Autonomous collision avoidance technology is the core of unmanned surface vehicles (USVs). Deep reinforcement learning (DRL) is a new approach to avoid collision for USVs. However, most research is based on the assumption of a fixed number of obstacles and ignores the collision prediction to improve safety. To address this problem, a novel “prediction-decision” collision avoidance model based on the deep deterministic policy gradient (DDPG) is proposed. First, a radiation-shaped state space is designed to make the DDPG that can be used in time-varying scenarios with stochastic obstacles. Then, the velocity obstacle (VO) is combined with the state space for training to realize the collision prediction. Subsequently, reward functions are designed using a reward-shaping technique to improve training efficiency and safety. Finally, virtual simulation experiments based on Unity3D and field tests are conducted to verify the algorithm’s performance. The results show that it can take safe collision avoidance actions in unknown environments and with generalization ability.

An efficient formation-keeping strategy is essential for unmanned surface vehicles (USVs) to achieve complex cooperation missions in the Marine Internet of Things (MIoT) system. However, traditional methods make generating an efficient strategy to adapt to different formation patterns difficult in dynamic MIoT. To address this, we enhance the deep deterministic policy gradient (DDPG) algorithm and propose a novel formation control strategy generation approach. First, we design a generic reward mechanism based on the virtual leader–follower strategy to adapt to different formation patterns, simplify the design process, and optimize the formation control. Then, we adopt the intrinsic curiosity module (ICM) to alleviate the problem of sparse rewards and the prioritized experience replay (PER) mechanism to improve the utilization of experience and accelerate the learning rate. In addition, a Gaussian noise model is integrated into the DDPG approach to simulate various external disturbances, which can improve the robustness of the generated strategy. Finally, we built a virtual simulation environment based on Unity3D and conducted field tests to verify the feasibility and superiority of our approach.

This paper proposes a stochastic process based remaining useful life (RUL) prediction method for hybrid systems in the presence of intermittent fault. The failure of an intermittently faulty component is determined as a fault appearance with its severity exceeding failure threshold. This failure process is referred to as an event-triggered cooperative failure process (ETCFP). To depict the ETCFP in the hybrid system, a doubly stochastic process model is developed. In this model, the fault occurrence process is characterized by a compound non-homogeneous Poisson process with mode- and degradation-dependent fault occurrence rate and random external impacts, while the degradation process is composed of the external impacts and a Wiener process with the mode-dependent drift coefficient. Then, a multi-stage expectation maximization algorithm is proposed to estimate the unknown parameters in the doubly stochastic process model. In this approach, the stochastic integrals are approximated by the numerical integration with progressively refined step sizes at each stage. After that, the formulation of RUL distribution is derived based on the failure mechanism of ETCFP. To solve the RUL distribution that consists of stochastic integrals, a particle filter based numerical method is employed. Finally, the experimental study is carried out on a hybrid circuit system to show the effectiveness of the proposed method.

The canal waterways of China still play an important role in the logistics and transportation industry. Unmanned technology helps to reduce costs and improve the safety of navigation. Real-time environmental awareness is vital to making unmanned surface vehicles (USVs) come true. This paper proposes a new lightweight environmental awareness method based on deep convolutional neural networks (DCNN) and a mixed attention mechanism for USVs in canals, which can simultaneously perform ship detection, segmentation, and surface and background segmentation tasks. The features of the ships, surface, and background are extracted by a shared feature extraction backbone network and hybrid attention mechanism, which improves the efficiency of visual environmental awareness. In addition, a dataset namedUSV-Canalis constructed to enrich the features of canal waterways for environmental awareness, which contains typical canal scenes and 3443 ship objects. To improve the generalization, multiple public datasets are mixed with theUSV-Canaldataset to build an integrated dataset to train our model, which boasts diversity in scene types and ship classes. The comparative and field experiments’ results show that 40.9% ofmAP, 95.8% ofmIoU,and 5 frames per second (FPS) inference speed can be achieved, and have good generalization, which can meet the requirements of environmental awareness of low-speed ships in canal waterwaysNote to Practitioners—The trained and validated model can ultimately be deployed on unmanned surface vehicles, and the required hardware platform is NVIDIA’s Jetson Nano, which is used for real-time perception of surrounding ships and navigable surfaces during navigation. The information can be integrated into the guidance, navigation, and control (GNC) system of USVs, achieving obstacle avoidance and ensuring safe navigation. It is vital to make autonomous navigation come true.

