Quantum Dempster-Shafer theory (QDST) derives a quantum mass function (QMF), a fuzzy metric obtained from multiple information sources based on quantum interference. In general, QMF effectively represents and processes uncertain information, but managing conflicts among multiple QMFs remains challenging. To address this issue, we propose a novel quantum conflict indicator (QCI) within the QDST framework. It is the first metric satisfying ideal conflict measurement properties, including non-negativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement. Based on QCI, a novel quantum conflict fusion method (QCI-Fusion) is introduced to fuse highly conflicting QMFs. Moreover, traditional methods, including QCI-Fusion, typically constructs the Quantum Frame of Discernment (QFoD) based on predicted labels, which makes it difficult to cover unseen classes. Therefore, a new decision architecture, QCI-Decision, is proposed for unsupervised detection that rejects out-of-distribution (OOD) samples while maintaining in-distribution (ID) classification. Experimental results show that the classification accuracy of QCI-Decision deviates from the original model predictions by at most 1.49%. Meanwhile, compared with the latest OOD detection methods, QCI-Decision improves the Area Under the Receiver Operating Characteristic Curve (AUC) by up to 0.6% and reduces the False Positive Rate at 95% True Negative Rate (FPR) by up to 1.63%. Moreover, compared with QCI-Fusion, QCI-Decision achieves approximately threefold faster fusion speed with negligible performance degradation, offering a promising solution for open-world quantum information decision.

The Internet of Everything (IoE) is accelerating the demand for intelligent, low-latency, and highly reliable communication systems to support automation and real-time decision-making. In dense urban and industrial environments, unmanned aerial vehicles (UAVs) are increasingly utilized to extend network coverage, improve connectivity, and enable dynamic data collection. However, managing RIS-assisted UAV-enabled IoE networks poses significant challenges, including accurate signal prediction, high computational complexity, and decentralized task assignment. To address these issues, we propose a novel RIS-empowered UAV-based Dynamic Area of Coverage (DAC) architecture. In this framework, UAVs equipped with reconfigurable intelligent surfaces (RIS) adaptively adjust the phase of reflected signals to optimize wireless channel conditions, suppress interference, and enhance signal quality.We formulate the Dynamic Area of Coverage with Location, Resource Allocation, and Trajectory Optimization (DAC-LRT) problem as a mixed-integer nonlinear programming (MINLP) model, aiming to jointly optimize UAV positioning, power distribution, and trajectory control to maximize real-time downlink capacity and ensure energy efficiency. To solve the DAC-LRT problem in dynamic and large-scale IoE environments, we design a Multi-Agent Distributed Deep Deterministic Policy Gradient (MAD3PG) algorithm. MAD3PG enables decentralized and adaptive policy learning by allowing UAVs to derive optimal actions directly from environmental observations. Simulation results demonstrate that our proposed approach significantly outperforms state-of-the-art methods, achieving improvements of 82.92%, 78.02%, and 71.9% in downlink capacity, coverage ratio, average throughput, and spectral efficiency over Deep Deterministic Policy Gradient (DDPG), Asynchronous Advantage Actor-Critic (A3C), and Greedy algorithms, respectively.

This article develops a dual-link coded event-triggered control (DL-CEC) for consensus in nonlinear multiagent system. To reduce the communication burden of signal transmission between the control box and actuator box or among agents and to enhance the security of information exchange, a dual-link coded scheme is proposed to compress each transmitted information into an L-length string. Furthermore, since the intrinsic complexity of nonlinear systems often causes traditional prescribed performance methods to fail in meeting constraints during the initial stages of operation, an adaptive prescribed performance (APP) scheme is introduced. By utilizing auxiliary functions, the APP is capable of dynamically adjusting performance boundaries, enabling seamless adaptation to varying initial system conditions. As a result, it ensures the tracking error is rigorously guaranteed to remain within a user-defined range over a prescribed time horizon, effectively accommodating diverse initial conditions of the system. By integrating DL-CEC with the APP method, the proposed control strategy ensures bounded consensus tracking with reduced communication cost and prescribed-time performance under arbitrary initial conditions. Simulation experiments corroborate the effectiveness and feasibility of the proposed approach.

This article studies the adaptive boundary control of a rotating body-beam system (RBBS) composed of a cantilevered beam with a tip payload connected to its upper end. The opposite end of the beam is fixed to the center of a rotational rigid disk. We assume that the dynamic process of the RBBS is affected by unknown disturbances and parameters. The external control actions, constituted by a control force exerted on the tip payload and a control torque acting on the disk, occur in dead zone nonlinearity and actuator failure. First, the mathematical expression of dead zone nonlinearity and actuator failure is combined and then divided into a desired control signal and a nonlinear input error. Second, by summing the input errors and external disturbances, adaptive boundary control and parameter compensation laws are designed for the RBBS to ensure vibration attenuation and regulate the rotating speed of the disk to a desired value. Third, the constructed control schemes ensure uniformity ultimately and boundedness regulation of the state variables, which is proved through the Lyapunov direct method. Finally, the effectiveness and robustness of the designed controllers are tested using numerical simulations.

Uncertainty in vessel control direction poses significant challenges in maritime operations, particularly in dynamic positioning, towing, and offshore wind farm maintenance. Traditional control methods struggle to handle uncertainties in system states for maritime operations, unmodeled dynamics, and multibody interactions. This study introduces an adaptive zone barrier Lyapunov control approach to address these challenges by ensuring vessel stability within a predefined operational zone while allowing adaptive parameter adjustments to compensate for uncertainties. The zone barrier Lyapunov function is employed to enforce safe operational constraints, preventing excessive deviations, while an adaptive control law dynamically estimates and adjusts system parameters. The proposed approach is validated through simulations of unmanned surface vehicle station-keeping and trajectory tracking. Results demonstrate that adaptive zone barrier Lyapunov control maintains vessel stability and maneuverability even when control direction is uncertain, outperforming conventional Lyapunov-based methods in handling constraint and robust effects. This study highlights the effectiveness of integrating zone barrier Lyapunov control with adaptive mechanisms for vessel motion control, offering a robust framework for enhancing maritime safety and operational efficiency in offshore environments.

The privacy-preserving control problem in discrete-event systems at the supervisory control layer is the central focus of this article. The key objective is to cosynthesize an edit function and a supervisor, working together to achieve the following goals: first, ensuring data opacity to prevent external intruders from deducing the system’s secret, second, ensuring the system performance adheres to safety and nonblockingness specification, and third, preserving the covert nature of the edit function, creating uncertainty about its presence to external intruders. By transforming this cosynthesis problem into a distributed supervisor synthesis problem in the Ramadge–Wonham supervisory control framework, this article introduces two heuristic synthesis approaches to incrementally cosynthesize an edit function and a supervisor. The effectiveness of the proposed approaches is demonstrated via a running example on location privacy.

The abundant knowledge of data and physics models can be simultaneously utilized in learning-based modeling, prediction, and control methods, which makes the balance between model efficiency, accuracy, and complexity. Thus, this work investigates the physics-informed neural networks (PINNs)-based adaptive optimized control method with essential learning designs for the whole learning framework. More specifically, the proposed method efficiently realizes the adaptive learning performance with PINNs modeled system dynamics via continuous learning with online data and system physics. Meanwhile, the PINNs model with autodifferentiation is employed by the adaptive dynamic programming approach to iteratively approximate the solution of the continuous-time Hamilton–Jacobi–Bellman equation with neural networks, providing more accuracy and efficiency over approaches that solely utilize either data-driven or physics-based models. As an outcome, the proposed method enables the PINNs-based learning control method to have superior performance in model transfer adaptation and learning efficiency. The proposed method is applied to automated vessel control problems, and its effectiveness and practical applicability are demonstrated through comparative simulations and hardware-in-the-loop tests.

Unplanned downtime and faults of wind turbines may lead to substantial economic losses. Therefore, continuous and efficient condition monitoring is essential to minimize the overall maintenance costs of wind farms. In this study, a residual-enhanced dual-model-driven method is proposed for wind turbine condition monitoring. Initially, detrended cross-correlation analysis and mutual information are employed to quantify nonlinear correlations and select informative characteristic parameters. Subsequently, a novel prediction architecture, termed RestoNet, is proposed to accurately model the normal operational behavior of wind turbines by leveraging residual information. RestoNet comprises two collaborative TimeXer sub-models, which are Transformer-based predictors for multivariate time series, and a fusion layer. One sub-model is designed to initially predict the future trend of the target characteristic parameter, while the other estimates in advance the potential future residuals generated by the former. The fusion layer then integrates the outputs of both sub-models to achieve accurate prediction of the target characteristic parameter. Finally, a health indicator (HI) is developed based on a radar chart of statistical features derived from model residuals, and health assessment is realized based on the probability distribution of the HI. Experimental results using real operational data from wind turbines validate the effectiveness of the proposed method in wind turbine condition monitoring. Quantitatively, RestoNet reduces the root mean square error (RMSE) by at least 20.76% compared with the baseline methods, indicating its superior predictive accuracy.

The intelligent fault diagnosis has achieved notable success in identifying known mechanical failures; however, reliably detecting out-of-distribution (OOD) faults remains a key challenge to achieve the diagnostic robustness. In industrial applications, vibration signals are typically collected as time-series data whose dynamic characteristics vary with load, speed, and environmental interference, with weak early fault patterns that blur class boundaries. As a result, models trained under limited laboratory conditions inevitably encounter unseen OOD inputs after deployment, requiring the ability to recognize and reject them reliably. Existing representation- and similarity-based OOD methods have shown promise but typically rely on single-granularity prototypes, capturing only coarse similarity structures and overlooking latent subclass relations—thus limiting the generalization under complex degradation modes. To address these limitations, we propose a multigranularity fuzzy inference (MgFI) framework for enhanced uncertainty quantification in fault diagnosis. MgFI models fine-grained subclass memberships on a hyperspherical manifold, aggregates them into class-level fuzzy sets, and infers coarse-grained In-distribution (ID) confidence through the hierarchical fuzzy reasoning. Extensive experiments demonstrate that MgFI substantially improves the OOD detection accuracy and provides a principled, interpretable framework for trustworthy open-set industrial diagnostics.

Classifiers fusion can be seen as a kind of multisource information fusion (MSIF), and classifiers fusion based on Dempster–Shafer (DS) evidence theory is an effective approach to improve the accuracy of classification tasks. However, different classifiers usually exhibit varying performances, making it challenging to achieve enhanced classification accuracy through direct fusion. Simultaneously, when the frame of discernment (FoD) of the target class expands, the number of focal elements involved in the fusion increases, resulting in a rapid growth in computational complexity. To enhance the classification performance while reducing the time cost of fusion, a novel weighted fusion of classifiers method based on approximate reasoning and reliability evaluation (WFC-AR-RE) is proposed in this article. Specifically, at first, the key focal elements are determined based on the outputs of classifiers, and an approximate basic belief assignment (BBA) is generated. Subsequently, the validation set is utilized to evaluate the performance of each classifier, thus obtaining the self-reliability of each BBA. Afterward, a novel divergence measure is introduced to quantify the discrepancy between BBAs, determining the relative reliability of each BBA. Finally, the fusion weight of each BBA is derived from its self-reliability and relative reliability, and Dempster’s rule is applied to combine the weighted BBA. The proposed WFC-AR-RE algorithm is applied to the MSIF system, and its effectiveness is demonstrated on 12 public datasets.

Adversarial attack adds an imperceptible perturbation on images to fool a model. Though existing adversarial attack methods have demonstrated great success in image classification tasks, they suffer inferior attack performances on object detection. We find that there exists multi-target gradient conflict (MGC) between different targets during attack process in object detection. To tackle this issue, we propose an effective pixel-level attack method, namely Selective Consistency Gradient Attack (SCGA). First, we select a dominant gradient direction by gradient ranking. Then, we introduce the gradient conflict rate to select targets with consistent gradients to improve attack efficiency. Finally, the perturbation is generated by gradient merging. Experiments on COCO 2017 validation subset verify the effectiveness of SCGA on both white-box attack and black-box attack, outperforming other methods with a large margin.

The fault diagnosis based on supervised learning has achieved remarkable results in the intelligent manufacturing, making it an important guarantee for long-term safe and stable operation in modern industry. However, the accuracy heavily relies on high-quality annotation labels, which are expensive to obtain, limiting the diagnosis models applicability in many scenarios. Although obtaining automatically annotated samples from annotators is a promising solution, the generated dataset is always containing incorrect labels (noisy labels), due to perceptual limitations, resulting in low or even invalid the accuracy of model. With the goal of handling this challenge, a diagnostic approach based on multi-granularity information fusion to combat noisy labels, called MgCNL, is proposed, to train the model with high-accuracy, without knowing the specific noise ratio. Specifically, inspired by granular-ball computing, a confidence evaluation method of labels is designed, so that samples with high confidence labels can be selected from dataset with noisy labels for supervised learning, thus avoiding the negative impact of incorrect labels on model performance. Finally, the efficacy was demonstrated on three datasets using different backbones: MgCNL successfully reduced the adverse impact of noisy labels, achieving significantly better results than other advanced methods in various noisy scenarios, which offers a competitive model training strategy for practitioners in intelligent manufacturing or industrial fault diagnosis who are hampered by the costs associated with sample labeling. Note to Practitioners—In modern industry, the cost of manual/expert annotation for high-quality data is is prohibitively expensive, and the data annotated by automatic annotators often contains noisy labels that seriously damages the accuracy of models, which makes many data-driven diagnosis models constrained by training data and difficult to put into practice, posing an urgent challenge to the automation and intelligence of the manufacturing industry. To address this challenge, this article proposed a robust training strategy called MgCNL, aimed at offsetting the negative impact of noisy labels, in the hope that automatic annotation strategy with lower cost can be more widely applied in model training tasks for industrial practice. MgCNL, based on multi-granularity information, can effectively select high-confidence samples from datasets for supervised learning, even under unknown proportions of noise labels, thus reducing the misleading impact of noisy labels on diagnostic models. As a result, MgCNL possesses the ability to robustly train high-accuracy diagnostic models in data with noisy labels, thus enabling automatic annotators to replace experts in dataset construction as a more economical and efficient potential technical approach. Meanwhile, MgCNL also brings value to datasets with uncertain labels, making them applicable without the need to invest significant human resources to verify label reliability.

Emission source microscopy (ESM) technique can be utilized for the localization of electromagnetic interference (EMI) sources in electronic systems, its performance greatly depends on the scanner accuracy and back-propagation method. In this paper, we introduce a novel spherical ESM system driven by 6-DOF manipulator, and investigate the back-propagation based on sphere wave expansion and robot control strategy. For spherical scanning aperture, we fuse the robot kinematics model and measurement constraints, and propose solving the optimal scanning grid with nonlinear programming method. For manipulator control, we present a graph-based learning framework (Gash-LKH) that combines sparse graph neural network with Lin-Kernighan heuristic (LKH) solver. This framework adopts the gated single-head attention module and parallel sparse graph feature abstracting channels, it can produce high-qualified edge candidate set that help subsequent LKH solver generate optimal scanning path with lower memory cost and less computation. Extensive experiments are conducted to validate the performance of Gash-LKH and Spherical ESM system, the results have demonstrated the feasibility and superiority of spherical ESM system in providing accurate microscopy and localization in EMI measurement.Note to Practitioners—The motivation of this paper is to develop an automated ESM system that realizes the spherical aperture scanning and pattern reconstruction of radiation source. Since adoption the back-propagation method based on sphere wave expansion, this system is supposed to achieve better microscopy performance and lower truncation error than other scanner. In this paper, we employ 6-DOF manipulator as scanner, and propose a complete spherical aperture generation method that produces the discrete and even scanning grid based on any source, frequency band and measurement constraints. Furthermore, we propose an end-to-end learning framework, Gash-LKH, to solve the optimal scanning path for given scanning aperture. The achieved accuracy and time-consumption of Gash-LKH is satisfactory for solving large-scaled and high-density scanning path planning. Note that the entire framework can be trained through random 3D-TSP instance dataset, and can be transferred to handle various radiation sources operating in microwave band. We have demonstrated the feasibility of proposed methodology and system through the experiments using Elfin-5 manipulator and benchmark sources. The results have offered the possibilities of achieving satisfactory localization and characterization in EMI measurement.

A memory output sliding mode load frequency control (MOSMLFC) strategy is proposed for multi-area interconnected power systems under historical-frequency-triggered denial-of-service (DoS) attacks. Due to the use of the open network, the multi-area power system is prone to cyber-attacks. Different types of attack models have been built to describe the actual attack behavior, so that effective strategies can be quickly formulated in the event of an attack. Therefore, a historical-frequency-triggered DoS attacks model is presented from the perspective of attackers, with the aim of destroying the stable state of the multi-area power system. It is assumed that attackers determine the timing of DoS attacks by monitoring the operational status of multi-area power systems and designing the triggering condition with historical frequency. A MOSMLFC strategy is investigated to ensure the security performance of multi-area power systems under historical-frequency-triggered DoS attacks, which applies the memory output information of the power system to realize the controller design. The security condition of multi-area power systems under historical-frequency-triggered DoS attacks is obtained by Lyapunov’s theorem and linear matrix inequality (LMI). Numerical examples are tested over the IEEE 10-generator 39-bus system and the results prove the usefulness and superiority of the proposed method. Note to Practitioners—Load frequency control is widely applied in multi-area power systems to achieve a balance between the load demand and generation. Frequent cyber-attacks are a threat to the normal operation of the power system. It is therefore necessary to develop appropriate strategies to defend against cyber-attacks. So far, there have been many different forms of cyber-attacks. This has prompted defenders to build different types of attack models to describe the actual attack behavior in order to preemptively formulate appropriate defensive strategies. Smart attacker may notice that certain characteristics of the target system are important, such as the power system frequency. This motivates us to propose a historical frequency-triggered DoS attack model that contributes to a deep understanding of the impact of cyber-attacks on the power system. We propose a unique sliding mode control approach to ensure the stable performance of power system state and output simultaneously.

In this paper, we propose the dynamic high-gain scaling technique and solutions to input saturation for uncertain strict-feedback nonlinear systems. Although high gain affords fast response and high accuracy for the improvement of tracking performance, there are two inescapable potential risks: (i) excessive high gain would amplify the negative effects from non-vanishing mismatched uncertainties; (ii) high gain may conflict with the limited regulation capacity. Herein, two strategies based on invariance property are applied to high-gain control, aiming to address these two risks separately. On the one hand, by defining the function of performance robustness evaluation (PRE), the scaling gain grows to speed up the convergence rate and then maintains at an acceptable high level while guaranteeing the robustness to uncertainties in an invariant set. On the other hand, for handling the input saturation, the control barrier function (CBF)-based quadratic program (QP) describes the function of performance safety evaluation (PSE) that helps assess the system safety and decide whether the compensation for saturation is necessary, as such, the balance between performance and saturation gets achieved. Numerical simulations and a semi-physical experiment are performed to investigate the performance of our proposed methodology. Note to Practitioners—The motivation of this article is that stringent time response constraints are necessary for security or to increase productivity in practical applications, while control constraints exist widely in control systems. The lack of constraint satisfaction may inevitably result in safety defects, performance degradation. Based on these observations, a balanced safety-critical high-gain control scheme is proposed for input-constrained nonlinear uncertain strict-feedback systems. The contribution focuses on finding a balanced relationship between rapid response ability, robustness to mismatched uncertainties, and finite regulation capability in the high-gain control framework, since excessive control gain would potentially amplify the effect of non-vanishing mismatched uncertainty or lead to unexpected control saturation. Moreover, the theoretical derivation demonstrates that: only the system has the tolerance of matched uncertainties when the high gain grows to infinity, otherwise there must exit a maximum value for high gain to maintain the robustness and stability; the PSE function assesses the system safety under saturation conditions and decides whether to continue improving control performance or compensate for saturation for the maintenance of stability.

