Autonomous vehicle trajectory prediction faces significant challenges in complex traffic scenarios due to sensor occlusions and limited single-viewpoint perception. Cooperative prediction among connected vehicles and infrastructure offers a promising solution. Existing approaches suffer from rigid architectural coupling, requiring homogeneous models and a fixed number of collaborators, making them impractical for dynamic real-world scenarios. Moreover, a critical yet underexplored domain shift problem arises when models trained on single-viewpoint datasets are applied to collaborative environments, leading to substantial performance degradation. To address these limitations, we propose the Bridging Infrastructure and Vehicles (BIV) cooperative prediction framework, a plug-and-play solution that enables collaboration among an arbitrary number of heterogeneous trajectory prediction systems. The BIV framework operates through three stages: individual prediction by each collaborator using their domain-specific models, cooperative sharing of predicted trajectories, and the fusion of predictions using a novel Dynamic Time Warping-based Non-Maximum Suppression (DTW-NMS) mechanism. The DTW-NMS abstracts potential future trajectories of target agents by leveraging the similarity of coordinate sequences to capture safety-critical maneuvers. The proposed framework effectively overcomes the domain shift between single-viewpoint training and collaborative deployment. As an offline collaborative approach, the BIV framework demonstrates superior performance compared to all existing offline methods, while also outperforming most online methods. The BIV framework surpasses the best offline collaborative trajectory prediction models, reducing minADE and minFDE by up to 23.15% and 25.67%, respectively, while lowering the miss rate by 25.93%.

The task performance of mobile manipulators can be significantly enhanced by whole-body control and optimization in complex scenarios. Due to the nonlinear properties of whole-body dynamics and parameter uncertainty, modeling accurate system dynamics is essential in addition to designing an effective control strategy. However, traditional control methods have high computational costs and fail to deal with the parameter errors caused by model linearization. To address these issues, we propose a model predictive control (MPC)-Net, a learning-based approach that facilitates rapid online optimization by combining deep learning with multiple MPCs. Firstly, we develop a parameter identification algorithm based on a deep neural model to estimate the unknown dynamics parameters. Although the control performance of the MPC approach positively correlated with the prediction horizon, a long horizon would result in additional computational costs. Thus, MPC-Net is constructed by combining multiple sub-MPC issues, and the nonlinear coefficients are obtained by using a deep neural network-based optimization framework. Furthermore, MPC-Net generates the solution by combining the outputs of multiple sub-MPC problems using the nonlinear transformation of learned coefficients. Experiments are conducted on a mobile manipulator, which demonstrates the proposed MPC-Net-based optimization control offers fast efficient computation and low tracking error performance.

Within the framework of differential graphical games, this paper investigates the leader-following output consensus problem for fully heterogeneous multiagent systems, where each follower incorporates both the influence of its own policy on its utility and that of its neighbors’ policies. To achieve output consensus, we design a novel fully distributed estimator-based control protocol without requiring prior knowledge of global network topology. Specifically, the fully distributed estimator is formulated through a graphical game perspective, modeling agent-neighbor interactions as zero-sum conflicts, to overcome the fragility of cooperative estimation in adversarial or resource-constrained environments. By employing min-max strategies, each agent can robustly optimize its local estimation policy against neighbors’ worst-case policies. To demonstrate the applicability of this approach to intelligent transportation networks, we develop a corresponding fully distributed platooning control algorithm that ensures coordinated motion of heterogeneous connected vehicles. Finally, numerical simulations and real-world experiments validate the effectiveness of the proposed protocols.

The problem of reinforcement learning (RL)-based fuzzy control for nonlinear systems with unknown dynamics via parallel composite policy iteration (PCPI) scheme is studied in this article. The main objective of this article is to solve the fuzzy algebraic Riccati equation (FARE), which is inherently complex and cannot be easily solved by traditional mathematical formulas. Policy iteration (PI) and value iteration (VI) algorithms proposed have been widely used to address this problem. However, these algorithms have the disadvantages of an initial stabilizing control policy, the persistent excitation (PE) condition, and huge amounts of data. To effectively alleviate these drawbacks, a novel PCPI algorithm is proposed in this article. Specifically, for each fuzzy subsystem, an adaptive parameter is designed to eliminate the requirement of an initial stabilizing control policy. In addition, an online model-free PCPI algorithm is proposed for the situation where the dynamic information of the fuzzy system is difficult to obtain. By substituting the stored historical data with online data, the PE condition is relaxed to the initial excitation (IE) condition. Concurrently, the corresponding algorithm can be executed independently and concurrently under each fuzzy rule, thereby fully exploiting the available computational resources. Finally, the effectiveness of the algorithms set forth in this article is verified through a single-link robot arm and quarter-car active suspension (QCAS) experiment.

This article investigates a two-person zero-sum game for sequential decision making processes with dynamically evolving performance preferences. First, we incorporate this problem within a game theoretic framework, and establish the existence and uniqueness conditions for the temporal equilibrium strategy. Moreover, we present a compact formulation for one player's best response to the other's strategy. Furthermore, the specific convergence rate is achieved through the design of utility parameters. Finally, we validate our approach on the optimal liquidation and competitive supply chain models.

This article addresses the distributed formation control problem for safety-certified uncertain multirobot systems with minimum restrictive connectivity maintenance (MRCM). Compared to this problem with deterministic models, it presents inherently more challenging requirements for safety and stability analysis. An integrated quadratic programming controller incorporating input-to-state safety control barrier functions (ISSf-CBFs) is developed to simultaneously enforce safety certification and MRCM. This controller builds upon a nominal design leveraging input-to-state stability and ISSf lemmas. The derived ISSf-CBFs provide formal guarantees for both safety certification and MRCM under uncertainties. The key to obtaining this controller lies in constructing ISSf-CBFs that enable safety certification and MRCM, from which the corresponding constraints are formally derived. Unlike existing methods, the strategy maximizes formation performance by executing formation tasks at full capability whenever possible, and only modifying the nominal controller when safety or MRCM constraints are violated. Finally, simulation and experiment validate the effectiveness of the proposed controller.

Multi-agent trajectory prediction at signalized intersections is pivotal for the safety of autonomous driving and the efficiency of intelligent transportation systems. However, conventional vehicle-centric approaches are limited by restricted perception ranges and occlusion. Vehicle-to-Everything (V2X) cooperation is widely regarded as an effective approach to alleviating these limitations. Furthermore, existing cooperative systems often suffer from complex coupling and selection bias, hindering universal and real-time service. To address these challenges, this paper introduces a novel Infrastructure-to-Everything (I2X) collaborative prediction scheme. This scheme decouples infrastructure capabilities from vehicle requests by independently forecasting and broadcasting trajectories for all detected vehicles. Building on this scheme, we propose I2XTraj, a dedicated infrastructure-based model that leverages three core mechanisms. First, a continuous signal-informed mechanism to adaptively encode real-time traffic light information. Second, a maneuver strategy awareness mechanism that integrates intersection geometric constraints to estimate maneuver distributions. Third, a spatial-temporal-mode attention network to refine multi-agent interactions. Extensive evaluations on two real-world datasets, V2X-Seq and SinD, demonstrate the superiority of our approach. In both single-infrastructure and collaborative scenarios, I2XTraj outperforms state-of-the-art methods by over 30% and 15%, respectively, confirming its strong generalizability and robustness in complex intersection environments.

This paper investigates path-guided cooperative circumnavigation control for autonomous vehicles with collision avoidance. Compared to the trajectory-guided control frameworks, which require vehicles to track time-dependent trajectories, the proposed method eliminates temporal constraints but introduces new design challenges. Coordination error variables based on separation angles and path information are defined, and a temporally unconstrained guidance law incorporating linear and angular velocities is proposed. The developed guidance law is inspired by chase-and-wait strategies, achieving structural simplicity. To address collision avoidance involving both inter-vehicle interactions and external obstacles for autonomous vehicles under model uncertainties and environmental disturbances, an input-to-state safe control barrier function (ISSf-CBF) is employed. The constructed ISSf-CBF systematically maps state-space safety constraints to control input constraints, thereby ensuring collision-free operations in dynamic environments. The key to achieving robust collision-free path-guided circumnavigation lies in synthesizing an input-to-state safety-certified controller by defining ISSf-CBF constraints. Simulation results verify the validity and effectiveness of the theoretical results.

This article focuses on the secure predefined-time sliding mode control problem for a third-order heterogeneous vehicle platoon. For the purpose of reducing the effect due to the sensor measurement deviation and relaxing the system conservatism, a state estimation algorithm is designed based on the historical data and threshold judgment. Furthermore, a rate-coded reversible privacy-preserving mechanism with dual encryption is proposed and applied to vehicle-to-vehicle communications, which can guarantee the protection of the critical system data and the realizability of the desired predefined-time convergence performance by utilizing the origin outputs. In order to avoid the singularity problem and enhance the resistance of the vehicle platoon to the external disturbance, a nonsingular sliding mode surface and a corresponding predefined-time controller are designed. Based on the predefined-time stability and the string stability theorems, the vehicle platoon can be proved to be a practical predefined-time stable (PPTS) and string stable. Finally, adequate validations of the third-order heterogeneous vehicle platoon demonstrate the fast convergence speed and good robustness of the proposed control scheme.

This paper investigates injection attacks incorporating stochastic noise against linear discrete time-varying system, which is more general but also more challenging to defend than deterministic injection attacks. Based on the optimization theory and novel key defining matrices, an optimal stochastic injection attack strategy is proposed. Unlike existing strategies, this strategy is stochastic, making it harder for defenders to predict. Therefore, the newly designed attack is expected to be widely used to disrupt system performance. By leveraging estimated data from the observer and the attack input, a virtual residual system is established, which more accurately reflects changes in the system error before and after the attack than the traditional error system. Using the state and output residuals along with the stochastic attack input, two performances are defined to ensure the stealthiness and effectiveness of the attack, respectively. Subsequently, an optimal attack problem with non-convex objective function and constraint is formulated. The key to acquiring the designed optimal stochastic injection attack strategy is to apply a semi-definite relaxation involving moment matrices for transforming this non-convex optimization problem into a convex optimization problem and solving it. Finally, the effectiveness of the proposed attack strategy is validated through numerical simulation of a networked mass-spring-damper system and a V-formation experiment involving three quadrotors. Note to Practitioners—The primary objective of this paper is to focus on the cyber security of discrete time-varying systems from the perspective of the attacker, which provides insight into the way of generating attack strategies under the stochastic case. The majority of existing injection attack strategies against intelligent systems are deterministic, and this can make the attacks less effective as the attacked system reconstructs and compensates for the attack signals, and the attack strategies tend to fail or are mostly ineffective. This paper synthesizes state estimation, semi-definite programming, optimization principles, and control theory to propose an optimal stochastic injection attack strategy. Compared with the deterministic injection attack strategies, the latter is harder to defend. Specifically, the operation of the attacker is divided into two phases: initial data eavesdropping and strategy generation. In the initial data eavesdropping phase, the attacker continuously eavesdrops and stores the initial traffic data of the system for a virtual state residual data estimation. In the strategy generation phase, the attacker generates the attack strategy using semi-definite programming with the help of the measured initial data. The mathematical analytical form of the proposed optimal stochastic injection attack strategy is given in detail. Subsequently, the effectiveness is verified by the numerical simulation and experiment based on a leader-following form consisting of three quadcopters, but not yet tested in production. In the future, we plan to design stochastic injection attack strategies with a data-driven framework.