Prognostics and health management (PHM) has garnered significant attention in industrial fields, particularly due to its successful application in managing battery degradation. However, current approaches are inadequate in addressing multiple thresholds, including both theoretical formulation and practical computational complexity. These limitations hinder the development and implementation of threshold-varying assessments, thereby impeding the advancement of PHM application. This article investigates prognostic applications with different failure thresholds and highlights the importance of failure threshold selection. In addition, theoretical evaluation and analysis are provided for multiple threshold settings, encompassing both discrete and continuous series. This introduces a novel technical domain for prognostic applications. The effectiveness of threshold-varying assessment is verified with several different approaches on real battery degradation experiments. Furthermore, we demonstrate the practical significance of threshold-varying assessments in enabling on-demand scheduling for maintenance or replacement of spare parts. Most importantly, to meet the real-time requirements of practical prognostic applications, this article also discusses the computational complexity of threshold-varying assessment and finds an applicable solution for this common difficulty.

The increasing adoption of lithium-ion batteries (LIBs) in low-carbon power systems is driven by their advantages, including long life, low self-discharge, and high energy density. However, LIB failures degrade performance and cause fire hazards. Effective fault diagnosis is thus critical yet challenging. This paper reviews LIB fault mechanisms, features, and methods with object of providing an overview of fault diagnosis techniques, emphasizing feature extraction’s critical role in detection via thresholds and isolation via multi-level strategies, and estimating detection quality. Several research gaps exist in current fault diagnosis techniques. Most techniques assume controlled conditions unlike complex real-world systems. Resource limitations often confine analytics to individual fault types, overlooking comprehensive approaches. Furthermore, many sensors lack the capability to detect precursor abnormalities, hampering early fault detection. To address these challenges, we advocate for advanced multiphysics and multiscale methods that incorporate sound, force, and thermal coupling to enhance fault diagnosis robustness in practical, complex systems. Multidimensional feature selection can prevent oversimplification in fault diagnosis. Overall, integrating analytics, sensing, and physics could enable comprehensive multidomain fault diagnosis under actual operating conditions.

The detection and evaluation of gait kinematics is vital for patient diagnosis and rehabilitation. Aiming at limitations of commonly used optical capture and wearable sensing systems in clinical applications, this paper proposes a thigh attitude angle prediction method based on the hip error tolerance from the kinematic data of two shank-mounted IMUs (Inertial Measurement Unit). The novelties of the proposed method are summarized as follows: i) It develops a parallel approach to regress variation of hip error tolerance for different subjects. This parallel approach, by simultaneously deconstructing the shank kinematics data via different regression algorithm including support vector machine, boosting tree, and stepwise linearity regression, is able to well accommodate the characteristics of both health subjects and patients. ii) It develops some evaluation indices based on gait symmetry, consistency, and activity to fully evaluate the human lower limbs motion performance in gait by only two IMUs. The effectiveness of the proposed method is verified by the experimental results among 8 healthy subjects and 16 cerebral infarction patients. For the healthy subjects, the estimated error of thigh prediction compared with Xsens and Vicon are 3.3 ± 0.3° and 3.5 ± 0.7°, respectively. For the patients, the estimated error compared with Xsens-measured angle is 4.8 ± 1.7°. Its broad significance in actual intelligent healthcare and robotics-assisted rehabilitation is three-fold: First, it is a recursive real-time method as it only needs data from the previous gait cycle to predict the thigh angle. Second, its accuracy meets the actual clinical needs. Third, it is a low-cost method that only needs two IMUs and has high potentials of clinical applications. Note to Practitioners—Gait is of great significance to quantify the degree of movement disorders in clinical practice, and the existing equipment is rarely able to meet the needs of full dimension, low cost, and low place constraints in clinical practice. This paper presents an innovative gait kinematic prediction method, which only uses two IMUs attached to the shanks to predict the orientation of thigh. Compared with related works, the proposed methods have achieved high-precision, real-time and low-cost acquisition. This paper is inspired by the problems of large number, high motion interference, and high cost of wearable gait measurement devices in clinical practice. The proposed method have potential to be integrated into exoskeletons and medical walking AIDS, which could greatly improve the comfort and control precision of the wearable system.

