This paper is concerned with the distributed coordination problem for the nonlinear multi-agent system (MAS) over a general digraph, where each agent is a multi-input multi-output system. The existing solutions are limited to the system without inputs coupling and with known, global Lipschitz, or linearly growing nonlinearities. To remove these requirements, we propose an integrated sub-optimal and control strategy for the more general nonlinear MAS. It consists of a fully distributed adaptive gradient optimization algorithm and a set of model-free prescribed performance controllers. Our approach ensures that the outputs of the MAS converge to the arbitrarily small neighborhoods of the optimal outputs; in particular, the reference-tracking performance is allowed to be freely predefined. Besides, the proposed control strategy is notably simple compared to the existing approaches, which typically employ function approximation, parameter identification, or derivative calculation. Finally, the simulation results illustrate the effectiveness and superiority of the proposed approach.

In this paper, we focus on trajectory planning for lane change maneuvers and elaborately consider the impacts of dynamic uncertainties from surrounding vehicles. First, a risk-aware corridor is developed to guarantee that the collision probability is below an acceptable risk level. In contrast to the existing studies, a more succinct and intuitive corridor is constructed based on an explicit safety check theorem. Since it relies solely on the distances between autonomous vehicles and surrounding ones, such a corridor can be easily converted into polytopic constraints and seamlessly integrated with an optimization scheme. Furthermore, a computationally tractable and hierarchical scheme is designed to decide the optimal merging instant and generate the expected trajectory. In this scheme, the trade-off between optimality and complexity in calculating merging instants can be well balanced through feasibility estimation and optimum searching. Furthermore, the trajectory is optimized by model predictive control (MPC) technique. Finally, both numerical simulations and naturalistic validations are conducted to verify the effectiveness. The comparison results indicate the convincing superiority of our scheme in achieving safer driving in an uncertain environment. The code is now available athttps://github.com/Aeolson/code_otp.git

Exploring complex environments using heterogeneous robot swarms (RSs) is a considerable challenge in terms of coordination, sensing, and information fusion. Existing approaches suffer from a lack of systematic analysis that fully exploits the complementary capabilities of heterogeneous agents. To bridge this gap, we propose a novel spatial calibration framework that integrates both virtual and physical calibration mechanisms to enable coordinated operation between two distinct robot swarms, RS-A and RS-B. RS-A, characterized by high mobility and a broad field of view, performs continuous, large-scale monitoring and identifies candidate regions of interest. RS-B, equipped with high-precision sensors, is dispatched to these regions to conduct fine-grained data collection and return accurate environmental information, facilitating comprehensive environmental mapping. To this end, we develop a distributed control method for spatial partitioning, position optimization, and information exchange within the swarm, based on improved coverage control and a flooding-based broadcast algorithm for intra-swarm communication. We further design a control architecture that enables inter-swarm collaboration. The proposed framework effectively addresses the limitations of homogeneous RSs in environmental exploration by integrating fast, coarse-grained surveillance with slow, fine-grained investigation through heterogeneous coordination. Finally, the effectiveness of our proposed framework is validated through simulation results.

Recent advances in robotics have expanded the potential of microrobot swarms (MRSs) in medicine, yet clinical deployment remains limited due to reliance on non-autonomous systems. This study proposes an event-triggered distributed coverage control framework that enables MRSs to autonomously detect and treat membrane lesions. To model lesion dynamics accurately, we introduce a coupled reaction-diffusion equation and a Hawkes process that capture spatial spread and temporal emergence. This model informs a modified Lloyd algorithm to guide MRSs toward the centroids of Voronoi cells, optimizing drug release over pre-existing lesion areas. Furthermore, we design an event-triggered mechanism prioritizing treatment of newly emerging lesions, redirecting microrobots to lesion centers for prioritized response. This adaptive framework effectively addresses lesion proliferation and promotes membrane healing. Simulations demonstrate improved coverage efficiency and lesion containment compared to conventional strategies.

The paper considers the event-triggered stabilizability of multi-agent systems (MAS). To reduce the frequency of control input update and information transmission, a novel distributed event-triggered control strategy with state estimation feedback is designed to achieve stabilizability. Event-trigger rules involving dynamic threshold are constructed, which ensure the convergence of systems states in finite time. By utilizing iSCC (independent strongly connected component) partition and Lyapunov stability theory, sufficient criteria for achieving finite time stabilizability have been obtained. Moreover, formation control under event-trigger scheme is addressed based on the obtained stabilizability results. Besides, it has been proven that the event-trigger interval has a positive lower bound, which can avoid Zeno behavior. Finally, the effectiveness of the theoretical results is verified by simulation.Note to Practitioners—In the fields of control and engineering, system stability has always been a research hotspot. Traditional stability analysis often focuses on the asymptotic stability. However, many practical scenarios require the system to reach a stable state within a finite time, i.e., finite time stabilizability, such as rapid response systems, emergency braking systems, and responding to emergencies. Hence, the research on finite time stabilizability has important practical value. In order to reduce the frequency of control updates, this paper proposes the finite time stabilizability under event-trigger scheme and designs a novel feedback controller based on state estimation, which has not been discussed before. At the same time, to explore the impact of network topology on stabilizability, the sufficient conditions for achieving finite time stabilizability of the system are obtained by iSCC graph partition. Dynamic threshold in event-triggering conditions to guarantee the implementation of finite time stabilizability is constructed. Finally, the theoretical results are applied to formation control. The research is of great significance for improving system performance and meeting real-time requirements.

This paper studies the problem of multi-agent trajectory prediction in crowded unknown environments. A novel energy function optimization-based framework is proposed to generate prediction trajectories. Firstly, a new energy function is designed for easier optimization. Secondly, an online optimization pipeline for calculating parameters and agents’ velocities is developed. In this pipeline, we first design an efficient group division method based on Frechet distance to classify agents online. Then the strategy on decoupling the optimization of velocities and critical parameters in the energy function is developed, where the slap swarm algorithm and gradient descent algorithms are integrated to solve the optimization problems more efficiently. Thirdly, we propose a similarity-based resample evaluation algorithm to predict agents’ optimal goals, defined as the target-moving headings of agents, which effectively extracts hidden information in observed states and avoids learning agents’ destinations via the training dataset in advance. Experiments and comparison studies verify the advantages of the proposed method in terms of prediction accuracy and speed. Note to Practitioners—Autonomous robots and vehicles are rapidly integrated into social life and industry, and the scenarios that robots work with multiple people or other moving objects in a crowded environment such as streets and factories will be quite common. One of the most important problems for the robot to solve is the real-time and accurate trajectory prediction of multiple agents around itself to ensure safe navigation. However, existing methods either require prior information to train models or critical parameters in advance or have insufficient prediction accuracy, which are not suitable for robot safe navigation in real applications. In this paper, we investigate the real-time multi-agent trajectory prediction problem for a robot in crowded unknown environments. To obtain the accurate predicted trajectories in real-time, we propose a new energy function optimization-based framework to forecast multi-agent trajectories in crowded unknown scenarios. This framework utilizes the observed data to infer unknown information without the dataset and optimizes the trajectories very efficiently, which can be adopted for robot motion planning and navigation in real-world environments.

This paper proposes a resilient model predictive control (MPC) strategy for constrained cyber-physical systems (CPSs) subject to disturbances and cyber attacks. The feedback sensor-controller (S-C) channel suffers from replay attack and the forward controller-actuator (C-A) channel suffers from false data injection (FDI) attack, and the defender has no prior information about the intruder. Considering that the abnormal behavior of intruder cannot be predicted, an expected one-step controllable set, and a series of minimally conservative constraints are developed to build attack detector. Two controllers are designed jointly based on infinite-horizon MPC to mitigate the negative effects caused by attack. Compared with the existing resilient control strategies, the attack model considered in this paper is less conservative, the resilient control structure is simpler, and it can avoid continuous channel refreshing caused by close-range attack. Robust constraint satisfaction, recursive feasibility and uniformly ultimate boundedness (UUB) are ensured for any admissible attack scenario and disturbance realization. Finally, simulations on a supply chain model show the efficacy of the algorithm.Note to Practitioners—With the wide application of wireless networks, vulnerabilities in the communication process can be easily exploited by intruders to launch malicious attacks. Resilient control can provide acceptable robustness and improve system safety. Additionally, many practical systems are constrained and disturbed, and MPC is one of the most effective methods for dealing with control problems in such systems. Its rolling optimization characteristics make it robust to disturbances. On the one hand, both the C-A channel and the S-C channel of the networked control system are vulnerable to attack. On the other hand, in a complex environment, the defender may not have prior information about the attacker, such as attack probability. Therefore, it is not practical to assume that only a single channel is attacked or that the algorithm relies on the attacker’s prior information. In this paper, a resilient MPC control structure is proposed to counter two types of attacks: replay attack and false data injection attack. One of its characteristics is that the attack model’s conservativeness is relatively low, and the structure is relatively simple, making the algorithm more applicable to actual needs. Another feature of the proposed approach is that it can be adapted directly to different types of attacks without modifying the overall resilient MPC architecture.