This paper addresses data-driven injection attack against unknown intelligent automation systems (IASs) with slowly time-varying delays, which is a more general but also more challenging to deal with than the model-based real-time systems. Using the control input and system output measurements, several new data-driven injection attack strategies based on compact form dynamic linearization (CFDL) and incremental triangular dynamic linearization (ITDL) are proposed. The attack strategies are more general than the existing ones, taking into account the unknown model parameters and time-varying delays in control-to-actuator as well as sensor-to-controller data transmission channels. Consequently, the new design attack results are anticipated to have wider applicability. Based on the established attack models of CFDL and ITDL, the data-driven optimal parameter estimation algorithms are employed to overcome the difficulty of the unknown model. Furthermore, with the help of the principle of the linear regression equation, the problem of seeking partial derivatives for the attack inputs with time delays is avoided. Several examples are presented to illustrate the validity of the designed attack strategies.Note to Practitioners—The primary objective of this paper is to focus on the cyber security of discrete-time intelligent automation systems from the viewpoint of the attacker, which provides insight into the way of generating attack strategies under the data-driven framework. The majority of the available attack strategies against intelligent automation systems are based on a priori knowledge of the system, without delays or with single-sided fixed time delay, and thus in reality the attack strategies fail or are mostly ineffective due to inaccessibility of the attacked system parameters in conjunction with communication network time delays. This paper synthesizes parameter estimation, dynamic linearization, optimality principle and control theory to propose data-driven injection attack strategies based on CFDL and ITDL, respectively. Compared with the CFDL-based attack strategy, the latter is more applicable in the situation where the attacker suffers from restricted memory space occupation and hashrate. Specifically, the operation of attacker has two phases: eavesdropping estimation and strategy generation. In the eavesdropping estimation phase, the attacker continuously eavesdrops and stores the traffic data of the system for parameter estimation as well as optimizes the estimated parameters. In the strategy generation phase, the attacker generates strategies with the assistance of optimal parameters. Mathematically analytical forms of the proposed two data-driven injection attack strategies are presented in detail. Then, their effectiveness is verified through numerical simulation and experiments based on a leader-following form consisting of five quadcopters, but not tested in production. In the future, we are intending to design the data-driven attack strategies for heterogeneous intelligent automation systems with time-varying delays under the framework of game.

Coalition structure is an effective cooperation architecture for task implementation in the field of multi-robot systems. Nevertheless, the presence of uncertainty inherently complicates the decision-making process for robots and may potentially result in suboptimal coordination. To tackle this challenge, this paper considers an uncertain multi-robot coalition formation scenario in which task information (the types of all tasks) is incompletely known to robots. Given the local beliefs of robots, the problem of multi-robot coalition formation under uncertainty is formulated as a coalition formation game. In this game, each robot is a rational and self-interested player and tends to join a coalition according to its preference. A polynomial-time coalition formation algorithm is proposed to identify the social agreement, i.e., Nash stable partition, among the robots. The convergence of the proposed algorithm is strictly guaranteed as long as the communication topology of the considered system is strongly connected. The coalition formation game is then extended to a dynamic game, and we propose a belief updating algorithm that enables robots to update their beliefs as the game is played repeatedly. Simulation results demonstrate the effectiveness of our proposed algorithms, and the robots will eventually learn the true type of each task.Note to Practitioners—The work reported in this article will be beneficial for deploying multi-robot systems to support cooperative surveillance applications. In these scenarios, robots can form stable coalitions to perform surveillance tasks, even when the task information is incompletely known due to sensor noise or limited sensor range. The problem of multi-robot coalition formation is known to be NP-hard, which becomes even more challenging when uncertainty is taken into account. This paper proposes game-based algorithms to solve the problem of multi-robot coalition formation under uncertainty. Some practical schemes are introduced as benchmarks to further illustrate the performance improvement brought by our proposed algorithms. The proposed algorithms are further evaluated through a real-world experiment, demonstrating their effectiveness for practical application.

This paper investigates anticipatory decision for unknown distributed systems with robustness concerns. Anticipatory decision focuses on action selection before observations appear at temporal scales. Firstly, anticipatory decision forms sequential feedback with min-max performance guarantees, while causality comes from time series analysis. Next, distribution, robustness and time consistency partition the optimization into spatial and temporal sub-games. The spatial sub-games dispel conflicts on distribution and robustness, while the temporal ones ensure stability and performance through time consistency. Finally, we propose a multi-step reinforcement learning algorithm under causality analysis and game theoretical framework. Numerical results demonstrate the effectiveness of the approach, and practical experiments show potential real-world applications. Note to Practitioners—This framework focuses on anticipatory decision for distributed systems, which suffer from distributed communication, unknown dynamics, environmental disturbances and state observation loss. Our framework has various application scenarios, e.g., internal surgical robots, low-light autonomous driving and non-GPS navigation, and these scenarios mainly involve dynamic environments and weak signal feedback. For example, decision-making in autonomous driving requires not only reacting to current environmental conditions but also anticipating future scenarios and uncertainties due to poor visibility. Most results deal these issues with model-driven approaches, while unknown dynamics render these methods inapplicable. For implementation, we propose a multi-step reinforcement learning algorithm for anticipatory decision framework with stability and robustness guarantees, and details mainly contain three parts: 1) We collect data during offline phase, and form the data structure, namely, current-next observation pair with multi-step decision and accumulated reward; 2) Strategies and value functions are approximated with neural networks through Monte-Carlo methods; 3) The strategy is deployed as sequential feedback in practical systems, and predicts multi-step decisions with single-step state observation. Finally, we select robot consensus with optical sensors as the implementation demo.

This paper aims to predict future optimal control inputs for unknown continuous-time linear systems. In contrast to most existing approaches that generate control input without observation loss, the proposed control scheme analyzes causality between observation and sequence feedback from the time-delay series model, allowing for the existence of observation loss. These time-delay series can be viewed as multiplayer games over a temporal scale, then a temporal game-theoretic approach ensures system stability and performance. By integrating the Bellman principle, a data-driven adaptive dynamic programming algorithm is proposed to avoid system knowledge. Furthermore, the designed parallel data-driven predictive algorithm reduces the computational complexity. Finally, the applicability and effectiveness of the methodology are demonstrated through numerical simulations and practical experiments. Note to Practitioners—This paper mainly concerns the predictive control of unknown continuous systems, which suffer from unknown dynamics and state observation loss. The proposed methods are suitable for weak information feedback and dynamically changing scenarios, such as autonomous driving in low visibility scenarios and endoscopic surgical robots. Most of the current processing methods are model-driven, which makes them unsuitable for unknown system dynamics changes. To address this issue, we propose a time-delay switched strategy for control prediction with stability and optimality guarantees. The practical application can be divided into three parts: i) Data collection: Collect historical multi-intervals accumulated inputs and outputs data, the amount of data should reach the requirement of the full rank of the data matrix; ii) Iterative learning: the optimal control strategy is learned from the historical data through an adaptive dynamic programming method; iii) Deployment: Control intervals are extended through temporal time-delay feedback on historical state trajectory, and length trigger conditions will switch these feedbacks logically for actuators. Finally, the Quanser QBot 2e robot is used as a demonstration example.

Nonlinear affine systems with relative degree two widely exist in the control field, and the unified output tracking control approach of these systems is still an open problem. This paper presents a formal framework for output tracking control design methodology for nonlinear affine systems with relative degree two. Specifically, a control barrier function is constructed to enforce the forward invariance or the stability of a set contained within a 0-sublevel set of the first-order derivative of pre-stablished Lyapunov function, thereby stabilizing the system output to the desired value. Additionally, by taking the first-order derivative of the elaborated control barrier function, control input terms with a nonzero coefficient are introduced into the stability condition, addressing the weakness of controller design using conventional control Lyapunov function approaches, which fails in states where the coefficient of the control input term is zero. Furthermore, the stability conditions are proposed in the form of linear inequality constraints involving control inputs, with which a collection of admissible controllers is provided. Then, by resorting to the pointwise minimum norm (PMN) technique, a numerical solution of an admissible controller can be derived in the context of a quadratic program. We finally demonstrate the effectiveness of the controller design method via two simulations, adaptive cruise control and vehicle lateral control.

In complex and variable terrains, affine formation control, with its flexible formation adjustment ability, can achieve various formation shapes to adapt well to the environment. Notably, the existing affine formation research based on stress matrices require the variation parameters for translation, rotation, scaling, and shearing of formations to be predesigned offline. To address this, we propose a novel method for online affine parameter adjustment that enables self-reconfiguration of formations in multiobstacle environments. By adopting artificial potential field environment excitation, the proposed motion planning algorithm can dynamically adjust the affine transformation parameters online, and realize the self-reconfiguration of formation shape to avoid collision. Then, a distributed model predictive controller is proposed for multiagent systems, which actively utilizes historical control input information to flexibly adjust controller performance while avoiding algebraic loops between neighboring agent controllers. The algorithm separates stability and performance optimization within the nonlinear model predictive control framework, ensuring both the feasibility and stability of the underlying optimization. Finally, the simulation results confirm the effectiveness of the proposed controller.

This article investigates the pursuit-evasion games for target capture in multiagent systems. To address this challenge, a novel data-driven optimal control policy is proposed, leveraging off-policy reinforcement learning and Nash-minmax strategies. First, a comprehensive framework for multiagent pursuit-evasion games is developed, modeled as a two-layer game structure. In this framework, interactions among agents within the same team are characterized as nonzero-sum games, while interactions between opposing teams are adversarial and treated as zero-sum games. Second, Nash-minmax strategies are introduced to solve the formulated multiagent pursuit-evasion games. These strategies effectively derive distributed Nash solutions for agents within the same team and adversarial worst-case policies for agents in opposing teams. Furthermore, to eliminate the reliance on prior knowledge of agent dynamics and initial stabilizing control gains, a data-driven optimal control policy is designed, ensuring the achievement of target capture. Finally, a numerical example is provided to demonstrate the effectiveness and practical applicability of the proposed approach.

This article aims to investigate the performance-guaranteed tracking problem for a class of uncertain nonlinear systems. The main goal is to attain control performance within prescribed-time (PT) limits despite the existence of mismatched disturbances. First, the mismatched disturbances are transformed into the equivalent forms. Second, for the unknown disturbance estimation, a PT extended state observer (PTESO) is developed to switch between the prescribed settling time ${\mathcal {T}}_{p}$ , the order is diminished to alleviate the occurrence of the peaking phenomenon. Furthermore, an ESO-based PT control strategy is constructed with time-varying gains. This allows real-time compensation of disturbances, and the prescribed performance is attained by virtue of a Lyapunov function employed combines both barrier and quadratic forms. Ultimately, the benefits and efficacy are illustrated via a numerical demonstration involving a wheeled mobile robot. The key features of this article include the observer capability to estimate unknown mismatching disturbances and the full effectiveness of the proposed controller for $t \in [t_{0}, \infty $ ), ensuring the convergence of tracking error to zero within any PT. Consequently, in addition to achieving the output tracking objective, the system can also exhibit favorable transient performance.

This article considers the formation control of multiple autonomous aerial vehicles (AAVs), where the AAVs operate in dynamic environments with multiple obstacles and narrow (constrained) areas. A new fuzzy model predictive interval output-constrained maneuver formation control method for AAVs is proposed. The interval output constraint algorithm utilizes the distance information to implement output constraints and formation changes, which are more practical but more challenging than the output constraint problem based on user-assigned settling time, especially in narrow areas. One of the distinctive advantages of the proposed output-constrained model predictive controller is that it can activate formation changes and output constraints for AAVs when passing through narrow space, and it can automatically restore the formation of AAVs and deactivate the output constraints when the AAVs are far away from the narrow space. Unlike most nonlinear model predictive control strategy, where predictive control relies on the accurate underlying dynamical system, the article introduces adaptive fuzzy updating law to receding horizon optimization algorithm to estimate and compensate unknown dynamics and external disturbances. Two potential field functions are designed to safely track in 3-D environments with obstacles. Finally, several examples are provided to illustrate the effectiveness of the proposed controller.

In this paper, we introduce a novel hierarchical decision-making framework that integrates control barrier functions (CBFs) with reinforcement learning (RL) to enhance the safety and efficiency of autonomous vehicle merging at on-ramps. The proposed technique is comprised of three layers: a behavioral planning layer that employs proximal policy optimization (PPO) algorithm aims to learn its driving behaviors, which are not necessarily compliant with vehicle dynamics; a motion control layer that adopts model predictive control (MPC) framework to ensure the kinematic feasibility to follow the trajectories with heading angles and velocities provided by the upper layer; and lastly, a control barrier function-guided layer, which is the core innovation of this paper, is implemented. It leverages the forward invariance of control barrier functions to design a safety controller that refines the output of MPC to maintain continuous driving safety. The refined results are then used as objective function, guiding subsequent updates in the learning process. Simulation results demonstrate that the proposed CBF-guided architecture significantly improves training efficiency and performance, achieving a 60% reduction in training time and 6% increase in success rate compared to standard RL methods.