Ensuring the side-by-side configuration of the automated surface vessels under the effect of the uncertainties of the environment attracted continued efforts on this challenging issue. This paper investigates the learning-based safety-enhanced adaptive side-by-side control algorithm that suitably defines and uses the modified adaptive control barrier functions to tackle the constrained control satisfied under the uncertainty environment by ensuring the control barriers on state-variables constraints. In addition to the inherent adaptivity of the adaptive control barrier functions, the finite-time auxiliary system is enabled to modify the adaptive control barrier functions, which considers the unknown part of the nominal model to ensure the satisfaction of system constraints, especially under uncertain situations. For the formulated quadratic programs with adaptive control barrier functions and control Lyapunov functions, the operator splitting quadratic program is employed in problem-solving, which effectively enlarges the robustness of problem-solving, making it particularly efficient and reliable for real-time applications. The effectiveness of the proposed method is demonstrated via comparative simulations under uncertain cases to show the superior adaptive ability under the model uncertainties, which can be employed in marine industry applications, e.g., carbon emissions estimation modeling and optimization.Note to Practitioners—This paper was motivated by enlarging the adaptive ability of optimization-based safe operation for safety-critical automated surface vessels control under the side-by-side configuration under the effect of the uncertainty of the environment. The proposed learning-based safety-enhanced adaptive side-by-side control algorithm promotes adaptivity and control performance with the usage of the finite-time auxiliary system to the refined adaptive control barrier functions in the optimization problem formulation. The problem-solving is accelerated with the usage of the operator-splitting quadratic program. These implements effectively enlarge the robustness of the proposed method, making it particularly efficient and reliable for industrial real-time applications. The proposed method employed a complicated system design with the outcome of a simple algorithm implemented with promoted adaptivity and robustness, which can be employed in industrial applications in future research to promote current control system performance.

Exploring procedurally-generated environments presents a formidable challenge in model-free deep reinforcement learning (RL). One state-of-the-art exploration method, adversarially guided actor–critic (AGAC), employs adversarial learning to drive exploration by diversifying the actions of the deep RL agent. Specifically, in the actor–critic (AC) framework, which consists of a policy (the actor) and a value function (the critic), AGAC introduces an adversary that mimics the actor. AGAC then constructs an action-based adversarial advantage (ABAA) to update the actor. This ABAA guides the deep RL agent toward actions that diverge from the adversary's predictions while maximizing expected returns. Although the ABAA drives AGAC to explore procedurally-generated environments, it can affect the balance between exploration and exploitation during the training period, thereby impairing AGAC's performance. To mitigate this adverse effect and improve AGAC's performance, we propose efficient adversarially guided actor–critic (EAGAC). EAGAC introduces a state-based adversarial advantage (SBAA) that directs the deep RL agent toward actions leading to states with different action distributions from those of the adversary while maximizing expected returns. EAGAC combines this SBAA with the ABAA to form a joint adversarial advantage, and then employs this joint adversarial advantage to update the actor. To further reduce this adverse effect and enhance performance, EAGAC stores past positive episodes in the replay buffer and utilizes experiences sampled from this buffer to optimize the actor through self-imitation learning (SIL). The experimental results in procedurally-generated environments from MiniGrid and the 3-D navigation environment from ViZDoom show our EAGAC method significantly outperforms AGAC and other state-of-the-art exploration methods in both sample efficiency and final performance.

Human activity recognition (HAR) based on wearable devices has received significant attention from scholars in recent years. Nevertheless, the lack of effective exploitation of multiview learning and limited capacity for uncertainty analysis still remain major challenges for high-precision and high-confidence activity recognition. Thus, this article proposes a novel multiview uncertainty-aware graph convolutional network (MVUAGCN) model. Specifically, MVUAGCN first divides the raw time series data into multiview data according to the sensor type, and then structures the derived data into multiview graph topology. After that, the multiview residual graph convolutional networks with the Chebyshev polynomial are deployed to generate the sources of evidence (SoEs). Then, all involved multiview SoEs are mapped into the evidence space through the Dirichlet distribution to obtain the uncertainty degree in MVUAGCN. Finally, all the mapped SoEs are fused sequentially and the decision is made according to the maximum probability. The comprehensive experimental evaluations were conducted on four publicly HAR datasets. With the nearly 5% improvement compared to CNN-based approaches, MVUAGCN achieves 99.06%, 100%, 97.84%, and 98.25% recognition accuracy for all the four datasets: PAMAP2, MHEALTH, OPPORTUNITY, and UCI HAR, respectively.

This article solves the entry capture problem (ECP) such that for any initial tracking error, it can be regulated into the prescribed performance constraints within a user-given time. The challenge lies in how to remove the initial condition limitation and to handle the ECP for nonlinear systems under unknown control directions and asymmetric performance constraints. For better tracking performance, we propose a unified tunnel prescribed performance (TPP) providing strict and tight allowable set. With the aid of a scaling function, error self-tuning functions (ESFs) are then developed to make the control scheme suitable to any initial condition (including the initial constraint violation), where the initial values of ESFs always satisfy performance constraints. In lieu of the Nussbaum technique, an orientation function is introduced to deal with unknown control directions while such way is capable of reducing the control peaking problem. Using ESFs, together with TPP and an orientation function, the resulted tunnel prescribed control (TPC) leads to a solution for the underlying ECP, which also exhibits a low complexity level since no command filters or dynamic surface control is required. Finally, simulation results are provided to further demonstrate these theoretical findings.

In this work, a novel reinforcement learning-based adaptive fault-tolerant control (FTC) scheme with actuator redundancy is presented for a nonlinear strict-feedback system with nonlinear dynamics and uncertainties. A learning-based switching function technique is established to steer different groups of actuators automatically and successively to mitigate the impact of faulty actuators by observing a switching performance index. The optimal tracking control problem (OTCP) of strict-feedback nonlinear systems is transformed into an equivalent optimal regulation problem of each affine subsystem via adaptive feedforward controllers. Subsequently, the designed objective functions associated with Hamilton-Jacobi-Bellman (HJB) estimate errors caused by neural network (NN) approximations can be minimized by the reinforcement learning algorithm without value or policy iterations. It is proved that the tracking objective can be achieved and all signals in the closed-loop system can be guaranteed to be bounded, as long as the minimum time interval between two successive failures is bounded. Theoretical results are verified by simulations.

A reliable adaptive-memory-derivative (AMD) event-triggered quantized sliding mode load frequency control (QSMLFC) method is proposed for the multiarea interconnected wind power system under frequency-based deception attacks. An AMD event-trigger scheme is proposed to promote the wind power system operation while saving the network resources, and the reliable AMD event-triggered QSMLFC method aims to reduce the frequency deviations of the interconnected wind power systems. A frequency-based deception attack model is developed for analyzing the security issues in network communications for wind power systems. The hysteresis quantizer is used to lower the communication rate. To validate the correctness of the control method, a sufficient reliability criterion is derived to prove the applicability of the AMD event-triggered QSMLFC. Three numerical examples and an IEEE 39-bus system simulation are presented to demonstrate that the reliable AMD event-triggered QSMLFC method can provide satisfactory stability performance for the wind power system under frequency-based deception attacks.

As the demand for efficient parcel delivery continues to grow in the logistics industry, optimizing multi-robot task assignment has become crucial for enhancing overall delivery performance. This paper addresses the precedence-constrained multi-truck multi-drone package delivery task assignment problem, where each truck coordinates with a drone to serve multiple dispersed customers under precedence constraints that specify the required order of service. While trucks deliver packages to designated customers, drones can simultaneously serve other customers, subject to their limited flight endurance and payload capacity. To tackle this challenge, a three-phase heuristic algorithm is proposed to minimize the total delivery time required to serve the last customer while ensuring all precedence constraints are satisfied. In the first phase, an extended minimum marginal cost algorithm is applied to quickly construct truck-only routes that comply with precedence constraints. In the second phase, a splitting algorithm combined with an endurance checking procedure is employed to generate hybrid truck–drone routes considering drone limitations. In the final phase, a variable neighborhood descent approach is introduced to further improve the solution by strategically perturbing the truck-only routes. Extensive simulations and experiments demonstrate that the proposed three-phase heuristic algorithm consistently achieves higher-quality solutions with reduced computation time compared with the widely used adaptive large neighborhood search method.

This article addresses the velocity-free predefined-time consensus tracking for multiagent systems (MASs) with input and output quantization via adaptive sliding mode control (SMC). First, a distributed predefined-time state observer is introduced to estimate the unmeasurable states. Therein, only the quantized position information is used, and the observation errors are ensured to be predefined-time bounded (PTB). Second, a class-${\mathcal {K}}_{\infty }$function is employed as the adaptive gain in the SMC to diminish the dependence on prior knowledge of lumped uncertainties and quantization parameters. Subsequently, a novel SMC-based quantized consensus tracking protocol is designed using time-varying functions to achieve the predefined-time consensus tracking of MASs. Specifically, with the proposed protocol, the consensus tracking errors are guaranteed to be PTB under input and output quantization. Finally, simulations are employed to validate the performance of the proposed protocol.

In this article, the globally adaptive neural network (NN) control problem is addressed for a class of nonlinear systems with mismatched uncertainties and unknown time-varying delays through a static gain function-based algorithm. The combination of the Lyapunov-Krasovskii functional (LKF), the static gain function-based backstepping technique and the radial basis function NN (RBF NN) approximation approach eliminates the effects of unknown time-varying delays and unknown nonlinearities. The additional terms generated by the derivative of LKF are divided into two parts, one part is dealt by virtual control laws and the other part is suppressed with static gain functions. Specifically, a smooth enough switching function is constructed, which ensures the global stability of the closed-loop system signals. Finally, the performance of the globally adaptive NN control scheme is revealed by simulation results.

In the realm of multi-agent systems, the effective coordination of agents for manipulation tasks poses a significant challenge. This study explores various strategies aimed at enhancing Multi-agent Reinforcement Learning (MARL) algorithms in the context of locomotion-based manipulation tasks. We systematically assess the performance of these strategies, incorporating reward shaping and algorithmic variations such as Proximal Policy Optimization (PPO) and Advantage Actor Critic (A2C). For better cooperation between agents, a prediction map is also implemented, informing the agents with a probability heat map of other agents based on their kinematic models. The experiments are conducted in the Isaac Sim simulation environment with Jetbot robots as agents. Results indicate distinct impacts of each strategy on critical performance metrics, including success rates, collision probabilities, and overall task efficiency. While some strategies exhibit notable improvements, others reveal limitations, emphasizing the nuanced challenges inherent in optimizing multi-agent systems for this task. These findings serve as an overview of current Reinforcement Learning (RL) optimization strategies, and could contribute valuable insights to the effective deployment of RL in complex, collaborative robotic scenarios.

In this paper, an artificial intelligence enabled energy-saving strategy (AIESS) is proposed to achieve high energy efficiency of hydraulic systems with a speed and displacement variable pump (SDVP). The AIESS, which integrates extreme gradient boosting (XGBoost) and genetic algorithm (GA), can generate the optimum combination of motor speed and pump displacement of SDVP in real time under actual working conditions. The XGBoost-based power model is used to accurately predict the input power of the SDVP considering environmental factors, e.g., oil temperature, then followed by GA for optimization, in which the XGBoost-based power model is the objective function. A test rig with a 16t hydraulic press and a stamping process is applied to validate the effectiveness of the proposed strategy. Compared to four other algorithm models, the XGBoost-based power model has the highest accuracy. The optimum combination can be obtained quickly by GA. The results show that the AIESS can reduce energy consumption by 28.6% during pressure-maintaining operation and by 11.2% during one stamping cycle compared to the previously proposed segmented control energy-saving strategy.Note to Practitioners—Energy-saving hydraulic system is a topic in the automation industry. The low energy efficiency of electro-hydraulic systems is mainly caused by poor flow rate matching. A speed and displacement variable pump (SDVP) is used to address this problem, but it is difficult to find the matched speed and displacement for the highest energy conversion efficiency of the SDVP due to the inaccuracy energy model and the challenge of coupling variables. This paper presents an energy-saving strategy integrated with machine learning and a genetic algorithm to handle this challenge. Relative variables of the machine learning model are picked through analysis of the mathematical energy model, and the usage of machine learning can fit the high-nonlinear energy model caused by uncertainties in both components and environmental factors. The trained model is then used as an objective function for a GA-based optimization process to generate the optimum motor speed and pump displacement combination. Considering the processing time of GA, this combination can be optimized before the operation for known working conditions. Finally, we have established a 16t hydraulic press to validate this energy-saving strategy and pre-optimized five combinations for a stamping process. The proposed strategy can reduce energy consumption by 11.2% compared with the previous strategy. This energy-saving strategy can also be applied to other automated hydraulic equipment, such as injection machines.

This work develops a dynamic compliant force control (DCFC) strategy for the robotic belt grinding system to suppress the vibrations and over-grinding phenomenon. First, the vibration mechanism is investigated, and the corresponding vibration models before and after contact are constructed, both of which can decompose the vibrations into three components: free, accompanying and forced vibrations. Next, the extra compliant hardware is equipped to the grinder to realize the dynamic adjustment of equivalent damping. The DCFC strategy considering mechanical compliance accompanied by the dynamic closed-loop control of the grinder damping is presented based on the empirical wavelet transform and multi-scale permutation entropy. Moreover, the cutting fluctuation ratio index is proposed to evaluate the severity of the over-grinding together with over-grinding time. Experiments demonstrate that compared with the general force control, the DCFC strategy can reduce the vibration amplitude from 3.01 mm/s$^2$to 0.97 mm/s$^2$, and the over-grinding time/cutting fluctuation ratio from 1.05 s/24.71% to 0.54 s/13.18%, consequently enhancing the grinding stability and quality.Note to Practitioners—This work is motivated by the need to maintain the grinding stability and suppress the over-grinding phenomenon for the robotic belt grinding system. The vibrations and over-grinding phenomenon caused by the weak rigidity and poor precision of the robot always result in incomplete grinding of the workpiece that needs partially manual regrinding. The proposed DCFC strategy enables a dynamic compliant contact force between the grinding tool and the robot, thus effectively suppressing the over-grinding phenomenon and enhancing the consistency of grinding quality, which is particularly suitable for considering material removal consistency in cut-in and cut-out areas. This method can be implemented by the user as described or be acquired as a standalone device, but some extra hardware needs to be equipped with the grinding tool to use this method.

Exploring sparse reward environments remains a major challenge in model-free deep reinforcement learning (RL). State-of-the-art exploration methods address this challenge by utilizing intrinsic rewards to guide exploration in uncertain environment dynamics or novel states. However, these methods fall short in procedurally-generated environments, where the agent is unlikely to visit a state more than once due to the different environments generated in each episode. Recently, imitation-learning-based exploration methods have been proposed to guide exploration in different kinds of procedurally-generated environments by imitating high-quality exploration episodes. However, these methods have weaker exploration capabilities and lower sample efficiency in complex procedurally-generated environments. Motivated by the fact that demonstrations can guide exploration in sparse reward environments, we propose improved exploration with demonstrations (IEWD), an improved imitation-learning-based exploration method in procedurally-generated environments, which utilizes demonstrations from these environments. IEWD assigns different episode-level exploration scores to each demonstration episode and generated episode. IEWD then ranks these episodes based on their scores and stores highly-scored episodes into a small ranking buffer. IEWD treats these highly-scored episodes as good exploration episodes and makes the deep RL agent imitate exploration behaviors from the ranking buffer to reproduce exploration behaviors from good exploration episodes. Additionally, IEWD adopts the experience replay buffer to store generated positive episodes and demonstrations and employs self-imitating learning to utilize experiences from the experience replay buffer to optimize the policy of the deep RL agent. We evaluate our method IEWD on several procedurally-generated MiniGrid environments and 3-D maze environments from MiniWorld. The results show that IEWD significantly outperforms existing learning from demonstration methods and exploration methods, including state-of-the-art imitation-learning-based exploration methods, in terms of sample efficiency and final performance in complex procedurally-generated environments.

This article investigates an output rate-coded secure control based on backstepping for the consensus of heterogeneous multiagent systems (MASs) with output-triggering condition. Due to the nondifferentiable virtual control inputs caused by discontinuous triggering signals, it presents a technical obstacle in implementing the recursive backstepping, rendering previous results inapplicable. To address this problem, an auxiliary high-order filter is elaborately constructed to guarantee the required-order derivatives of virtual control inputs. As this filter is driven by local triggering consensus error, it allows our MASs to have different system orders and relative degrees. Besides, to reduce communication bit and strengthen communication security, we propose a novel distributed rate-coded algorithm from an encoding-decoding viewpoint. When it is triggered, agent's output is encrypted into an L-length codeword, then transmitted to neighbors without exposing any sensitive system states or real control inputs. It is shown that all the closed-loop system signals are ultimately bounded, and the mean square consensus tracking error can be reduced by appropriately selecting control parameters. Simulations illustrate our theoretical finding.

In this article, saturation-tolerant prescribed control (SPC) is investigated for a class of multiinput-multioutput (MIMO) nonlinear systems. The key challenge lies in how to guarantee both input and performance constraints simultaneously for nonlinear systems especially under external disturbance and unknown control directions. We propose concise finite-time tunnel prescribed performance (FTPP) for better tracking performance, which features tight allowable set and user-specified settling time. To comprehensively tackle the conflict between the above two constraints, an auxiliary system is designed to explore their interconnections instead of neglecting their contradictions. By introducing its generated signals into FTPP, the obtained saturation-tolerant prescribed performance (SPP) has the ability to degrade or recover the performance boundaries in the light of different saturation conditions. Consequently, the developed SPC, together with nonlinear disturbance observer (NDO), can effectively improve the robustness and reduce the conservatism against external disturbances, input, and performance constraints. Finally, comparative simulations are presented to showcase these theoretical findings.

This article introduces a novel approach called terminal sliding-mode control for achieving time-synchronized convergence in multi-input-multi-output (MIMO) systems under disturbances. To enhance controller design, the systems are categorized into two groups: 1) input-dimension-dominant and 2) state-dimension-dominant, based on signal dimensions and their potential for achieving thorough time-synchronized convergence. We explore sufficient Lyapunov conditions using terminal sliding-mode designs and develop adaptive controllers for the input-dimension-dominant case. To handle perturbations, we design a multivariable disturbance observer with a super-twisting structure, which is integrated into the controller. By utilizing the sliding-mode technique and the disturbance observer, the proposed controller ensures simultaneous convergence of all output dimensions. In the state-dimension-dominant case, where a full-rank system matrix is absent, only specific output elements converge to equilibrium simultaneously. We conduct comparative simulations on a practical system to highlight the effectiveness of our proposed method for the input-dimension-dominant case. Statistical results reveal the benefits of shorter output trajectories and reduced energy consumption. For the state-dimension-dominant case, we present numerical examples to validate the semi-time-synchronized property.

High-performance learning-based control for the typical safety-critical autonomous vehicles invariably requires that the full-state variables are constrained within the safety region even during the learning process. To solve this technically critical and challenging problem, this work proposes an adaptive safe reinforcement learning (RL) algorithm that invokes innovative safety-related RL methods with the consideration of constraining the full-state variables within the safety region with adaptation. These are developed toward assuring the attainment of the specified requirements on the full-state variables with two notable aspects. First, thus, an appropriately optimized backstepping technique and the asymmetric barrier Lyapunov function (BLF) methodology are used to establish the safe learning framework to ensure system full-state constraints requirements. More specifically, each subsystem's control and partial derivative of the value function are decomposed with asymmetric BLF-related items and an independent learning part. Then, the independent learning part is updated to solve the Hamilton-Jacobi-Bellman equation through an adaptive learning implementation to attain the desired performance in system control. Second, with further Lyapunov-based analysis, it is demonstrated that safety performance is effectively doubly assured via a methodology of a constrained adaptation algorithm during optimization (which incorporates the projection operator and can deal with the conflict between safety and optimization). Therefore, this algorithm optimizes system control and ensures that the full set of state variables involved is always constrained within the safety region during the whole learning process. Comparison simulations and ablation studies are carried out on motion control problems for autonomous vehicles, which have verified superior performance with smaller variance and better convergence performance under uncertain circumstances. The effectiveness of the safe performance of overall system control with the proposed method accordingly has been verified.