The issue related to distributed containment control for multiple networked mechanical systems with inherent nonlinearities, dynamic leaders, unknown external disturbances, parameter uncertainties, and constrained network communication is investigated by designing distributed adaptive event-triggered sliding-mode controllers in this study. To lessen the number of state updates and network resource loss of networked mechanical systems, a time-varying-threshold-based adaptive event-triggered mechanism is constructed. An adaptive sliding-mode estimator is established to estimate the inaccurate states. Then, integrated with the aforementioned event-triggered strategy and adaptive sliding-mode estimator, discontinuous and continuous distributed adaptive event-triggered sliding-mode control laws without requiring each follower to get the upper bounds of the leaders’ states derivatives are, respectively, devised to compensate for the influences of nonlinearities, disturbances, and parameter uncertainties. To further attenuate the negative effects of unknown disturbances, inherent nonlinearities, and chattering, a distributed adaptive event-triggered sliding-mode control protocol with boundary layer function is designed. Eventually, the Lyapunov stability theory is utilized to testify that the adaptive error and containment error are uniformly ultimately bounded. A practical example is furnished to verify the validity of the present sliding-mode containment control strategies.Note to Practitioners—This work aims to develop the distributed containment control approach for multiple networked nonlinear mechanical systems, which is of great significance in the fields of deep space exploration, environment monitoring and joint rescue. We put forward an event-based adaptive estimation and containment control framework for multiple networked nonlinear mechanical systems, which solves practical problems such as transmission frequency, limited computation capability, and network resources. An adaptive sliding-mode estimator and adaptive event-triggered mechanism are, respectively, devised to estimate the inaccurate states and reduce data transmission frequency. Despite the effects of communication interruption and unknown disturbances, an event-triggered adaptive control protocol based on sliding-mode estimator is designed, which is applied to a planar manipulator with two degree-of-freedom.

The event-triggered almost global attitude consensus problem is considered in this article for multiple rigid body systems with prescribed performance. Two kinds of attitude consensus protocols using axis-angle vectors are proposed at the kinematic level with different prescribed performance constraints. The first protocol aims to achieve the event-triggered attitude consensus almost globally under jointly connected graphs. Based on a prescribed performance function with local states, the configuration space of parameterized attitude representations is shown to be positively invariant which almost globally covers . The second protocol is designed to reach attitude consensus with the prescribed transient behavior guaranteed in the event-triggered setting. By defining a prescribed performance function using the metric on axis-angle spaces, a dynamic event-triggered framework is designed to ensure both the attitude geometric topology constraint and prescribed convergence performance. Finally, numerical results are given to show the validness of the two control protocols.

The prescribed-time semi-global control for a class of time-varying uncertain systems under a nonlinear growth condition is achieved via linear time-varying feedback. The involved nonlinear uncertainties are categorized as unmatched uncertainties (depending on states and time) and matched uncertainties (depending on time only). Both state feedback and observer-based output feedback are constructed relying on the properties of parametric Lyapunov equations and the time-varying gains acquired by solving scalar differential equations. The proposed output feedback approach features a separation principle, that is, the construction of prescribed-time observer and prescribed-time state feedback is conducted separately. The proposed control scheme is validated by simulations carried out on a standard mechatronics system with complicated loads.

This article focuses on the issue of the quantization-based event-triggered integral sliding-mode controller design for networked multiagent systems (MASs) encountering interferences under limited network bandwidth. An integral sliding manifold (ISM) is designed to address the effect of disturbances and ensure the desired dynamic performance of the system. We establish an event-triggered mechanism (ETM) with an exponential decay rate to conserve the limited communication resources. Then, a uniform quantizer is added to quantify the triggered state signals to lessen the network transmission burden caused by the digital network. Combining the designed ETM with a static uniform quantizer, the quantized trigger state signals are sent to decoders through the digital network to construct a quantized ISM. Subsequently, an event-triggered integral sliding-mode controller under quantization technology is developed to ensure the asymptotic average consensus of networked MASs. By testifying that every network agent has a lower positive bound, the viability of the proposed ETM is demonstrated, thereby ensuring the absence of Zeno behavior. Eventually, two simulation examples are proffered to confirm the efficacy of the quantization feedback-based event-triggered sliding-mode control methodology.

Periodical time-delay scenario is often encountered in industrial manufacturing processes. However, the presence of time delays and periodical coefficients brings challenges to controller design and system analysis, which thereby hinders the performance improvement of such systems. In this work, the dynamics of milling systems are transformed into a time-invariant finite-dimensional uncertain model described by Fourier series and Padé approximation. An adaptive active control law is accordingly designed to stabilize such complex dynamics. With the assistance of LaSalle–Yoshizawa theorem, conditions are derived to guarantee sufficiently large stability regions of the corresponding closed-loop system. A numerical case study is conducted on a standard two degrees of freedom milling perturbation system to substantiate the superiority of the proposed adaptive active control technique in terms of enlarged stable operational regions.

In this article, the deception attack detection problem is studied in scenarios involving multisecurity level transmission channels. Powerful attackers can construct stealthy deception attacks by exploiting data from reliable and unreliable channels. From the perspective of data correlation, we develop three detection schemes with different resource consumption. First, a fully security channel is utilized to establish innovation-based time-varying data correlation, which triggers residual covariance variation under attacks. Second, a noise-encryption mechanism is introduced without requiring the fully security channel. For the initial two methods, we propose a targeted optimization method to improve the detection performance by exploiting the quantified residual covariance variation. Third, we propose a time-shift coding method from the perspective of dynamic system stability, which is rigorously proved to be sensitive to attack behavior. For these proposed methods, we quantify the residual covariance variation induced by attacks and achieve detection by the$\chi ^{2}$test and generalized likelihood ratio test. Finally, the efficiency and reliability of these detection schemes are validated by examples.

In complex traffic environments, the driving performance of the leader vehicle in a platoon can be greatly impacted by sudden and unexpected changes in vehicle acceleration rates. This phenomenon is known as unknown jerk dynamics (JDs), and it can lead to more extreme car-following behaviors (CFBs) in platoon tracking control, which may raise safety and traffic capacity issues. To tackle these concerns, this work studies cooperative platoon tracking control and intermittent optimization problems for connected autonomous vehicles (CAVs) with unknown reaction time delays (RTDs) using a nonlinear car following model (NCFM). In a free-design but directed communication network, we assume that the leader CAV’s external inputs have unknown but bounded parameters both for the JDs and RTDs, while only a small number of nearby follower CAVs are aware of the leader CAV’s acceleration signals. To solve these issues, we consider that each follower CAV implements a distributed observer law, which provides a reference signal stated as an estimated JD of the leader CAV. Then, a distributed platoon tracking control protocol is proposed to construct cooperative tracking controllers with identical inter-vehicle constraints (ICs). This maintains the desired safety distance between the CAVs and allows each follower CAV to track its leader CAV only through local information exchange. In addition, we present a robust intermittent optimization design and a novel intermittent sampling condition that can guarantee optimally scheduled feedback gains for the cooperative platoon tracking controllers to minimize the control cost in the presence of unknown JDs and RTDs under non-identical ICs. Simulation case studies are conducted to demonstrate the effectiveness of the proposed approaches. We also demonstrate the efficient development of such a distributed cooperative car-following model for the platoon’s motion (or as an intelligent speed advising system for automated or human-driven vehicles), resulting in a trip that is safe, comfortable, and energy efficient.

Privacy of user is becoming increasingly significant in constructing efficient multiagent energy management systems for multimicrogrid (MMG). As an emerging privacy-protection method, federated learning (FL) has been used to prevent data breaches in the MMG-related field. However, with the ever-growing participants, the underlying communication burden existing in FL is evident. Besides, since the neural network layers collectively determine an agent's performance, the possible difference in layer convergence speeds would cause the inconsistency problem, that is, the FL may degrade the convergence rate of those fast-convergent layers, which weakens the overall performance of the agent. To address these issues, a communication-efficient FL (CEFL) algorithm is proposed in this study. Considering the cooperative relationship among layers, a layer evaluation (LE) mechanism is developed in CEFL to evaluate layer contribution through the Shapley value (SV), a profit distribution approach for coalitions. In this way, only partial layers with the highest contributions are selected to be uploaded to the server. In addition, instead of average parameters aggregation, a communication-efficient parameter aggregation method is proposed in CEFL to update the parameters of the global model (GM), in which an aggregation model (AM) is developed to receive parameters for aggregation. The performance of the proposed CEFL is verified by the numerical analysis of MMGs with 3-8 MGs participating. Furthermore, experiments investigate the influence of the hyperparameter in the CEFL and also demonstrate performance improvements, compared with the other four state-of-the-art algorithms.