In this article, the global exponential synchronization problem is investigated for a class of delayed nonlinear memristive neural networks (MNNs) with reaction-diffusion items. First, using the Green formula, Lyapunov theory, and proposing a new fuzzy adaptive pinning control scheme, some novel algebraic criteria are obtained to ensure the exponential synchronization of the concerned networks. Furthermore, the corresponding control gains can be promptly adjusted based on the current states of partial nodes of the networks. Besides, a fuzzy adaptive aperiodically intermittent pinning control law is also designed to synchronize the fuzzy MNNs (FMNNs). The controller with intermittent mechanism can obtain appropriate rest time and save energy consumption. Finally, some numerical examples are provided to confirm the effectiveness of the results in this article.

Human-robot skill transfer is an important means for robots to learn skills and has received more and more attention and research in recent years. Typically, to ensure effective skill transfer, a skill is demonstrated several times by a human, from which a robot learns the features contained in the demonstrations and reproduces the skill in a new environment. However, it is necessary to consider the cases such as errors in human demonstrations and sensor issues, resulting in imperfect demonstrations, unrelated data, information loss, and variations in the lengths and amplitudes of the demonstrations. Therefore, this brief proposes a new trajectory alignment and filtering method for extracting relatively useful information from multiple demonstrations. This method can be used in conjunction with most probabilistic movement learning methods (this brief uses probabilistic movement primitives (ProMPs) as an example) for learning from demonstrations (LfDs), so that the robot can eventually learn and generate trajectories for completing skills from multiple demonstrations of varying quality. The effectiveness of the proposed method is verified by simulation results.

This article investigates robust predictive control problem for unknown dynamical systems. Since the dynamics unavailability restricts feasibility of model-driven methods, learning robust predictive control (LRPC) framework is developed from the aspect of time consistency. Under feedback-like control causality, the robust predictive control is then reconstructed as spatial-temporal games, and we guarantee stability through time-consistent Nash equilibrium. For gradation clarity, our framework is specified as four-follow contents. First, multistep feedback-like control causality is drawn from time series analysis, and Takens' theorem provides theoretical support from steady-state property. Second, control problem is reconstructed as games, while performance and robustness partition the game into temporal nonzero-sum subgames and spatial zero-sum ones, respectively. Next, multistep reinforcement learning (RL) is designed to solve robust predictive control without system model. Convergence is proven through bounds analysis of oscillatory value functions, and properties of receding horizon are derived from time consistency. Finally, data-driven implementation is given with function approximation, and neural networks are chosen to approximate value functions and feedback-like causality. Weights are estimated with least squares errors. Numerical results verify the effectiveness.

This article introduces a sustainable human-guided reinforcement learning (RL) framework to address the challenge of learning performance degradation when the human guidance is suspended. First, a compensation reward based on the historical similarity between the RL agent and human guidance history is designed to ensure the continued influence of human guidance. To avoid cumulative errors in value function approximation caused by fitting the new reward, including the compensation reward, a novel RL paradigm is proposed, which bypasses value function fitting and directly optimizes the policy using historical similarity. This paradigm develops a new historical similarity-based learning objective for RL to leverage human guidance more efficiently and achieve alignment with human behavior. Furthermore, the proposed paradigm enables the fine-tuning of the RL agent to address the long-tail problem. Experimental results demonstrate the advantages of the proposed method in terms of sustainable guidance and optimal performance in the autonomous driving, achieving a 15% increase in optimal performance compared with existing state-of-the-art (SOTA) methods.

This article investigates the optimal group consensus problem (GCP) in multiagent systems (MASs). To address this problem, a novel distributed optimal control policy is designed in the framework of off-policy reinforcement learning (RL). First, a framework for multiagent differential graphical games is formulated. Second, a min-max strategy is then introduced to ensure the achievement of group consensus through a data-driven value iteration (VI) approach. Finally, the presented consensus control policy is extended to address the group formation tracking problem (GFTP) of nonholonomic mobile robots, with a numerical example to illustrate the efficacy of the proposed results. Compared with the existing literature, this article has the following contributions: 1) A group of agents are decomposed into multiple subgroups to accomplish different consensus objectives; 2) the prior knowledge of agents’ dynamics and initial stabilizing control gains can be eliminated; and 3) the performance index function (PIF) for each agent is designed to integrate not only its individual control policy but also that of its neighboring agents.

This article investigates the problem of finite-horizon optimal fault-tolerant control for heterogeneous nonlinear discrete-time multiagent systems. A distributed optimal fault estimation and optimal fault-tolerant control scheme is presented based on the Stackelberg game and graphical game frameworks, in which agent systems and corresponding fault estimators are treated asthe “leader” and “follower” in the Stackelberg game, and the agent systems constitutes a graphical game. This is more general than the existing integration design of fault estimation as well as fault-tolerant control, as it better reveals the interaction between the agent system and its fault estimator. To achieve interactive Stackelberg equilibrium, an auxiliary controller for fault estimator is designed, which exhibits non cooperative properties with the fault-tolerant controller of the agent. Then, a finite-time distributed estimator for discrete-time case is developed for the first time to rapidly estimate the matrix and state of the leader agent. The coefficient matrices of the considered fault signals obey a hidden semi-Markov switching rule, which can encompass the existing actuator and sensor fault models. Further, two critic neural networks are established to obtain approximate solutions of interactive Stackelberg equilibrium online by employing the adaptive dynamic programming technology. Finally, simulation examples are provided to verify the efficacy and applicability of the proposed scheme.

This paper proposes a GPU-accelerated optimization framework for collision avoidance problems where the controlled objects and the obstacles can be modeled as the finite union of convex polyhedra. A novel collision avoidance constraint is proposed based on scale-based collision detection and the strong duality of convex optimization. Under this constraint, the high-dimensional non-convex optimization problems of collision avoidance can be decomposed into several low-dimensional quadratic programmings (QPs) following the paradigm of alternating direction method of multipliers (ADMM). Furthermore, these low-dimensional QPs can be solved parallel with GPUs, significantly reducing computational time. High-fidelity simulations are conducted to validate the proposed method’s effectiveness and practicality.

This paper aims to investigate the security problem of nonlinear cyber-physical systems (CPSs), which poses a challenge to handle compared with linear CPSs. A series of optimization problems for nonlinear CPSs under injection attack are constructed, which are based on a general model of the nonlinear systems with repetitive operation characteristics and a novel introduction of the key technical lemma. These optimization results are more general than the existing injection attack results and the requirements for attackers to obtain system information are relaxed. Also, the form of switching applied to the attack strategy possesses several advantages, including high stealthiness, lower cost, and more flexibility. Therefore, the new optimal injection attack strategies are expected to be more widespread and provide a basis for the design of defense strategies. The key to acquiring the designed optimal attack strategies is to adopt the linear input/output (I/O) data model for these systems via introducing an estimation term of the improved projection estimation method into the linear model. Finally, a networked GLUON-6L3 manipulator example validates the effectiveness of the proposed methods. Note to Practitioners—The main purpose of this paper is to study the cyber security of nonlinear cyber-physical systems from the perspective of attackers, which can help defenders fully understand the behavior of attackers. Most of the existing attack strategies aimed at linear systems or have a priori knowledge of the attacked systems. However, there are great limitations and difficulties in practical application. In this paper, the new attack strategies are proposed by combining system identification, iterative learning and control theory, which relaxes the requirement of the attacker’s ability. In detail, the attacker only needs to obtain the control input and output data of the attacked system and design the attack strategy according to it. Among them, the first-in, first-out queue storage method is applied to remove the requirements for the storage capability of the attacker. In practical applications, the attacker does not need to store the I/O data continuously, but only store the initial value and the data of two successive iterations. The mathematical analytic forms of two optimal attack strategies are given, and then their effectiveness are verified on a welding work of a networked six-axis manipulator. In the future, we will investigate the design of attack strategies for nonlinear systems with heterogeneous dynamics and multiple delays.

Distributed machine learning has emerged as a promising data processing technology for next-generation communication systems. It leverages the computational capabilities of local nodes to efficiently handle large datasets, creating highly accurate data-driven models for analysis and prediction purposes. However, the performance of distributed machine learning can be significantly hampered by communication bottlenecks and node dropouts. In this article, a novel unmanned aerial vehicle (UAV)-enabled hierarchical distributed learning architecture is proposed to support machine learning applications, e.g., regional monitoring. Multiple UAV receivers (URs) are introduced as wireless relays to improve the communication between the UAV transmitters (UTs) and the cloud server. Our objective is to identify the optimal UT-UR association to maximize the social welfare of the network, which is distinctly different from the existing works that focus on the unilateral profit-maximizing problem. We formulate a two-side many-to-one matching game to model the UT-UR association problem, and a two-phase many-to-one matching algorithm is designed to identify the stable matching. The validity of our proposed scheme is verified through in-depth numerical simulations.

In this article, a novel model-free policy gradient reinforcement learning algorithm is proposed to solve the tracking problem for discrete-time heterogeneous multiagent systems with external disturbances over switching topology. The dynamics of the followers and the leader are unknown, and the leader's information is missing for each agent due to the switching topology. Therefore, a distributed adaptive observer is introduced to learn the leader's dynamic model and estimate its state for each agent. For the tracking problem, an exponential discount value function is established and the related discrete-time game algebraic Riccati equation (DTGARE) is derived, which is the key to obtaining the control strategy. Furthermore, a data-based policy gradient algorithm is proposed to approximate the solution of the GAREs online and the utilization of agents' accurate knowledge is avoided. To improve the efficiency of data utilization, an offline dataset and the experience replay scheme are used. In addition, the lower bound of the exponential discount value is explored to ensure the stability of the systems. In the end, a simulation is provided to show the validity of the proposed method.

In this paper, the cooperative output regulation problem is investigated for heterogeneous nonlinear multi-agent systems allowing frequent arrivals and departures of agents. Inspired a generally switched systems with multi modes and multi dimensions, the migration of agents is described by a switched communication network, where the number of agents and the graph of topology could be simultaneously changed at the switching instant. In this paper, this switching topology is modeled as a quasi-mode-dependent persistent dwell time switched system with size-varying modes for more generalization. Furthermore, the assumption of existing a directed spanning tree can be removed during the special time interval with a limit on the maximum dwell time, which makes the stability analysis for such switching directed topology more challenging. Hence, a quasi-time-dependent Lyapunov function is utilized to construct a novel distributed reference generator, which can approach the exosystem in a globally uniformly exponential manner. And based on the Takagi-Sugeno fuzzy set theory, a fuzzy cooperative controller is developed for each agent to track the estimating signal. Finally, a multi-pendulum system is adopted to illustrate the feasibility and applicability of the derived results.

This work investigates stability and localizability of local-bearing-based multiagent systems without common orientation in the presence of measurement noise, which are more general but also more challenging to deal with than global-bearing-based multiagent systems under ideal environment. Based on local-bearing unbiased estimator constructed from the historical information and a newly designed time-varying gain, a robust prescribed-time orientation estimation algorithm is proposed to ensure that the local reference frame of the follower agent is aligned with the global one. The local bearing information is more easily obtained than global one. Therefore, the new orientation estimation result is expected to be more widely applicable. The robust orientation estimation algorithm is then applied to the problem of localization estimation, and a prescribed-time distributed localization estimation algorithm is developed. The distinctive advantage of this work is that only the local bearing information is used, the fast and controllable localization estimation is achieved. The global convergence is derived based on the cascade system. Some simulation and experiment results are provided to prove the effectiveness of the proposed estimation algorithms.

This article investigates optimal control for a class of large-scale systems using a data-driven method. The existing control methods for large-scale systems in this context separately consider disturbances, actuator faults, and uncertainties. In this article, we build on such methods by proposing an architecture that accommodates simultaneous consideration of all of these effects, and an optimization index is designed for the control problem. This diversifies the class of large-scale systems amenable to optimal control. We first establish a min-max optimization index based on the zero-sum differential game theory. Then, by integrating all the Nash equilibrium solutions of the isolated subsystems, the decentralized zero-sum differential game strategy is obtained to stabilize the large-scale system. Meanwhile, by designing adaptive parameters, the impact of actuator failure on the system performance is eliminated. Afterward, an adaptive dynamic programming (ADP) method is utilized to learn the solution of the Hamilton-Jacobi-Isaac (HJI) equation, which does not need the prior knowledge of system dynamics. A rigorous stability analysis shows that the proposed controller asymptotically stabilizes the large-scale system. Finally, a multipower system example is adopted to illustrate the effectiveness of the proposed protocols.