This article studies an event-triggered tunnel prescribed control (TPC) for uncertain nonlinear systems under any initial condition. A more general entry capture problem (ECP) is introduced, where the tunnel prescribed performance is satisfied after a certain period of system operation, as opposed to starting from the beginning, thereby, making the control design complex yet challenging. In this case, the normally employed prescribed performance control becomes invalid due to the singularity problem arising from the initial condition violation. An error self-tuning function is proposed to provide a unified approach for handling different initial conditions, which can be extended to other methods. In order to deal with unknown control directions, an orientation function is employed in lieu of Nussbaum-type function, by which the initial control input is always equal to zero avoiding a large initial value. We then develop an event-triggered mechanism from an encoding–decoding viewpoint, by which only 1-bit string, either 1 or 0, is required for each communication between control and actuator. In this way, such event-triggered mechanism can further reduce communication burden and improve communication security at the same time. The developed event-triggered TPC provides an effective solution for the underlying ECP and exhibits low-complexity level since no additional filters or time derivatives of virtual control inputs are required in the control design. Finally, comparative simulations are conducted to illustrate the abovementioned theoretical finding.

Unmanned aerial vehicles (UAVs) represent an essential component of advanced intelligent equipment that can be used as an aerial perception system by installing various sensors such as vision, hearing, touch, taste, and smell to achieve intelligently integrated perception of environments. However, these perception system with environmental information may be threatened by various internal and external attacks, causing a great challenge to the security of the UAV. The original security system relied on an expert knowledge base to prevent attacks, but the weaknesses of lacking proactivity and flexibility are gradually exposed. The strong resistance and survivability of biological systems can be used to fill this capability gap and provide new ideas for the security of the UAV perception system. Therefore, an endogenous security framework (ESUAV-NI) based on the neural system and immune system is proposed in this article. Through breeding artificial intelligence (AI) vaccines and distributed neural hierarchical control, we achieve the security protection for the UAV perception system. Moreover, we evaluated the AI vaccine breeding approach in the ESUAV-NI by conducting extensive experiments on internal threats and external aerial imagery camouflage data, respectively. The results show that the proposed approach has a superior performance for the UAV perception system.

This paper studies synchronized optimization control for the cooperatively connected autonomous vehicle platoon formulation applicable in various driving scenarios and accommodates multiple vehicles dynamically entering or exiting the platoon. More specifically, the proposed algorithm consists of bi-layered synchronized optimization that enables the ultimate optimized control to attain the synchronized convergence property, and also importantly, ensures the satisfaction of safety performance requirements. The first layer of the proposed approach involves formulating the platoon dynamics and ensuring that the platoon operates within safe boundaries while optimizing its overall performance. To achieve this, the prescribed performance control is utilized to ensure that the state-variables remain within a predefined region throughout the synchronized optimization process. In the second layer, the control optimization takes into account the vehicle dynamics and actuators of either heterogeneous or homogeneous individual vehicles, improving performance and coordination within the platoon. In each optimization layer, the optimized backstepping is utilized, and the norm-normalized sign function is appropriately incorporated with the decomposition design to establish the learning framework with the outcome that attains the synchronized properties simultaneously. The adaptive dynamic programming and gradient-constrained method are utilized in the learning design to iteratively optimize system control while keeping the learning parts within the admissible policy region. Importantly, it is rigorously shown that this particular development and methodology attains the noteworthy time-synchronized stability property and outcome that all vehicle agents arrive at the desired relative position at the same time with synchronized convergence. Additionally, it is also shown that the methodology of our specific algorithmic strategy significantly also attains the desired outcomes of “string stability” (jointly with the above-mentioned desired outcomes of “time-synchronized stability”). To evaluate its effectiveness, comparative studies with different methods are carried out to showcase the significantly better desired outcomes attained with this methodology of synchronized optimization. Further evaluations in scenarios involving dynamic entry and exit of multiple vehicles demonstrate the corresponding capability and effectiveness in achieving the desired objectives.

The rise of the Internet of Things has spurred the growth of wireless applications, particularly Wi-Fi-based indoor localization, which is gaining prominence owing to its cost-effectiveness. Nevertheless, the accuracy of Wi-Fi-based indoor localization is hindered by signal instability. To address this limitation, we introduce an interval random analysis approach for uncertain Wi-Fi-based indoor localization. Specifically, this approach employs an interval random parameter lognormal shadowing model for radio map enhancement and adaptive Bayesian comprehensive learning (IRPLS-ABCL) particle swarm optimization (PSO) for location estimation accuracy enhancement. The process comprises two stages: offline training and online localization. During the offline phase, we establish the interval random parameter lognormal shadowing model, considering the parameters as interval random variables, rather than precise values, in a sparse reference point scenario. In the online phase, we use a double-panel fingerprint homogeneity model to assess fingerprint similarity and apply the adaptive Bayesian comprehensive learning PSO algorithm to enhance localization precision. The experimental results show that the proposed algorithm can achieve the best performance in terms of localization accuracy based on the predicted average received signal strength (RSS), reaching 1.89 m.

The belief functions (BFs) introduced by Shafer in the mid of 1970s are widely applied in information fusion to model epistemic uncertainty and to reason about uncertainty. Their success in applications is however limited because of their high-computational complexity in the fusion process, especially when the number of focal elements is large. To reduce the complexity of reasoning with BFs, we can envisage as a first method to reduce the number of focal elements involved in the fusion process to convert the original basic belief assignments (BBAs) into simpler ones, or as a second method to use a simple rule of combination with potentially a loss of the specificity and pertinence of the fusion result, or to apply both methods jointly. In this article, we focus on the first method and propose a new BBA granulation method inspired by the community clustering of nodes in graph networks. This article studies a novel efficient multigranular belief fusion (MGBF) method. Specifically, focal elements are regarded as nodes in the graph structure, and the distance between nodes will be used to discover the local community relationship of focal elements. Afterward, the nodes belonging to the decision-making community are specially selected, and then the derived multigranular sources of evidence can be efficiently combined. To evaluate the effectiveness of the proposed graph-based MGBF, we further apply this new approach to combine the outputs of convolutional neural networks + attention (CNN + Attention) in the human activity recognition (HAR) problem. The experimental results obtained with real datasets prove the potential interest and feasibility of our proposed strategy with respect to classical BF fusion methods.

This article focuses on the intralayer-dependent impulsive synchronization of multiple mismatched multilayer neural networks (NNs) with mode-mixed effects. Initially, a novel multilayer NN model that removes the one-to-one interlayer coupling constraint and introduces nonidentical model parameters is first established to meet diverse modeling requirements in complex applications. To help the multilayer target NNs with mismatched connection coefficients and time delays achieve synchronization, the hybrid controller is designed using intralayer-dependent impulsive control and switched feedback control approaches. Furthermore, the mode-mixed effects caused by the intralayer coupling delays and switched intralayer topologies are incorporated into the novel model and analysis method to ensure that the subsystems operating within the current switching interval can effectively use the topology information of the previous switching intervals. Then, a novel analysis framework including super-Laplacian matrix, augmented matrix, and mode-mixed methods is developed to derive the synchronization results. Finally, the main results are verified via the numerical simulation with secure communication.

Guaranteed safety and performance under various circumstances remain technically critical and practically challenging for the wide deployment of autonomous vehicles. Safety-critical systems in general, require safe performance even during the reinforcement learning (RL) period. To address this issue, a Barrier Lyapunov Function-based safe RL (BLF-SRL) algorithm is proposed here for the formulated nonlinear system in strict-feedback form. This approach appropriately arranges and incorporates the BLF items into the optimized backstepping control method to constrain the state-variables in the designed safety region during learning. Wherein, thus, the optimal virtual/actual control in every backstepping subsystem is decomposed with BLF items and also with an adaptive uncertain item to be learned, which achieves safe exploration during the learning process. Then, the principle of Bellman optimality of continuous-time Hamilton-Jacobi-Bellman equation in every backstepping subsystem is satisfied with independently approximated actor and critic under the framework of actor-critic through the designed iterative updating. Eventually, the overall system control is optimized with the proposed BLF-SRL method. It is furthermore noteworthy that the variance of the attained control performance under uncertainty is also reduced with the proposed method. The effectiveness of the proposed method is verified with two motion control problems for autonomous vehicles through appropriate comparison simulations.

This article addresses the entry capture problem (ECP) of uncertain nonlinear systems under asymmetric performance constraints. We show that such ECP is commonly encountered in practice that has not been well addressed, whose tracking error is free from any performance constraints initially then is driven into the prescribed region in finite time. For better-transient performance, a unified tunnel prescribed performance (TPP) is developed to provide strict and tight allowable set. By utilizing a scaling function, together with an error scaling function (ESF), and a more general error-dependent transformation function (ETF), we propose an event-triggered tracking control strategy leading to a solution for the underlying ECP with various initial conditions and asymmetric performance constraints. This control strategy is of significant simplicity, stemming from that only 1-bit signal is needed for each data transmission between controller and actuator. We also show that the tracking error (including the initial-constraint violation) is regulated into the prescribed region in a given time globally. Finally, simulations are conducted to illustrate the above theoretical findings.

This article investigates the control barrier performance function (CBPF)-based event-triggered cooperative formation control of underactuated unmanned surface vehicles (USVs) under the consideration of input saturation. Compared with the cooperative formation commonly studied in existing literature, three distinct features of the present work are: 1) the conflict between consensus performance-related constraint and the control capability limitation gets balanced based on CBPF-based control; 2) the CBPF-based path updating alleviates the negative cooperative coupling for performance constraint maintenance; and 3) the parallel dynamic event-triggering (PDET) mechanism under the nonrecursive design framework reduces the update frequency of the controllers by adjusting the triggering threshold and gain in parallel. Numerical simulations are provided to verify the validity of the obtained theoretical results.

This article addresses the adaptive control problem for strict-feedback nonlinear time-varying systems with unknown control coefficients and external disturbances. To solve the problem of multiple unknown control coefficients, a new lemma based on a general class of Nussbaum functions rather than a specific Nussbaum function is proposed. By lumping all the time-varying parameters and control coefficients into an augmented time-varying vector, and combining the congelation of variables approach, a robust adaptive controller, without the restriction of the persistent excitation (PE) conditions, is constructed for the systems with external disturbances to guarantee the boundedness of all closed-loop variables. Then, by proper transformation, a novel adaptive control scheme is developed to achieve asymptotic stability for strict-feedback nonlinear time-varying systems without external disturbances. In addition, a tracking control method is acquired from the proposed adaptive control method. Finally, two simulations and an actual experiment demonstrate the applicability of the proposed schemes.

The classical optimal control of the linear system assumes that the system is stabilizable, thereby deriving the optimal control with the outcome that the solution inherently stabilizes the system. Such optimization does not distinctly address stabilization and optimization as separate concerns, leading to a situation where, as the system expands in size and complexity, the optimal controller suffers performance decreases and becomes increasingly sensitive and fragile. In this article, nested optimized control (NOC) and nested optimized adaptive control (NOAC) are introduced to explicitly handle the stabilization, optimization/adaptation for unknown parameters separately in an effort to strike a balance between guaranteed stability and optimal control. The robustness of the classical optimal control is inherent in the design itself, and the stability margin is relatively small subject to parameter uncertainties. In our NOC, the robustness is explicitly handled by the state feedback control and its stability margin is larger than the classical one, because of the introduction of the explicit state feedback control loop, the next optimized control loop is introduced for system performance. Note that, the term optimized rather than optimal is used here as it is not the classical optimal control anymore, but a fundamental change in design methodology. To further improve the stability margin due to parameter uncertainties, adaptive control is introduced to approximate the parameters in an effort to further improve the stability margin. The effectiveness of the proposed method is demonstrated through comparative examples that highlight its advantages.

To address the problem of ensuring the target is within the field of view when robot vision tracking, this paper designs an adaptive neural network-based trajectory tracking controller for the motion control of an omnidirectional mobile robot during target tracking. First, the desired orientation angle of the omnidirectional robot is designed based on the relative positions of the human and the robot, taking into account the limitations of the camera field of view. Subsequently, a kinematic model of the robot is designed considering skidding and sliding environmental disturbances, and an adaptive neural network control scheme is proposed to cope with the uncertainty of the disturbance parameters. Theoretical and experimental proof that the solution is feasible. Simulation results show that the proposed control scheme can perform the target tracking task. Finally, the feasibility and effectiveness of the control scheme are verified through practical experiments.

Motivated by the thermoelastic coupling effect arising in microelectromechanical systems (MEMS), event-triggered control of second-order ODE-heat systems coupled at intermediate point is investigated. The event-triggered control includes two parts, one is the feedback control signal, and the other is the event-triggered mechanism that decides when to update the control input. First, we design an event-triggered controller by using Zero-Order Hold to a continuous-time controller. Then, a dynamic triggering condition is established. Next, we derive that the existence of a minimal dwell-time, which avoids the occurrence of Zeno behavior. By utilizing Lyapunov-Krasovskii functional method, the global exponential stability of the closed-loop system is demonstrated. Finally, the simulation data based on the thermoelastic coupling system is displayed to demonstrate the availability of the theoretical results.

In this article, we propose an intelligent collaborative system for robotic navigation and control (CNaC) governed by the Euler-Lagrange equation. First, a state reconstruction based on neural networks navigation (SR-NNN) law is designed to estimate the current position of the robot for intelligent CNaC. The SR-NNN makes full use of partial truth information and the mighty local fitting ability of neural networks. In the absence of landmark, SR-NNN still exhibits navigation performance with high precision. The maximum root-mean-squared error (RMSE) of DR is 0.096 and the maximum RMSE of SR-NNN is 0.053, which has been improved by 55%. In addition, the motion model obtained by SR-NNN online training can avoid the error introduced by the predetermined motion model and overcome the interference of the external environment. The intelligent CNaC still can achieve satisfactory control performance based on the estimated position given by the SR-NNN rather than the ground truth which is formed by postprocessing. The intelligent CNaC has been demonstrated by simulation tracking sample and real experiments, which verifies the effectiveness of the intelligent CNaC.

In this article, a globally adaptive neural-network tracking control strategy based on the dynamic gain observer is proposed for a class of uncertain output-feedback systems with unknown time-varying delays. A reduced-order observer with novel dynamic gain is proposed. An n th-order continuously differentiable switching function is constructed to achieve the continuous switching control of the system, thus further ensuring that all the closed-loop signals are globally uniformly ultimately bounded (GUUB). It is proved that by adjusting the designed parameters, the tracking error converges to a region which can be adjusted to be small enough. The effectiveness of the control scheme is demonstrated by two simulation examples.

This article investigates the distributed active fault-tolerant cooperative control problem for leader-follower multiagent systems (MASs) in the presence of multiple faults, communication delays, and external disturbances. A new distributed consensus protocol is put forward to ensure the state consensus of MASs, which can be served as a nominal controller in fault-free cases with communication delays and external disturbances. A novel distributed time-delay intermediate observer, which can estimate system states and multiple faults simultaneously, is derived based on the time-delay closed-loop system equation. By integrating a fault compensation mechanism into the nominal controller, a distributed active fault-tolerant consensus controller is constructed for the follower agents to eliminate the adverse effects of multiple faults. Simulation examples are provided to demonstrate the effectiveness of the proposed method.

This article studies the problem of saturation-tolerant prescribed control (SPC) for a class of nonlinear time-delay systems with unknown control directions. We propose a unified finite-time tunnel prescribed performance (FTPP), which not only provides more tight allowable set leading to smaller overshoot, but also drives the tracking error into the prescribed set within a known time. To remove the implicit assumption that both input and performance constraints need to be satisfied simultaneously, an auxiliary system is developed to establish a balance between these two constraints instead of ignoring their interconnection and conflict. With aid of its generated nonnegative signals, the developed saturation-tolerant prescribed performance (SPP) possesses flexible performance. Namely, SPP can temporarily enlarge the performance boundaries to guarantee both constraints when input saturation occurs, and fast recover back to the prescribed boundaries when input saturation disappears. Consequently, a low-complexity SPC for uncertain nonlinear systems is developed, which can always guarantee both input and performance constraints even under unknown time delay and unknown control directions. Finally, comparative simulation is provided to illustrate the merits of the presented control strategy.

Synthetic Aperture Radar (SAR) image segmentation stands as a formidable research frontier within the domain of SAR image interpretation. The fully convolutional network (FCN) methods have recently brought remarkable improvements in SAR image segmentation. Nevertheless, these methods do not utilize the peculiarities of SAR images, leading to suboptimal segmentation accuracy. To address this issue, we rethink SAR image segmentation in terms of sequential information of transformers and cross-modal features. We first discuss the peculiarities of SAR images and extract the mean and texture features utilized as auxiliary features. The extraction of auxiliary features helps unearth the distinctive information in the SAR images. Afterward, a feature-enhanced FCN with the transformer encoder structure, termed FE-FCN, which can be extracted to context-level and pixel-level features. In FE-FCN, the features of a single-mode encoder are aligned and inserted into the model to explore the potential correspondence between modes. We also employ long skip connections to share each modality’s distinguishing and particular features. Finally, we present the connection-enhanced conditional random field (CE-CRF) to capture the connection information of the image pixels. Since the CE-CRF utilizes the auxiliary features to enhance the reliability of the connection information, the segmentation results of FE-FCN are further optimized. Comparative experiments conducted on the Fangchenggang (FCG), Pucheng (PC), and Gaofen (GF) SAR datasets. Our method demonstrates superior segmentation accuracy compared to other conventional image segmentation methods, as confirmed by the experimental results.

This article proposes an adaptive neural trajectory tracking control scheme forn-DOF robotic manipulators subjected to parameter variations, unknown functions, and time-varying external disturbances. First, the computed torque control (CTC) method is designed to reduce the system's nonlinearity. Second, radial basis function neural networks (RBFNNs) are constructed to approximate the uncertainties due to parameter variations and unknown functions. It is also important to note that the RBFNN's centers and widths are defined by state constraints. As a result of the nonlinear disturbance observer (NDO), the RBFNNs' approximation errors and disturbances are estimated to further improve tracking performance. The barrier Lyapunov function (BLF) ensures the closed-loop system's stability, guaranteeing tracking performance while preventing state constraint violation. Furthermore, sensitivity analysis provides a ranking of the importance of design parameters in influencing dynamic responses. Finally, simulations on a seven-degrees of freedom robotic manipulator are performed to validate the effectiveness of the proposed method.

In recent years, wearable sensor-based human activity recognition (HAR) is becoming more and more attractive, especially in health monitoring and sports management. However, in order to obtain high-quality HAR, it is often necessary to get sufficient labeled activity data, which is very difficult, time-consuming, and costly in a natural environment. To tackle this problem, multisource domain adaptation (DA) is a promising method that aims to learn enough multisource prior knowledge from labeled activity data, and then transfer this learned knowledge to the target unlabeled dataset. Thus, this article presents a novel multisource weighted DA with evidential reasoning (w-MSDAER) for HAR, which can effectively utilize complementary knowledge between multiple sources. Specifically, we first use the strategy of distribution alignment to learn local domain-invariant classifiers based on multisource domains. And then the reliabilities of these derived classifiers are comprehensively evaluated according to the belief function based technique for order preference by similarity to ideal solution (BF-TOPSIS). Finally, the discounting fusion method is used to fuse the local classification results. Comprehensive experiments are conducted on two open-source datasets, and the results show that the proposed w-MSDAER significantly outperforms other state-of-art methods.

This paper investigates the task assignment problem for one truck and one drone to deliver packages to a group of customer locations. The truck, carrying a large number of packages, can only travel between a group of prescribed street-stopping/parking locations to replenish the drone with both packages and batteries. The drone can carry multiple packages simultaneously to serve customers sequentially within its limited operation range. The objective is to reduce the amount of time it takes the drone to deliver the necessary package to the last customer while taking into account its operation range and loading capacity. First, the package delivery task assignment problem is shown to be an NP-hard problem, which guides us to design heuristic task assignment algorithms. Secondly, based on graph theory, a lower bound on the minimum time for the drone to serve the last customer is achieved to approximately evaluate the performance of a task assignment algorithm. Third, several decoupled heuristic algorithms are designed to sequentially plan the routes for the drone and the truck. Two coupled heuristic algorithms, namely the improved nearest inserting algorithm and the improved minimum marginal-cost algorithm, are proposed to simultaneously plan the routes for the drone and the truck. Numerical simulations demonstrate that the improved minimum marginal-cost algorithm reduces the total service time by 14.93% and 14.06% on average compared with the existing decoupled two-phase algorithm TPA and the coupled greedy algorithm, respectively. In the best case, it reduces the total service time by 41.71% and 40.11% compared with the TPA and the coupled greedy algorithm, respectively.