This article proposes a distributed passivity-based bilateral teleoperation control for optimizing the velocity/force manipulability of the coordinated remote redundant manipulators during the task execution. Following the leader–follower paradigm, the control connects a local haptic device with a leader remote manipulator and coordinates all the leader and follower remote manipulators. The approach is novel in reconciling the potential conflicts between the pose synchronization task and the manipulability optimization task for the remote manipulators by two-layer auxiliary systems. The first layer decouples the pose synchronization constraints into separable position and orientation constraints, and the second layer optimizes the manipulability under the position and orientation constraints. The approach is robust by designing smooth controls for the manipulators without knowing their dynamic parameters. Finally, the control renders the bilateral teleoperator output strictly passive for stable physical interactions with the human user and the environment. Comparative experiments verify the effectiveness of the proposed control in the presence of time-varying communication delays.

For some natural networks, edge dynamics are a scientific representation, and physical quantities can be better characterized by edges than nodes, such as transportation and social networks. Topology is a paramount determinant for characterizing system performance. To bridge the gaps between the topology structure and stabilizability, we propose a technique to achieve the desired independent strongly connected component (iSCC) partition by adding edges to change the topology structure. Besides, based on iSCC partition as the central tool for grasping the stabilizability of node and edge dynamics, it has been proven that the stabilizability realization of first-order multiagent systems directly depends on the topology structure. Furthermore, the relationship between node stabilizability and edge stabilizability is explored from a graph theory perspective through the transformation mechanism from a node digraph to an edge digraph. In particular, the stabilizability results of second-order multiagent systems reveal that the stabilizability of node dynamics and edge dynamics depends not only on the topology, but also on the feedback coefficientsk1,k2. Ultimately, simulation experiments are provided to verify the correctness and effectiveness of the proposed control protocol.

This article studies nonconservative stability conditions of continuous-time switched linear systems under mode-dependent dwell time (MDT). To establish a unified analysis approach for switched systems with stable and/or unstable subsystems, a concept called “dictionary” is introduced to characterize admissible MDT switching sequences. Subsequently, two equivalent nonconservative conditions of the global uniform asymptotic stability (GUAS) are obtained based on quadratic Lyapunov functions (LFs). Moreover, the stability results are transformed into convex conditions for facilitating the controller design. In addition, the developed stability results are applied toL2-gain analysis andH∞controller design for the continuous-time switched linear system subject to external disturbances. Simulations are provided to validate the effectiveness and the superiority over existing results.

Microwave cavity filters are essential electromechanical coupling devices in communication systems. Structural-parameter tuning by experienced operators improves the filter performance but is demanding and time-consuming. The automatic tuning method has received extensive research attentions using data-driven modeling approaches. However, two main issues affect the accuracy and efficiency of the model construction: 1) features of tuning processes, as model inputs, have limited adaptability and extraction accuracy to different resonant states and 2) models require plentiful training data and the training process is time-consuming. Thus, dynamic hybrid models are developed in this study with self-selected inputs, self-organized samples, and a self-learning structure. First, spatial features are extracted to flexibly depict the tuning characteristic, and double-domain (spatial or circuital) features are selected adaptively to accommodate distinct resonance states. Second, a trustworthiness-curiosity-driven active sampling method is exploited to attain fewer and better-training data. Third, an improved glsms broad learning system acrlong BLS is developed using new modules of incremental node calculation and weight pruning, characterized by more lightweight and flexible structures. The proposed method is effective and flexible demonstrated by simulations and experiments, and the tuning task of microwave cavity filters is fulfilled in a more accurate and efficient manner.

This article investigates the cooperative rendezvous control problem for perturbed heterogeneous marine systems composed of an autonomous underwater vehicle (AUV) and an autonomous surface vehicle (ASV). A novel Lyapunov-based model predictive control (LMPC) framework is presented to accomplish safe and precise rendezvous under input limitations and external disturbances. First, by incorporating the prescribed performance control (PPC) technique into the LMPC framework, we transform the original ascending state of the AUV into a self-constrained state, which serves as the decision variable of the model predictive control (MPC) optimization problem. Then, PPC-aided auxiliary control laws based on disturbance observers (DOBs) are designed to establish a robust contractive constraint to provide stability margins. Combining the LMPC with the PPC technique makes the original state-constrained problem an equivalent state-constraint-free problem. By addressing the MPC problem for the equivalent unconstrained system, the proposed method preserves the rendezvous safety. With the robust contractive constraint, the proposed safety-preserving LMPC (SP-LMPC) controller can inherit robustness and stability from the robust auxiliary control laws. Furthermore, theoretical analyses are conducted to assess recursive feasibility and closed-loop stability. With comprehensive theoretical support, the proposed method provides a new framework to simultaneously address state constraints and disturbances for highly nonlinear marine systems. Finally, simulations and comparisons are conducted to demonstrate the effectiveness and advantages of the proposed algorithm.

Safety as a fundamental requirement for human-swarm interaction has attracted a lot of attention in recent years. Most existing approaches solve a constrained optimization problem at each time step, which has a high real-time requirement. To deal with this challenge, this article formulates the safe human-swarm interaction problem as a Stackerberg-Nash game, in which the optimization is performed over the entire time domain. The leader robot is supposed to be in a dominant position, interacting directly with the human operator to realize trajectory tracking and responsible for guiding the swarm to avoid obstacles. The follower robots always take their best responses to leader's behavior with the purpose of achieving the desired formation. Following the bottom-up principle, we first design the best-response controllers, that is, Nash equilibrium strategies, for the followers. Then, a Lyapunov-like control barrier function-based safety controller and a learning-based formation tracking controller for the leader are designed to realize safe and robust cooperation. We show that the designed controllers can make the robotic swarms move in a desired geometric formation following the human command and modify their motion trajectories autonomously when the human command is unsafe. The effectiveness of the proposed approach is verified through simulation and experiments. The experiment results further show that safety can still be guaranteed even when there exists a dynamic obstacle.

This article focuses on distributed nonconvex optimization by exchanging information between agents to minimize the average of local nonconvex cost functions. The communication channel between agents is normally constrained by limited bandwidth, and the gradient information is typically unavailable. To overcome these limitations, we propose a quantized distributed zeroth-order algorithm, which integrates the deterministic gradient estimator, the standard uniform quantizer, and the distributed gradient tracking algorithm. We establish linear convergence to a global optimal point for the proposed algorithm by assuming Polyak-Łojasiewicz condition for the global cost function and smoothness condition for the local cost functions. Moreover, the proposed algorithm maintains linear convergence at low-data rates with a proper selection of algorithm parameters. Numerical simulations validate the theoretical results.

Addressing external disturbances has been a critical issue for control design to ensure reliable operation of systems. This article investigates the tracking control problem for the uncertain nonlinear systems with the strong external disturbance and the prescribed performance. The flexible performance-based control scheme is developed by introducing an external disturbance criterion into the prescribed performance. It is capable of guaranteeing the prescribed performance if the external disturbance is less than a specified threshold and degrading that in light of the user-appointed rule otherwise. Particularly, the disturbance interval observer is synthesized to generate the boundaries of the external disturbances and realize the judgment of that criterion. With the generated boundaries, the interval-type auxiliary system is designed to provide the modified performance functions (MPFs) that characterize performance requirement and degradation rule simultaneously. Based on the positive system theory and the Lyapunov method, it is theoretically shown that the system output can always track the reference signal and satisfy the constraints of MPFs. Finally, both the numerical simulation and the application of flight control design verify that the results are effective and valid.

This article aims to develop a robust model predictive control (MPC) scheme for the trajectory tracking control of autonomous underwater vehicles (AUVs) subject to bounded disturbances. Based on the error dynamics model derived from the AUV dynamics and the desired trajectory, an improved tube-based MPC scheme is then developed. The tube-based MPC solves two optimal control problems, the first solves a standard problem for the nominal system which defines a reference state trajectory, and the other attempts to steer the state of the disturbed system to stay in a tube centered around the reference state trajectory thereby enabling robust control of the AUV systems. For tube-based nonlinear MPC, finding a local linear feedback to characterize the tube is challenging. To address it, we replace the local linear feedback controller with an ancillary one that incorporates the tightening constraints to ensure the disturbed system state stays in the online optimized tube. The simulation results demonstrate the effectiveness of the proposed method.