In this article, a multistage prognosis method is developed for the nonlinear electromechanical system in the presence of imperfect preventive maintenance (IPM) and degradation coupling effect (DCE). In this method, a composite degradation model incorporating IPM and DCE is proposed, by which the resultant remaining useful life (RUL) can be dynamically updated. First, the effects related to internal degradation, external degradation, and cumulative IPM are defined to facilitate the establishment of degradation model. Then, after the fault diagnosis with enhanced isolability using bond graph and improved temporal causal graph, degradation data obtained by cubature Kalman filter are used to estimate degradation model coefficients. Due to the RUL variation with occurrences of the first IPM and new fault, the multistage prognosis method is developed to update RULs at different stages that are distinguished by the first IPM of faulty component or the DCE caused by newly occurring fault. Finally, the proposed methods are validated by experimental results.

Accurate bearing fault diagnosis and prognosis (FDP) is critical for optimal maintenance schedules, safety and reliability. The existing methods face some problems and challenges in detecting the starting time for prognosis and using a single model to describe fault dynamics. With these motivations, this article presents a hybrid bearing FDP framework with fault detection and automatic fault model selection. In the proposed approach, the convolutional neural network is used to detect fault and select the appropriate fault dynamic model. To improve the performance, power spectrum of vibration signals are fused with operating conditions to built information maps for fault detection and model selection. After a fault is detected, a Bayesian method is triggered to estimate the fault state and predict the remaining useful life. In the prognosis, the Dempster–Shafer theory is employed to fuse prediction results from different models if necessary. The proposed approach is verified with bearings under different operating conditions. Experimental results and comparison studies verify the high accuracy and efficiency of the proposed method.

The collision avoidance in real-time is crucial for unmanned surface vehicles (USVs) in a complex environment. Traditional methods make it hard to ensure the balance of control decisions. To balance safety and practicality, a collision avoidance algorithm based on deep reinforcement learning (DRL) and a two-level incentive reward based on the principle of complementarity is proposed. To address the vital sparse reward problem of Deep Deterministic Policy Gradient (DDPG), the trajectory evaluation function of the dynamic window algorithm (DWA) is referred to construct the primary reward strategy, and a secondary incentive reward is constructed based on velocity obstacle (VO) to eliminate potential collision risks. To improve the efficiency of training, the electronic chart (EC) and Unity3D are used to build an immersive simulation platform. Based on it, simulations are made to verify the performance. In addition, field experiments are first conducted in various encounter scenarios to verify the effectiveness. The results show that it can take safe collision avoidance actions and get practical paths in various situations.

Deep learning-based diagnosis methods currently face some challenges and open problems. First, domain knowledge of fault modes and operating conditions are not integrated in most existing approaches, which results in low diagnosis accuracy and training efficiency. Second, existing methods treat all features with indiscriminate attention, which causes unnecessary computation and even false diagnosis results in some cases. Third, multitask diagnosis becomes more important for health maintenance. To address these challenges, this article proposes a deep residual convolutional neural network with an enhanced discriminate feature learning capability and information fusion for multitask bearing fault diagnosis. In the proposed approach, domain knowledge is integrated with monitoring data to build the information map. Two attention modules are introduced to enhance the discriminate feature learning ability. Two classifiers are employed for multitask diagnosis. Experiments on two bearing cases demonstrate that the proposed approach has significant improvements in terms of diagnostic accuracy and training efficiency.

The uncertainties in the degradation process have always been regarded as a major challenge in the practical applications of Prognostics and Health Management. This article discusses the uncertainties in the degradation process of Lithium-ion batteries, points out their potential consequences in practical applications, and then we summarizes some commonly adopted aftertreatment solutions for them. To proceed realistic analysis, we present a non-accelerated degradation experiment with several Lithium-ion batteries. The experiment lasted for more than ten months and the data highlighted the uncertain fluctuations and periodic waves in SOH degradation process. Furthermore, this article reveals the delayed correlation between SOH degradation and changing environmental temperature. Finally, we provide some possible solutions for guiding practical applications of our finding.