In this article, a globally neural-network-based adaptive control strategy with flat-zone modification is proposed for a class of uncertain output feedback systems with time-varying bounded disturbances. A high-order continuously differentiable switching function is introduced into the filter dynamics to achieve global compensation for uncertain functions, thus further to ensure that all the closed-loop signals are globally uniformity ultimately bounded (GUUB). It is proven that the output tracking error converges to the prespecified neighborhood of the origin. The effectiveness of the proposed control method is verified by two simulation examples.

In this article, we are devoted to the global stabilization for ordinary differential equation (ODE)-parabolic partial differential equation (PDE)-ODE-coupled systems subject to spatially varying coefficient, where a nonlinear ODE is located at the driving end and a linear ODE is located at the other end. By means of infinite-dimensional and finite-dimensional backstepping transformations, both state-feedback and output-feedback controllers are established to assure the global exponential stability of the resulting closed-loop system. Besides, the boundedness and exponential convergence of the controllers are also investigated. Finally, the availability of the theoretical results is illustrated by simulation data.

In this article, we study the adaptive stability for parabolic partial differential equation (PDE)-ordinary differential equation (ODE) cascade systems with actuator dynamics, where the actuator dynamics are nonlinear subject to unknown parameters. Compared with a class of PDE–ODE coupled systems that the control input only acts on the PDE boundary and the linear sandwiched system without uncertainty, the structure of such systems is more complex. First of all, infinite-dimensional backstepping transformation is adopted. The original PDE-ODE cascade system is changed to a new system that is easier to design. On this basis, finite-dimensional backstepping transformation and adaptive compensation technology are combined to develop a state-feedback controller. Then, the boundedness of all the signals in the closed-loop system is proved by the Lyapunov functional analysis. Furthermore, the control law and the original system states eventually converge to zero. Finally, different simulation data are presented to illustrate the validity of the theoretical results.

In this article, the output feedback control design problem for a class of strict-feedback nonlinear systems with unknown state time-varying delays is addressed. Two output feedback control schemes are considered which are based on full-order and reduced-order observer, respectively. Lyapunov–Krasovskii functionals with static gain are used to develop novel memoryless control strategies. The above work ensures that all the signals in the closed-loop system are bounded and the system is asymptotically stable. Finally, two simulation examples illustrate the effectiveness of the two control schemes we proposed.

Adaptive fuzzy control via disturbance observer (DOB) is proposed for uncertain strict-feedback nonlinear systems with unknown disturbances and full-state constraints. By constructing an integral barrier Lyapunov function, it is ensured that all states in the strict-feedback system will not exceed their preset constraints. Moreover, the fuzzy-logic system is adopted to approximate the uncertainty of nonlinear functions in this system. To efficiently estimate the approximation error and unknown disturbances, this work designs a new DOB and ensures that the estimation error of lumped disturbances converge to a smaller and bounded set. The proposed control based on the Lyapunov criterion ensures that all signals in the nonlinear system are semi-global uniformly and ultimately bounded. Two simulation examples illustrate the efficiency of the proposed control.

In this article, a decentralized adaptive control strategy is proposed for a class of large-scale nonlinear systems with interconnections. Each subsystem contains multiple state delays, asymmetric dead-zone inputs, and bounded time-varying disturbances. Every error subsystem is first decomposed according to input matrix assumption. Then, the decentralized adaptive controller is developed to handle uncertain functions in the subsystem. The nonlinear control gain function is constructed and used in adaptive control design. Two robust adaptive control schemes are, respectively, addressed for the cases of matched and mismatched time-delay nonlinearities. It is proved that all closed-loop signals are bounded, and the tracking error converges to an adjustable region. A simulation example is presented to show the effectiveness of the proposed method.

Abstract This paper proposes a state estimation approach ‘robust strong tracking unscented Kalman filter with unknown inputs’ that can be applied to non‐linear systems with unknown inputs. Specifically, the non‐linear state and measurement equations are linearised by statistical linearisation. Then, the estimation equation of the unknown input is derived based on the weighted least squares method. The multiple suboptimal fading factor is introduced into a priori error covariance matrix to improve the tracking ability for the inaccuracy of the system model and the abrupt change of state variables caused by unknown inputs. Finally, based on the unbiased minimum variance estimation, the unbiased state estimation and the error covariance matrix are derived. Singular value decomposition is performed on the error covariance matrix to improve the stability of the algorithm. Simulated results validate the effectiveness of the proposed method.

Human pose estimation has achieved significant progress on images with high imaging resolution. However, low-resolution imagery data bring nontrivial challenges which are still under-studied. To fill this gap, we start with investigating existing methods and reveal that the most dominant heatmap-based methods would suffer more severe model performance degradation from low-resolution, and offset learning is an effective strategy. Established on this observation, in this work we propose a novel Confidence-Aware Learning (CAL) method which further addresses two fundamental limitations of existing offset learning methods: inconsistent training and testing, decoupled heatmap and offset learning. Specifically, CAL selectively weighs the learning of heatmap and offset with respect to ground-truth and most confident prediction, whilst capturing the statistical importance of model output in mini-batch learning manner. Extensive experiments conducted on the COCO benchmark show that our method outperforms significantly the state-of-the-art methods for low-resolution human pose estimation.

High-quality data transmission synchronization process is frequently expected in light of secure communication mechanisms (SCMs), especially for space laser communication among the satellite constellation. To improve security and reliability during the data transmission processes prominently, control problems of chaotic synchronization synchronouslyat the same timeare explored. In this paper, several novel error synchronization control protocols are proposed to solve these problems. First, by introducing a norm-normalized sign function (NNSF), unique (fixed-) time-synchronized stability is manifested, such that all non-zero state elements reach the origin synchronously at the same time. And upper bounds of synchronous resident time calculated by offered protocols are irrelevant/relevant to initial states of the error systems. Second, integrated with the (fixed-) time-synchronized stability theories, the (fixed-) time-synchronized sliding mode controllers with special convergent performance are established for two representative types of chaotic systems. Third, the ratio-persistent performance plays a dominating role for simultaneous convergence of the errors. Further, the innovation of the algorithms is reflected in that the decrypted signal is completely consistent with the transmitted message signal within synchronized settling time. Finally, in the simulation, not only theoretical verifications, but also practical verifications of image encryption and decryption processes verify the effectiveness of the SCMs.

The output-based event-triggered cooperative output regulation problem is addressed for constrained linear heterogeneous multiagent system in this article. In light of the robust control theory,$H_{\infty }$leader-following consensus with respect to exogenous signals, including both disturbance to be rejected and reference state of leader to be tracked, is guaranteed. Meanwhile, the system performance alleviates degradation through a model recovery anti-windup technique while encountering input saturation. Furthermore, the follower’s self-state observer, the leader-state observer, and the anti-windup auxiliary system are integrated into a comprehensive system, and a unified event-triggering mechanism of full states is addressed. A fixed lower bound of sampled interval is adopted such that the frequency of data transmission gets reduced and no Zeno-behavior happens. Both the input and output of the follower’s controller and anti-windup compensator hold constant, respectively, during the event-triggered intervals such that the resulting output-based event-triggered controller can be directly implemented in a digital platform. Finally, a simulation example is provided to illustrate the effectiveness.

This article proposes a saturation-tolerant prescribed control (SPC) for a class of multiinput and multioutput (MIMO) nonlinear systems simultaneously considering user-specified performance, unmeasurable system states, and actuator faults. To simplify the control design and decrease the conservatism, tunnel prescribed performance (TPP) is proposed not only with concise form but also smaller overshoot performance. By introducing non-negative modified signals into TPP as saturation-tolerant prescribed performance (SPP), we propose SPC to guarantee tracking errors not to violate SPP constraints despite the existence of saturation and actuator faults. Namely, SPP possesses the ability of enlarging or recovering the performance boundaries flexibly when saturations occur or disappear with the help of these non-negative signals. A novel auxiliary system is then constructed for these signals, which bridges the associations between input saturation errors and performance constraints. Considering nonlinearities and uncertainties in systems, a fuzzy state observer is utilized to approximate the unmeasurable system states under saturations and unknown actuator faults. Dynamic surface control is employed to avoid tedious computations incurred by the backstepping procedures. Furthermore, the closed-loop state errors are guaranteed to a small neighborhood around the equilibrium in finite time and evolved within SPP constraints although input saturations and actuator faults occur. Finally, comparative simulations are presented to demonstrate the feasibility and effectiveness of the proposed control scheme.

This article addresses the formation control problem of flapping-wing vehicles (FWVs) under the model uncertainty and the measurement inaccuracy. A two-layer formation strategy is adopted, which consists of a formation control layer for the leaders, and a containment control layer for the followers. In both layers, attitudes and positions are required by the formation geometry. A formation state estimation algorithm is designed to achieve the desired formation states from local neighborhoods. In FWVs, attitude angles are usually achieved from angular velocities, whose measurement error accumulates during integration and leads to divergence of the system. In order to solve this problem, we explore the coupling property between the translational motion and the rotational motion of FWVs, and design a coupling-based estimation method for attitude angles. To compensate for the model uncertainty, the measurement error, and the estimation error, adaptive neural networks are developed together with the control algorithm. The stability of both the control algorithm and the estimation algorithm is guaranteed based on the Lyapunov stability theory. Simulations are conducted to validate our method, and the results illustrate its effectiveness.

In this technical correspondence, a multilayer formation (MLF) control problem is considered and solved by a unified framework. The agents in each layer present a sort of hierarchical distinction: receive information from former layers, communicate inside the current layer, and send information to subsequent layers. With an arbitrary number of layers, we extend the previous result from undirected graphs to directed ones. The proposed controller achieves MLF without using the distributed estimators and the acceleration information. This removes the induced discontinuities and alleviates the system complexity. It is then proved that the closed-loop errors are semiglobally uniformly ultimately bounded. Simulations are presented to illustrate the effectiveness of this approach.

In this article, based on polynomial differential inclusions, we propose a heuristic iterative approach for estimating the domains of attraction for nonpolynomial systems. First, we use the fuzzy model to construct a polynomial differential inclusion for the nonpolynomial system, which can be equivalently written as a time-invariant uncertain polynomial system. Then, beginning with an initial inner estimation, we present an iterative approach to enlarge this initial inner estimation by calculating common Lyapunov-like functions. Furthermore, the domains of attraction are estimated by combining this iterative approach with heuristic construction of differential inclusions. In the end, our heuristic iterative approach is implemented with linear semidefinite programming and then tested on some nonpolynomial examples with comparisons to the existing methods in the literature.

In this article, a robust adaptive output-feedback control approach is presented for a class of nonlinear output-feedback systems with parameter uncertainties and time-varying bounded disturbances. A reduced-order filter driven by control input is proposed to reconstruct unmeasured states. The state estimation error is shown to be bounded by dynamic signals driven by system output. The bound estimation technique is employed to estimate the unknown disturbance bound. Based on the backstepping design with three sets of tuning functions, an adaptive output-feedback control scheme with the flat-zone modification is proposed. It is shown that all the signals in the resulting closed-loop adaptive control systems are bounded, and the output tracking error converges to a prespecified small neighborhood of the origin. Two simulation examples are provided to illustrate the effectiveness and validity of the proposed approach.

In this article, we investigate the saturation-tolerant prescribed control (SPC) for multiinput and multioutput nonlinear systems with unknown control directions and actuator faults. We propose a concise tunnel prescribed performance (TPP) with the control design independent of initial conditions and smaller overshoots achieved due to its tight feasible region. A novel auxiliary system, to tactfully establish a feedback mechanism between input saturation and prescribed performance, is constructed. By introducing the generated nonnegative modifications into the TPP, the resulted saturation-tolerant prescribed performance (SPP) is capable of flexibly degrading performance constraints in the case of saturation; and recovering back to the user-specified performance in the case without saturation. Furthermore, the proposed control scheme guarantees not only finite-time convergence, but also SPP-constrained tracking performance despite the input saturation and uncertainties. Finally, comparative results are provided to demonstrate the distinctive merit of the proposed SPC more than the traditional prescribed performance control.

This article studies the adaptive fuzzy fault tolerant control (FTC) problem for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with unknown control directions in the presence of time-varying asymmetric output constraints. Our contribution includes a step forward beyond usual FTC results to exhibit that the system output of nonsquare and square MIMO systems is uniformly bounded against actuator faults by a novel FTC methodology without the fault detection unit, as well as stay in the preselected constraints. To obtain new results, an equivalent unconstrained system is established by employing an error transformation technique. Furthermore, a learning-based switching function scheme is proposed to automatically activate different groups of actuators without human intervention for attenuating the influence of faulty actuators. By this means, no explicit fault detection and isolation units are needed to result in reducing the risk of false alarm or missed detections and expediting the responsiveness of the controller. Moreover, the obstacle caused by unknown control directions is circumvented by a novel technique combining the matrix decomposition technique and Nussbaum-type function. It is proved that the desired tracking performance with prescribed output bounds and the boundedness of all the signals in the closed-loop system can be guaranteed via an improved average dwell time approach. Finally, simulation results demonstrate the merits of the proposed controller.

This article investigates the task assignment problem in which multiple dispersed robots need to visit a set of target locations while trying to minimize the robots’ total travel distance. Each robot initially has the position information of all the targets and of those robots that are within its limited communication range, and each target demands a robot with some specified capability to visit it. We propose a decentralized auction algorithm which first employs an information consensus procedure to merge the local information carried by each communication-connected (CC) robot subnetwork. Then, we apply a marginal-cost-based strategy to construct conflict-free target assignments for the CC robots. When the communication network of the robots is not connected, we demonstrate that the robots’ total travel distance might in fact increase when their communication range grows, and more importantly, such a somewhat counterintuitive fact holds for a range of algorithms. Furthermore, the proposed algorithm guarantees that the total travel distance of the robots is at most twice of the optimal when the communication network is initially connected. Finally, Monte Carlo simulation results demonstrate the satisfying performance of the proposed algorithm.

Previous research on finite-time control focuses on forcing a system state (vector) to converge within a certain time moment, regardless of how each state element converges. In the present work, we introduce a control problem with unique finite/fixed-time stability considerations, namely time-synchronized stability (TSS), whereat the same time, all the system state elements converge to the origin, and fixed-TSS, where the upper bound of the synchronized settling time is invariant with any initial state. Accordingly, sufficient conditions for (fixed-) TSS are presented. On the basis of these formulations of the time-synchronized convergence property, the classical sign function, and also anorm-normalized sign function, are first revisited. Then in terms of this notion of TSS, we investigate their differences with applications in control system design for first-order systems (to illustrate the key concepts and outcomes), paying special attention to their convergence performance. It is found that while both these sign functions contribute to system stability, nevertheless an important result can be drawn that norm-normalized sign functions help a system to additionally achieve TSS. Furthermore, we propose a fixed-time-synchronized sliding-mode controller for second-order systems; and we also consider the important related matters of singularity avoidance there. Finally, numerical simulations are conducted to present the (fixed-) time-synchronized features attained; and further explorations of the merits of the proposed (fixed-) TSS are described.

In this article, the output-feedback control problem for a class of nonlinear time-delay systems has been considered. Unmeasured system states and the time-varying delays which appear in all state variables bring great challenges to the controller design. Novel Lyapunov–Krasovkii functionals with control gains are proposed for a delay-independent controller. The system is proved to be asymptotically stable driven by the controller with suitable design parameters. Finally, we apply the new control scheme to a two-stage chemical reactor system and the time-delay tension leg platform system, the effectiveness of the control scheme is illustrated in the simulation results.

In this article, adaptive state feedback stabilization is addressed for a class of delayed uncertain systems with external disturbance, where the time delays and the bound parameters of the delayed states and disturbance are assumed to be unknown. By introducing a new Lyapunov–Krasovskii functional, we present a memoryless control strategy to stabilize nonlinear time-delay systems through newly defined control gain function. The main contribution of this article lies in the construction of control gain function. Multiple gain functions can be revealed in a function set. The asymptotic stability of the closed delayed nonlinear system is ensured. Simulation examples are presented to show the effectiveness of the proposed method.

This article studies the task assignment problem for a fleet of dispersed vehicles to efficiently visit a set of target locations where some target locations might be unreachable for one or several vehicles. The objectives are to visit as many target locations as possible by using the minimum number of vehicles while minimizing the vehicles' total travel time. We first propose a target merging strategy to deal with the optimization problem, which is in general NP-hard, and show that for the special case of a single vehicle, it requires linear time to calculate the maximum number of targets to be visited. Second, we design a longest path-based algorithm and analyze the cases in which the objective to visit the maximum number of targets by using the minimum number of vehicles can be obtained through the proposed algorithm within linear running time. Once the targets to be visited and the corresponding employed vehicles are determined, the marginal-cost-based target inserting principle to be discussed guarantees that the chosen targets will be visited within a computable finite maximal travel time, which is at most twice of the optimal when the cost matrix is symmetric. Integrating the longest path-based algorithm with two target inserting principles used to minimize the vehicles' total travel time, we design two two-phase task assignment algorithms. Furthermore, we propose a one-phase algorithm to optimize the multiple objectives simultaneously by improving a co-evolutionary multipopulation genetic algorithm. Numerical simulations show that the proposed task assignment algorithms can lead to satisfying solutions against popular genetic algorithms.

Considering various cyberattacks aiming at the Internet of Vehicles (IoV), secure pose estimation has become an essential problem for ground vehicles. This paper proposes a pose estimation approach for ground vehicles under randomly occurring deception attacks. By modeling attacks as signals added to measurements with a certain probability, the attack model has been presented and incorporated into the existing process and measurement equations of ground vehicle pose estimation based on multisensor fusion. An unscented Kalman filter-based secure pose estimator is then proposed to generate a stable estimate of the vehicle pose states; i.e., an upper bound for the estimation error covariance is guaranteed. Finally, the simulation and experiments are conducted on a simple but effective single-input-single-output dynamic system and the ground vehicle model to show the effectiveness of UKF-based secure pose estimation. Particularly, the proposed scheme outperforms the conventional Kalman filter, not only by resulting in more accurate estimation but also by providing a theoretically proved upper bound of error covariance matrices that could be used as an indication of the estimator’s status.

This article investigates the task planning problem where one vehicle needs to visit a set of target locations while respecting the precedence constraints that specify the sequence orders to visit the targets. The objective is to minimize the vehicle's total travel distance to visit all the targets while satisfying all the precedence constraints. We show that the optimization problem is NP-hard, and consequently, to measure the proximity of a suboptimal solution from the optimal, a lower bound on the optimal solution is constructed based on the graph theory. Then, inspired by the existing topological sorting techniques, a new topological sorting strategy is proposed; in addition, facilitated by the sorting, we propose several heuristic algorithms to solve the task planning problem. The numerical experiments show that the designed algorithms can quickly lead to satisfying solutions and have better performance in comparison with popular genetic algorithms.

Action recognition is a critical task for social robots to meaningfully engage with their environment. 3-D human skeleton-based action recognition has been an attractive research area in recent years. Although the existing approaches are good at action recognition, it is a great challenge to recognize a group of actions in an activity scene. To tackle this problem, at first, we partition the scene into several primitive actions (PAs)-based upon motion attention mechanism. Then, the PAs are described by the trajectory vectors of the corresponding joints. After that, motivated by text classification based on word embedding, we employ a convolutional neural network (CNN) to recognize activity scenes by considering motion of joints as "word" of activity. The experimental results on the dataset of human activity scenes show the efficiency of the proposed approach.