This article studies the resilient control against mixed denial of service (DoS) attacks for cyber-physical systems (CPSs). Different from existing results, this work considers the presence of both zero- and hold-input attacks, where a unified model is introduced to describe mixed DoS attacks. Upon this model, the original CPS is reformulated as a switched system subject to a time-varying delay. To characterize the occurrence frequency and duration of zero- and hold-input attacks, the type-dependent average dwell time (ADT) switching is adopted. In the meantime, multiple discontinuous Lyapunov functions (MDLFs) suitable to the type-dependent ADT switching are employed. By virtue of the switching scheme and MDLFs, a piecewise feedback controller is designed to guarantee global uniform exponential stability and$H_\infty$performance of the closed-loop system. In addition, the developed control law is extended to an observer-based version, accommodating the case when the system state is not fully measurable. Finally, the effectiveness of our theoretical results is verified by two numerical examples.

The utilization of large-scale distributed renewable energy (RE) promotes the development of the multimicrogrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy sufficiency. The multiagent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this article tackles this practical yet challenging issue by proposing a federated MADRL (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm, thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data are not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge National Laboratory distributed energy control communication laboratory MG (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL.

In nature, when encountering unexpected uncertainty, animals tend to react quickly to ensure safety as the top priority, and gradually adapt to it based on recent valuable experience. We present a framework, namely EVOLutionarymodel-baseduncertainty obserVER (EVOLVER), to mimic the bio-behavior for robotics to achieve rapid transient reaction ability and high-precision steady-state performance simultaneously. In particular, the Koopman operator is leveraged to explore the latent structure of internal and external disturbances, which is subsequently utilized in anevolutionarymodel-based disturbance observer to estimate the eventual disturbance. The resulting observer can guarantee a provable convergence in optimal conditions. Several practical considerations, including construction of a training dataset, data noise handling, and lifting functions selection, are elaborated in pursuit of the theoretical optimality in real applications. The lightweight feature of our framework enables online computation, even on a microprocessor (STM32F7 with 100 Hz control frequency). The framework is thoroughly evaluated by one simulation and three experiments. The experimental scenarios include: 1) Trajectory prediction of an irregular free-flying object subject to aerodynamic drag, 2) indoor and outdoor agile flights of a quadrotor subject to wind gust, and 3) high-precision end-effector control of a manipulator subject to base moving disturbance. Comparison results show that the performance of our proposed EVOLVER is superior to several state-of-the-art model-based and learning-based schemes.

This article focuses on the performance analysis of distributed controllers for general linear multiagent systems in the sense of convergence time and energy consumption. First, the specified-time optimal controller is obtained using the linear operator theory-based method, and then the optimal topology is deduced. Second, to analyze the impact of communication topology on energy consumption, two distributed, suboptimal specified-time controllers are developed for undirected and directed graphs, respectively. By utilizing the inverse optimality method and Lyapunov function scaling, the performance in terms of the bounds of the gaps between the energy consumption of the suboptimal and optimal control laws is derived, which evaluates the effectiveness of the suboptimal controllers. Finally, as the simulation results show, the performance can specify appropriate settling times for applications with different energy budgets and facilitate optimizing the communication topology to reduce the energy gap.

This paper proposes a robust output feedback model predictive control (MPC) framework for constrained linear networked control systems (NCSs) subject to random packet dropouts and external disturbances. The proposed output feedback MPC scheme consists of a state observer that accommodates the random measurement loss and a state feedback model predictive controller that stabilizes the perturbed system. According to the proposed observer, the estimation error dynamics can be represented by a switched system. By developing a generalized robust positive invariant (GRPI) set under the switched system formulation, the estimation error can be confined in this invariant set, which serves as the explicit error bound of state estimation. Then, the GRPI set is utilized to tighten the state and input constraints in the MPC optimization problem to alleviate the effects of random packet dropouts and disturbances. As a result, the system can be stabilized by the proposed output feedback model predictive controller while both state and input constraints are fulfilled. Simulation results are provided to validate the effectiveness of the proposed method.

This paper proposes a novel quantized control strategy for network-based linear systems subject to multi-input-multi-output (MIMO) quantization. A logarithmic quantization scheme is adopted for characterizing the quantization effect on system dynamics. A sufficient and necessary condition on the asymptotic stability is established for quantized MIMO systems. To improve the numerical testability of the obtained results, a polytopic approach approximating the MIMO quantization uncertainties is developed. By constructing a novel Lyapunov function that has dependence on the MIMO quantization uncertainties, asymptotic stability criteria are established for closed-loop quantized MIMO systems. The conditions on the existence of state-feedback controllers that guarantee the closed-loop stability are derived based on the proposed technique that decouples the controller gains and the parameters of MIMO quantization uncertainties. The proposed method and the associated theoretical results are extended to the disturbance attenuation case. Finally, the theoretical results are applied to a benchmark example and a converter circuit to illustrate their efficacy and superiority.

The adaptive tracking control problem of leader-following nonlinear multiagent systems (MASs) subject to unknown perturbations and limited network bandwidth is investigated by the robust adaptive event-triggered sliding-mode control method. A distributed integral sliding mode is established to realize the finite-time reachability of the states of the leader-following nonlinear MAS. An adaptive triggering control mechanism is then put forward to dynamically adjust the triggering interval, thus reducing the actuator wear and unnecessary network resource consumption. The positions and velocities of the leader-following nonlinear MAS subject to unknown external disturbances are, respectively, driven to the equilibrium point by constructing a distributed event-based robust adaptive sliding-mode protocol. Via the Lyapunov stability theory and Barbalat lemma, sufficient conditions to ensure the adaptive tracking performance are derived for leader-following nonlinear MASs. Three simulation examples to verify the efficacy of the proposed event-based robust adaptive sliding-mode controller design are presented.

This article is concerned with a class of discrete-time hybrid fuzzy systems subject to semi-Markov switching, in which the sojourn time of each mode is with upper and lower bounds. A practical scenario oftransitional asynchronyis taken into account for the first time, where the switchings of controllers to be designed lag behind the ones of the controlled plant, and the lags depend on the transition between adjacent modes. By means of the semi-Markov kernel approach, numerically testable stability criteria are obtained, based on which existence conditions of the anticipated stabilizing controller capable of overcoming the transitional asynchrony are derived. Compared with the previous studies assuming the mode-independent or mode-dependent lags, the derived results are less conservative. Two illustrative examples including a class of bicopters are given to demonstrate the effectiveness and potential of the designed anti-transitional-asynchrony controllers.

Cyber-physical systems (CPSs) have attracted increasing attention in recent years due to their promise for substantial and long-term benefits to society, economy, environment, and citizens. In addition, the rapid advances in computing, communication, and storage technologies have resulted in a revolution in the information communication technology domain and domination in the industry context. The utilization of CPSs in industrial settings has led to industrial cyber-physical systems (ICPSs), which, in conjunction with the information-driven interactions, enables large-scale cooperation in industrial facilities and among all the stakeholders of the value chain. Hence, the research on ICPSs is essential, especially with respect to the engineering of such systems for industrial applications. This article presents an overview of recent developments in ICPSs. We first introduce the architecture of ICPSs. Then, we review the developments of ICPSs in relevant research domains. Finally, this article concludes by presenting some potential future research directions on ICPSs.

This article addresses the event-triggered coordinated control problem for multiple Euler-Lagrange systems subject to parameter uncertainties and external disturbances. Based on the event-triggered technique, a distributed coordinated control scheme is first proposed, where the neural network-based estimation method is incorporated to compensate for parameter uncertainties. Then, an input-based continuous event-triggered (CET) mechanism is developed to schedule the triggering instants, which ensures that the control command is activated only when some specific events occur. After that, by analyzing the possible finite-time escape behavior of the triggering function, the real-time data sampling and event monitoring requirement in the CET strategy is tactfully ruled out, and the CET policy is further transformed into a periodic event-triggered (PET) one. In doing so, each agent only needs to monitor the triggering function at the preset periodic sampling instants, and accordingly, frequent control updating is further relieved. Besides, a parameter selection criterion is provided to specify the relationship between the control performance and the sampling period. Finally, a numerical example of attitude synchronization for multiple satellites is performed to show the effectiveness and superiority of the proposed coordinated control scheme.