This paper investigates the wire frequency characterization and proposes a fault classification method of electrical wires. A data-driven machine learning-based fault diagnostic method is developed. In the proposed method, an incident signal containing different frequency components is sent to wires with different hard and soft faults. The transmitted signals are collected at the terminal. By analyzing the spectrum of the collected signals, the amplitude values at different frequencies are extracted as features for fault diagnosis. A support vector machine method is adopted to train and test for different fault conditions. The proposed method is verified with two different types of wires with different fault severity levels. The results show that the proposed multiple frequency signal transmission method can be used to diagnosis the fault class of wires.

Load-carrying capability is an essential criterion in legged robots' practical application. This paper proposes an unpowered hydraulic auxiliary system to improve the legged robot's loading capability and energy efficiency. For humans, it has been widely hypothesized that intra-abdominal pressure can reduce potential injurious compressive force imposed on spinal discs when a person lifts heavy objects. Inspired by this human biomechanical phenomenon, we design a novel loading-carrying strategy using hydraulic cylinders, valves and accumulators. Different from ordinary powered hydraulic systems, this design provides continuous support force to share the load applied on knee joint actuator without consuming extra energy. The bent-leg theoretical model is constructed to validate the design and analysis. A bipedal hydraulic-assisted electric leg prototype (HyELeg) is fabricated and tested for squatting and walking gaits. The results show that with hydraulic assistance, the prototype can save energy by 45.9% for squatting with a load of 125% of its own body weight, and the walking performance is enhanced by 16.9% in energy efficiency with carrying a load of 67.5% of its own body weight.

It is inevitable for human to bear the gravitational and inertial force when carrying loads. The impact force exerted on human body is originated from the inertial force which can increase the energy expenditure and cause injury to human body. This paper proposes a semi-active hover backpack with controllable air damper to minimize the inertial force. The skyhook damper model of hover backpack is established which is the dynamic target of the practical backpack. Sliding mode control is designed to eliminate the tracking error and the effectiveness of the control method is analyzed. Simulation and experiment are conducted and comparative results are stated. The results demonstrate that the semi-active hover backpack with sliding mode control can reduce the inertial force.

The state-of-health (SOH) is a critical factor in guaranteeing the safe operation and reliability of Lithium-ion battery-related equipment. One of the main challenges is the accuracy and practicality of health indicator (HI) extraction and suitable algorithm for SOH diagnosis and prognosis. This paper proposes a new method that implements an extended Kalman filter (EKF) with an external HI to diagnose and prognose the SOH of Lithium-ion batteries (LIBs) and the results are expressed in form of a probability distribution function (PDF). First, aging experiments are conducted on LIBs. Second, an HI that has a strong relation to the SOH of LIBs is extracted from the terminal voltage of batteries. Third, EKF algorithm is implemented to diagnose and prognose the SOH of batteries. Fourth, the proposed method is verified with a series of experiments. The results demonstrate the effectiveness of the proposed method in terms of SOH diagnostic and prognostic.

This paper proposes a modeling method for reflectometry response of natural aged cable insulation. In the proposed modeling, a finite element method (FEM) is used to simulate the electromagnetic field of cable sections with different degrees of insulation aging. From the model, the S-parameters of cable sections are extracted. A series of equivalent circuit models of cable sections are then built based on the S-parameters to test the reflectometry response of different aging conditions. The reflectometry response signals are analyzed to build a cable insulation degradation model. The results of model are verified with the experiment data to demonstrate the effectiveness of the proposed modeling method.

Prognosis of discrete component with intermittent fault in hybrid systems is challenging since the component has only two states (i.e., ON and OFF) and no associated physical parameter in the model can quantify the degradation. This article aims to solve the discrete component prognosis problem under the model-based paradigm. First, the fault detection and isolation module help find the possible faulty discrete components. Based on the isolated possible faulty discrete components, Levy flight biogeography-based optimization is proposed to identify the faulty discrete component states, as well as the fault appearing and fault disappearing instants. Second, a Weibull function-based degradation model which can capture the duration evolution of intermittent fault of discrete component in observation window (OW) is developed using coordinate reconstruction approach, and the degradation model coefficients can be calculated from the fault identification results. After that, the concept of failure threshold for faulty discrete component is defined based on the ratio of fault duration to OW, which enables the prognosis of intermittent fault in discrete component. Finally, the proposed methodologies are validated by experiment results.Note to Practitioners—This article is motivated by the intermittent fault prognosis problem of discrete components (e.g., relays and hydraulic valves) in hybrid systems. Existing fault prognosis researches do not consider discrete component which is an important part of hybrid systems. For the intermittent fault prognosis of discrete component, the observation window (OW) concept and coordinate reconstruction (CR) method are proposed to establish the degradation model, and the ratio of fault duration to OW is used to define the failure threshold of discrete component. To show the effectiveness of the proposed methods, an application on a hybrid circuit system is considered. It is noted that the degradation pattern (e.g., increase of frequency or duration of intermittent fault) of discrete components may vary in different systems, while the degradation process can be quantified by the OW and CR methods developed in this article, which enables the prognosis of intermittent fault in discrete component for various hybrid industrial systems. The proposed approach can be applied to industrial hybrid systems if the following conditions are satisfied: 1) the hybrid bond graph model of the monitored system can be established, based on which the fault detection and isolation can be implemented and 2) the monitored system contains multiple discrete components suffering from intermittent faults whose appearing and disappearing instants can be identified by certain method.