This article is concerned with the real-time localization problem for the dynamic multi-agent systems with measurement and communication noises under directed graphs. The barycentric coordinates are introduced to describe the relative position between agents. A novel robust distributed localization estimation algorithm based on iterative learning is proposed. The relative-distance unbiased estimator constructed from the historical iterative information is used to suppress the measurement noise. The designed stochastic approximation method with two iterative-varying gains is used to inhibit the communication noise. Under the zero-mean and independent distributed conditions on the measurement and communication noises, the asymptotic convergence of the proposed methods is derived. The numerical simulation and the QBot-2e robot experiment are conducted to test and verify the effectiveness and the practicability of the proposed methods.

Understanding 3-D scene geometry from videos is a fundamental topic in visual perception. In this article, we propose an unsupervised monocular depth and camera motion estimation framework using unlabeled monocular videos to overcome the limitation of acquiring per-pixel ground-truth depth at scale. The photometric loss couples the depth network and pose network together and is essential to the unsupervised method, which is based on warping nearby views to target using the estimated depth and pose. We introduce the channelwise attention mechanism to dig into the relationship between channels and introduce the spatialwise attention mechanism to utilize the inner-spatial relationship of features. Both of them applied in depth networks can better activate the feature information between different convolutional layers and extract more discriminative features. In addition, we apply the Sobel boundary to our edge-aware smoothness for more reasonable accuracy, and clearer boundaries and structures. All of these help to close the gap with fully supervised methods and show high-quality state-of-the-art results on the KITTI benchmark and great generalization performance on the Make3D dataset.

This article is devoted to dealing with exponential synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) under the framework of aperiodic sampling and state quantization. First, by taking the effect of aperiodic sampling and state quantization into consideration, a novel quantized sampled-data (QSD) controller with time-varying control gain is designed to tackle the exponential synchronization of INNs. Second, considering the available information of the lower and upper bounds of each HTVD, a refined Lyapunov-Krasovskii functional (LKF) is proposed. Meanwhile, an improved looped-functional method is utilized to fully capture the characteristic of practical sampling patterns and further relax the positive definiteness requirement for LKF. Consequently, less conservative exponential synchronization conditions with extra flexibility are derived. Finally, a numerical example is employed to demonstrate the effectiveness and advantages of the proposed synchronization method.

This article investigates the formation tracking-containment control problem for multiple unmanned aerial vehicles (UAVs), while considering collision avoidance in the three-dimensional (3-D) environment. A distributed model predictive formation control method is developed for UAVs with obstacle avoidance. Unlike most existing trajectory tracking control schemes, the proposed method is derived from a smooth shifting function and a distributed Lyapunov-based model predictive controller, together with two collision-free functions, to obtain an improved control algorithm with the following characteristics: 1) the swarm system of UAVs realizes the cooperative hunting process from tracking formation to containing formation, fencing a target to their convex hull; 2) the Lyapunov-based model predictive formation control method inherits the stability properties of backstepping technique and adopts the receding horizon optimization of model predictive control (MPC) technique; and 3) by exploiting the Lyapunov-based MPC algorithm and collision-free functions, the swarm system of UAVs can avoid collision with obstacles or collision with each other in 3-D scenarios with multiple obstacles. Finally, the simulation and comparison results confirm that the proposed controller outperforms the traditional backstepping controller in terms of safety and tracking performance.

This article systematically studies the issue of adaptive neural network (NN) output-feedback control for uncertain nonlinear systems using event-triggered output. First, to tackle the problem of unmeasurable states, a compact state observer using event-triggered output is constructed. Then, since the event-triggered output signals are discontinuous, the virtual control laws in backstepping design are no longer differentiable. Hence, the dynamic surface control scheme is introduced to resolve this problem. Unlike existing work requiring system functions to satisfy Lipschitz continuity condition, adaptive NN control is incorporated into the designed algorithm to relax the above constraint. What is more, the event-triggered mechanism is also used for parameter estimation to avoid waste of computing and communication resources. Finally, the results of comparative simulations and the DC brush motor experiment are depicted to demonstrate the practicality and effectiveness of the proposed method.

In order to realize the operability and safety of unmanned aerial vehicles (UAVs) in confined areas, this article investigates an adaptive anti-disturbance performance guaranteed fuzzy formation control problem for quadrotor UAVs by using aperiodic signal updating. The unknown dynamics are approximated by using fuzzy logic systems. A disturbance observer is constructed for each UAV, including position subsystem (outer-loop) and attitude subsystem (inner-loop), to reduce the negative effects of UAVs with disturbances in complex flight environments. To avoid the potential internal collision among the multiple UAVs, a prescribed performance function that widens the initial value range of the consistency error is designed to keep the formation error within the specified range. Intermittent output signals generated by event-triggered control strategy of attitude subsystem are used to reduce sensors data transmission on each UAV, thereby saving energy and communication resources. Via the Lyapunov stability theory, the formation error can converge to a prescribed boundary range. Finally, the validity of the proposed control strategy is illustrated by simulation results.

Exception handling is a mechanism in modern programming languages. Studies have shown that the exception handling code is error-prone. However, there is still limited research on detecting exception handling bugs, especially for C++ programs. To tackle the issue, we try to precisely represent the exception control flow in C++ programs and propose an analysis method that makes use of the control flow to detect such bugs. More specifically, we first extend control flow graph by introducing the concepts of five different kinds of basic blocks, and then modify the classic symbolic execution framework by extending the program state to a quadruple and properly processing try, throw and catch statements. Based on the above techniques, we develop a static analysis tool on the top of Clang Static Analyzer to detect exception handling bugs. We run our tool on projects with high stars from GitHub and find 36 exception handling bugs in 8 projects, with a precision of 84%. We compare our tool with four state-of-the-art static analysis tools (Cppcheck, Clang Static Analyzer, Facebook Infer and IKOS) on projects from GitHub and handmade benchmarks. On the GitHub projects, other tools are not able to detect any exception handling bugs found by our tool. On the handmade benchmarks, our tool has a significant higher recall.

Third-party Python modules are usually implemented as binary extensions by using native code (C/C++) to provide additional features and runtime acceleration. In native code, the heap-allocated PyObjects are managed by the reference counting mechanism provided in Python/C APIs for automatic reclaiming. Hence, improper refcount manipulations can lead to memory leaks and use-after-free problems, and cannot be detected by simply pairing the occurrence of source and sink points. To detect such problems, state-of-the-art approaches have made groundbreaking contributions to identifying inappropriate final refcount values before returning from native code to Python. However, not all problems can be exposed at the end of a path. To detect those hidden in the middle of a path in native code, it is also crucial to track the lifecycle state of PyObjects through the refcount and lifecycle operations in API calls. To achieve this goal, we propose the PyObject State Transition Model (PSTM) recording the lifecycle states and refcount values of PyObjects to describe the effects of Python/C API calls and pointer operations. We track state transitions of PyObjects with symbolic execution based on the model, and report problems when a statement triggers a transition to buggy states. The program state is also expanded to handle pointer nullity checks and smart pointers of PyObjects. We conduct experiments on 12 open-source projects and detect 259 real problems out of 280 reports, which is twice as many bugs as state-of-the-art approaches. We submit 168 real bugs to those active projects, and 106 issues are either confirmed or resolved.

This paper addresses the bearing-only distributed localization, which is more accurate and more reliable than traditional localization technologies. A new bearing-only distributed localization framework is proposed to solve localization problems for both static sensor networks and mobile multi-agent systems (MASs). Based on the complex coordinate representation, a novel bearing-only distributed localization algorithm for sensor networks is proposed. The algorithm combines the orientation estimation and the position estimation to solve the orientation alignment problem, which makes the compasses no longer needed for the localized networks. The localization for the mobile MASs is also studied and the corresponding localization algorithm is designed, which is more general and more challenging. The key to obtaining positions of moving agents, is the velocity estimator in the proposed localization algorithm which makes the estimated positions and velocities converge to the true value simultaneously. A distinctive advantage of the localization framework proposed in this paper is that the bearing-only localization algorithms can be applied to more general systems, such as sensor networks and MASs. Simulations and experiment are presented to verify the proposed algorithms.

In this paper, the time-varying formation tracking (TVFT) control problem is concerned for multi-agent systems (MASs). The primary objective is to achieve asymptotical convergence for formation tracking error subject to nonparametric and nonvanishing uncertainties. Firstly, in order to estimate lumped unmodeled dynamics and external disturbances, an event- triggered fuzzy extended state observer (FESO) inspired by our previous works is established for follower group, in which efficient nonlinear observation pattern and convenient linear numerical tractability are integrated. For rationally scheduling transmission, a dynamic event-triggered mechanism is applied to form adaptively regulating strategy. Secondly, a distributed TVFT control law is developed by utilizing neighborhood formation tracking errors. Different from conventional disturbance-tolerant methodologies, total disturbance compensation is introduced to attenuate uncertainty influence in real time. Consequently, active disturbance rejection formation tracking control architecture is systematically constructed for uncertain MASs. Moreover, considering switching topology, the controller design algorithm is presented by piecewise Lyapunov analysis. Finally, the effectiveness and advantages of the proposed event-triggered FESO-based active disturbance rejection control protocol is illustrated by an numerical example on unmanned aerial vehicle swarm system.

In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.

In this article, an extended state observer (ESO) design problem is investigated for uncertain nonlinear systems subject to limited network bandwidth. First, for rational information exchange scheduling, a dynamic event-triggered (DET) communication protocol is proposed. Different from the traditional static event-triggered strategies with fixed thresholds, an internal dynamic variable is introduced to be adaptively adjusted by a dual-directional regulating mechanism. Thus, more desirable tradeoff between observation performance and communication resource efficiency is achieved. Second, inspired by our early work on Takagi-Sugeno fuzzy ESO (TSFESO), a novel paradigm of event-triggered TSFESO is initially proposed. Third, under the DET mechanism, the TSFESO design approach is derived to carry out exponential convergence for estimation error dynamics. Finally, the effectiveness of the proposed method is verified by numerical examples. The nonlinear estimating efficiency and linear numerical tractability are integrated in TSFESO. In addition, a generalized ESO formulation is developed to allow some nonadditive uncertainties incompatible with total disturbance, such as improved event-triggered strategy, and thus, the application sphere of ESO is further expanded.

Motion control is critical in mobile robot systems, which determines the reliability and accuracy of a robot. Due to model uncertainties and widespread external disturbances, a simple control strategy cannot match tracking accuracy with disturbance immunity, while a complex controller will consume excessive energy. For precise motion control with disturbance immunity and low energy consumption, a control method based on an enhanced reduced-order extended state observer (ERESOBC) is proposed to control the motor-wheels dynamic model of a differential driven mobile robot (DDMR). In this method, only unknown state error and negative disturbance are estimated by the enhanced reduced-order extended state observer (ERESO), which reduces the required energy of the observer. In addition, a simple state-feedback-feedforward controller is used to track the reference signal and compensate for negative disturbance. Through numerical simulation and application example, the tracking performance and disturbance rejection performance of DDMR are compared with the traditional control method based on enhanced extended state observer (EESOBC), and the results show the superiority of the ERESOBC method.

This article investigates the output consensus problem of heterogeneous linear multiagent systems under directed communication graphs. A novel adaptive dynamic event-triggered mechanism is proposed, intending to further save system resources consumed in communication between agents and controller update of agents themselves, and remove the assumption that the global information associated with the communication topology should be known in advance for the design of control parameters. Unlike the existing related adaptive event-triggered algorithms, the proposed algorithm could theoretically guarantee the existence of a strictly positive constant on the interevent time intervals for both communication between agents and controller update. Furthermore, it is shown by the simulation that the addition of a dynamic variable has the potential to further optimize the control cost when compared to the addition of exponential function signal or$L_{1}$signal usually adopted in existing adaptive event-triggered mechanisms. Then, the obtained results are extended from the strongly connected graph to the directed communication graph only containing a spanning tree. Finally, numerical simulation results are conducted to demonstrate the effectiveness of the proposed mechanism.