This article addresses an improved co-design method of dynamical controller and asynchronous integral-type event-triggered mechanisms (ETMs) for a class of linear systems with external disturbances and measurement noises. First, a dynamical controller is designed for a linear disturbed plant, and two independent integral-type ETMs are synthesized to be embedded in the plant output and control input channels. Then, an augmented hybrid system is constructed, in which the integral-type ETMs in the two channels are not required to be activated synchronously and both channels are affected by their measurement noises. The proposed asynchronous integral-type event-triggered control (IT-ETC) scheme for two-fold signal transmissions can not only avoid the Zeno behavior strictly, but save more communication resources than the static event-triggered control (S-ETC) strategy. Moreover, a criterion is provided to guarantee that the hybrid system is${\mathcal {L}}_{2}$stable, and an improved co-design method is further synthesized to simultaneously obtain the design parameters of ETMs and feasible solutions of the dynamical controller. As a result, a tradeoff can be achieved between the robustness of the control system and the occupancy rate of communication resources. Compared with the S-ETC strategy, the simulation results have illustrated the effectiveness and superiority of the proposed asynchronous IT-ETC scheme.

This article addresses the path-guided flocking control of unmanned surface vehicles (USVs) suffering from fully unknown kinetics. A model-free learning and anti-disturbance control method is developed to achieve path-guided flocking without using prior knowledge of model nonlinearities, ocean disturbances, or control input gains. Specifically, data-driven concurrent learning extended state observers (CLESOs) based on fuzzy systems are presented to estimate the unknown kinetics of USVs. With the proposed CLESO, a model-free path-following control law is proposed for a leader USV to follow a parameterized path. Then, model-free flocking control laws based on potential functions are proposed for follower USVs to avoid collisions and maintain network links within available communication ranges. Through cascade stability analysis, the closed-loop system is proven to be globally asymptotically stable. Simulation results substantiate the proposed CLESO-based anti-disturbance control approach for path-guided flocking of a swarm of USVs.

This article studies the distributed robust optimal control for discrete-time linear multiagent systems (MASs) with parametric uncertainties, where digraphs that only contain a directed spanning tree are allowed. Using the linear quadratic regulator approach, an optimal control protocol is presented. The presented controller is fully distributed, since the global information of graphs is unneeded for the design and implementation of the presented controller. The global performance index of MASs can be minimized by using the presented control protocol, and the optimal solution is independent with the information of parametric uncertainties. Finally, some simulated examples are provided to show the effectiveness of the proposed approaches.

This article proposes a model predictive control as a secure service (MPCaaSS) framework for cyber–physical systems (CPSs) in the presence of both cyber threats and external disturbances. First, in order to take advantage of the cloud-edge computing, we design a double-layer controller architecture by using a novel control parameterization based on Gaussian radial basis functions. In this controller architecture, the cloud-side controller optimizes the controller parameters of the edge-side controller, whereas the edge-side controller implements the real-time control law using the generated controller parameters. Second, in order to securely transmit data packets, we integrate an encoding scheme and an elliptic curve cryptography (ECC)-based encryption into the proposed MPCaaSS framework. Then, the controller parameters and the state measurements can be encrypted such that no malicious attackers can corrupt and intercept the transmission. It is shown that the recursive feasibility of MPCaaSS is achieved under some sufficient conditions, and the robust stability of the closed-loop system is guaranteed if the optimization problem is recursively feasible. Simulated examples are conducted to demonstrate the effectiveness of the proposed method.

This paper investigates the fixed-time stabilization problem for a class of nonlinear systems via event-triggered control. The event-triggered mechanism can be applied to the nonlinear system with the coexistence of low-order and high-order nonlinearities. In order to deal with these low-order and high-order terms as well as achieve the objective of fixed-time stabilization, the initial value of the system is divided into two cases, and then the event-triggered controller is designed, respectively. By switching control theory, it is proved that the nonlinear system is globally fixed-time stable under the designed controller and the Zeno behavior can be excluded. Finally, two simulations show that the proposed technology is effective.

In this article, the consensus problem of linear systems is revisited from a novel geometric perspective. The interaction network of these systems is assumed to be piecewise fixed. Moreover, it is allowed to be disconnected at any time but holds a quite mild joint connectivity property. The system matrix is marginally stable and the input matrix is not of full-row rank. By directly examining the subspace determined by the network, we first establish convergence by resorting to an observability condition. Then, according to joint connectivity, we are able to extend this convergence uniformly to the entire orthogonal complement of the consensus manifold. In this way, we work out the necessary and sufficient condition for exponential consensus. It turns out that, with a suitably designed feedback matrix, exponential consensus can be realized globally and uniformly if and only if a jointly (δ,T) -connected condition and an observability condition relying only on the system and input matrices are satisfied. We also characterize the lower bound of the convergence rate. Simple yet effective examples are presented to illustrate the findings.

This article studies a distributed model-predictive control (DMPC) strategy for a class of discrete-time linear systems subject to globally coupled constraints. To reduce the computational burden, the constraint tightening technique is adopted for enabling the early termination of the distributed optimization algorithm. Using the Lagrangian method, we convert the constrained optimization problem of the proposed DMPC to an unconstrained saddle-point seeking problem. Due to the presence of the global dual variable in the Lagrangian function, we propose a primal-dual algorithm based on the Laplacian consensus to solve such a problem in a distributed manner by introducing the local estimates of the dual variable. We theoretically show the geometric convergence of the primal-dual gradient optimization algorithm by the contraction theory in the context of discrete-time updating dynamics. The exact convergence rate is obtained, leading the stopping number of iterations to be bounded. The recursive feasibility of the proposed DMPC strategy and the stability of the closed-loop system can be established pursuant to the inexact solution. Numerical simulation demonstrates the performance of the proposed strategy.

This article studies the containment control problem for a group of linear systems, consisting of more than one leader, over switching topologies. The input matrices of these linear systems are not required to have full-row rank and the switching can be arbitrary, making the problem quite general and challenging. We propose a novel analysis framework from the viewpoint of a state transition matrix. Specifically, according to the inherent linearity, we successfully establish a connection between state transition matrices of the above multileader system and a virtual leader-following system obtained by combining those leaders. This enlightening result relates the containment problem to a consensus one. Then, by analyzing the property of the state transition matrix, we uncover that each component of any follower's state converges to the convex hull spanned by the corresponding components of the leaders', provided some mild conditions are satisfied. These conditions are derived in terms of the concept of a positive linear system. A special case of the second-order linear system is further discussed to illustrate these conditions. Moreover, two different design methods of the feedback gain matrix are provided, which additionally require that the network topology contains a united spanning tree all the time.

This article is concerned with the robust cooperative optimal control of nonlinear multiagent systems (MASs) with external disturbances and modeling uncertainties. Using the super-twisting algorithm, a continuous sliding-mode control protocol is presented for high-order nonlinear MASs with multiple inputs. The sliding-mode dynamics is modeled by the Takagi-Sugeno fuzzy approach, and the nominal control protocol that guarantees the robust optimization of the cost function is designed. Directed topologies are allowed using the presented protocol, and many assumptions about topologies are removed. Finally, three numerical examples are reported to demonstrate the effectiveness and improved performance of the presented protocol.

This article studies the formation stabilization problem of asynchronous nonlinear multiagent systems (MAS) subject to parametric uncertainties, external disturbances, and bounded time-varying communication delays. A self-triggered min–max distributed model predictive control (DMPC) approach is proposed to address this problem. At triggering instants, each agent solves a local min–max optimization problem based on local system states and predicted states of neighbors, determines its next triggering instant, and broadcasts its predicted state trajectory to the neighbors. As a result, the communication load is greatly alleviated while retaining robustness and comparable control performance compared to periodic DMPC algorithms. In order to handle time-varying delays, a novel consistency constraint is incorporated into each local optimization problem to restrict the deviation between the newest predicted states and previously broadcasted predicted states. Consequently, each agent can utilize previously predicted states of its neighbors to achieve cooperation in the presence of the asynchronous communication and time-varying delays. The proposed algorithm’s recursive feasibility and MAS’s closed-loop stability at triggering instants are proven. Finally, numerical simulations are conducted to verify the theoretical results.