Efficient task assignments can significantly improve agent management and reduce communication load and energy consumption. This article investigates the cooperative control problem for multiagent systems with an active task assignment strategy, in which whether an agent exchanges the information with neighbors depends on a perceived mission. By defining a set of missions, a task assignment mechanism for cooperative control problem is first proposed, in which the tasks will be scheduled by intermittent communication signals associated with the actual optimization requirements. By allowing appropriate task assignment conditions, a class of tracking cooperative control protocol is designed and accordingly, the stability of the closed-loop systems under the intermittent communication will be guaranteed. We also consider a case that the communication links between the followers and the leader can be optimized. To maximize the quality of information interaction, a leadership competition mechanism is introduced to design the tracking cooperative control protocol. As an application, cooperative surveillance using a group of rotary-wing air vehicles is considered. Numerical simulation demonstrates the effectiveness of the proposed approaches.

This article proposes a novel dynamic fault-tolerant control model to address the optimal tracking cooperative control problem for cyber-physical systems, by considering that all systems can be endowed as a multiagent system and the admissible levels of the actuator fault can be resiliently management. Different from previous works, the feedback gain for the cooperative controller design is no longer fixed, and actuator outage behaviors can be solved by a resilient control way. By introducing a sampling manner, a robust optimal framework is first developed to determine the appropriate feedback gain under a cost constraint for the dynamic fault model. The dynamic fault-tolerant control protocol is, then, designed to achieve the cooperative behaviors. Moreover, a fault management mechanism is proposed, in which the fault parameter is reset as an initial value when the fault growth is greater than the admissible level. By this design, the tracking cooperative behaviors can be achieved in a resilient management process. Two examples are presented to illustrate the effectiveness of the proposed theories.

Due to the fact that single sensor data contains only partial information about complex systems, multiple sensors are often embedded to simultaneously monitor the health state and predict the remaining useful life (RUL). This brings new challenges to traditional approaches that focus on single sensor in terms of data fusion, optimization, and uncertainty management. To address these challenges, this article proposes a novel RUL prediction and uncertainty management framework for multisensor systems. In this framework, a composite 1-D health indicator (1-D HI) is obtained from multiple sensors by optimizing some HI characteristics, including monotonicity, robustness, fitting error, and range information, to better describe the underlying degradation process. A multiobjective grasshopper optimization algorithm is used to achieve the optimal weight vector of the fusion model. Then, an unscented particle filter is introduced to predict the RUL by combining the degradation model constructed from HI and the composite 1-D HI as measurement. To manage the uncertainty in prognosis, a probability distribution of failure threshold and noise parameter adjustment are developed. Experimental results on aircraft turbine engine degradation and comparison with state-of-the-art methods are presented to demonstrate the effectiveness of the proposed framework in RUL prediction of multisensor systems.

Cyber-physical systems (CPS) refers to the integrative system consisting of interconnected computing and control devices interacting with the physical infrastructure via sensors and actuators. Recently, there is a swift growth of CPSs ranging from smart grids to smart buildings, robotics, and other industrial control systems. They have formed the keystone of the sustainable growth of the economy, manufacturing, and smart and connected communities. Due to extensive applications of CPSs, their resilience, reliability, and security are paramount. Many factors, however, pose significant threats to CPSs and lead to high economic losses and social impacts. Software defects also make CPSs vulnerable to security attacks and coordinated cyber and physical attacks. To address this issue, emerging technologies and methods for understanding and improving the resilience, reliability, and security of CPSs are needed. This Special Section aims to provide a platform to help define, understand, and quantify the resilience, reliability, and security of CPSs.