In this article, a model-free predictive control algorithm for the real-time system is presented. The algorithm is data driven and is able to improve system performance based on multistep policy gradient reinforcement learning. By learning from the offline dataset and real-time data, the knowledge of system dynamics is avoided in algorithm design and application. Cooperative games of the multiplayer in time horizon are presented to model the predictive control as optimization problems of multiagent and guarantee the optimality of the predictive control policy. In order to implement the algorithm, neural networks are used to approximate the action-state value function and predictive control policy, respectively. The weights are determined by using the methods of weighted residual. Numerical results show the effectiveness of the proposed algorithm.

A connected vehicle platoon with unknown input delays is studied in this article. The control objective is to stabilize the connected vehicles, ensuring all vehicles are traveling at the same speed while maintaining a safety spacing. A decentralized control law using only onboard sensors is designed for the connected vehicle platoon. A novel switching-type delay-adaptive predictor is proposed to estimate the unknown input delays. By using the estimated unknown input delays, the control law can guarantee the stability of the successive vehicles. The platoon control adopts a one-vehicle look-ahead topology structure and a constant time headway (CTH) policy, which makes the desired spacing between vehicles vary with time. In this framework, the stability of the connected vehicles can be derived through the analysis of each pair of two successive vehicles in the platoon. Finally, an example is presented to illustrate the applicability of the obtained results.

For time-varying delayed nonlinear systems with parameter uncertainties and persistent disturbances, an online and an offline robust fuzzy model predictive control algorithms are proposed in this article. Both methods guarantee the input-to-state stability of the system. Furthermore, a novel alternative optimization (AOP) approach and a dual-mode optimal control (OP)/AOP strategy are proposed to prevent the performance deterioration of the online OP approach due to the challenges in addressing the bilinear matrix inequality (BMI) constraints. With the established AOP and dual-mode OP/AOP techniques, the optimization problem constrained by BMIs is transformed into convex, and a significantly more precise approximation of the ellipsoidal minimal robust positively invariant set can be calculated. Besides, the system can eventually converge into a more compact ellipsoidal set. These two alternative optimization methods can be easily extended to various nonlinear systems. A numerical example and a continuous-time stirring tank example are provided to validate the effectiveness and advantages of the established methodologies.

This article investigates the tracking problem of event-triggered prescribed performance fuzzy fault-tolerant control (FTC) for unknown Euler–Lagrange systems with actuator faults and external disturbances. First, the barrier Lyapunov functions (BLFs) and prescribed performance functions are synthesized to guarantee that the tracking errors satisfy the preset transient performance. Different from existing prescribed performance control methods, which require the initial values of the tracking errors to be within the prescribed performance functions, an error transformation method is introduced to ensure that the tracking errors with any bounded initial values can enter the preset boundaries within a preset time. Then, considering the unavailability of system parameters, the fuzzy logic systems are used to approximate unknown parameters of the system. What is more, to solve the problem of limited communication and computing resources in practical systems, an improved event-triggered control (ETC) scheme is proposed, which can reduce the communication and computation burden without satisfying the input-to-state stability assumption. Meanwhile, the Zeno phenomenon can be avoided. Furthermore, the effects of actuator faults and the event-triggered mechanism are handled by Nussbaum gain technology. Finally, the superiority of the proposed control algorithm is verified by simulation results.

This article studies the asynchronous sliding mode control for nonlinear Markov jump systems (NMJSs), whose transition probabilities is piecewise-constant and subjected to the persistent dwell-time switching rule. By resorting to the interval type-2 Takagi–Sugeno (IT2 T-S) fuzzy set theory, the nonlinearity and uncertainty features of the investigated systems are modeled. Taking the uncertain membership functions (MFs) of IT2 T-S fuzzy systems into consideration, the nonparallel distributed compensation strategy is employed for designing the MFs of the controller such that they do not need to be obtained in time. Furthermore, in view of the difficulty of accurately acquiring the mode of NMJSs with complex transition probabilities, the hidden Markov model is employed to provide a detected mode for constructing the sliding mode law. Afterward, a novel fuzzy sliding surface is proposed, and an asynchronous IT2-fuzzy-model-based sliding mode control law is designed for forcing the closed-loop systems to move on the predefined sliding surface. Thereafter, a novel two-mode-dependent Lyapunov-like function is conceived for the studied systems, where modes of NMJSs and transition probabilities are permitted to change simultaneously. By applying an improved technique to handle the parameteric matrix inequalities, some less-conservative sufficient conditions in the form of linear matrix inequality are obtained for ensuring the stochastic exponentially asymptotical stability of the sliding mode with$H_\infty$performance. Finally, a mass-spring mechanical system is presented to substantiate the validity of the proposed main results.

This article addresses the exponential synchronization problem for a class of fuzzy inertial memsirtor-based neural networks with mixed time-varying delays. First, the inertial items are described as second-order systems and transformed into first-order systems by utilizing a appropriate variable substitution. Then, the fuzzy state-feedback control strategy and fuzzy adaptive control strategy are designed to ensure the exponential synchronization under the framework of Filippov solutions. The exponential synchronization algebraic conditions are obtained by choosing a proper Lyapunov–Krasovskii functional. Finally, two numerical simulations are provided to validate the effectiveness and benefit of the proposed results.

This article is devoted to designing a novel aperiodic sampled-data-based event-triggered control strategy for Takagi–Sugeno fuzzy systems. First, via taking the structural features of fuzzy subsystems and the available information of fuzzy membership functions into consideration, an improved fuzzy-dependent adaptive event-triggered mechanism, which designs different adaptive event-triggered mechanisms for corresponding fuzzy subsystems, is proposed to provide extra design flexibility and further optimize communication efficiency. Then, the two-side looped-functional method and dynamic partitioning approach are introduced in the construction of the novel Lyapunov–Krasovskii functional (LKF). These two methods contribute to deriving preferable stability criterion and stabilization approach via relaxing the positive definite constraint on LKF and fully utilizing the inner system state during the whole aperiodic sampling interval. Eventually, two simulation examples are introduced to verify the effectiveness of the proposed control strategy and its advantages in lightening communication frequency.

This article investigates the stability and stabilization problem of aperiodic sampled-data nonlinear systems in the framework of interval type-2 (IT-2) fuzzy models. First, by introducing two adjustable parameters and splitting the sampling intervals into four nonuniform intervals, a refined two-side looped-functional method is constructed to fully utilize inner state information during the whole aperiodic sampling interval. Simultaneously, the positive definiteness constraint for the individual matrix in Lyapunov–Krasovskii functional can be further relaxed. Then, via constructing a novel fuzzy Lyapunov–Krasovskii functional (FLKF) together with a fuzzy-dependent-switching scheme, the available features of fuzzy membership functions (FMFs) can be further taken into consideration to increase the design flexibility. Consequently, the stability condition and corresponding controller design approach for aperiodic sampled-data IT-2 fuzzy systems can be obtained with less design conservatism and larger sampling intervals. Finally, two simulation examples are employed to demonstrate the validity and superiority of the proposed method.

This article investigates the real-time localization problem of dynamic multiagent systems with repetitive operation characteristics under directed graph. A distributed localization estimation algorithm based on iterative learning is proposed. The barycentric coordinates calculated based on the relative distance are used to estimate the real coordinates of the agent. Different from the traditional estimation methods along the time axis, the proposed method utilizes the information of iteration axis simultaneously. In this method, the current estimation coordinates are updated by using the estimation coordinates of the same sampling time in previous iteration, the estimation accuracy is improved, and the velocity constraint is removed. Additionally, the real-time localization problem of dynamic multiagent systems under arbitrary deployment is concerned. An improved distributed localization estimation algorithm with signed coefficients based on iterative learning is proposed. Meanwhile, the results are also extended to the localization estimation of multiagent systems with arbitrary deployment in 3-D space. By introducing Richardson iteration and infinite norm, the global asymptotic convergence of the proposed methods is guaranteed. Finally, numerical simulations and the Qbot-2e robot experiment are provided to show the effectiveness and validity of the obtained results.

This article studies the decentralized control of large-scale systems with sparsity and communication delays. The large-scale system is defined over a directed connected graph and the information structure is partially nested. Based on the decomposition of the noise history, the optimal problem of the overall large-scale system can be decomposed into independent subproblems. Hence, the data-driven decentralized control method is investigated to find the optimal controllers using adaptive dynamic programming (ADP), which could release the dependence on the knowledge of model. In addition, state feedback and output feedback policy iteration algorithms are developed, respectively. Rigorous stability analysis shows that the proposed algorithms can stabilize the large-scale systems asymptotically. Finally, the effectiveness of the proposed theoretical methods is demonstrated by the application of heavy duty vehicle (HDV) platooning.

This article investigates the exponential synchronization problem for a class of memristor-based neural networks with mixed time-varying delays and parameter perturbations, and inertial items are considered (MINNs). A periodically intermittent control protocol is designed to guarantee the exponential synchronization between two MINNs. Then, by adopting nonsmooth analysis, Halanay inequality, and Lyapunov theory, the exponential synchronization criteria for MINNs under the proposed controller are obtained. Furthermore, instead of the reduced-order method, the synchronization of MINNs is considered under the framework of the second-order system directly, which is different from existing literature. Some numerical simulations are presented to show the validity of the proposed criteria in the end.

Real-time semantic segmentation aims to generate high-quality prediction in limited time. Recently, with the development of many related potential applications, such as autonomous driving, robot sensing and augmented reality devices, semantic segmentation is desirable to make a trade-off between accuracy and inference speed with limited computation resources. This paper introduces a novel effective and light-weighted network based on Separable Pyramid Module (SPM) to achieve competitive accuracy and inference speed with fewer parameters and computation. Our proposed SPM unit utilises factorised convolution and dilated convolution in the form of a feature pyramid to build a bottleneck structure, which extracts local and context information in a simple but effective way. Experiments on Cityscapes and Camvid datasets demonstrate our superior trade-off between speed and precision. Without pre-training or any additional processing, our SPMNet achieves 71.22% mIoU on Cityscapes test set at the speed of 94 FPS on a single GTX 1080Ti GPU card.

The problem of the stochastic event-based distributed fusion estimation for a class of Gaussian systems is investigated. Considering the deterministic event-triggers destroying the Gaussian property of system states, the stochastic event-triggered mechanisms (SETMs) are used, which also can relieve the network transmission burden. Under the stochastic transmission schedules, a two-step fusion estimation method is developed. The first step, with the consideration of channel fading, the local estimation of each sensor is proposed by using the measurements from itself and its neighbors. The second step, the fusion algorithm is designed to eliminate the disagreements among local estimations of each sensor. Finally, experiment is carried out to demonstrate the advantages of the proposed distributed fusion estimation.

In this article, the output synchronization problem of passive multiagent systems (MASs) with transmission delays and switching graphs is addressed by a novel logic-based distributed switching mechanism. Our result shows that synchronization is reached for arbitrarily large and bounded constant, time varying, or distributed delays, which, compared with the existing results for passive MASs, has an obvious advantage. This delay robustness holds under the very weak connectivity assumptions on the underlying graph, that is, as long as the graph is uniformly jointly strongly connected and switches with a dwell time. The proposed algorithm is applied to the position synchronization problem of multiple robotic manipulators to show its applicability.

This article investigates the consensus problem of general linear multiagent systems under directed communication graphs with event-triggered mechanisms. First, a novel distributed static event-triggered mechanism with a state-dependent threshold is proposed to solve the consensus problem, both with a positive lower bound on the average time interval of the communication among agents and updates of controllers. Thus, the Zeno behavior is excluded for communication among agents and controller updates. Next, to further reduce the frequencies of communication among agents and updates of controllers, a distributed dynamic event-triggered mechanism is introduced. By applying the static and dynamic mechanisms, the problem can be addressed with the reduced use of system resources compared with that in most existing control algorithms. Finally, numerical simulations are presented to verify the effectiveness of the results.

This article is concerned with the problem of compensation-based output feedback control for Takagi-Sugeno fuzzy Markov jump systems subject to packet losses. The phenomenon of packet losses is assumed to randomly occur in the feedback channel, which is modeled by a Bernoulli process. Employing the single exponential smoothing method as a compensation scheme, the missing measurements are predicted to help offset the impact of packet losses on system performance. Then, an asynchronous output feedback controller is designed by the hidden Markov model. Based on the mode-dependent Lyapunov function, some novel sufficient conditions on the controller existence are derived such that the closed-loop system is stochastically stable with strict dissipativity. Besides, an algorithm for determining the optimal smoothing parameter is proposed. Finally, the validity and advantages of the design approach are manifested by some simulation results.