Distributed formation control is presented for networked Euler-Lagrange systems (ELSs) over a directed interaction topology. This problem is defined by a layered framework in which information flow both among the leaders and among the followers is described by different layers. To empower the formation to make a variety of geometric transformations, we present the necessary and sufficient conditions for affine maneuverability under a directed graph. Unlike most existing results using a diagonal stabilizing matrix to achieve the stabilizability of affine formation, this fully distributed approach is feasible without any global information. Next, we propose an adaptive control law for agents in each layer, where the closed-loop errors are driven to a neighborhood of the origin in finite time. Adaptive neural networks are integrated to tackle the model uncertainties in ELSs by updating the norm of the weight matrix, which can simplify the control design and alleviate the computational burden compared with traditional ones. The simulation results are given to show the effectiveness of the proposed approach.

In this article, a control scheme based on optimized backstepping (OB) technique is developed for a class of nonlinear strict-feedback systems with unknown dynamic functions. Reinforcement learning (RL) is employed for achieving the optimized control, and it is designed on the basis of the neural-network (NN) approximations under identifier-critic-actor architecture, where the identifier, critic, and actor are utilized for estimating the unknown dynamic, evaluating the system performance, and implementing the control action, respectively. OB control is to design all virtual controls and the actual control of backstepping to be the optimized solutions of corresponding subsystems. If the control is developed by employing the existing RL-based optimal control methods, it will become very intricate because their critic and actor updating laws are derived by carrying out gradient descent algorithm to the square of Bellman residual error, which is equal to the approximation of the Hamilton-Jacobi-Bellman (HJB) equation that contains multiple nonlinear terms. In order to effectively accomplish the optimized control, a simplified RL algorithm is designed by deriving the updating laws from the negative gradient of a simple positive function, which is generated from the partial derivative of the HJB equation. Meanwhile, the design can also release the condition of persistence excitation, which is required in most existing optimal controls. Finally, effectiveness is demonstrated by both theory and simulation.

Continued great efforts have been dedicated toward high-quality trajectory generation based on optimization methods; however, most of them do not suitably and effectively consider the situation with moving obstacles; and more particularly, the future position of these moving obstacles in the presence of uncertainty within some possible prescribed prediction horizon. To cater to this rather major shortcoming, this work shows how a variational Bayesian Gaussian mixture model (vBGMM) framework can be employed to predict the future trajectory of moving obstacles; and then with this methodology, a trajectory generation framework is proposed which will efficiently and effectively address trajectory generation in the presence of moving obstacles, and incorporate the presence of uncertainty within a prediction horizon. In this work, the full predictive conditional probability density function (PDF) with mean and covariance is obtained and, thus, a future trajectory with uncertainty is formulated as a collision region represented by a confidence ellipsoid. To avoid the collision region, chance constraints are imposed to restrict the collision probability, and subsequently, a nonlinear model predictive control problem is constructed with these chance constraints. It is shown that the proposed approach is able to predict the future position of the moving obstacles effectively; and, thus, based on the environmental information of the probabilistic prediction, it is also shown that the timing of collision avoidance can be earlier than the method without prediction. The tracking error and distance to obstacles of the trajectory with prediction are smaller compared with the method without prediction.

In the original belief function (BF) theory, a precise-valued belief structure has been widely used to represent uncertain information. However, this mentioned belief structure is difficult to effectively measure the specific hesitant situation, especially when decision makers have a set of possible values for the belief assignments of focal elements. In order to model the hesitant nature of the behavior of people to make a decision under uncertainty, we propose a hesitant fuzzy belief structure (HFBS) that is based on the BF theory and the recent hesitant fuzzy set theory. We also present the novel rule of combination of HFBS that is used and evaluated in a wearable human activity recognition (HAR) system coupled with an extreme learning machine. The evaluation of this new HFBS approach is done from two benchmark datasets. We clearly show its effectiveness and its superiority compared to various methods used classically for the wearable HAR.

This article addresses the finite-time tracking control for multi-input and multi-output (MIMO) nonlinear nonstrict feedback systems with actuator faults and saturations. First, a fuzzy state observer is constructed to approximate the unmeasured system states, where the restrictions of the known actuator faults are removed from the observer design. Based on the state observer, a novel adaptive output feedback control is then proposed to achieve favorable tracking performance even if actuator saturations and faults occur. Also, the nonlinear functions in the MIMO nonlinear systems are not required to follow the linearly parameterization or growth conditions making the control design more generally available. Furthermore, the dynamic surface control technique is adopted to avoid tedious analytic computations inherent in the backstepping procedure. It can be proved that the proposed control can not only guarantee the closed-loop system states bounded, but also regulate the tracking errors to a small neighborhood around the equilibrium in finite time despite the existence of the actuator saturations and faults. Finally, comparative simulations are carried out to demonstrate the feasibility and effectiveness of the theoretical results.

This article develops a new strategy for robust fault-tolerant control (FTC) of robot manipulators using an adaptive fuzzy integral sliding-mode control (ISMC) and a disturbance observer (DO). First, an ISMC is developed for the FTC system. The major features of the approach are discussed. Then, to enhance the performance of the system, a fuzzy logic system approximation and a DO are introduced to approximate the unknown nonlinear terms, which include the model uncertainty and fault components, and to estimate the compounded disturbance and then are integrated into the ISMC. Next, a switching term based on an adaptive two-layer supertwisting algorithm is designed to compensate the disturbance estimated error and guarantee stability and convergence of the whole system. The nominal controller of the ISMC is reconstructed using a backstepping control technique to achieve the stability for the nominal system based on the Lyapunov criterion. The computer simulation results demonstrate the effectiveness of the proposed approach.

G-images refer to image data defined on irregular graph domains. This article elaborates on a similarity-preserving FuzzyC-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images. To preserve the membership similarity between an arbitrary image pixel and its neighbors, a Kullback–Leibler divergence term on partition matrix is introduced as a part of FCM. As a result, similarity-preserving FCM is developed by considering spatial information of image pixels for its robustness enhancement. Due to superior characteristics of a wavelet space, the proposed FCM is performed in this space rather than the Euclidean one used in conventional FCM to secure its high robustness. Experiments on synthetic and real-world G-images demonstrate that it indeed achieves higher robustness and performance than the state-of-the-art segmentation algorithms. Moreover, it requires less computation than most of them.

This article is concerned with the challenge of guaranteeing output constraints for fault-tolerant control (FTC) of a class of unknown multi-input single-output (MISO) nonlinear systems in the presence of actuator faults. Most industrial systems are equipped with redundant actuators and a fault detection-isolation mechanism for accommodating unexpected actuator faults. To simplify the system design and reduce the risk of false alarm or missed detection brought by the detection unit, a learning-based switching function scheme is proposed to automatically activate different sets of actuators in a rotational manner without human intervention. By this means, no explicit fault detection mechanism is needed. An additional step has been made to guarantee that the system output remains in user-defined time-varying asymmetric output constraints all the time during the occurrence of failures by utilizing error transformation techniques. The stability of the transformed system can equivalently deliver the result that the original system output stays in the required bounds. Hence, system crash or further catastrophic outcomes can be avoided. A neural network is integrated to embody the adaptive FTC design for dealing with unknown system dynamics. The dynamic surface control (DSC) technique is also invoked to decrease complexity. Furthermore, the stability analysis is carried out by the standard Lyapunov approach to guarantee that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, the simulation results are provided to verify the effectiveness of the proposed scheme.

In this article, we investigate the inner estimations of the minimal domains of attraction (MDA) for uncertain nonlinear systems, whose uncertainties are modeled by parameters defined in a semialgebraic set. We begin from an initial inner estimation of MDA and then enlarge this initial inner estimation by iterative calculating common Lyapunov-like functions with a linear sum of squares programming-based approach. Afterwards, this enlarged inner estimation of MDA is further improved by iterative computations of parameter-dependent Lyapunov-like functions. Especially, we use a simple semialgebraic set, described by a polynomial level-set function, to under-approximate this improved estimation. In the end, our methods are implemented and tested on several uncertain examples with comparisons to existing methods in the literatures.

In this article, tracking control is considered for a class of uncertain multi-input-multi-output (MIMO) nonlinear systems, where the time-varying parameters, the time-varying control coefficient and the time-varying disturbance are assumed to be unknown but to be bounded. Three stable adaptive tracking schemes for a given reference signal are proposed by devising different control algorithms. In the first scheme, bounded-error tracking is achieved in the sense that the tracking error converges exponentially to an adjustable region around the origin, where the σ-modification adaptive laws are used to ensure the boundedness of all closed-loop signals. In the second scheme, asymptotic tracking is obtained in the sense that the tracking error converges to zero asymptotically, where the strictly positive and integral functions are employed in the control law to ensure the signal boundedness and zero-error tracking. In the third scheme, exponential tracking is gotten in the sense that the tracking error exponentially converges to zero with a given convergence speed, where exponential functions are incorporated into control law and adaptive laws to ensure system stability and the faster convergence. Three adaptive tracking schemes are, respectively, applied to nonlinear chaotic Chua's circuit with control inputs. The parametric model is developed for Chua's circuit with uncertain parameters and external disturbances. The effectiveness of the proposed control algorithms is demonstrated by comparative simulation studies.

This paper studies the precedence-constrained task assignment problem for a team of heterogeneous vehicles to deliver packages to a set of dispersed customers subject to precedence constraints that specify which customers need to be visited before which other customers. A truck and a micro drone with complementary capabilities are employed where the truck is restricted to travel in a street network and the micro drone, restricted by its loading capacity and operation range, can fly from the truck to perform the last-mile package deliveries. The objective is to minimize the time to serve all the customers respecting every precedence constraint. The problem is shown to be NP-hard, and a lower bound on the optimal time to serve all the customers is constructed by using tools from graph theory. Then, integrating with a topological sorting technique, several heuristic task assignment algorithms are proposed to solve the task assignment problem. Numerical simulations show the superior performances of the proposed algorithms compared with popular genetic algorithms.

Pose estimation with state or measurement constraints has been frequent in autonomous vehicle navigation. Aiming at incorporating constraints inherently, this article proposes a dynamic potential field (DPF)-based formulation to represent states, measurements, and constraints on connected Riemannian manifolds. The state equation and the output equation are derived in the DPF forms, which imply the probabilistic inference with states and measurements. Constraints are incorporated by projecting points toward a constraint subset in the state space and the measurement space, where the DPF representing constraints is created. An information fusion scheme has been designed for DPFs, which are obtained from multisensor measurements and constraints. KITTI and self-collected sequences have been used in experiments, during which it is observed that the rotational drift is corrected and translational errors are reduced, thanks to the fusion of stereo visual odometry (SVO), heading measurements, and road maps.

This paper addresses the trajectory analysis, mission design, and control law for multiple microsatellites to cooperatively circumnavigate a host spacecraft. This cooperative circumnavigation (CCN) problem is defined to drive a group of networked microsatellites to a predefined planar ellipse concerning a host spacecraft while maintaining a geometric formation configuration. We first design several potential functions to guide the microsatellites to the given planar elliptical orbit with a proper radius. Next, the affine Laplacian matrix is introduced to characterize the desired formation shape of microsatellites. Based on the potential functions and the Laplacian matrix, a CCN control law is finally proposed. Then, the simulation results of eight microsatellites with earth-orbiting mission scenarios are given, where the natural trajectory motion is incorporated which consumes nearly zero-fuel.

Multisensor fusion strategies have been widely applied in human activity recognition (HAR) in body sensor networks (BSNs). However, the sensory data collected by BSNs systems are often uncertain or even incomplete. Thus, designing a robust and intelligent sensor fusion strategy is necessary for high-quality activity recognition. In this article, Dezert-Smarandache theory (DSmT) is used to develop a novel sensor fusion strategy for HAR in BSNs, which can effectively improve the accuracy of recognition. Specifically, in the training stage, the kernel density estimation (KDE)-based models are first built and then precisely selected for each specific activity according to the proposed discriminative functions. After that, a structure of basic belief assignment (BBA) can be constructed, using the relationship between the test data of unknown class and the selected KDE models of all considered types of activities. In order to deal with the conflict between the obtained BBAs, proportional conflict redistribution-6 (PCR6) is applied to fuse the acquired BBAs. Moreover, the missing data of the involved sensors are addressed as ignorance in the framework of the DSmT without manual interpolation or intervention. Experimental studies on two real-world activity recognition datasets (The OPPORTUNITY dataset; Daily and Sports Activity Dataset (DSAD)) are conducted, and the results shows the superiority of our proposed method over some state-of-the-art approaches proposed in the literature.

This paper considers the problem of asynchronous H∞control for networked Markovian jump systems subject to probabilistic packet dropouts and communication delays in the measurement channel. A new dynamic output-feedback-based asynchronous controller is proposed wherein the dynamic output-feedback controller modes need not synchronize with the system modes. By utilizing results from stochastic Lyapunov-Krasovskii stability theory, sufficient conditions in terms of matrix inequalities are derived such that the closed-loop networked Markovian jump system is stochastically stable and achieves the prescribed H∞performance. Using the Schur complement technique and under the assumption that the input matrix is full rank, the sufficient condition is reduced to a linear matrix inequality and the dynamic output-feedback-based asynchronous controller is synthesized. A detailed numerical example with simulation results are presented to evaluate the proposed controller design scheme.

In this paper, heading reference-assisted pose estimation (HRPE) has been proposed to compensate inherent drift of visual odometry (VO) on ground vehicles, where an estimation error is prone to grow while the vehicle is making turns or in environments with poor features. By introducing a particular orientation as “heading reference,” a pose estimation framework has been presented to incorporate measurements from heading reference sensors into VO. A graph formulation is then proposed to represent the pose estimation problem under the commonly used graph optimization model. Simulations and experiments on KITTI data set and our self-collected sequences have been conducted to verify the accuracy and robustness of the proposed scheme. KITTI sequences and manually generated heading measurement with Gaussian noises are used in simulation, where rotational drift error is observed to be bounded. Compared with a pure VO, the proposed approach greatly reduces average translational localization error from 153.85 to 24.29 m and 23.80 m in self-collected stereo visual sequences with traveling distance over 4.5 km at the processing rates of 19.7 and 11.1 Hz, for the loosely coupled and tightly coupled models, respectively.

This paper addresses the distributed formation-containment (DFC) problem for multiple Euler-Lagrange systems with model uncertainties via output feedback in both constant and time-varying formation cases. First, a novel definition of the DFC problem is proposed using a two-layer framework. Since only parts of the followers can acquire the states of the dynamic leader, we design a distributed finite-time sliding-mode estimator to obtain accurate estimations of the desired position and velocity for each agent. Next, to deal with the absence of velocity sensors, we propose two DFC control laws combined with the high-gain observer for the leaders and the followers, respectively, while the time-varying formation in the first layer and the leader-based containment in the second layer can be achieved. Further, the adaptive neural networks are applied to deal with the model uncertainties due to their superior approximation capability. The uniform ultimate boundedness of all the state errors can be guaranteed by Lyapunov stability theory. In addition, a unified framework is given which can be transformed to four other basic distributed problems. Finally, simulation examples are presented to illustrate the feasibility of the theoretical results.

In this paper, the formation tracking control is studied for a multiagent system (MAS) with communication limitations. The objective is to control a group of agents to track a desired trajectory while maintaining a given formation in nonomniscient constrained space. The role switching triggered by the detection of unexpected spatial constraints facilitates efficiency of event-triggered control in communication bandwidth, energy consumption, and processor usage. A coordination mechanism is proposed based on a novel role "coordinator" to indirectly spread environmental information among the whole communication network and form a feedback link from followers to the leader to guarantee the formation keeping. A formation scaling factor is introduced to scale up or scale down the given formation size in the case that the region is impassable for MAS with the original formation size. Controllers for the leader and followers are designed and the adaptation law is developed for the formation scaling factor. The conditions for asymptotic stability of MAS are discussed based on the Lyapunov theory. Simulation results are presented to illustrate the performance of proposed approaches.

In this paper, a tracking control approach for surface vessel is developed based on the new control technique named optimized backstepping (OB), which considers optimization as a backstepping design principle. Since surface vessel systems are modeled by second-order dynamic in strict feedback form, backstepping is an ideal technique for finishing the tracking task. In the backstepping control of surface vessel, the virtual and actual controls are designed to be the optimized solutions of corresponding subsystems, therefore the overall control is optimized. In general, optimization control is designed based on the solution of Hamilton-Jacobi-Bellman equation. However, solving the equation is very difficult or even impossible due to the inherent nonlinearity and complexity. In order to overcome the difficulty, the reinforcement learning (RL) strategy of actor-critic architecture is usually considered, of which the critic and actor are utilized for evaluating the control performance and executing the control behavior, respectively. By employing the actor-critic RL algorithm for both virtual and actual controls of the vessel, it is proven that the desired optimizing and tracking performances can be arrived. Simulation results further demonstrate effectiveness of the proposed surface vessel control.

This paper proposes an improved disturbance observer to realize accurate contact force estimation using the joint torque sensor. The joint torque sensor separates the dynamics of the link side from the motor side of the robot manipulator. Therefore, only computing the partial dynamics on the link side can realize external force estimation. This can considerably reduce the modeling workload and error terms that may affect the estimation results. Furthermore, this paper presents that the observed residual value during free motion can be considered as the friction dynamics, which is approximated by the neural network (NN) due to its inherent capacity in approximating nonlinear functions. After that, the estimation accuracy of the observer is considerably improved. Compared to other local NN approximation method, we analyzes the properties of the friction force in detail to select appropriate excitation trajectory for accurate global approximation results using only limited training data. We have presented that the suitable excitation trajectory and the use of global basis function are the sufficient conditions for global friction approximation. The proof of this theorem is also given. The Kalman filter is also utilized to reduce the noise of the estimation results in real time. The experimental results also demonstrate the efficacy of the proposed method, which accurately estimates the contact force for flexible joint robots.

This paper proposes an optimized tracking control approach using neural network (NN) based reinforcement learning (RL) for a class of nonlinear dynamic systems, which requires both tracking and optimizing to be performed simultaneously. Generally, for obtaining optimal control solution, Hamilton-Jacobi-Bellman equation is expected to be solvable, but, owing to strong nonlinearity, the equation is solved difficultly or even impossibly by analytical methods. Therefore, adaptive NN approximation based RL is usually considered. In the optimized control design, for driving output state following to the desired trajectory, an error term is split from optimal performance index function, and then both actor and critic NNs are built to perform RL algorithm. Actor NN aims to execute control behaviors, and critic NN aims to appraise control performance and make feedback to actor. The proof of stability concludes that the desired control performances are obtained. A numerical simulation is designed and implemented, and the desired results are shown.

In this paper, we present a novel approach for tracking control with switching formation in nonomniscient constrained space for multiagent system (MAS). The introduction of switching formation results from the situation where MAS is maneuvering in restricted path which is often the case for real world application. The preplanned trajectory may be inaccurate due to the lack of sufficient environmental information. In this case, agents may have to rapidly avoid collisions with unexpected obstacles or even switch the formation to guarantee the passability. A concept of avoidless disturbance is proposed. To solve the undesirable chattering on resulting trajectory caused by avoidless disturbance and the existing adaptation algorithm for neural network (NN) weights, a local path replanning approach is designed such that the potential force generated by avoidless disturbance is acted on the original desired trajectory outputting the locally replanned path for an agent. An NN-based controller is designed and the performance is validated using Lyapunov functions. Simulations are carried out to illustrate the effectiveness of proposed strategies.

This paper develops a novel control methodology for tracking control of robot manipulators based on a novel adaptive backstepping nonsingular fast terminal sliding mode control (ABNFTSMC). In this approach, a novel backstepping nonsingular fast terminal sliding mode controller (BNFTSMC) is developed based on an integration of integral nonsingular fast terminal sliding mode surface and a backstepping control strategy. The benefits of this approach are that the proposed controller can preserve the merits of the integral nonsingular fast terminal sliding mode control (NFTSMC) in terms of high robustness, fast transient response, and finite-time convergence, as well as backstepping control strategy in terms of globally asymptotic stability based on Lyapunov criterion. However, the major limitation of the proposed BNFTSMC is that its design procedure is dependent on the prior knowledge of the bound value of the disturbance and uncertainties. In order to overcome this limitation, an adaptive technique is employed to approximate the upper bound value; yielding an ABNFTSMC is recommended. The proposed controller is then applied for tracking control of a PUMA560 robot and compared with other state-of-the-art controllers, such as computed torque controller, PID controller, conventional PID-based sliding mode controller, and NFTSMC. The comparison results demonstrate the superior performance of the proposed approach.