This paper presents an efficient, robust, comfortable, and real-time motion planning framework for on-road autonomous vehicles. This proposed framework aims to enhance the performance of motion planning in complex environments such as driving in the urban area. It uses a path velocity decomposition method to separate the motion planning problem into path planning and velocity planning. The novelty lies in the use of Historical data in the$SL$coordinate in the framework of a tree version of Rapidly-exploring Random Graph (RRT*) technique in path planner, called HSL-RRT*, which grows the path tree efficiently by the data from previous planning cycle. The velocity planner uses a Nonlinear Model Predictive Controller (NMPC) to generate optimal velocity along the path generated from the path planner, taking account of vehicle constraints and comfort. Analytic and simulation results are presented to validate the approach, with a special focus on the robustness and efficiency of the algorithm operating in complex scenarios.

This paper proposes a heterogeneous coupling network framework to address the cooperative tracking control problem for multiagent systems with dynamic interaction topology and bounded intermittent communication. By considering the underlying dynamic interaction topology and introducing the adjustable heterogeneous coupling weighting parameters, a bounded consensus condition of cooperative tracking control is proposed. With considering a bounded intermittent communication condition, a class of intermittent cooperative tracking control protocol is designed based on the combination of the individual agent dynamic and the exchange of information among the agents under an appropriate consensus speed constraint. It is proved in the sense of Lyapunov that the cooperative tracking control for the closed-loop multiagent systems can be achieved under the dynamic interaction topology, an appropriate feedback gain matrix, and the intermittent communication information of all agents. The results are further extended to the information consensus protocol with intermittent coordinated constraint information. Finally, two examples are presented to verify the effectiveness.

Machine condition prediction plays an important role in industries. In condition-based maintenance, measurements are used to detect, identify, and predict the onset and evolution of potential faults. The information is then used to optimally schedule the maintenance activities and logistics. With the development of machine learning and big data, deep learning algorithms become important tools in condition prediction due to their excellent capabilities in data processing, feature extraction, and modeling. This paper presents a novel approach for machine health condition prognosis based on Long Short Term Memory (LSTM) and Particle Filtering (PF). In this work, machine condition data are used to train a LSTM model offline, which is then employed as a prognostic model in the framework of Bayesian approach to online predict the evolution of the machine fault state. Particle filtering is utilized to calculate the posterior probability density function. Case studies of a cracked carrier plate are presented to verify the effectiveness of the proposed approach. The results demonstrate that the proposed approach can predict machine condition more accurately.

Weather analysis and support plays an important role in mission planning and risk evaluation of aircraft and unmanned aerial vehicles. The impact of adverse weather on the aviation industry has been well documented. Although long-term efforts have made tremendous progress in this area, the current weather support tools rely on deterministic weather analysis that cannot meet the increasing requirements. A more flexible, accurate, and reliable probabilistic analysis is desirable for probabilistic aviation decision aid. To achieve this goal, this paper proposes an ensemble forecasting-based framework in which the probabilistic analysis of adverse weather, 3-D receding horizon field D* mission planning, and mission risk evaluation are integrated for a robust and flexible decision aid. The innovations of the proposed system include advanced multiple parameter probabilistic data analysis for probabilities of adverse weather and its confidence analysis, planning, and risk evaluation. Real ensemble weather forecasting data are used to demonstrate the efficiency of the proposed approach.

This paper addresses the leader-follower consensus problem of multivehicle wirelessly networked uncertain systems with nonlinear dynamics and actuator fault and proposes a class of distributed discontinuous communication protocols based only on the relative states among neighboring vehicles. By introducing a novel fault model for multivehicle wirelessly networked uncertain systems, fault tolerant consensus can be achieved with different fault modes of the actuators. It is proved in the sense of Lyapunov that, if the conditions of dwell time and the intermittent communication rate are satisfied, the leader-follower consensus can be achieved for closed-loop multivehicle wirelessly networked uncertain systems with nonlinear dynamics and actuator fault under the topology that frequently but not always contains a spanning tree rooted at the leader. Furthermore, the results are extended to the collision avoidance and formulation control problems. Four examples are presented to demonstrate the effectiveness of the proposed approaches.