This article addresses the aperiodic sampled-data control problem for flexible spacecraft with stochastic actuator failures. Flexible spacecraft dynamics are approximated by a group of T-S fuzzy models due to strong nonlinearity, and the multi-stochastic failures of spacecraft are depicted by a time-continuous and state-discrete Markov chain. To reduce the design conservativeness, a membership-sampling-dependent Lyapunov-Krasovskii functional (MSDLKF) is introduced to utilize the information of fuzzy membership functions and aperiodic sampling modes. Furthermore, a number of reliable fuzzy controllers are designed to obtain the exponential attitude stabilization under the circumstances of stochastic failures. At the same time, disturbance attenuation is ensured. The solution of the fuzzy controller gains can be obtained by solving a set of linear matrix inequalities (LMIs). In the end, an example of the practical flexible spacecraft system is given to illustrate the feasibility and validity of the proposed fuzzy control methods.

In this article, the cooperative output regulation problem of heterogeneous multiagent systems has been investigated. It is assumed that only a few agents could know the system matrix of the exosystem and no agent knows the topological information. Under these conditions, a novel distributed online algorithm is proposed to estimate the information relevant to the topology. Based on this algorithm, a distributed event-triggered adaptive observer is designed such that each agent can observe the exosystem. It is theoretically shown that the proposed distributed controller will make the multiagent system achieve cooperative output regulation asymptotically. Finally, a simulation is presented to show the effectiveness of the result.

For the extended state observer (ESO) design, the most challenging issue is to exploit efficient nonlinear observation performance, while maintaining desirable linear numerical tractability under noncontinuous transmission. In this article, in order to address this issue, the sampled-data ESO design is investigated for a class of uncertain nonlinear systems. First, based on the fuzzy modeling approach with reasonable fuzzy rules and sets, the nonlinear functions of the ESO are suitably approximated by several local linear models weighted by membership functions. Then, a novel methodology of Takagi–Sugeno fuzzy extended state observer (TSFESO) is developed for the first time. By virtue of fuzzy membership functions, the estimating action is implemented in a nonlinear pattern, while taking advantages of Takagi–Sugeno fuzzy formulation, the observer gains can be calculated in a linear manner. As a result, the nonlinear estimating efficiency and linear numerical tractability are integrated in a unified framework. Second, for the aperiodic sampling case, the exponential convergence criterion for the TSFESO is presented by constructing a sampling- and time-dependent Lyapunov functional, in which the information on sampling action is taken into full consideration for desirable feasibility. Moreover, the observer design approach is also put forward. Finally, the superiorities and effectiveness of the proposed approaches are demonstrated by numerical examples.

This article is concerned with extended state observer (ESO) design for uncertain nonlinear systems. First, different from standard ESO exclusively applicable for integral chain systems, a ESO formation including nonlinear and linear types is proposed for general state-space models. Inspired by our previous work, a event-triggered generalized fuzzy ESO (GFESO) is developed. Second, in order to schedule transmission rationally, an improved total disturbance-resilient dynamic event-triggered mechanism (TDRDETM) is put forward. Third, under TDRDETM, the GFESO design approach is presented in sense of exponential convergence. Finally, numerical examples illustrate the effectiveness of the given methods.

This article discusses the interval type-2 (IT2) Takagi–Sugeno (T-S) fuzzy asynchronous controller design problem for nonlinear multiagent systems via a dynamic event-triggered scheme in the discrete-time context. To formulate the asynchronous phenomena between the system modes and the anticipant controller modes, the hidden Markov model is proposed. The primary attention is focused on the explicit design of the dynamic event-triggered strategy that can be dynamically adjusted in line with system information, which mitigates the communication burden efficiently. On this occasion, the information renewal of the controller is aperiodic. Furthermore, the nonlinear characteristics are effectually disposed through utilizing a unique IT2 T-S fuzzy model, which is with mismatched membership functions (MFs). As a result, the resulting closed-loop fuzzy multiagent systems are accompanied by mismatched MFs and asynchronous modes, whereafter, via solving the convex optimization problem, the desired controller gains are acquired. Eventually, the validity and practicability of the developed control scheme are illustrated by two examples.

In this article, the problem of the distributed$H_\infty$filtering in finite-time horizon is investigated for a class of Takagi–Sugeno (T-S) fuzzy systems with sensor saturation and unknown bounded noise. Considering the network bandwidth limitation, a dynamic event-triggered mechanism (ETM) is proposed. Due to an asynchronous problem of premise variables induced by the dynamic ETM, the partition approach is applied. Under the partition region, a novel piecewise T-S fuzzy distributed$H_\infty$filtering is derived. By constructing the Lyapunov function, sufficient conditions for the finite-time$H_\infty$performance of the estimation error system are given. Furthermore, to minimize the interference from disturbance to distributed filtering, the optimal problem of disturbance attenuate parameter$\gamma$is solved. Finally, a simulation example is presented to verify the effectiveness of the proposed algorithm.

This article investigates the optimally distributed consensus control problem for discrete-time multiagent systems with completely unknown dynamics and computational ability differences. The problem can be viewed as solving nonzero-sum games with distributed reinforcement learning (RL), and each agent is a player in these games. First, to guarantee the real-time performance of learning algorithms, a data-based distributed control algorithm is proposed for multiagent systems using offline system interaction data sets. By utilizing the interactive data produced during the run of a real-time system, the proposed algorithm improves system performance based on distributed policy gradient RL. The convergence and stability are guaranteed based on functional analysis and the Lyapunov method. Second, to address asynchronous learning caused by computational ability differences in multiagent systems, the proposed algorithm is extended to an asynchronous version in which executing policy improvement or not of each agent is independent of its neighbors. Furthermore, an actor-critic structure, which contains two neural networks, is developed to implement the proposed algorithm in synchronous and asynchronous cases. Based on the method of weighted residuals, the convergence and optimality of the neural networks are guaranteed by proving the approximation errors converge to zero. Finally, simulations are conducted to show the effectiveness of the proposed algorithm.

In this article, the consensus problem of linear multiagent systems (MASs) is investigated, where an event-triggered mechanism combined with sampler is introduced to rationally utilize the network bandwidth and the communication energy. Different from most existing results on the event-triggered consensus of MASs, a time-varying communication delay (CD) with a more general form is constructed in this article. With the help of a new class of Lyapunov functional and advanced inequalities, some novel criteria for achieving the asymptotic consensus of the considered MASs under different CD cases are obtained. For demonstrating the superiority of the proposed method from the conservatism and the practicability, a compared example and an application of spacecraft formation flying are proposed, respectively.

In this article, the distributed fusion estimation issue with the dimensionality reduction strategy under DoS attacks and deception attacks is investigated for a class of stochastic uncertain systems with fading measurements. The stochastic uncertainties existed in the system and measurement equations are represented by state-dependent noises. The fading measurements are depicted by stochastic variables with known statistics. Then, a novel attack and compensation model is proposed to display the randomly occurring behaviors of the DoS attacks and the deception attacks within a unified framework. Furthermore, a distributed multisensor fusion estimation (DMSFE) algorithm is presented. An explicit form of dimensionality reduction is designed against attacks. Stability conditions are derived such that the mean square errors (MSEs) of the proposed DMSFE are bounded. A sequential covariance intersection fusion estimator (SCIFE) is designed to prevent the cross fusion covariance matrices calculating, which owns lower accuracy by smaller computation cost than DMSFE. An illustrative example is provided to show the effectiveness and merits of the proposed algorithm.

This article proposes a unified framework to investigate the leader-following and leaderless consensus problem of general linear multiagent systems under the directed communication topology only containing a spanning tree. To further reduce the use of resources for the control objective, an energy-saving control algorithm is introduced composed of double dynamic event-triggered mechanisms that work independently: one intends to control the communication of agents with their neighbors and the other to decide the update of controllers. It is shown that the control algorithm performs well compared to most existing control algorithms in terms of the communication cost between agents and the update cost of controllers. In addition, a new procedure to choose parameters is obtained by the developed Lyapunov stabilty method, with the potential of achieving less conservative parameters than related results. Finally, the effectiveness of the proposed control scheme is verified by numerical simulations.

In this article, the problem of a nonlinear system states estimation with multisensor stochastic scheduling is investigated. In order to solve the non-Gaussian property induced by the nonlinear transformation, the unscented transformation (UT) technique is applied. Since the sensor networks channel is limited, the stochastic event-triggered mechanisms (SETMs) are proposed to reduce the network transmission burden. Under the SETMs, the modified unscented Kalman filter is proposed. Additionally, the sufficient conditions are given to guarantee the stabilities of the error covariance and the estimation error. Finally, extensive examples are carried out. Performances evaluation and comparison with existing methods are given to demonstrate the superiority of the proposed methods.

This paper studies the fixed-time distributed localization problem for directed network based on bearing measurements. The orientation of the global coordinate system is not available to nodes whose local coordinate systems do not to be aligned. First, the barycentric coordinate representation is obtained relying only on the bearing information. Then, a fixed-time distributed network localization algorithm is proposed. By applying the proposed algorithm, the localization problem is converted to the consensus tracking problem for localization errors. When nodes distribution and communication topology meet the requirements, one can prove that the position estimates can convert to the truth after a fixed time. Finally, the simulation verifies the validity of the algorithm.

This article is concerned with the issue of dynamic event-based non-fragile dissipative state estimation for a type of stochastic complex networks (CNs) subject to a randomly varying coupling as well as fading measurements, where the variation of coupling is governed by a Markov chain. To characterize the measurement fading phenomenon for different nodes, a Rice fading model is considered with known statistics information of the coefficients. For the sake of further resource saving, a dynamic event-triggering strategy (ETS), which is proved to release less data packets than the static one, is implemented to govern the measurements transmission for each sensor to its corresponding estimator. The main objective of this article is to determine a dynamic event-based non-fragile estimator such that, for all possible parameter fluctuations in estimator gains, the estimation error system is stochastically stable with a strict (Υ1, Υ2, Υ3)-γ-dissipativity. Through intensive stochastic analysis, sufficient conditions are then derived in terms LMI to guarantee the existence of the desired state estimator. Finally, the effectiveness of the proposed results are verified by two practical examples of Chua's circuit and quadruple-tank process system (QTPS).

This article studies the problem of finite frequency fault detection filtering design for uncertain nonlinear systems based on interval type-2 Takagi-Sugeno fuzzy models. It is assumed that the frequencies of disturbances and faults are in finite frequency sets, respectively. The objective is to design an admissible filter such that the fault detection system is asymptotically stable with prescribed finite frequency \mathscr H∞and \mathscr H-performances. Based on Fourier transform and Projection lemma, finite frequency filtering synthesis results are obtained. Then, a novel membership-function-dependent finite frequency fault detection filtering design approach is proposed by using the information of the lower and upper membership functions together with the footprint of uncertainties. Two algorithms with linear matrix inequality constraints are developed to optimize the finite frequency \mathscr H∞performance and the finite frequency \mathscr H-performance, respectively. Finally, simulation studies are provided to show the effectiveness of the proposed method.

In this article, the event-triggered asynchronous control problem of nonlinear multiagent systems is investigated based on Takagi-Sugeno fuzzy models. In order to rationally utilize network resources and elaborately avoid unnecessary continuous monitoring, an event-triggered mechanism with a new dynamic threshold parameter and a sampler are considered, respectively. Besides, an asynchronous operation method is adopted to deal with the mismatched premise variables between the fuzzy system and fuzzy controller. Based on the Lyapunov stability theory and appropriate inequality, some sufficient criteria in the form of linear matrix inequalities are obtained to ensure the stability of the closed-loop system. Finally, an illustrative example is provided to demonstrate the effectiveness and superiority of the proposed method.