Deep space exploration has significant meaning both in science and economy; however, it is very hard to obtain the relevant information due to its complexity. In this study, the autonomous celestial navigation method is utilised. To achieve high accuracy of the celestial navigation in a deep space environment, the improved filtering algorithm–spherical simplex unscented particle filter (SSUPF) is implemented, which adopts the spherical simplex unscented Kalman filter (SSUKF) algorithm to generate the important sampling density of particle filter (PF). According to simulation results, the authors derive that the SSUPF method can greatly increase the performance of the navigation system compared with unscented Kalman filter (UKF), SSUKF and unscented PF (UPF), and the computational burden of SSUPF is reduced by 24% in comparison with UPF.

Data-defect would affect the data quality and the analysis results of data mining. This paper presents a data-defect inspection method with kernel-neighbor-density-change outlier factor (KNDCOF). The definition of kernel neighbor density is proposed to represent the density of each object in database, and the ascending distance series (ADS) of each object is calculated based on the kernel distance between the object and its neighbors. Then, the average density fluctuation (ADF) of the object is established according to the weighted sum of the square of density difference between the object and others in ADS. Finally, the KNDCOF of the object is equal to the ratios of the ADF of the object and the average ADF of neighbors of the object. The degree of the object being an outlier is indicated by the KNDCOF value. The experiments are performed on three real data sets to evaluate the effectiveness of the proposed method. The experimental results verify that the proposed method has higher quality of data-defect inspection and does not increase the time complexity.

This paper presents a novel content-driven associative memory (CDAM) to associate large-scale color images based on the subjects that represent the images' content. Compared to traditional associative memories, CDAM inherits their tolerance to random noise in images and possesses greater robustness against correlated noise that distorts an image's spatial contextual structure. A three-layer recurrent neural tensor network (RNTN) is designed as the network model of CDAM. Multiple salient objects detection algorithm and partial radial basis function (PRBF) kernel are proposed for subject determination and content-driven association, respectively. Convergence of the RNTN is analyzed based on the properties of PRBF kernels. Extensive comparative experiment results are provided to verify the CDAM's efficiency, robustness, and accuracy.

This study develops a fault estimation and accommodation scheme for the image-based visual servoing system to eliminate the effects of the faults due to the image feature extraction task, which is named as bias virtual sensor fault. First, a bias virtual sensor fault in visual servoing is declared. Then, fault diagnosis, which includes fault detection, isolation, and estimation, is designed based on the means of particle filter. Finally, a fault accommodation law is developed based on the information obtained from the fault estimation to compensate for the effects of the fault in the system. The proposed fault estimation and accommodation is verified through simulation and experimental studies, and the results show that the system can estimate and eliminate the unknown fault effects effectively.

In this paper, a control technique named optimized backstepping is first proposed by implementing tracking control for a class of strict-feedback systems, which considers optimization as a design philosophy of the high-order system control. The basic idea is that designing the actual and virtual controls of backstepping is the optimized solutions of the corresponding subsystems so that overall control of the high-order system is optimized. In general, optimization control is designed based on the solution of Hamilton-Jacobi-Bellman equation, but solving the equation is very difficult due to the inherent nonlinearity and intractability. In order to overcome the difficulty, the neural network (NN)-based reinforcement learning strategy of actor-critic architecture is used. In every backstepping step, the actor and critic NNs are constructed for executing control behavior and evaluating control performance, respectively. According to the Lyapunov stability theorem, it is proven that the desired control performance can be obtained. Finally, a simulation example is carried out to further demonstrate the effectiveness of the proposed control approach.

Accurate high-temperature measurement is very important for process monitoring of an industrial system. Infrared thermometers usually can handle no more than 1000 °C and should use some expensive accessories for higher temperature measurements. This paper proposes a low-cost pyrometry system with nonlinear multisense partial least squares (NMSPLS). The ordinary camera with different filters is designed to collect the images of hot object at different wavelengths, and the NMSPLS is presented for predicting the temperature of the hot object from the obtained images. For the proposed method, the obtained images are represented by the multisense tensor, where red, green, and blue are regarded as three different dimensions in a sense of the tensor, respectively. The proposed method integrates an outer model and a nonlinear inner model. For the outer model, the independent variables and the dependent variables are projected into a low-dimensional common latent subspace. The weight matrices are calculated from the independent variables by the tucker decomposition, and the single value decomposition is adopted for extracting the latent variables (Lvs) based on the covariance between the independent variables and the dependent variables. For the nonlinear inner model, the neural network is adopted and the extracted Lvs are used as the input and the output of the neural network, respectively. Two real experiments are performed for estimating the proposed method. The experimental results verify that the proposed method can be applied for pyrometry and have higher effectiveness.

The control problem of an uncertain n -degrees of freedom robotic manipulator subjected to time-varying output constraints is investigated in this paper. We describe the rigid robotic manipulator system as a multi-input and multi-output nonlinear system. We devise a disturbance observer to estimate the unknown disturbance from humans and environment. To solve the uncertain problem, a neural network which utilizes a radial basis function is used to estimate the unknown dynamics of the robotic manipulator. An asymmetric barrier Lyapunov function is employed in the process of control design to avert the contravention of the time-varying output constraints. Simulation results validate the validity of the presented control scheme.

In this paper, a novel finite time fault tolerant control (FTC) is proposed for uncertain robot manipulators with actuator faults. First, a finite time passive FTC (PFTC) based on a robust nonsingular fast terminal sliding mode control (NFTSMC) is investigated. Be analyzed for addressing the disadvantages of the PFTC, an AFTC are then investigated by combining NFTSMC with a simple fault diagnosis scheme. In this scheme, an online fault estimation algorithm based on time delay estimation (TDE) is proposed to approximate actuator faults. The estimated fault information is used to detect, isolate, and accommodate the effect of the faults in the system. Then, a robust AFTC law is established by combining the obtained fault information and a robust NFTSMC. Finally, a high-order sliding mode (HOSM) control based on super-twisting algorithm is employed to eliminate the chattering. In comparison to the PFTC and other state-of-the-art approaches, the proposed AFTC scheme possess several advantages such as high precision, strong robustness, no singularity, less chattering, and fast finite-time convergence due to the combined NFTSMC and HOSM control, and requires no prior knowledge of the fault due to TDE-based fault estimation. Finally, simulation results are obtained to verify the effectiveness of the proposed strategy.

In this paper, we propose a method of reference adaptation for robots in physical interactions with unknown environments. A cost function is constructed to describe the interaction performance, which combines trajectory tracking error and interaction force between the robot and the environment. It is minimized by the proposed reference adaptation based on trajectory parametrization and iterative learning. An adaptive impedance control is developed to make the robot be governed by the target impedance model. Simulation and experiment studies are conducted to verify the effectiveness of the proposed method.

In this paper, robust visual tracking scheme is achieved through a novel sparse tracking via collaborative motion and appearance (TCMA). A coarse-to-fine framework with both motion and holistic appearance information is taken into consideration. In coarse search, we employ an optical flow map for the generation of motion particles. A rough estimation of target image patch is obtained using l2-regularized least square method in coarse search stage. In fine search, a novel smooth term is proposed in the cost function to improve the robustness of the tracker. With this smooth term, the object appearance in the previous frame will also affect the calculation of sparse coefficient in the current frame. It allows the tracker involving temporal information between consecutive frames instead of only considering single frame appearance information as in the conventional sparse coding-based tracking algorithms. In order to reserve the original and latest appearance information simultaneously in the template, a quadratic-function-like weight allocation scheme combining with particle contributed histogrammic correlation is developed in the updating stage. Both qualitative and quantitative studies are conducted on a set of challenging image sequences. The superior performance over other state-of-the-art algorithms is verified through the experiment.

There are two main challenges, drift and scale ambiguity, restricting monocular visual odometry from an extensive application on real autonomous navigation. In this paper, an iterative localization framework is presented to globally localize a mobile vehicle equipped with a single camera and a freely available digital map. Inspired by the concept of cloud, a new Gaussian-Gaussian Cloud model is proposed to give a unified representation of the measurement randomness and scale ambiguity in monocular visual odometry. In this model, a collection of cloud drops is generated. Both the drift and scale ambiguity are considered and represented simultaneously in each cloud drop. To reduce the measurement uncertainties of any drop in Gaussian-Gaussian Cloud, road constraints from the open source map-OpenStreetMap-are utilized. The map is first converted to a template edge map and a shape matching step is then implemented to assign the probability of each cloud drop, indicating to what degree the drop accords with road constraints. A parameter estimation scheme is used to narrow down the scale ambiguity of monocular visual odometry while resampling cloud drops. Evaluations on the KITTI benchmark data set and our self-collected data set have demonstrated the stability and accuracy of the proposed approach.

In this paper, we present an adaptive control of robotic manipulators with parametric uncertainties and motion constraints. Position and velocity constraints are considered and they are unified and converted into the constraint of the nominal input. An adaptive neural network control is developed to achieve trajectory tracking, while the problems of motion constraints are addressed by considering the saturation effect of the nominal input. The uniform boundedness of all closed-loop signals is verified through Lyapunov analysis. Simulation and experiment results on a 2-degree-of-freedom robotic manipulator demonstrate the effectiveness of the proposed method.

Due to the robustness against the uncertainties, conventional sliding mode control (SMC) has been extensively developed for fault-tolerant control (FTC) system. However, the FTCs based on conventional SMC provide several disadvantages such as large transient state error, less robustness, and large chattering, that limit its application for real application. In order to enhance the performance, a novel adaptive third-order SMC, which combines a novel third-order sliding mode surface, a continuous strategy and an adaptation law, is proposed. Compared with other innovation approaches, the proposed controller has an excellent capability to tackle several types of actuator faults with an enhancing on robustness, precision, chattering reduction, and time of convergence. The proposed method is then applied for an attitude control of a spacecraft and the results demonstrate the superior performance.

In this paper, we introduce a novel control scheme based on the social force model for robots navigating in human environments. Social proxemics potential field is constructed based on the theory of proxemics and used to generate social interaction force for design of robot motion control. A combined kinematic/dynamic control is proposed to make the robot follow the target social force model, in the presence of kinematic velocity constraints. Under the proposed framework, given a specific social convention, robot is able to generate and modify its path smoothly without violating the proxemics constraints. The validity of the proposed method is verified through experimental studies using the V-rep platform.

Wheeled mobile robots (WMRs) are often used for maintenance of round pipes or ducts, which can typically be represented as a cylindrical workspace. Working in round pipes or ducts, kinematic models of WMRs are different from those applying on a plane and thus pose significant challenges in terms of kinematic analysis and motion control. To address these challenges, the kinematic properties of WMRs in a cylindrical workspace are analyzed in this paper. First, we discuss the kinematic properties of a single wheel in a cylindrical workspace. Then, we analyze the geometric constraints of WMRs in round pipes or ducts with analytical geometry. Based on these analyses, kinematic properties of WMRs in cylindrical workspaces are discussed with screw theory. A control law based on biaxial clinometer information is proposed, and it enables the robot to move horizontally in round pipes or ducts. Finally, the motion of a single wheel purely rolling in a cylindrical workspace is simulated. Experiments using a car-like mobile robot moving in round ducts are carried out to show the feasibility of the proposed algorithm.

Central catadioptric omnidirectional images exhibit serious nonlinear distortions due to the involved quadratic mirrors. Therefore, features based on the conventional pin-hole model are hard to achieve satisfactory performances when directly applied to the distorted omnidirectional images. This paper analyzes the catadioptric geometry to facilitate modeling the nonlinear distortions of omnidirectional images. Different to the conventional imaging model, the prior information is considered in catadioptric system. A parameterized neighborhood mapping model is proposed to efficiently calculate the neighborhood of an object based on its measurable radial distance in the image plane. On the basis of the parameterized nonlinear model, a distortion-invariant fragment-based joint-feature mixture model of Gaussian is presented for human target tracking in omnidirectional vision. Under the framework of Gaussian Mixture Model, the problem of feature matching is converted into the feature clustering. The joint probability distribution of a joint-feature class is modeled by a mixture of Gaussian. A weight contribution mechanism is designed to flexibly weight the fragments contribution based on their responses, which leads to a robust tracking even under serious partial occlusion. Finally, experiments validate the advantage of the proposed algorithm over other conventional approaches. Catadioptric omnidirectional cameras have been widely used in robotics and surveillance fields for visual sensing due to its big field-of-view. However, conventional visual models use large-scale statistical sampling for feature extraction in catadioptric sensor, which may consume lot of computational cost. For practical applications, a parameterized model that can accurately and efficiently formulate distortion of catadioptric image is desirable. Integrating of the priori of system, a parameterized neighborhood model is presented to directly extract distorted image content in image, which can significantly improve the efficiency of algorithm. To robustly handle challenging occlusion in the distorted image, a flexible fragment-based joint-feature framework is presented for robust non-rigid human target tracking. Compared with the conventional tracking methods applied to catadioptric vision, the proposed tracking approaches leads to much better performance from the perspective of efficiency and robustness.

In this paper, the fault diagnosis (FD) problem in image-based visual servoing with eye-in-hand configurations is investigated. The potential failures are detected and isolated based on approximating parameters related. First, the failure scenarios of the visual servoing systems are reviewed and classified into the actuator and sensor faults. Second, a residual generator is proposed to detect the failure occurrences, based on the Kalman filter. Third, a decision table is proposed to isolate the fault type. Finally, simulation and experimental results are given to validate the efficacy and the efficiency of the proposed FD strategies.

This paper studies the optimal distribution of feet forces and control of multilegged robots with uncertainties in both kinematics and dynamics. First, a constrained dynamics for multilegged robots and the constrained environment model are established by considering both kinematic and dynamic uncertainties. Under an external wrench for multilegged robots, the foot forces and moments of the supporting legs can be formulated as quadratic programming problems subject to linear and nonlinear constraints. The neurodynamics of recurrent neural network is developed for foot force optimization. For the obtained optimized tip-point force and the motion of legs, we propose a hybrid task-space trajectory and force tracking based on fuzzy system and adaptive mechanism that are used to compensate for the external perturbation, kinematics, and dynamics uncertainties. The tracking of task-space trajectory and constraint force is achieved under unknown dynamical parameters, constraints, and disturbances. Extensive simulations have been provided to verify the effectiveness of the proposed scheme.

In this paper, we deal with the problem of tracking control for a class of uncertain nonlinear systems in strictfeedback form subject to completely unknown system nonlinearities, hard constraints on full states, and unknown time-varying bounded disturbances. Integral barrier Lyapunov functionals are constructed to handle the unknown affine control gains (g(·)) with state constraints simultaneously. This removes the need on the knowledge of control gains for control design and avoids the conservative step of transforming original state constraints into new bounds on tracking errors. Neural networks (NNs) are used to approximate the unknown continuous packaged functions. To enhance the robustness, adapting parameters are developed to compensate the unknown bounds on NNs approximations and external disturbances. Design parameters-dependent feasibility conditions are formulated as sufficient conditions for the existence of feasible design parameters to guarantee the state constraints, and an offline constrained optimization step is proposed to obtain the optimal design parameters prior to the implementation of the proposed control. It is proved that the proposed control can guarantee the semiglobal uniform ultimate boundedness of all signals in closed-loop system, all states are ensured to remain in the predefined constrained state space, and tracking error converges to an adjustable neighborhood of the origin by choosing appropriate design parameters. Simulations are performed to validate the proposed control.

Central catadioptric omnidirectional images exhibit serious nonlinear distortions due to quadratic mirrors involved. Conventional visual features developed based on the perspective model are hard to achieve a satisfactory performance when directly applied to the distorted omnidirectional image. This paper presents a parameterized neighborhood model to efficiently calculate the adaptive neighborhood of an object based on the measurable radial distance in image plane. On the basis of the parameterized neighborhood model, a distortion invariant joint-feature framework implemented with contour-color fragment mixture model of Gaussian is proposed for visual tracking in catadioptric omnidirectional camera system. Under the framework of Gaussian Mixture Model, the problem of feature matching is converted into feature clustering. A weight contribution mechanism is presented to flexibly weight the fragments based on their responses, which makes the system robustly guided by limited visible fragments even when serious partial occlusion happens. The experiments validate the performance of the proposed algorithm.

This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori, and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new n th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method.

In this note, adaptive neural network (NN) control is investigated for a class of uncertain nonlinear systems with asymmetric saturation actuators and external disturbances. To handle the effect of nonsmooth asymmetric saturation nonlinearity, a Gaussian error function-based continuous differentiable asymmetric saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided using dynamic surface control. Using radial basis function NN, adaptive control is developed to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design constants. The effectiveness of the proposed control is demonstrated in the simulation study.

In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

This paper investigates optimal feet forces distribution and control of quadruped robots with uncertainties in both kinematics and dynamics. First, a constrained dynamics of quadruped robots is established. The distribution of required forces and moments on the supporting legs of a quadruped robot can be formulated as a problem for minimizing an objective function subject to form-closure constraints and balance constraints of external force. The dynamics of recurrent neural network for realtime force optimization are proposed. For the obtained optimized tip-point force and the motion of legs, we propose the hybrid motion/force control based on adaptive fuzzy system to compensate for the external perturbation and the task-space tracking errors in the environment. The proposed control can confront the uncertainties including approximation task space error and external perturbation. The verification of the proposed control is conducted using the extensive simulations.

We investigate the state estimation problem in cyber-physical systems (CPS) where the dynamical physical process is measured by a wireless sensor and the measurements are transmitted to a remote state estimator. It has been shown that the estimation performance strongly depends on the wireless communication quality. To enhance the estimation performance, we apply the cognitive radio technique to the system and propose a CHAnnel seNsing and switChing mEchanism (CHANCE) to explore opportunistic accessibility of multiple channels. We consider two types of wireless channels, i.e., one unlicensed channel which can be accessed freely and several licensed channels which have been pre-assigned to primary users. For the single-licensed-channel case, we develop a necessary condition for the estimation stability based on the physical process dynamics, channel quality and the channel sensing accuracy. This condition becomes also sufficient under certain conditions. We also derive the conditions under which the estimation performance is guaranteed to be improved by CHANCE. The above results are then extended to multi-licensed-channel cases. Simulations based on a particular linear system show that, the long-run mean estimation error covariance with CHANCE is at least 63% less than that without CHANCE. It is also shown that CHANCE outperforms the existing RANDOM mechanism in terms of estimation performance.

This paper presents adaptive neural tracking control for a class of uncertain multiinput-multioutput (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine pure-feedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularity-free adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded. Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this paper.

This paper investigates optimal feet forces' distribution and control of quadruped robots under external disturbance forces. First, we formulate a constrained dynamics of quadruped robots and derive a reduced-order dynamical model of motion/force. Consider an external wrench on quadruped robots; the distribution of required forces and moments on the supporting legs of a quadruped robot is handled as a tip-point force distribution and used to equilibrate the external wrench. Then, a gradient neural network is adopted to deal with the optimized objective function formulated as to minimize this quadratic objective function subjected to linear equality and inequality constraints. For the obtained optimized tip-point force and the motion of legs, we propose the hybrid motion/force control based on an adaptive neural network to compensate for the perturbations in the environment and approximate feedforward force and impedance of the leg joints. The proposed control can confront the uncertainties including approximation error and external perturbation. The verification of the proposed control is conducted using a simulation.

In this paper, the direct adaptive neural control is proposed for a class of uncertain nonaffine nonlinear systems with unknown nonsymmetric input saturation. Based on the implicit function theorem and mean value theorem, both state feedback and output feedback direct adaptive controls are developed using neural networks (NNs) and a disturbance observer. A compounded disturbance is defined to take into account of the effect of the unknown external disturbance, the unknown nonsymmetric input saturation, and the approximation error of NN. Then, a disturbance observer is developed to estimate the unknown compounded disturbance, and it is established that the estimate error converges to a compact set if appropriate observer design parameters are chosen. Both state feedback and output feedback direct adaptive controls can guarantee semiglobal uniform boundedness of the closed-loop system signals as rigorously proved by Lyapunov analysis. Numerical simulation results are presented to illustrate the effectiveness of the proposed direct adaptive neural control techniques.