A remote multirobot system (RMRS) outfitted with wireless sensors for large-scale data collection may need to be tele-driven by several human users simultaneously. The long distances between the users’ local robots and the RMRS can inject time-varying delays in their communications. This article enables such multiuser teleoperation of an RMRS through a control strategy that robustly synchronizes an RMRS with tree topology and proximity-limited one-hop communications among its robots, enables multiple users to tele-guide the RMRS and to feel the actions of the other users over time-delayed communications between the users’ local robots and the RMRS, and contains the RMRS to the stationary convex hull spanned by the local robots of all users in the steady state. A control design constrained by the connectivity of the RMRS and by the passivity of the teleoperator guarantees effective coordination, safe teleoperation, and steady-state containment. The design is a dynamic feedforward–feedback passivation strategy facilitated by a suitable decomposition of the teleoperator into interconnected subsystems. The analysis of the storage functions, and thus of the input–output relations, of all subsystems and their interconnections proves the properties of the design. Comparative experiments in a teleoperation testbed with four local and ten remote robots validate its practical efficacy.

This article deals with the event-triggered leader-following guaranteed cost consensus control problem for second-order nonlinear multiagent systems, in which the guaranteed cost function is proposed to facilitate to enhance the consensus tracking regulation performance. To reduce the frequency of information transmission, a distributed event-triggered mechanism, which broadcasts the triggered states to its neighbours for each agent, is designed, and the triggering condition is then constructed for leader-following second-order nonlinear multiagent systems. By employing Lyapunov–Krasovskii method and Barbalat’s lemma, some sufficient conditions are derived to ensure the leader-following consensus and guaranteed cost performance for second-order nonlinear multiagent systems. It is also exhibited that the constructed triggering condition can efficaciously exclude the Zeno behavior. To testify the efficacy of the proposed theoretical methodology, a simulation example is offered.

A remote team of robots may be teleoperated by multiple users to explore unstructured environments and to tackle unforeseen emergencies therein. During a large-scale environmental search, each user may visually observe a unique hazard endangering the remote robot connected to their local robot. Therefore, each user may want to tele-drive the remote robot team to a location different than the target locations of other users. This paper resolves the possible conflicts among the multiple user commands through a distributed clustering algorithm that allocates to each user a number of remote robots proportional to the urgency of their request. A pivotal design challenge in the teleoperation context is to ensure that the remote robots allocated to each user are topologically reachable from the user’s local robot within the induced communication subnetwork. The proposed design overcomes this challenge through a reachability-constrained integer linear program that modulates the interconnections of the remote robots on the fly. A comparative experiment on a platform with 2 local and 12 remote robots validates the practical efficacy of the proposed clustering algorithm.

With the rapid development of functional requirements in the emerging Industry 4.0 era, modern industrial control systems (ICSs) are no longer isolated islands, making them more vulnerable to various cyberattack threats. Cyberattacks on ICSs may have disruptive consequences, such as significant social and economic losses. To proactively address the security issue of ICSs, this article presents a unified architectural approach from the perspectives of cyberthreats on ICSs, security-related ICS technologies, and methods for ICSs. It incorporates secure networks, secure control systems, secure physical processes, and their interactions seamlessly into a unified framework. To increase the resistance of ICSs against intrusions, the network security in our architectural approach is to secure the data in motion through the integration of secure network architecture, secure industrial network protocols, and secure end-to-end communications. The protection of control systems in our architectural approach is risk-based and hierarchical and encompasses prevention- and tolerance-centric defenses. It provides a layer-by-layer defense so that an acceptable level of cybersecurity risk is achieved and maintained. Aiming to maintain the stable operation of physical ICS processes, the secure control in our architectural approach implements a security process against process-aware attacks through a resilient safety control scheme. The global and systematic architectural approach presented in this article for the ICS cybersecurity will help facilitate the design and implementation of cyberattack-resilient ICSs in the networked world. For further development of ICS security technologies, emerging challenges are identified and discussed to motivate future research efforts.

In this article, by analyzing the eigenvalues and eigenvectors of Laplacian L , we investigate the controllability of multiagent systems under equitable partitions. Two classes of nontrivial cells are defined according to the different numbers of links between them, which are completely connected nontrivial cells (CCNCs) and incompletely connected nontrivial cells. For the system with CCNCs, a necessary condition for controllability is found to be choosing leaders from each nontrivial cell, the number of which should be one less than the cardinality of the cell. It is shown that the controllability is affected by three factors: 1) the number of the links between nontrivial cells; 2) the rank of the connection matrix; and 3) the odevity of the capacity of the nontrivial cells. In the case of nontrivial cells under the equitable partition, there are automorphisms of interconnection graph G , which induce the eigenvectors of L with zero entries. For the system with automorphisms, by taking advantage of the property of eigenvectors associated with L , we propose several graphical necessary conditions for controllability. In addition, by the PBH rank criterion, the controllable subspaces of the system with different classes of nontrivial cells are compared. Finally, a necessary and sufficient condition for controllability under minimum inputs is given.

In this article, we investigate a robust distributed model predictive control (DMPC) scheme for tracking the consensus of linear multiagent systems (MASs) subject to additive disturbances and time-varying communication delays. A terminal constraint set is constructed by the Lyapunov-Razumikhin functional, and a corresponding local controller is designed for each agent. Furthermore, the sufficient conditions ensure that the terminal constraint set is provided in the form of linear matrix inequalities (LMIs). The recursive feasibility of the proposed algorithm is guaranteed based on the designed terminal constraint set, terminal cost, and local controller. Moreover, the closed-loop system is shown to be input-to-state stable (ISS). An illustrative example is given to verify the effectiveness of the presented approach.

This article studies integral-type event-triggered model predictive control (MPC) of continuous-time nonlinear systems. An integral-type event-triggered mechanism is proposed by incorporating the integral of errors between the actual and predicted state sequences, leading to reduced average sampling frequency. Besides, a new and improved robustness constraint is introduced to handle the additive disturbance, rendering the MPC problem with a potentially enlarged initial feasible region. Furthermore, the feasibility of the designed MPC and the stability of the closed-loop system are rigorously investigated. Several sufficient conditions to guarantee these properties are established, which is related to factors, such as the prediction horizon, the disturbance bound, the triggering level, and the contraction rate for the robustness constraint. The effectiveness of the proposed algorithm is illustrated by numerical examples and comparisons.

This article studies an efficient nonlinear model-predictive control (NMPC) scheme for trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV). By augmenting the desired trajectory to a reference dynamical system, we can make the tracking task fit into the standard NMPC framework. In order to alleviate the heavy computational burden caused by solving the corresponding NMPC optimization problem online, we develop an improved continuation/generalized minimal residual ( [Formula: see text]/GMRES) algorithm. Compared with the standard C/GMRES method, the inequality constraint is relaxed by imposing the penalty term on the cost function. To guarantee the closed-loop system stability, we introduce a contraction constraint. Based on the proposed numerical algorithm and the stability constraint, we develop a novel efficient-NMPC algorithm to achieve acceptable control performance with reduced computational complexity. The numerical convergence of [Formula: see text]/GMRES solutions and the closed-loop stability of efficient-NMPC are theoretically analyzed in the presence of the input constraint. Finally, the numerical simulations, software-in-the-loop (SIL) simulations, and the real-time experiment are given to demonstrate the effectiveness of the proposed [Formula: see text]/GMRES algorithm and efficient-NMPC scheme.

This paper proposes a strategy to overcome the threats posed to connectivity-preserving synchronization of Euler-Lagrange networks by time-varying delays and bounded actuation. It first introduces a suitable distributed negative gradient plus damping injection controller, based on which it establishes that the local connectivity of time-delay Euler-Lagrange networks can be preserved by appropriately regulating the interagent connections and increasing the damping injection locally. Yet, actuator saturation may impede the proper modulation of the interagent connections and the injection of sufficient damping and, thus, may threaten the maintenance of connectivity. This paper then develops an indirect coupling control framework which integrates the bounded actuation constraints into the control design. The framework endows every agent with a virtual proxy and couples initially adjacent agents through their virtual proxies. The interproxy couplings then tackle the time-varying delays while the agent-proxy couplings account for the saturation of actuators. Lyapunov-Krasovskii analysis proves that the indirect coupling strategy can drive time-delay Euler-Lagrange networks with bounded actuation to connectivity-preserving synchronization by limiting the energy of the agent-proxy and interproxy couplings according to the actuation constraints. Experiments with Geomagic Touch haptic robots validate the proposed designs compared to a conventional proportional plus damping controller.