This article is devoted to event-triggered stabilization for a class of interval type-2 (IT2) fuzzy systems with aperiodic sampling. First, the IT2 Takagi-Sugeno fuzzy model and sampled-data controllers are established subject to mismatched membership functions. Second, considering a nonuniform sampling case, a refined adaptive event-triggered communication scheme is proposed in a hierarchy form to dynamically adjust the direction and rate of the event-triggered threshold parameter by state changing trend and relative state error, respectively. Thus, a complete dual-directional regulating mechanism with sensitivity to state variation is reasonably created to give extra flexibility, which is beneficial for a preferable tradeoff between control performance and network resource. Third, considering the practical behaviors on the sampling interval, a novel integral type of time-dependent Lyapunov function is constructed. Then, the stability criterion and the controller design approach are derived. Finally, the numerical examples are provided to demonstrate the effectiveness and advantages of the proposed methods.

This article focuses on the problem of fault detection filtering design for discrete-time interval type-2 Takagi-Sugeno (T-S) fuzzy systems in finite frequency domain. Considering the fact that external disturbances and faults are usually reside in finite frequency ranges, the finite frequency H∞and H-performances are introduced to reflect the disturbance robustness and fault sensitiveness in finite frequency domain, respectively. Based on discrete-time Fourier transform and its properties, finite frequency performance analysis results are first obtained. Then, by exploiting the information on upper and lower membership functions, the membership-function-dependent filtering design conditions in the form of linear matrix inequalities are established for discrete-time interval type-2 T-S fuzzy systems in finite frequency domain. With the obtained filter, a fault detection scheme is then proposed and it is shown that the resulting fault detection system is asymptotically stable with prescribed finite frequency H∞and H-performances. Finally, the effectiveness of the proposed method is validated by simulation studies.

This article is concerned with the event-triggered guaranteed cost control for a class of Markovian jump systems with time-varying delays and partly unknown transition probabilities, which is described by the Takagi-Sugeno fuzzy model. For the sake of saving network bandwidth, an event-triggered mechanism related to system modes is developed in the feedback channel, in which network-induced delay randomly occurs. The stability criterion for the system with a guaranteed cost index is derived by the Lyapunov-Krasovskii functional. Moreover, sufficient conditions that ensure the existence of the admissible fuzzy controllers are given. By means of the proposed approach, the fuzzy control gains and event-triggered parameters can be codesigned. Finally, some simulation results are presented to demonstrate the superiority and effectiveness of the developed method.

This article is concerned with the problem of imperfect premise matching asynchronous H∞output tracking control for Takagi-Sugeno fuzzy Markov jump systems. A hidden Markov model is established due to the fact that the modes information of the system may not be accurately transmitted to the controller, which is used to depict the asynchronous phenomenon between the system modes and controller modes. The packet loss in the communication process is described by a stochastic variable subject to Bernoulli distribution. Then, based on a novel Lyapunov function, the mode-dependent and fuzzy-basis-dependent stability criteria are derived and the asynchronous control scheme is developed subject to an H∞tracking performance. Finally, two examples are provided to demonstrate the effectiveness of the proposed approach.

This article considers the exponential stability and aperiodic sampled-data control problem for nonlinear systems based on a class of Takagi–Sugeno fuzzy models. The fuzzy-dependent-switching control strategy together with a novel time-varying sampled-data controller is proposed to deal with the exponential stabilization problem of such systems. Mixed-fuzzy dependent Lyapunov–Krasovskii functionals (MFDLKFs), which fully make use of available characteristics of the sampling patterns, the signs and the upper bounds of the time derivative of fuzzy membership functions, are constructed for the purpose of reducing the design conservatism. Based on the proposed MFDLKFs, a novel exponential stabilization criterion for the fuzzy systems with aperiodic sampling is established in terms of linear matrix inequalities, which is less conservative and obtains a larger sampling interval compared with existing results. Finally, a simulation example is employed to demonstrate the effectiveness and superiority of the proposed fuzzy-dependent-switching control scheme.

This paper is concerned with the stability of the vehicular platoon consisting of a leader and multiple cooperative autonomous driving followers. The objective of the platoon control is to ensure all vehicles traveling at the same speed while maintaining a safety spacing. To achieve the objective, a novel control framework consisting of the distributed adaptive event-triggered observer and the car-following control protocol is proposed for the vehicular platoon control. The condition that only few following vehicles can access the information of the leader is considered. In order to design controllers while avoid using any global information, such as the system matrix and the state of the leader, a distributed event-triggered observer is proposed, such that each vehicle can observe the dynamics of the leader. Based on this observer, both collision avoidance and limited communication source of each vehicle are simultaneously considered in the design of the observer. It is shown that under the proposed control framework, the platoon can achieve asymptotical stable, meanwhile, the amount of transmission data and communication cost among vehicles can be reduced. Finally, numerical simulations are presented to show the applicability of the obtained results.

By incorporating a logic-based switching mechanism into high-gain linear controllers, global output-feedback adaptive stabilization is achieved for a class of nonlinear systems with unknown input-output-dependent lower-triangular growth rate and uncertain control coefficient. When a controller candidate, associated with a Lyapunov candidate, is connected into the closed-loop, the logic unit constantly supervises the change rate of the Lyapunov candidate. If it does not decrease as rapidly as predicted, another controller candidate will be switched in to replace the former one. It is theoretically proved that there is only a finite number of switching times, and asymptotic stability of the closed-loop system is guaranteed. Compared with existing results, the logic-based switching adaptive control approach can tolerate strong input-output-dependent nonlinearities, as well as large uncertainties from the system dynamics including the control coefficient. Finally, a numerical example is provided to illustrate the feasibility and the effectiveness of the proposed approaches.

This article is concerned with the issues of finite-time control for a class of continuous-time nonlinear semi-Markovian jump systems (SMJSs) with saturation constraint via observed-based control. Both sensor and actuator saturations, and unknown nonlinearities are considered simultaneously. The main purpose of this article is to derive parameter selection sufficient conditions by designing an observed-based controller. These conditions ensure that the system is finite-time boundedness (FTB). By employing some reasonable assumptions and constructing an appropriate semi-Markovian Lyapunov function, some novel finite-time stabilization criteria are obtained, which guarantee the FTB for the underlying systems over the whole finite-time interval. Meanwhile, the observed-based controller is designed. Finally, a practical example is provided to illustrate the effectiveness and merits of the proposed methods.

This paper investigates the distributed formation control problem of multiple nonholonomic wheeled mobile robots within real environments. The formation pattern of the system adopts leader-follower structure and the communication topology among the multirobot system is modeled by a directed graph. Slippage is hard to avoid due to the possible existence of ice, sand, or muddy roads. To overcome the effect of slippage, an adaptive trajectory tracking controller for leader robot is designed, such that the leader robot can keep up with the virtual reference trajectory and estimate the real value of unknown slipping ratio accurately. In addition, it is difficult for each follower robot to obtain leader's states in a large formation system, so distributed formation controllers are designed based on distributed observers. It is shown that the proposed controllers can realize formation objective and overcome the slippage constraints at the same time. Finally, the effectiveness of the proposed controllers is verified by simulation results.

This paper is concerned with the sliding mode control problem for a class of continuous-time switched neural networks with mode-dependent average dwell time (MDADT). The considered continuous-time switched neural networks are motivated by biological neural networks which contain a nonlinear term and a changeable switched signal. The concept of MDADT is introduced, in which every subsystem has its own dwell time before switching to another subsystem. Moreover, a novel sliding mode controller is designed by an event-triggered mechanism which is based on the observer error and the system mode, where its triggered condition can be more conservative and practical than the existing triggered conditions. Sufficient conditions are derived to ensure that the closed-loop system is stochastically exponentially stable in terms of linear matrix inequalities. The designed sliding mode controller can promote the sliding mode motion of the system state. Finally, an illustrative example is provided to demonstrate the effectiveness and merits of the proposed method.

A dynamic event-triggered$\mathcal {H}_{\infty }$controller of the singular system based on stochastic cyber-attacks is studied in this article. To save network resources, a novel way is given, the cyber-attacks are considered as phenomena randomly occurring via network communication. First, sufficient conditions are derived, which ensure the closed-loop singular system to be regular, impulse free and asymptotically stable under a prescribed$\mathcal {H}_{\infty }$norm bound. Second, from some elegant linearization techniques, the method of design corresponding controller is put forward. Finally, some examples are given to illustrate the effectiveness of the obtained theoretical results.

In this paper, the distributed remote state estimation problem for conditional dynamic linear systems in mobile sensor networks with an event-triggered mechanism is investigated. The distributed mixture Kalman filtering method is proposed to track the state of the maneuvering target, which uses particle filtering to estimate the nonlinear variables and apply Kalman filtering to estimate the linear variables. An event-based distributed filtering scheme is designed, which is an energy-efficient way to transmit data between sensors and estimators. In addition, by using the mutual information theory, an optimal control problem is formed to control the position of sensors so that the target tracking process can be achieved quickly. Finally, a simulation example about the maneuvering target tracking is provided to corroborate the effectiveness of the filtering method and the control performance for sensors.

This paper is concerned with the adaptive backstepping control problem for a cloud-aided nonlinear active full-vehicle suspension system. A novel model for a nonlinear active suspension system is established, in which uncertain parameters, unknown friction forces, nonlinear springs and dampers, and performance requirements are considered simultaneously. In order to deal with the nonlinear characteristics, a backstepping control strategy is developed. Meanwhile, an adaptive control strategy is proposed to handle the uncertain parameters and unknown friction forces. In the cloud-aided vehicle suspension system framework, the adaptive backstepping controller is updated in a remote cloud based on the cloud storing information, such as road information, vehicle suspension information, and reference trajectories. Finally, simulation results for a full vehicle with 7-degree of freedom model are provided to demonstrate the effectiveness of the proposed control scheme, and it is shown that the addressed controller can improve the performances more than 80% compared with passive vehicle suspension systems.

This paper is devoted to the stability and stabilization for Takagi-Sugeno fuzzy systems with time-varying delays. First, an improved matrix inequality is presented to bound both strictly and nonstrictly proper rational functions, which is more general than the existing versions of reciprocally convex lemmas. Second, by suitable operations on parameter-dependent polynomial multiplied by state rate, a couple of novel intermediary-polynomial-based functions (IPFs) are developed in delay-product types. Benefitting from slack matrices of IPFs, a certain degree of flexibility is furnished. More importantly than that, by feat of adjustment of the variable parameter, the resulting conditions will be further endowed with additive freedom, which relaxes the feasible space in a distinctive manner. Third, by utilizing IPFs along with triple integrals, the stability criteria and the controller design approach are derived by some advanced integral inequalities. Resorting to elaborate construction of IPFs, the strengths of bounding techniques are sufficiently exploited, and the information on delay derivative is adequately reflected. Consequently, more desirable performances are achieved, while without excessive computational complexity. Finally, the effectiveness of the proposed methods is verified by numerical examples.

In this paper, the event-triggered distributed Kalman filtering problem is considered for a class of networked multisensor fusion systems (NMFSs) under sensor energy and network bandwidth constraint. A general event-triggered scheme is employed for the NMFSs to reduce the energy consumption and communication burden between the sensor nodes and fusion center (FC) under the communication bandwidth constraints. Local estimation information is allowed to transmit partial components to FC over the network with limited bandwidth. A group of binary variables are introduced to describe the component transmitting process when the triggering condition is violated. Furthermore, the untransmitted local estimation signals are compensated by the previous transmitted one, and a recursively event-triggered distributed fusion Kalman filter in the linear minimum mean square error sense is designed from the restructured local unbiased estimators. At each time instant, a set of binary variables are determined by an optimal judgement criterion. Finally, a simulation example is provided to illustrate the effectiveness and advantages of the proposed methods.

In this paper, the event-triggered H∞state estimation problem is investigated for a two-degree-of-freedom quarter-car suspension system operated over a switching-channel network environment. First, the channel-switching is governed by a nonhomogeneous Markov chain whose probability transition matrix is time-varying. Then, a Markov jump linear system model is adopted to represent the overall networked system in view of the event-triggered communication scheme, signal quantization and random packet losses on account of the limited network bandwidth. By virtue of the Lyapunov functional and linear matrix inequality method, the event-triggered H∞state estimation problem is transformed into an optimization problem that switching-channel-dependent estimators are designed such that the estimation error system is exponentially stable in the mean square sense and achieves a desired performance level. Finally, a simulation example is used to demonstrate the validity of proposed design method.