This paper addresses the nonlinear robust braking control of aircraft. We consider the unknown aerodynamic forces and moments which will degrade the brake performance significantly. Moreover, for the transport or commercial air-crafts, weight variation will influence the braking control torque calculation. In this paper, robust adaptive control is proposed for aircraft braking system. By integrating a neural network (NN) estimator to approximate unknown aerodynamic forces and moments, the proposed control can effectively suppress the aerodynamic uncertainties and weight variation. The brake torque input constraint is also discussed in this paper. Simulation results clearly demonstrate the advantages and effectiveness of the proposed method.

In this paper, we address the problem of non-metric navigation for mobile robot in indoor environment. With pure vision sensors, we tackle the most fundamental problem in autonomous mobile robot navigation - to achieve obstacle avoidance ability in mobile robot. We approach the problem by extracting time-to-contact information using optical flow and motion analysis. Our method is based on flow divergence which contains qualitative depth information of the environment. In addition, we presented a flexible robot heading decision making framework that is able to incorporate higher level navigation task on top of obstacle avoiding behaviour. A state-machine based control scheme is utilized for the coordination of the robot's action defined under a behaviour based design. Through virtual simulation and physical experiments, we demonstrated the effectiveness of our non-metric navigation strategy in unknown environment.

Motivated by applications in robot-assisted physical rehabilitation, this paper presents an adaptive control design for robot manipulators operating in an ellipsoidal constrained region. The ellipsoidal constraint problem is more challenging than the box constraint problem tackled in previous works, since the nonlinear constraint boundary cannot be handled in a decoupled manner along the dimensions of the task space. We introduce a novel Barrier Lyapunov Function (BLF) which contains a quotient of the squared norm of the tracking error over the ellipsoidal task space constraint. This function allows the task space constraint to be handled directly without requiring an intermediate mapping to the error space. We show that, under the proposed BLF-based adaptive control, the end-effector always remains in the constrained region despite the perturbing effects of online parameter adaptation and also the presence of bounded external disturbances. A simulation example illustrates the performance of the proposed control.

Controlling nonstrict-feedback nonlinear systems is a challenging problem in control theory. In this paper, we consider adaptive fuzzy control for a class of nonlinear systems with nonstrict-feedback structure by using fuzzy logic systems. A variable separation approach is developed to overcome the difficulty from the nonstrict-feedback structure. Furthermore, based on fuzzy approximation and backstepping techniques, a state feedback adaptive fuzzy tracking controller is proposed, which guarantees that all of the signals in the closed-loop system are bounded, while the tracking error converges to a small neighborhood of the origin. Simulation studies are included to demonstrate the effectiveness of our results.

Researchers have proposed various machine learning algorithms for traffic sign recognition, which is a supervised multicategory classification problem with unbalanced class frequencies and various appearances. We present a novel graph embedding algorithm that strikes a balance between local manifold structures and global discriminative information. A novel graph structure is designed to depict explicitly the local manifold structures of traffic signs with various appearances and to intuitively model between-class discriminative information. Through this graph structure, our algorithm effectively learns a compact and discriminative subspace. Moreover, by using$L_{2, 1}$-norm, the proposed algorithm can preserve the sparse representation property in the original space after graph embedding, thereby generating a more accurate projection matrix. Experiments demonstrate that the proposed algorithm exhibits better performance than the recent state-of-the-art methods.

This paper presents sliding-mode-observer (SMO)-based adaptive sliding mode control (SMC) and neural network (NN) control for effective tracking of the slip ratio applicable to electric vehicles (EVs) and hybrid EVs (HEVs), where electric motors are used to achieve braking in addition to propulsion. The proposed SMO alleviates the difficulty in choosing its gains. To adapt the road condition parameter for better performance, a Lyapunov-based adaptation is integrated with the sliding-mode controller. The resulting adaptive controller performs very well in achieving slip tracking in the face of parameter uncertainties. Furthermore, to cope up with the uncertainties and unknown nonlinearity involved with the vehicle slip dynamics, a nonmodel-based NN controller is developed using the function approximation properties of the multilayer perceptrons.

In this paper, model-free impedance control is designed for the safe human-robot interaction. A passive impedance model is imposed on the robot and a control method is proposed to guarantee the robot dynamics governed by the target model. The proposed method does not require any model information except for upper bounds of system matrix. It is thus easy to apply to practical implementation. The rigorous analysis of the control performance and robustness is presented. The validity of the proposed method is verified on the six degrees-of-freedom (DOF) PUMA 560 robot arm through simulation.

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

In this paper, we consider the problem of allocating machine resources among multiple agents, each of which is responsible to solve a flowshop scheduling problem. We present an iterated combinatorial auction mechanism in which bid generation is performed within each agent, while a price adjustment procedure is performed by a centralized auctioneer. While this approach is fairly well-studied in the literature, our primary innovation is in an adaptive price adjustment procedure, utilizing variable step-size inspired by adaptive PID-control theory coupled with utility pricing inspired by classical microeconomics. We compare with the conventional price adjustment scheme proposed in Fisher (1985), and show better convergence properties. Our secondary contribution is in a fast bid-generation procedure executed by the agents based on local search. Putting both these innovations together, we compare our approach against a classical integer programming model as well as conventional price adjustment schemes, and show drastic run time improvement with insignificant loss of global optimality.

To improve the classification performance of k-NN, this paper presents a classifier, called k -NS, based on the Euclidian distances from a query sample to the nearest subspaces. Each nearest subspace is spanned by k nearest samples of a same class. A simple discriminant is derived to calculate the distances due to the geometric meaning of the Grammian, and the calculation stability of the discriminant is guaranteed by embedding Tikhonov regularization. The proposed classifier, k-NS, categorizes a query sample into the class whose corresponding subspace is proximal. Because the Grammian only involves inner products, the classifier is naturally extended into the high-dimensional feature space induced by kernel functions. The experimental results on 13 publicly available benchmark datasets show that k-NS is quite promising compared to several other classifiers founded on nearest neighbors in terms of training and test accuracy and efficiency.

In this paper, adaptive neural network (NN) control is investigated for a class of block triangular multiinput-multioutput nonlinear discrete-time systems with each subsystem in pure-feedback form with unknown control directions. These systems are of couplings in every equation of each subsystem, and different subsystems may have different orders. To avoid the noncausal problem in the control design, the system is transformed into a predictor form by rigorous derivation. By exploring the properties of the block triangular form, implicit controls are developed for each subsystem such that the couplings of inputs and states among subsystems have been completely decoupled. The radial basis function NN is employed to approximate the unknown control. Each subsystem achieves a semiglobal uniformly ultimately bounded stability with the proposed control, and simulation results are presented to demonstrate its efficiency.

In this paper, robust adaptive neural network (NN) control is investigated for a general class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems with unknown control coefficient matrices and input nonlinearities. For nonsymmetric input nonlinearities of saturation and deadzone, variable structure control (VSC) in combination with backstepping and Lyapunov synthesis is proposed for adaptive NN control design with guaranteed stability. In the proposed adaptive NN control, the usual assumption on nonsingularity of NN approximation for unknown control coefficient matrices and boundary assumption between NN approximation error and control input have been eliminated. Command filters are presented to implement physical constraints on the virtual control laws, then the tedious analytic computations of time derivatives of virtual control laws are canceled. It is proved that the proposed robust backstepping control is able to guarantee semiglobal uniform ultimate boundedness of all signals in the closed-loop system. Finally, simulation results are presented to illustrate the effectiveness of the proposed adaptive NN control.

In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line neural network (NN) control using only output measurements. A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: 1) for any initial compact set, how to determine a priori the compact superset, on which NN approximation is valid; and 2) how to ensure that the arguments of the unknown functions remain within the specified compact superset. By ensuring boundedness of the BLF, we actively constrain the argument of the unknown functions to remain within a compact superset such that the NN approximation conditions hold. The semiglobal boundedness of all closed-loop signals is ensured, and the tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.

This paper presents adaptive neural tracking control for a class of non-affine pure-feedback systems with multiple unknown state time-varying delays. To overcome the design difficulty from non-affine structure of pure-feedback system, mean value theorem is exploited to deduce affine appearance of state variables x(i) as virtual controls α(i), and of the actual control u. The separation technique is introduced to decompose unknown functions of all time-varying delayed states into a series of continuous functions of each delayed state. The novel Lyapunov-Krasovskii functionals are employed to compensate for the unknown functions of current delayed state, which is effectively free from any restriction on unknown time-delay functions and overcomes the circular construction of controller caused by the neural approximation of a function of u and [Formula: see text] . Novel continuous functions are introduced to overcome the design difficulty deduced from the use of one adaptive parameter. To achieve uniformly ultimate boundedness of all the signals in the closed-loop system and tracking performance, control gains are effectively modified as a dynamic form with a class of even function, which makes stability analysis be carried out at the present of multiple time-varying delays. Simulation studies are provided to demonstrate the effectiveness of the proposed scheme.

In this paper, we investigate the leader-follower formation control of underactuated Autonomous Underwater Vehicles (AUVs). By using position measurements from the leader, we design a virtual vehicle such that the trajectory of the virtual vehicle converges to the reference trajectory of the follower. A position tracking control is designed for the follower to track the virtual vehicle using Lyapunov and backstepping synthesis. Simulation results demonstrated the effectiveness of the proposed scheme.

This paper proposes a novel approach, Simultaneous Path Planning and Topological Mapping (SP2ATM), to address the problem of path planning by registering the topology of the perceived dynamic environment as opposed to the conventional grid representation. The local topology is encoded, concurrent and incremental with path planning, by extracting only the admissible free space. The resulting Admissible Space Topological Map (ASTM) then serves as the minimum information to facilitate path planning in the 3D configuration space. Experimental results obtained from our mobile robot X1 in a complex planar environment, validates completeness and optimality of the algorithm.

In this paper, adaptive control is investigated for a class of discrete-time nonlinear multi-agent systems (MAS). Each agent is of uncertain dynamics and is affected by other agents in its neighborhood. An agent is able to sense the outputs of the agents inside its neighborhood but is unable to sense those outside its neighborhood. Among all the agents, there is a hidden leader, which knows the desired tracking trajectory, but it is affected by and can only affect those agents inside its neighborhood while all other agents are not aware of its leadership. The decentralized adaptive control is designed for each agent by using the information of its neighbors. Under the proposed decentralized adaptive controls, both rigid mathematical proof and simulation studies are provided to show that all the agents are guaranteed to reach their common goal, i.e., following the desired reference.

Face detection plays an important role in developing human-robot interaction (HRI) for social robots to recognize people. In this paper, we introduce an intelligent vision system that is able to detect human face from background and filter out all the non-face but face-like images. The human face is detected using Ada boost-based Haar-Cascade classifier and the real human face detection is improved using extreme learning machine (ELM). The proposed robot vision system for human detection is tested through realtime experiments.

In this paper, adaptive variable structure neural control is investigated for a class of nonlinear systems under the effects of time-varying state delays and uncertain hysteresis inputs. The unknown time-varying delay uncertainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design, and the effect of the uncertain hysteresis with the Prandtl-Ishlinskii (PI) model representation is also mitigated using the proposed control. By utilizing the integral-type Lyapunov function, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded (SGUUB). Extensive simulation results demonstrate the effectiveness of the proposed approach.

In this paper, adaptive neural network (NN) tracking control is investigated for a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems in triangular control structure with unknown nonsymmetric dead zones and control directions. The design is based on the principle of sliding mode control and the use of Nussbaum-type functions in solving the problem of the completely unknown control directions. It is shown that the dead-zone output can be represented as a simple linear system with a static time-varying gain and bounded disturbance by introducing characteristic function. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation term for the upper bound of the optimal approximation error and the dead-zone disturbance, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded, with tracking errors converging to zero under the condition that the slopes of unknown dead zones are equal. Simulation results demonstrate the effectiveness of the approach.

In this paper, coupled dynamics are presented for two cooperating mobile robotic manipulators manipulating an object with relative motion in the presence of uncertainties and external disturbances. Centralized robust adaptive controls are introduced to guarantee the motion, and force trajectories of the constrained object converge to the desired manifolds with prescribed performance. The stability of the closed-loop system and the boundedness of tracking errors are proved using Lyapunov stability synthesis. The tracking of the constraint trajectory/force up to an ultimately bounded error is achieved. The proposed adaptive controls are robust against relative motion disturbances and parametric uncertainties and are validated by simulation studies.

In this paper, adaptive neural control is investigated for a class of unknown nonlinear systems in pure-feedback form with the generalized Prandtl-Ishlinskii hysteresis input. To deal with the nonaffine problem in face of the nonsmooth characteristics of hysteresis, the mean-value theorem is applied successively, first to the functions in the pure-feedback plant, and then to the hysteresis input function. Unknown uncertainties are compensated for using the function approximation capability of neural networks. The unknown virtual control directions are dealt with by Nussbaum functions. By utilizing Lyapunov synthesis, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach.

An integer programming (IP) model is proposed for integrated resource allocation and operation scheduling for a multiple job-agents system. Each agent handles a specific job-list in a bidirectional flowshop. For the individual agent scheduling problem, a formulation is proposed in continuous time domain and compared with an IP formulation in discrete time domain. Of particular interest is the formulation of the machine utilization function-both in continuous time and discrete time. Fast heuristic methods are proposed with the relaxation of the machine capacity. For the integrated resource allocation and scheduling problem, a linear programming relaxation approach is applied to solve the global resource allocation and a fast heuristic method is applied to solve each scheduling subproblem. The proposed solution is compared experimentally with that from the integer programming solver by CPLEX.

10.1145/1409540.1409547

This paper is concerned with simultaneous stability of a collection of continuous-time linear plants whose feedback control loops are closed via a shared digital communication network. Because of the limitation of communication capacity, only a limited number of controller-plant connections can be accommodated at any time instant. Therefore, it is necessary to carefully design the scheduling policy so as to achieve simultaneous stabilization for all these control loops. In the paper, sufficient condition on the existence of such a scheduling policy is presented for the collection of networked LTI systems with sampled-data controllers and network-induced uncertain delays. It turns out that the condition is only based on the convergence rate of the closed-loop system and the divergence rate of the open-loop plant. The proof for this schedulability condition is in a constructive way, which can also serve as a systematic way for the scheduling policy design.

In this paper, we present an active vision system for human-robot interaction purposes that includes robust face detection, tracking, recognition and facial expression analysis. The system will search for human faces in view, zoom on the face of interest based on the face recognition database, track it and finally analyze the emotion parameters on the face. After detection using Haar-cascade classifiers, the variable parameters of the camera are changed adaptively to track the face of the subject by employing the Camshift algorithm, and to extract the facial features which are used for face recognition and facial expression analysis. Embedded Hidden Markov Model is used for face recognition and nonlinear facial mass-spring model is employed to describe the facial muscle’s tension. The motion signatures are then classified using Multi-layer Perceptrons for facial expression analysis. This system can be used as a comprehensive and robust vision package for a robot to interact with human beings.

In this paper, we propose an interactive system for reconstructing human facial expression. In the system, a nonlinear mass-spring model is employed to simulate twenty two facial musclespsila tensions during facial expressions, and then the elastic forces of these tensions are grouped into a vector which is used as the input for facial expression recognition. The experimental results show that the nonlinear facial mass-spring model coupled with the SVM classifier is effective to recognize the facial expressions. Finally, we introduce our robot that can make artificial facial expressions. Experimental results of facial expression generation demonstrate that our robot can imitate six types of facial expressions.

A very important aspect in developing human-robot interaction (HRI) is the ability to recognize people by sound source recognition. In this paper, we introduce an intelligent audio human detection system that is able to recognize user’s voice, and identified it from background sound. The sound sources recognition for human robot interaction is investigated using an unsupervised learning algorithm, neighborhood linear embedding (NLE), which is able to extract the intrinsic features such as neighborhood relationships, global distributions and clustering property of a given data set. Furthermore, motivated by the scale adaptivity of human’s perception, several scale invariant metrics are designed to enhance the intrinsic feature extraction performance of NLE. Simulations on different sound sources recognition are studied to demonstrate effective applications of the scale invariant NLE algorithm for robust sound recognition and identification to improve auditory system of robot for human robot interaction.

In this paper, we investigate stability of a nonlinear tracking control for a mobile robot in connection with the method of artificial potential trench. We first refine the concept of potential trench function and propose several approaches to construct potential trench functions. Then we synthesize a control law that stabilizes a robot while tracking a goal point moving on a given segment (smooth curve) and extend it to a simple case of formation control. Results from computer simulation verify and demonstrate the effectiveness of this novel control.

In this paper, adaptive neural network (NN) control is investigated for a class of nonlinear pure-feedback discrete-time systems. By using prediction functions of future states, the pure-feedback system is transformed into an n-step-ahead predictor, based on which state feedback NN control is synthesized. Next, by investigating the relationship between outputs and states, the system is transformed into an input-output predictor model, and then, output feedback control is constructed. To overcome the difficulty of nonaffine appearance of the control input, implicit function theorem is exploited in the control design and NN is employed to approximate the unknown function in the control. In both state feedback and output feedback control, only a single NN is used and the controller singularity is completely avoided. The closed-loop system achieves semiglobal uniform ultimate boundedness (SGUUB) stability and the output tracking error is made within a neighborhood around zero. Simulation results are presented to show the effectiveness of the proposed control approach.

In this paper, output feedback adaptive neural network (NN) controls are investigated for two classes of nonlinear discrete-time systems with unknown control directions: 1) nonlinear pure-feedback systems and 2) nonlinear autoregressive moving average with exogenous inputs (NARMAX) systems. To overcome the noncausal problem, which has been known to be a major obstacle in the discrete-time control design, both systems are transformed to a predictor for output feedback control design. Implicit function theorem is used to overcome the difficulty of the nonaffine appearance of the control input. The problem of lacking a priori knowledge on the control directions is solved by using discrete Nussbaum gain. The high-order neural network (HONN) is employed to approximate the unknown control. The closed-loop system achieves semiglobal uniformly-ultimately-bounded (SGUUB) stability and the output tracking error is made within a neighborhood around zero. Simulation results are presented to demonstrate the effectiveness of the proposed control.

In this paper, we investigate the operation of the queue-formation structure (or Q-structure) in multi-robot teams with limited communications. Information flow is divided into two time scales: (i) the fast time scale where the robots' reactive actions are determined based only on local communications, and (ii) the slow time scale, where information required is less demanding, can be collected over a longer time with intermittent information loss. Therefore, there is no need for global information at all times, reducing the overall communication load. In addition, a dynamic target determination algorithm, based on the Q-structure, is used to produce a series of targets that incrementally guide each robot into formation. It provides greater control over the distance between robots on the same queue for better formation scaling. An analysis of the convergence of the system of robots is provided. Simulation studies verify the effectiveness of the scheme.

Direct adaptive partial state feedback control is presented to achieve semiglobally input-to-state practically stable (ISpS) disturbance attenuation with output tracking for a class of uncertain time-varying nonlinear systems in which the unmeasured dynamics do not possess a constant disturbance attenuation level (CDAL). Identifying a necessary condition for the existence of a CDAL, direct adaptive neural networks (NNs) control is developed, where the universal approximation property of NNs and the domination design are employed together to overcome the difficulties due to the lack of state information, unknown system nonlinearities, and unknown state-dependent disturbance attenuation gain. The proposed method is coherent in the sense that it is applicable to the case in which a CDAL exists.

In this paper, we investigate the operation of the queue-formation structure (or Q-structure) in multirobot teams with limited communication. Information flow can be divided into two time scales: (1) the fast-time scale where the robots' reactive actions are determined based only on local communication and (2) the slow-time scale, where information required is less demanding, can be collected over a longer time, and intermittent information loss can be afforded. Therefore, there is no need for global information at all times, reducing the overall communication load. In addition, a dynamic target determination algorithm, based on the Q-structure, is used to produce a series of targets that incrementally guide each robot into formation. It provides greater control over the distance between robots on the same queue, instead of relying on inter robot repulsive distance, and, allows better formation scaling. An analysis of the convergence of the system of robots and realistic simulation studies are provided.