This paper proposes an accurate cooperative control strategy to address the distributed adaptive consensus problem for multiple subsystems of the process industrial plants by constructing a two-layer node-to-node communication framework. In the present framework, each subsystem is modeled by an agent, and all the subsystems and the information flow are regarded as a multiagent uncertain system. By introducing proper assumptions, a class of distributed adaptive consensus protocol for accurate cooperative control is designed by adaptive weighting factors, appropriate feedback gains, and limited state information. It shows that distributed adaptive consensus of accurate cooperative control can be achieved for closed-loop multiagent uncertain systems with the two-layer node-to-node communication framework, if each follower is affected by at least one leader for some uniformly bounded communication time intervals. The results are further extended to nonlinear situations. Two application examples are presented to verify the effectiveness of the proposed approaches.

This paper proposes a frequency adaptive discrete Fourier transform (DFT) based repetitive control (RC) scheme for DC/AC converter. DFT-based RC is an improved RC scheme aiming to eliminate waveform distortion on selected harmonics without influencing the overall system stability. However, the traditional DFT-based RC is insufficient to accommodate frequency fluctuation as fractional delay period in one signal cycle will lead to the large magnitude pulse deviates away from the desired frequency. To address the problem, virtual variable sampling method is proposed to develop a flexible and precise way to achieve a fixed integer delay with virtual variable sampling. By applying this method, the DFT-based RC can easily accomplish frequency adaptation. Comparing the traditional DFT-based RC, it not only reduces computation load but also increases the system stability. A complete series of experiments of programmable AC power source under frequency tuning are presented to verify the effectiveness of the proposed method.

Estimation State-of-Charge (SOC) of Lithium-ion batteries is a main function of battery management system (BMS), which play critical roles in the application of batteries. The applications in electrical vehicles and consumer electronics require a time efficient algorithm to produce accurate SOC estimation. Extended Kalman filter (EKF) is widely used in state estimation because it provides a simple and efficient solution for nonlinear systems. In order to further reduce the computation cost, Lebesgue sampling based EKF (LS-EKF) is developed, which is able to eliminate unnecessary computations. In this paper, the SOC is estimated by the proposed LS-EKF method based on an equivalent circuit model. By this means, the SOC estimation is much faster than traditional EKF method, which makes it feasible for online applications. This method is verified by SOC experimental results. The results show that the LS-EKF based algorithm has good performance and low computation cost.

In the past decades, fault diagnosis and prognosis (FDP) approaches were developed in the Riemann sampling (RS) framework, in which samples are taken and algorithms are executed in periodic time intervals. With the increase of system complexity, a bottleneck of real-time implementation of RS-based FDP is limited calculation resources, especially for distributed applications. To overcome this problem, a Lebesgue sampling-based FDP (LS-FDP) is proposed. LS-FDP takes samples on the fault dimension axis and provides a need-based FDP philosophy in which the algorithm is executed only when necessary. In previous LS-FDP, the Lebesgue length is a constant. To accommodate the nonlinear fault dynamics, it is desirable to execute FDP algorithm more frequently when the fault growth is fast while less frequently when fault growth is slow. This requires to change the Lebesgue length adaptively and optimize the selection of Lebesgue length based on fault state and fault growth speed. The goal of this paper is to develop an improved LS-FDP method with adaptive Lebesgue length, which enables the FDP to be executed according to fault dynamics and has low cost in terms of computation and hardware resource needed. The design and implementation of adaptive LS-FDP (ALS-FDP) based on a particle filtering algorithm are illustrated with a case study of Li-ion batteries to verify the performances of the proposed approach. The experimental results show that ALS-FDP keeps close monitoring of fault growth and is accurate and time-efficient on long-term prognosis.