In recent years, the investigations on cyber-physical systems (CPS) have become increasingly popular in both academia and industry. A primary obstruction against the booming deployment of CPS applications lies in how to process and manage large amounts of generated data for decision making. To tackle this predicament, researchers advocate the idea of coupling edge computing, or edge-cloud computing into the design of CPS. However, this coupling process raises a diversity of challenges to the quality-of-services (QoS) of CPS applications. In this article, we present a survey on edge computing or edge-cloud computing assisted CPS designs from the QoS optimization perspective. We first discuss critical challenges in service latency, energy consumption, security, privacy, and reliability during the integration of CPS with edge computing or edge-cloud computing. Afterwards, we give an overview on the state-of-the-art works tackling different challenges for QoS optimization, and present a systematic classification during outlining literature for highlighting their similarities and differences. We finally summarize the experiences learned from surveyed works and envision future research directions on edge computing or edge-cloud computing assisted CPS optimization.

This Special Section on ‘`Cloud-Edge Computing for Cyber-Physical Systems and Internet-of-Things’' is oriented to the dissemination of a few of those latest research and innovation results, covering many aspects of design, optimization, implementation, and evaluation of emerging cloud-edge solutions for CPS and IoT applications. The selected high-quality contributions cover a broad range of novel technologies and application scenarios in CPS and IoT. We hope that these accepted papers will produce long-lasting impacts, as well as stimulating and encouraging the international community to work on this exciting and impactful topic.

In this work, an adaptive learning model predictive control (ALMPC) scheme is proposed for the trajectory tracking of perturbed autonomous ground vehicles (AGVs) subject to input constraints. In order to estimate the unknown system parameter, we propose a set-membership-based parameter estimator based on the recursive least-squares (RLS) technique with the ensured nonincreasing estimation error. Then, the estimated system parameter is employed in MPC to improve the prediction accuracy. In the proposed ALMPC scheme, a robustness constraint is introduced into the MPC optimization to handle parametric and additive uncertainties. For the designed robustness constraint, its shape is decided off-line based on the invariant set, whereas its shrinkage rate is updated online according to the estimated upper bound of the estimation error, leading to further reduced conservatism and slightly increased computational complexity compared with the robust MPC methods. Furthermore, it is theoretically shown that the proposed ALMPC algorithm is recursively feasible under some derived conditions, and the closed-loop system is input-to-state stable (ISS). Finally, a numerical example and comparison study are conducted to illustrate the efficacy of the proposed method.

This article proposes a novelH∞state estimation scheme for a family of Takagi-Sugeno fuzzy semi-Markov jump systems with indirectly accessible mode information and nonideal data transmission. To address estimation of the indirectly accessible modes, the observed-mode sequence emitted by emission probabilities is utilized in this article. By extending the classic Lyapunov stability theory, a set of novel convex stability criteria is proposed by eliminating the nonconvex terms in stabilization conditions with the aid of certain techniques. The proposed stability criteria are utilized to ensure theH∞performance of the studied fuzzy systems. In addition, numerically checkable conditions on the existence of a fuzzy observed-mode-dependent estimator are formulated to guarantee the σ-error mean square stability of the underlying error system with a guaranteedH∞disturbance attenuation level. The developed theoretical results are illustrated by an application of a single-link robotic arm.

This article studies the formation tracking problem of a team of autonomous underwater vehicles (AUVs) with the ocean current disturbances. A distributed Lyapunov-based model predictive controller (DLMPC) is designed such that AUVs can keep the desired formation while tracking the reference trajectory, despite the presence of external disturbances. The DLMPC inherits the stability and robustness of the extended state observer (ESO)-based auxiliary control law and invokes online optimization to improve formation tracking performance of the multi-AUV system. The closed-loop stability of the multi-AUV system is guaranteed by the stability constraint that utilizes the ESO-based auxiliary controller and the associated Lyapunov function. Furthermore, the inter-AUV collision avoidance can be achieved by incorporating well-designed artificial potential fields-based cost term in the formation tracking cost function. Extensive simulations on the Saab Falcon AUVs are carried out, demonstrating the superior control performance and robustness of the proposed method.

In a distributed multi-master-multi-slave teleoperation system, the human users may compete against each other for the control of the team of slave robots. To win the competition, one operator would send the largest command to the slave group. For the sake of team cohesion, the slave group should follow the command of the winning operator and ignore the commands of the other users. To enable (i) the slave team to identify the winning operator, and (ii) each slave to determine whether to admit or discard the command it receives from its operator, this paper proposes a dynamic decision-making protocol that distinguishes the decision variable of the slave commanded by the winner from the decision variables of all other slave robots. The protocol only requires the slaves to exchange and evaluate their decision variables locally. Lyapunov stability analysis proves the theoretical convergence of the proposed decision-making algorithm. An experimental distributed winner-take-all teleoperation in a 3-masters-11-slaves teleoperation testbed validates its practical efficacy.

This paper investigates the stability analysis and fuzzy control problems for a class of discrete-time fuzzy systems with hidden semi-Markov stochastic uncertainties. The nonlinear plant is described via the Takagi-Sugeno (T-S) fuzzy model, and the parameter uncertainties are represented by a hidden semi-Markov chain. Owing to the semi-Markov kernel (SMK), the probability density functions (PDFs) of sojourn time for different modes in describing the stochastic uncertainties can address different types of distributions according to different target modes. A novel Lyapunov function that depends on the hidden mode and the observed mode with the elapsed time is proposed to analyze the stability and the H∞performance of the fuzzy system. Then, the sufficient criteria for the elapsed time-dependent and observed-mode-dependent fuzzy controller are achieved by exploiting the observations of hidden modes, ensuring that the closed-loop system is σ-error mean square stable with guaranteed H∞performance. A cart-pendulum system is used to demonstrate the effectiveness and applicability of the proposed theoretical results.

In this article, we are concerned with the synchronization problem of Kuramoto oscillators under the sampled-data updating law. This article is motivated by the needs of synchronization of Kuramoto oscillators in the presence of periodic and asynchronous coupling updates. Based on the periodical sampled-data method, a sufficient condition ensuring synchronization under periodic updates is derived. In order to relax the requirement of having all data updated simultaneously, an event-triggered law is designed to implement asynchronous coupling updates. Our synchronization analysis does not rely on any linearization technique around equilibrium points. Instead, we employ the Lyapunov stability theory and nonsmooth analysis technique to deduce the synchronization conditions and estimate the region of attraction. The effectiveness of the proposed sampled-data coupling is illustrated by numerical simulations.

In this article, we consider the distributed synchronization problem of dynamic networked systems with adaptive nonlinear couplings. Based on how the information is collected, the interactions between subsystems are characterized by nonlinear relative state couplings and nonlinear absolute state couplings. In both cases, we show that the considered nonlinear interactions can be used to simulate the couplings with disturbed relative or absolute states. In order to implement the nonlinear couplings in a fully distributed fashion, adaptive control laws are proposed for the adjustment of coupling strengths between connected subsystems. It is shown that the connected network topology is sufficient to ensure the synchronization of dynamic networked systems with the proposed adaptive nonlinear coupling methods. Different from many existing works, the σ -modification technique is used to suppress the increase of the coupling strengths with an additional benefit of preventing the coupling strengths from increasing. Simulation examples are given to assess the performance of the proposed adaptive nonlinear couplings.

The event-triggered tracking control problem of second-order multiagent systems in consideration of system nonlinearities is investigated by utilizing the distributed sliding-mode control (SMC) approach. An event-triggered strategy is proposed to decrease the controller sampling frequency and save the network communication resources; the triggering condition is then established for leader-following multiagent systems. In this article, by utilizing the distributed event-based sliding-mode controller, the system state of second-order multiagent systems with system nonlinearities is capable of approaching the integral sliding-mode surface in finite time. A novel integral sliding-mode surface is constructed in this article to guarantee the consensus tracking performance in the existence of system nonlinearities as the state trajectories of second-order integrator systems move on the constructed sliding manifold. By employing the Lyapunov approach, sufficient conditions are deduced to ensure that the consensus tracking performance is obtained for the closed-loop system. Furthermore, it is presented that the triggering scheme can effectively reduce state updates and eliminate the Zeno behavior. A simulation example is provided to testify the validity of our proposed methodology.

This article presents an efficient peer-to-peer energy-sharing framework for numerous community prosumers to reduce energy costs and to promote renewable energy utilization. Specifically, for day-ahead and real-time energy management of prosumers, an intercommunity energy-sharing strategy and an intracommunity energy-sharing strategy are proposed, respectively. In the former strategy, prosumers can share energy with any community peers, and community aggregators represent their own prosumers to coordinate energy sharing. A two-phase model is designed. In the first phase, the optimal energy-sharing profiles of prosumers are derived to minimize the global energy costs, and in the second phase, equilibrium-based energy-sharing prices are induced considering the individual interests of prosumers. In the latter strategy, prosumers share energy only with its community peers for time saving to handle real-time uncertainties collaboratively to reduce real-time costs. The framework efficiency is verified by the simulation cases on a typical distribution network.