This paper studies a hierarchical approach for incrementally driving a nonholonomic mobile robot to its destination in unknown environments. The A* algorithm is modified to handle a map containing unknown information. Based on it, optimal (discrete) paths are incrementally generated with a periodically updated map. Next, accelerations in varying velocities are taken into account in predicting the robot pose and the robot trajectory resulting from a motion command. Obstacle constraints are transformed to suitable velocity limits so that the robot can move as fast as possible while avoiding collisions when needed. Then, to trace the discrete path, the system searches for a waypoint-directed optimized motion in a reduced 1-D translation or rotation velocity space. Various situations of navigation are dealt with by using different strategies rather than a single objective function. Extensive simulations and experiments verified the efficacy of the proposed approach.

In this paper, adaptive robust force/motion control strategies are presented for mobile manipulators under both holonomic and nonholonomic constraints in the presence of uncertainties and disturbances. The proposed control is robust not only to parameter uncertainties such as mass variations but also to external ones such as disturbances. The stability of the closed-loop system and the boundedness of tracking errors are proved using Lyapunov stability synthesis. The proposed control strategies guarantee that the system motion converges to the desired manifold with prescribed performance and the bounded constraint force. Simulation results validate that the motion of the system converges to the desired trajectory, and the constraint force converges to the desired force.

This paper proposes a practical method for planning paths incrementally for mobile robots in unknown environments using the latest sensory information. A* algorithm was modified in this research for it to be able to handle an occupancy grid map with unknown information. Then the paper presented an algorithm that is able to robustly and incrementally searching for an optimal path based on the partial map simultaneously built by the robot. Waypoints was generated to further optimize the obtained path and were sequentially traced by the robot in a simple, reactive way. Extensive simulations and experiments were carried out to verify the proposed planning algorithm

Adaptive smart neural network controller design is presented in this paper for wheeled mobile robots with unknown dynamics. The controller is constructed at the dynamical level. The smart neural control scheme is designed such that the current control action not only can utilize the knowledge that neural networks learned from the past experience, but also keep the learning ability in the operational phase and finish the same control task in a 'smarter' way. The proposed neural control scheme can act smartly in the operational phase after the networks have been well trained in the training phase, in a way similar to the control process of human in learning to accomplish some complicated control tasks. All the system states are shown to be able to track the desired trajectory. Numerical simulation is conducted to verify the effectiveness of the proposed method

The use of self organizing agents to generate suitable task schedules for embodied autonomous multi-robot teams is investigated in this paper. A dandelion graph structure is used for defining agent neighborhood relations and for representing schedules, so as to support the agent flocking framework. The inter- and intra-roam-space movement algorithms govern the movement of individual agents. Although task information such as due dates and durations can be available for scheduling, in a dynamically changing environment, these values can only be estimated and are often subjected to changes. Therefore, the proposed scheme aims at allowing robot teams to adapt to unforseen circumstances, while at the same time, still making use of available time information for preliminary scheduling and planning. Realistic simulations are performed and verifies the effectiveness of the approach

In this paper, an image is regarded as a collection of image patches that can be referred to as points with certain intrinsic structures/patterns in high-dimensional space. These structures contain vital information of image features and thus provide a novel method for image feature representation. To discover these intrinsic structures, we first propose neighborhood linear embedding (NLE), an unsupervised learning algorithm, to discover neighborhood relationship and global distribution of input data simultaneously. Secondly, NLE is extended to discover the clustering structure of data by incorporating with a Euclidean distance histogram and a series of band pass filters. Finally, by combining with a dimensionality reduction technique, the discovered intrinsic structures are visualized and manipulated in low-dimensional space in the format known as embeddings. The proposed NLE allows the discovery process to adapt to the characteristics of input data. In addition, it is revealed that an image feature composed of image patches can be tracked by tracking the contour containing embeddings of the corresponding image patches

This paper presents adaptive repetitive learning control for trajectory tracking of uncertain robotic manipulators. Through the introduction of a novel Lyapunov-like function, the proposed method only requires the system to start from where it stopped at the last cycle, and avoids the strict requirement for initial repositioning for all the cycles. In addition, it is more applicable, as it only requires the variables to be learned in an iteration-independent manner, rather than satisfying the periodicity requirement in a number of the conventional methods. With the adoption of fully saturated learning, all the signals in the closed loop are guaranteed to be bounded, and the iterative trajectories are proven to follow the profiles of desired trajectories over the entire operation interval. The effectiveness of the proposed method is shown through extensive numerical simulation results.

In this paper, an algorithm for the complete multi-robot coverage of a connected space with unknown obstacles is presented. The proposed algorithm mainly operates by maintaining, as far as possible, small uncovered regions between covered areas and obstacles. In addition, the bounds on the amount of repeated coverage and time required for complete coverage are also investigated. Furthermore, it is shown that repeated coverage can occur only around regions where the paths between obstacles are less than twice the width of the robots’ coverage range. This property holds even when the robots have no a priori knowledge of the environment, and therefore helps to prevent unnecessary wastage of time and resources.

10.1360/03yf0601

Following the idea of using first-order time derivatives, this paper presents a general recurrent neural network (RNN) model for online inversion of time-varying matrices. Different kinds of activation functions are investigated to guarantee the global exponential convergence of the neural model to the exact inverse of a given time-varying matrix. The robustness of the proposed neural model is also studied with respect to different activation functions and various implementation errors. Simulation results, including the application to kinematic control of redundant manipulators, substantiate the theoretical analysis and demonstrate the efficacy of the neural model on time-varying matrix inversion, especially when using a power-sigmoid activation function.

In this paper, adaptive neural network (NN) control is investigated for a class of discrete-time multi-input-multi-output (MIMO) nonlinear systems with triangular form inputs. Each subsystem of the MIMO system is in strict feedback form. First, through two phases of coordinate transformation, the MIMO system is transformed into input-output representation with the triangular form input structure unchanged. By using high-order neural networks (HONNs) as the emulators of the desired controls, effective output feedback adaptive control is developed using backstepping. The closed-loop system is proved to be semiglobally uniformly ultimate bounded (SGUUB) by using Lyapunov method. The output tracking errors are guaranteed to converge into a compact set whose size is adjustable, and all the other signals in the closed-loop system are proved to be bounded. Simulation results show the effectiveness of the proposed control scheme.

In this paper, the cooperative backoff adaptive scheme (COBOS) is proposed for task allocation amongst a team of heterogeneous robots. The COBOS operates in regions with limited communication ranges, and is robust against robot malfunctions and uncertain task specifications, with each task potentially requiring multiple robots. The portability of tasks across teams (or when team demography changes) is improved by specifying tasks using basis tasks in a matrix framework. The adaptive feature of COBOS further increases the flexibility of robot teams, allowing robots to adjust their actions based on past experience. In addition, we study the properties of COBOS: operation domain; communication requirements; computational complexity; and solution quality; and compare the scheme with other task-allocation mechanisms. Realistic simulations are carried out to verify the effectiveness of the proposed scheme.

In this paper, we present a novel approach for representing formation structures in terms of queues and formation vertices, rather than with nodes, as well as the introduction of the new concept of artificial potential trenches, for effectively controlling the formation of a group of robots. The scheme improves the scalability and flexibility of robot formations when the team size changes, and at the same time, allows formations to adapt to obstacles. Furthermore, for multirobot teams to operate successfully in real and unstructured environments, the instant goal method is used to effectively solve the local minima problem.

In this paper, an Instant Goal approach is proposed for collision-free boundary following of obstacles of arbitrary shape and globally convergent path planning in unknown environments. Firstly, for effective knowledge representation and manipulation, a vector representation is presented, which not only saves much space but also conforms to the physical properties of range sensors. Secondly, the concept of Instant Goals is introduced enabling the robot to perform boundary following in a "natural" human-like manner, with additional measures taken to ensure that the robot is moving "forward" along the boundary, even if the obstacle is of arbitrary shape and disturbing obstacles are present. Collision checking is performed simultaneously and, when needed, collision avoidance is efficiently incorporated in. Based on the approach of boundary following, a realistic sensor-based path planner with global convergence property is designed for the robot capable of acquiring discrete and noisy range data. Realistic simulation experiments validate the effectiveness of the proposed approaches.

Practical adaptive neural control is presented for a class of nonlinear systems with unknown time delays in strict-feedback form. Using appropriate Lyapunov-Krasovskii functionals, the unknown time delays are compensated for. Controller singularity problems are solved by practical neural network control. A novel differentiable control function is provided such that the practical design can be carried out in the decoupled backstepping design. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness of all the signals in the closed-loop system, and the tracking error is proven to converge to a small neighborhood of the origin.

Without knowledge of background or motion feature, detecting humans from a 2D image is still a tough task. In this paper, a novel stereo-based method to detect human objects for mobile service robots is proposed. Human objects are detected from the stereo spatial space through three distinct steps: (i) human oriented scale-adaptive filtering to aggregate and enhance the evidence of human presence, (ii) human like object segmentation, and (iii) human object identification based on the matching of a deformable head shoulder template to the evidence from both stereo and edge information. Systematic evaluation of the experimental results show that the high accuracy rates for human detection have been achieved with fewer constraints on the human operator, robot and the environment which they are in.

In this paper, motion estimation is proposed by fusing audio and video sensor data. The audio system consists of three microphones arranged on a Y-shaped structure, mounted on a pan-tilt camera. The camera forms the video system. Together, the audio and video system enables the 3D position of the sound source to be estimated. Using the position estimates, a motion model, consisting of the translational velocity and acceleration of the source, is in turn estimated using a Kalman filter. The motion model allows the sound source to be tracked in real time. This fusion estimation system has many potential applications such as video conferencing and security monitoring for intruders. Simulation results show that the motion estimation is satisfactory.

A new application architecture is designed for continuous, real-time, distributed wireless sensor networks. We develop a wireless sensor network for machinery condition-based maintenance (CBM) using commercially available products, including a hardware platform, networking architecture, and medium access communication protocol. We implement a single-hop sensor network to facilitate real-time monitoring and extensive data processing for machine monitoring. A LabVIEW graphical user interface is described that allows for signal processing, including FFT, various moments, and kurtosis. A wireless CBM sensor network implementation on a heating and air conditioning plant is presented as a case study.

In this paper, robust adaptive control strategy is presented for a wheeled mobile robot in the presence of model perturbations that violates the nonholonomic assumption. The nonholonomic constraint of the vehicle is assumed to be violated by an unknown slippage. Consequently, a perturbed kinematic model of the system is obtained. Using backstepping, the proposed controller is constructed at the dynamical level. The robust adaptive controller is to eliminate the needs for the LIP form of the system dynamics and the exact bounds of the system dynamics. All the system states are shown to be able to track the desired trajectory. The simulation results demonstrate the effectiveness of the proposed controllers.

In this paper, we propose the representation of formations in terms of queues and queue vertices rather than with nodes, as well as the use of artificial potential trenches for the formation control of a team of robots. This addresses the issues of scalability, flexibility and stability which are required for the formation control of a large team of networked homogeneous robots with explicit communication capabilities. In addition, the ability of multi robot teams to negotiate obstacles is important for the operation of the teams in real and unstructured environments. The obstacle avoidance behavior considers the relative position and velocity, thus taking into account the presence of moving obstacles. In addition, local minima problems due to deep crevices of obstacles and dead ends of narrow paths, are solved by the application of the instant goal method.

In this paper, adaptive neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms. The MIMO systems consist of interconnected subsystems, with couplings in the forms of unknown nonlinearities and/or parametric uncertainties in the input matrices, as well as in the system interconnections without any bounding restrictions. Using the block-triangular structure properties, the stability analyses of the closed-loop MIMO systems are shown in a nested iterative manner for all the states. By exploiting the special properties of the affine terms of the two classes of MIMO systems, the developed neural control schemes avoid the controller singularity problem completely without using projection algorithms. Semiglobal uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop of MIMO nonlinear systems is achieved. The outputs of the systems are proven to converge to a small neighborhood of the desired trajectories. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. The proposed schemes offer systematic design procedures for the control of the two classes of uncertain MIMO nonlinear systems. Simulation results are presented to show the effectiveness of the approach.

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.

In this paper, adaptive neural control is presented for a class of strict-feedback nonlinear systems with unknown time delays. The proposed design method does not require a priori knowledge of the signs of the unknown virtual control coefficients. The unknown time delays are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. It is proved that the proposed backstepping design method is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop. In addition, the output of the system is proven to converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approach.

In this paper, adaptive neural network (NN) control is investigated for a class of multiinput and multioutput (MIMO) nonlinear systems with unknown bounded disturbances in discrete-time domain. The MIMO system under study consists of several subsystems with each subsystem in strict feedback form. The inputs of the MIMO system are in triangular form. First, through a coordinate transformation, the MIMO system is transformed into a sequential decrease cascade form (SDCF). Then, by using high-order neural networks (HONN) as emulators of the desired controls, an effective neural network control scheme with adaptation laws is developed. Through embedded backstepping, stability of the closed-loop system is proved based on Lyapunov synthesis. The output tracking errors are guaranteed to converge to a residue whose size is adjustable. Simulation results show the effectiveness of the proposed control scheme.

In this paper, robust adaptive neural network (NN) control is presented to solve the control problem of nonholonomic systems in chained form with unknown virtual control coefficients and strong drift nonlinearities. The robust adaptive NN control laws are developed using state scaling and backstepping. Uniform ultimate boundedness of all the signals in the closed-loop are guaranteed, and the system states are proven to converge to a small neighborhood of zero. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. The proposed adaptive NN control is free of control singularity problem. An adaptive control based switching strategy is used to overcome the uncontrollability problem associated with x0 (t0) = 0. The simulation results demonstrate the effectiveness of the proposed controllers.

In this paper, for joint torque optimization of redundant manipulators subject to physical constraints, we show that velocity-level and acceleration-level redundancy-resolution schemes both can be formulated as a quadratic programming (QP) problem subject to equality and inequality/bound constraints. To solve this QP problem online, a primal-dual dynamical system solver is further presented based on linear variational inequalities. Compared to previous researches, the presented QP-solver has simple piecewise-linear dynamics, does not entail real-time matrix inversion, and could also provide joint-acceleration information for manipulator torque control in the velocity-level redundancy-resolution schemes. The proposed QP-based dynamical system approach is simulated based on the PUMA560 robot arm with efficiency and effectiveness demonstrated.

In this paper, relative azimuth audio localization is investigated using two microphones and an asymmetric mask. The Helmholtz-Kirchhoff integral method is employed to obtain information on ITD (Interaural Time Differences) and IID (Interaural Intensity Differences) which are two important cues for sound localization. It is proved that the proposed system can locate a sound source with relative azimuth in nearly 360 degrees.

This paper focuses on adaptive control of nonaffine nonlinear systems with zero dynamics using multilayer neural networks. Through neural network approximation, state feedback control is firstly investigated for nonaffine single-input-single-output (SISO) systems. By using a high gain observer to reconstruct the system states, an extension is made to output feedback neural-network control of nonaffine systems, whose states and time derivatives of the output are unavailable. It is shown that output tracking errors converge to adjustable neighborhoods of the origin for both state feedback and output feedback control.

The Global Positioning System (GPS) has been widely used in land vehicle navigation applications. However, the positioning systems based on GPS alone face great problems in the so-called urban canyon environments, where the GPS signals are often blocked by high-rise buildings and there are not enough available satellite signals to estimate the positioning information of a fix. To solve the problem, a constrained method is presented by approximately modeling the path of the vehicle in the urban canyon environments as pieces of lines. By adding this constraint, the minimum number of available satellites reduces to two, which is satisfied in many urban canyon environments. Then, different approaches using the constrained method are systematically developed. In addition, a state-augmentation method is proposed to simultaneously estimate the positions of the GPS receiver and the parameters of the line. Furthermore, the interacting multiple model method is used to determine the correct path which the vehicle follows after passing an intersection of roads. Simulation results show that this approach can solve the urban canyon problems successfully.

In this paper, direct adaptive neural-network (NN) control is presented for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. By utilizing a special property of the affine term, the developed scheme,avoids the controller singularity problem completely. All the signals in the closed loop are guaranteed to be semiglobally uniformly ultimately bounded and the output of the system is proven to converge to a small neighborhood of the desired trajectory. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulation results are presented to show the effectiveness of the approach.

This paper presents a robust adaptive neural control design for a class of perturbed strict feedback nonlinear system with both completely unknown virtual control coefficients and unknown nonlinearities. The unknown nonlinearities comprise two types of nonlinear functions: one naturally satisfies the "triangularity condition" and can be approximated by linearly parameterized neural networks, while the other is assumed to be partially known and consists of parametric uncertainties and known "bounding functions." With the utilization of iterative Lyapunov design and neural networks, the proposed design procedure expands the class of nonlinear systems for which robust adaptive control approaches have been studied. The design method does not require a priori knowledge of the signs of the unknown virtual control coefficients. Leakage terms are incorporated into the adaptive laws to prevent parameter drifts due to the inherent neural-network approximation errors. It is proved that the proposed robust adaptive scheme can guarantee the uniform ultimate boundedness of the closed-loop system signals.. The control performance can be guaranteed by an appropriate choice of the design parameters. Simulation studies are included to illustrate the effectiveness of the proposed approach.

A class of energy-based position controllers for a kind of flexible spacecraft is proposed. Closed-loop stability of the original distributed parameter system can be achieved, as well as asymptotic stability for the truncated system, which is obtained through representing the deflection of the appendage by an arbitrary finite number of flexible modes. The feedback gains of the controller are tuned by a genetic algorithm (GA) optimization process to achieve good results for tip motion based on some suitable fitness functions. Numerical simulations are carried out on a kind of spacecraft with one flexible appendage, and satisfactory results are obtained.

10.1109/ROBOT.2001.932759

Model-free controllers are presented for multi-link smart materials robots. The controllers are derived from the basic energy-work relationship in the absence of the system model which is complex and difficult. To obtain for multi-link smart materials robots. The smart materials bonded along the links are used to apply additional control to suppress the residue vibration effectively. One can achieve not only the closed-loop stability of the original system, but also the asymptotic stability of the truncated system, which is obtained through representing the deflection of each link by an arbitrary finite number of flexible modes. Simulation results are provided to show the effectiveness of the presented approach.

The paper first describes the problem of goals unreachable with obstacles nearby when using potential field methods for mobile robot path planning. Then, new repulsive potential functions are presented by taking the relative distance between the robot and the goal into consideration, which ensures that the goal position is the global minimum of the total potential.

This paper presents a novel control method for a general class of nonlinear systems using neural networks (NNs). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the output is measurable, by using a high-gain observer to estimate the derivatives of the system output, an adaptive output feedback NN controller is proposed. The closed-loop system is proven to be semi-globally uniformly ultimately bounded (SGUUB). In addition, if the approximation accuracy of the neural networks is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussions.

In this paper, two new parametric network models of robots are presented based on the systems' own functions. The complete dynamics of robots are given in a ready-to-used format which is constructed by finite dimensional static parametric networks for the inertia matrix and the potential energy (or the gravitational forces). As a result, dynamic models of robots can be automatically generated by software once given the number of degrees of freedom (DOF) and the sequence of the joint types, without knowing other parameters such as the lengths and the twist angles of the links. An existing adaptive controller is used as an example to show that some of the controllers can be easily modified such that adaptive controllers can be automatically generated. It is shown that all the closed-loop signals are bounded and tracking error goes to zero.

This paper considers improving the tip regulation performance of a joint-PD controlled single-link flexible manipulator by introducing nonlinear strain feedback. The controller is developed by applying Lyapunov's direct method. The stability of the closed-loop system is theoretically proven based on the partial differential equations (PDE) which govern the motion of the flexible robot, instead of using the traditional truncated models. The controller is very simple in its form, and only the measurements of joint angle, joint velocity, and strain of the bending beam are needed for implementation. The controller is very robust as well because it is independent of system parameters.