This paper proposes an adaptive explicit synchronization framework to address the cooperative control for heterogeneous uncertain dynamical networks under switching communication topologies. The main contribution is to develop an adaptive explicit synchronization algorithm, in which the synchronization state can be completely tracked by each agent in real time rather than only be measured after the synchronization process of all agents is over. By introducing appropriate assumptions, a class of adaptive explicit synchronization protocols is designed by using a combination of the virtual leader's states, the neighboring agents' relative information, distributed feedback gain, and distributed average weighted parameters. It is proved in the sense of Lyapunov that, if the dwell time is larger than a positive threshold, the cooperative control problem for the closed-loop heterogeneous uncertain dynamical networks under switching of strongly-connected communication topologies can be solved by the proposed adaptive explicit synchronization algorithm. Furthermore, by assuming that the topology is frequently strongly-connected, it shows that intermittent adaptive explicit synchronization can be achieved with well-designed control parameters. Two examples are presented to demonstrate the effectiveness of the proposed theory.

This paper proposes a global cooperative control framework to address leader-follower consensus of constraints subsystems of industrial plants, in which each subsystem is modeled as an agent and all the subsystems and networks of information flow construct a multiagent system. The focus of this paper is to solve the global leader-follower consensus for multiagent systems with input saturation via low-high gain feedback approach and parametric algebraic Riccati equation approach, in which the feedback gain design is distributed and decoupled from network topologies. By introducing appropriate assumptions, a class of low-high gain feedback protocol is designed based on the states of local neighbors to reach the global stability. It is proved in the sense of Lyapunov that, if the dwell time is larger than a positive threshold, the global leader-follower consensus for the closed-loop linear multiagent systems with input saturation under the derived topology containing a directed spanning tree can be achieved. The results are further extended to leader-follower consensus for nonlinear multiagent systems with the design of nonlinear low-high gain feedback protocol. As industrial applications of the proposed low-high gain scheduling approaches, the controller design of vibration in mechanical systems and satellite formation systems are revisited. Numerical simulations with cooperative control of industries subsystems show the effectiveness of the proposed approach.

In this paper, three iterative learning control (ILC) schemes are developed in the multirate signal processing domain. One is pseudo-downsampled ILC, in which the input update rate is different from the sampling rate of feedback system. The second one is a two-mode ILC, in which the input update rates of ILC are different at low and high frequency bands. The third one is a cyclic pseudo-downsampled ILC, which extends the first scheme by shifting downsampling points in different iterations. Theoretical background and design approaches of these multirate schemes are addressed. Experimental results are presented to highlight the traits of each scheme. The advantage is that these schemes have the ability to learn those error component beyond the learnable bandwidth of a conventional ILC and, therefore, can improve the tracking accuracy substantially. Additionally, the multirate ILC schemes have the abilities to produce good learning transient with the presence of initial state error.

The earlier works by Zhang, B. et al, (2004) on cutoff-frequency phase-in ILC show that the scheme can suppress initial state error/position offset properly and improve tracking accuracy. However, since cutoff frequency is set high in initial phase of operation cycles, the improvement of tracking accuracy is mainly in this phase and the tracking error in later phase of operation cycles can still be large. A uniformly good tracking accuracy is favorable in many applications. In this paper, a sliding cutoff-frequency phase-in ILC is proposed to achieve this goal. In this scheme, cutoff frequency profile moves along the time axis after some cycles according to the assessment of tracking performance. Experimental results on an SCARA robot show that this scheme can further improve the tracking accuracy and generate a uniform tracking error over the entire operation interval

In this paper, a discrete wavelet transform-based cutoff frequency tuning method is proposed and experimental investigation is reported. In the method, discrete wavelet packet algorithm, as a time-frequency analysis tool, is employed to decompose the tracking error into different frequency regions so that the maximal error component can be identified at any time step. At each time step, the passband of the filter is from zero to the upper limit of frequency region where the maximal error component resides. Hence, the filter is a function of time as well as index of cycle. The experimental results show that this method can suppress higher frequency error components at proper time steps. While at the time steps where the major tracking error falls into lower frequency range, the cutoff frequency of the filter is set lower to reduce the influence of noises and uncertainties. This way, learning transient and long-term stability can be improved.

In this paper, a frequency tuning method based on time-frequency analysis of error signal is presented for iterative learning control (ILC). Qualitative analysis and experimental investigation are presented. The method uses wavelet packet algorithm to decompose the error signal so that the maximal error component at any time step can be identified. The cutoff frequency of the filter at each time step is set to cover the frequency band up to the frequency region where the maximal error component resides. The proposed method allows high frequency error components enter the learning at proper time steps. While at other time steps, the cutoff frequency is set low to guarantee the good learning transient and long-term stability.