This paper is concerned with the event-based security control problems for a set of discrete-time stochastic systems suffered from randomly occurred attacks, especially the denial-of-service attacks and the deception attacks. An attack model with compensation is established to describe combinations of attacks to the networked control systems. An event-triggered mechanism is employed to debase the communication load by transmitting measurement signals when a definite triggering condition is satisfied. A definition of security probability is proposed to describe the transient state of controller systems. The aim of this paper is to design a dynamic output feedback controller, thereupon the closed-loop system achieves the described security in probability. Some novel sufficient conditions are proposed to guarantee the input-to-state stability of the system in probability, and the controller gains are designed by solving a set of matrix inequalities. The upper bound of the quadratic cost function is also derived. Finally, simulation examples are applied to illustrate the effectiveness of the proposed design scheme.

Autonomous vehicles require a collision-free and comfortable motion trajectory at every time instant. It is a common method to generate a feasible path to the target state and append an optimization-based method for path postprocessing. In this paper a Hybrid A* based motion planning algorithm is presented for autonomous vehicles under unstructured circumstances. Firstly, the Hybrid A* algorithm is improved with a better heuristic function and a better search policy to realize a less-time consuming graph search in consideration of vehicle's motion model. Then, nonlinear optimization algorithm is applied to optimize the generated path further and realizes high quality of security and smoothness of the discrete trajectory. Finally, Catmull-Rom interpolation is combined to make waypoints continuous and easy-to-control. Simulation results concerning different tasks are described to demonstrate the validity of the proposed algorithm.

This paper addresses the distributed adaptive event-triggered H∞filtering problem for a class of sectorbounded nonlinear system over a filtering network with timevarying and switching topology. Both topology switching and adaptive event-triggered mechanisms (AETMs) between filters are simultaneously considered in the filtering network design. The communication topology evolves over time, which is assumed to be subject to a nonhomogeneous Markov chain. In consideration of the limited network bandwidth, AETMs have been used in the information transmission from the sensor to the filter as well as the information exchange among filters. The proposed AETM is characterized by introducing the dynamic threshold parameter, which provides benefits in data scheduling. Moreover, the gain of the correction term in the adaptive rule varies directly with the estimation error and inversely with the transmission error. The switching filtering network is modeled by a Markov jump nonlinear system. The stochastic Markov stability theory and linear matrix inequality techniques are exploited to establish the existence of the filtering network and further derive the filter parameters. A co-design algorithm for determining H∞filters and the event parameters is developed. Finally, some simulation results on a continuous stirred tank reactor and a numerical example are presented to show the applicability of the obtained results.

This paper is concerned with the target tracking problem over a filtering network with dynamic cluster and data fusion. A novel distributed consensus-based adaptive Kalman estimation is developed to track a linear moving target. Both optimal filtering gain and average disagreement of the estimates are considered in the filter design. In order to estimate the states of the target more precisely, an optimal Kalman gain is obtained by minimizing the mean-squared estimation error. An adaptive consensus factor is employed to adjust the optimal gain as well as to acquire a better filtering performance. In the filter's information exchange, dynamic cluster selection and two-stage hierarchical fusion structure are employed to get more accurate estimation. At the first stage, every sensor collects information from its neighbors and runs the Kalman estimation algorithm to obtain a local estimate of system states. At the second stage, each local sensor sends its estimate to the cluster head to get a fused estimation. Finally, an illustrative example is presented to validate the effectiveness of the proposed scheme.

This brief is concerned with stability analysis for delayed neural networks (DNNs). By establishing polynomials and introducing slack variables reasonably, some improved delay-product type of auxiliary polynomial-based functions (APFs) is developed to exploit additional degrees of freedom and more information on extra states. Then, by constructing Lyapunov-Krasovskii functional using APFs and integrals of quadratic forms with high order scalar functions, a novel stability criterion is derived for DNNs, in which the benefits of the improved inequalities are fully integrated and the information on delay and its derivative is well reflected. By virtue of the advantages of APFs, more desirable performance is achieved through the proposed approach, which is demonstrated by the numerical examples.

This article aims to point out an error in the concerned paper and a possible correction to address the error. The correction is achieved by several revisions made on the corresponding assumption, event-triggering function, adaptive laws, and event-triggered control law.

The H∞output tracking control problem of the networked control systems (NCSs) under an adaptively adjusted event-triggered scheme is investigated in this paper. Firstly, a novel adaptively adjusted event-triggered scheme developed in the NCSs with stochastic sensor faults is proposed to choose the necessary packets of sampled data to be transmitted through the networks. Then, the considered system is described as a time-delay system with a delay-distribution for investigation. Based on the established model, a novel stability criterion and the state-feedback controller design with a desired performance of H∞output tracking control for the systems are both derived by using Lyapunov functional. An algorithm is also presented to explain the process of adaptively adjusted event-triggered scheme method. Finally, a satellite tracking case is provided to demonstrate the effectiveness of the proposed approach.

This paper is concerned with the guaranteed cost control problem for a class of Markov jump discrete-time neural networks (NNs) with event-triggered mechanism, asynchronous jumping, and fading channels. The Markov jump NNs are introduced to be close to reality, where the modes of the NNs and guaranteed cost controller are determined by two mutually independent Markov chains. The asynchronous phenomenon is considered, which increases the difficulty of designing required mode-dependent controller. The event-triggered mechanism is designed by comparing the relative measurement error with the last triggered state at the process of data transmission, which is used to eliminate dispensable transmission and reduce the networked energy consumption. In addition, the signal fading is considered for the effect of signal reflection and shadow in wireless networks, which is modeled by the novel Rice fading models. Some novel sufficient conditions are obtained to guarantee that the closed-loop system reaches a specified cost value under the designed jumping state feedback control law in terms of linear matrix inequalities. Finally, some simulation results are provided to illustrate the effectiveness of the proposed method.

This paper investigates the cooperative output regulation problem for heterogeneous multiagent systems (MASs) under switching topology. Two novel distributed event-triggered adaptive control strategies based on state feedback and output feedback are developed, which can avoid using the minimal nonzero eigenvalue of Laplacian matrix associated with global system topologies. It is shown that under the proposed control protocols, MASs could achieve asymptotic tracking and disturbance rejection, and meanwhile, the amount of transmission data and communication cost among agents can be reduced. Then, the leader-following consensus problem of MASs is given as an application of our main results. Finally, an example is presented to verify the effectiveness of the proposed control schemes.

In this paper, the distributed H∞state estimation problem is investigated for a class of filtering networks with time-varying switching topologies and packet losses. In the filter design, the time-varying switching topologies, partial information exchange between filters, the packet losses in transmission from the neighbor filters and the channel noises are simultaneously considered. The considered topology evolves not only over time, but also by event switches which are assumed to be subjects to a nonhomogeneous Markov chain, and its probability transition matrix is time-varying. Some novel sufficient conditions are obtained for ensuring the exponential stability in mean square and the switching topology-dependent filters are derived such that an optimal H∞disturbance rejection attenuation level can be guaranteed for the estimation disagreement of the filtering network. Finally, simulation examples are provided to demonstrate the effectiveness of the theoretical results.

This paper considers the problem of distributed H∞filtering for a class of switched repeated scalar nonlinear systems with randomly occurred sensor nonlinearities and asynchronous switching due to practical reasons. The possibility of randomly occurred sensor nonlinearities is described by a Bernoulli stochastic variable, and the asynchronous switching filtering means that the mode of the plant is different from the mode of the designed filter possibly. A distributed filtering network is used to estimate the system state instead of a filter to improve reliability in case of faults of the filter. A distributed mode-dependent filter is designed by constructing a unified mode-dependent Lyapunov function and solving a set of linear matrix inequalities. Some novel sufficient conditions are obtained by using the average dwell time switching mechanism such that the augmented filtering error system is stochastically exponentially stable and achieves a prescribed H∞disturbance attention index. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed designed method.

In this paper, we use the combination of graph theory and consensus algorithm to realize the formation control of nonholonomic wheeled robots. A novel edge-weight function is designed so that the desired formation-shape can be achieved. In addition, the consensus problem is usually solved on the assumption that the robots are modeled as particle model, but this assumption is not suitable when we are dealing with real robots, so we design a novel algorithm to meet the nonholonomic constraints of wheeled robots. Finally, the effectiveness of the proposed method is verified by the simulation results of an example.

This paper presents a novel control method of extended virtual constraint to mimic human movement for a three-link planar robot with moving torso. Inspired by two supine yoga movements, the dynamic model of bipedal walker is modified accordingly, which enlarges application of generalized planar biped robot and provides a more stable and robust walking gait. Due to the continuity of kinematics and discreteness of impact, the walking motion is regarded as a hybrid system, whose zero dynamics determines the stable state of robot. Hence, a within-stride feedback controller is designed based on input-output linearization and extension of virtual constraint. Moreover, poincaré return map is adopted to analyze the stability of walking gait. The simulation results demonstrate the validity of proposed control law, leading to asymptotically stable walking with modified planar biped robot.

This paper is concerned with the problem of vertical attitude estimation of a two-degree-of-freedom quarter-car suspension system by designing a distributed filtering network, where several distributed filters estimate vehicle heave motion cooperatively under consideration of external disturbance, network channel noises, and measurement error. The sampled data are transmitted through wireless networks. In order to reduce network traffic load and save communication resources, a novel periodic event-triggered sampling scheme is proposed, under which data are transmitted only when the proposed triggering condition is violated. Codesign of event-triggered and distributed filters is derived to guarantee well H∞robustness to the system noises considered above. Finally, the experiments are given to show the effectiveness of the proposed filtering system.

A novel distributed adaptive output feedback control strategy is developed to solve the cooperative output regulation problem of heterogeneous general linear multi-agent systems(MASs) under switching topology. The proposed control strategy avoids using the minimal non-zero eigenvalue of Laplacian matrix when calculating gain matrix, and the communication topology is assumed to contain a directed spanning tree only frequently. It is shown that individual agents could track external signal asymptotically and achieve disturbance rejection. Ultimately, a simulation is presented to exemplify the effectualness of the main result.

This paper addresses the H∞filtering problem for networked control systems with quantization and multiple packet dropouts. The effects of measurement channel and control channel quantization as well as packet dropouts are considered simultaneously due to limited communication capacity and unreliable communication links. Stochastic variables satisfying the Bernoulli random binary distribution are utilized to model the multiple packet dropouts. Sufficient conditions are proposed such that the filtering error system is exponentially mean-square stable while the H∞disturbance rejection attenuation constraint is satisfied. Then, the explicit expression of the desired filter gains is described in terms of the solution to linear matrix inequalities (LMIs). Finally, a numerical example is employed to demonstrate the effectiveness of the proposed filter design approach.

The stabilization problem is investigated for continuous-time Takagi-Sugeno (T-S) fuzzy systems with time delay and impulsive effects in this paper. Some delay-independent and delay-dependent stabilization approaches are developed for both state feedback and observer-based feedback cases, which are based on the Lyapunov-Krasovskii functional approach and a parallel distributed compensation scheme. The time delay is dealt by Lyapunov theory allied with the Halanay Lemma. The restriction on the interval between each impulsive instant and output sampled instant needs not be equal to or less than a constant scalar, but it only requires an inequality satisfaction. When the impulsive effects vanish, the results can be extended to the corresponding sufficient conditions for the cases of a nonlinear continuous time-delay system with a state feedback and observer-based sampled output feedback. Finally, some examples are given to demonstrate the effectiveness of the proposed method.

In this paper, a continuous-time Takagi-Sugeno (T-S) fuzzy system with impulsive effects that are controlled through network is investigated. Network signal-transmission delays and signal-quantization effects are simultaneously considered. The network is with two time-varying additive delays and limited capacity. First, a quantized output-feedback networked control system (NCS) model is established to describe the impulsive NCSs through a channel with limited capacity. Then, based on the Lyapunov-Krasovskii functional approach and a parallel-distributed compensation scheme, a delay-dependent stabilization approach is developed for the impulsive NCSs, which guarantees that the closed-loop system is asymptotically stable. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.

This paper is concerned with the problems of quantized H∞control for networked control systems with random communication delays. The quantizer considered here is logarithmic type. A quantized state-feedback controller is designed, such that the closed-loop system is exponentially mean-square stable in the sense of mean square and achieves an optimal H∞disturbance attenuation level. Sufficient conditions for exponentially mean-square stable of closed-loop system is given in terms of linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of the proposed approach.