This article considers the resilient consensus problems of discrete-time complex cyber-physical networks under F-local deception attacks. A resilient consensus algorithm, where extreme values received are removed by each node, is first introduced. By utilizing the presented algorithm, a necessary and sufficient condition to ensure resilient consensus in the absence of trusted edges is then provided by means of network robustness. We further generalize the notion of network robustness and present the necessary and sufficient condition for the achievement of resilient consensus in the presence of trusted edges. In addition, we show that through appropriately assigning the trusted edges, the resilient consensus can be reached under arbitrary communication network. Finally, the validity of the theoretical findings is demonstrated by simulation examples.

This article presents a resilient model predictive control (MPC) framework to attenuate adverse effects of denial-of-service (DoS) attacks for cyber-physical systems (CPSs), where the system dynamics is modeled by a linear time-invariant system. A DoS attacker targets at blocking the controller to actuator (C-A) communication channel by launching adversarial jamming signals. We show that, in order to guarantee exponential stability of the closed-loop system, several conditions for resilient MPC should be satisfied. And these established conditions are explicitly related to the duration of DoS attacks and MPC parameters such as the prediction horizon and the terminal constraint. Two key techniques, including the μ-step positively invariant set and the modified initial feasible set are exploited for achieving exponential stability in the presence of DoS attacks. Moreover, the maximum allowable duration of the DoS attacker is also obtained by using the μ-step positively invariant set. Finally, the effectiveness of the proposed MPC algorithm is verified by simulated studies and comparisons.

The consensusability problems of general linear multiagent systems considering directed topologies are explored from a frequency domain perspective in this paper. By investigating the properties of Laplacian spectra, a consensus criterion is established based on the stability of several complex weighted closed-loop systems. Furthermore, for single-input multiagent systems, frequency domain consensusability criteria are proposed on the basis of the stability margins, which depend on the H∞norm of the complementary sensitivity function determined by the agents' unstable poles. The corresponding design procedure is also developed. A numerical example is also presented to validate the proposed consensusability results.

Under consensus-based economic dispatch (ED) algorithm, multiple agents, which control local generation units, cooperatively minimize the total generation cost subject to the balance of the generation and expected demand in smart grids. As ubiquitous time delays on communication links exist in communication networks, studying the effect of delays on the dispatch performance is of both theoretical merit and practical value for the efficient and stable operation of smart grids. In this paper, we consider a well-developed consensus-based ED protocol under constant time delays. We find that there always exists a sufficiently small learning gain parameter under finite constant delays such that the convergence of the consensus-based algorithm is guaranteed. Further, an analytical expression of the upper bound is established for the learning gain parameter, which is determined by the largest delay, the weight matrix and the parameters of generation cost functions. In order to guarantee the optimality of the final solution, we propose the updating rule for iterations when initial states are not received by their neighbors due to time delays. The optimality of the final solution under the proposed updating rule is analyzed. We validate our theoretical results through extensive simulation studies.

During swarm teleoperation, the operator may threaten the distance-dependent inter-robot communications and, with them, the connectivity of the slave swarm. To prevent the operator from disconnecting the swarm, this paper develops a constructive strategy to dynamically modulate the interconnections of, and the local damping injections at, all slave robots. Lyapunov-based set invariance analysis shows that the strategy preserves all interaction links in the tree network while synchronizing the slave swarm. By properly limiting the impact of the user command rather than rejecting it entirely, the proposed explicit gain update law enables the operator to guide the motion of the slave swarm to the extent to which it does not endanger swarm connectivity. An experiment illustrates that the proposed strategy can maintain the tree network connectivity of a teleoperated swarm.

In this paper, we propose an event-triggered rendezvous control method for multiple two-wheeled mobile robots (2WMRs) subject to time-varying communication delays. By checking the integral-type event-triggering conditions asynchronously and periodically, each 2WMR determines whether or not to sample and broadcast its states. When the information used in an agent's controller is updated, the 2WMR calculates its x (or y ) control input, and then a Rotate&Compensate&Run Rendezvous Scheme is provided for 2WMRs to update their states. We present a sufficient condition for 2WMRs to asymptotically reach rendezvous, and the convergence analysis is conducted using the Lyapunov functional approach. Experiments are further presented to validate the effectiveness of the proposed control method.

This paper investigates the sliding-mode control (SMC) problem of Takagi-Sugeno (T-S) fuzzy multiagent systems (MASs). A cooperative fuzzy-based dynamical sliding-mode (SM) controller is designed and the overall closed-loop T-S fuzzy MAS is constructed. A new model transformation method for T-S fuzzy MASs is presented to transform the fuzzy weighting matrix into a set of fuzzy weighting scalars. By applying the method of linear matrix inequality, a general stability analysis approach for T-S fuzzy MASs is proposed. Moreover, the energy-cost constraint problem is studied by using the linear quadratic regulator method. Finally, numerical examples are provided to illustrate the effectiveness of the proposed theoretical approaches and the improved performance compared to existing results.

This paper investigates the dynamic coverage control problem for a group of agents with unknown density function. A cost function, depending on a certain metric and the density function, is defined to describe the performance of coverage network. Since the optimal deployment of agents is closely depending on the density function, we employ the Bayesian prediction approaches to estimate the density function. Moreover, a novel coverage-control-customized algorithm is proposed to acquire the Bayesian parameters. The merits of this Bayesian-based spatial estimation algorithm are the consideration of measurement noise and the capability of dealing time-varying density function. However, the estimated density function from Bayesian framework follows normal distribution, which leads the cost function to a stochastic process. To deal with this type of cost function, a discrete control scheme is proposed to steer the agents approaching to a near-optimal deployment. The mean-square stability of the proposed coverage system is further analyzed. Finally, numerical simulations are provided to verify the effectiveness of the proposed approaches.

In network systems, a group of nodes may evolve into several subgroups and coordinate with each other in the same subgroup, i.e., reach cluster synchronization, to cope with the unanticipated situations. To this end, the leader-following practical cluster synchronization problem of networks of generic linear systems is studied in this paper. An event-based control algorithm that can largely reduce the amount of communication is first proposed over directed communication topologies. In the proposed algorithm, each node decides itself when to transmit its current state to its neighbors and how to update its controller according to the estimations of the states of it and its neighbors. Then, the Lyapunov method is utilized to perform the convergence analysis. It shows that the practical cluster synchronization can be ensured by choosing appropriate parameters no matter what kind of estimation for the state is applied. Furthermore, the Zeno behavior is also excluded for each node under some mild assumptions. Besides, three kinds of common estimations for the states including zero-order hold model, first-order approximate model, and high-order model-based estimations are, respectively, analyzed from the perspective of the exclusion of Zeno behavior. Finally, the validity of the proposed algorithm is demonstrated, the effects of the concerned parameters are simply presented, and the effects of the three estimations are also compared through several simulations.

In this paper, we aim to investigate the optimal synchronization problem for a group of generic linear systems with input saturation. To seek the optimal controller, Hamilton-Jacobi-Bellman (HJB) equations involving nonquadratic input energy terms in coupled forms are established. The solutions to these coupled HJB equations are further proven to be optimal and the induced controllers constitute interactive Nash equilibrium. Due to the difficulty to analytically solve HJB equations, especially in coupled forms, and the possible lack of model information of the systems, we apply the data-based off-policy reinforcement learning algorithm to learn the optimal control policies. A byproduct of this off-policy algorithm is shown that it is insensitive to probing noise that is exerted to the system to maintain persistence of excitation condition. In order to implement this off-policy algorithm, we employ actor and critic neural networks to approximate the controllers and the cost functions. Furthermore, the estimated control policies obtained by this presented implementation are proven to converge to the optimal ones under certain conditions. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm.

We investigate scaled group consensus problems of multiagent systems with first/second-order linear continuous dynamics. For a complex network consisting of two subnetworks with different physical quantities or task distributions, it is concerned with this case that the agents' states in one subnetwork converge to a consistent value asymptotically, while the states in the other subnetwork approach another value with a ratio of the former. For the case of the information exchange being directed, novel consensus protocols are designed for both first-order and second-order dynamics to solve the scaled group consensus problems. By utilizing algebra theory, graph theory, and Lyapunov stability theory, several necessary and sufficient conditions are established to guarantee the agents' states reaching the scaled group consensus asymptotically. Finally, several simulation results are presented to demonstrate the effectiveness of the theoretical results.

This paper proposes the precise tracking control for the multiple linear switched reluctance machines (LSRMs) with network-induced time delays by using the motor positions compensation method. Considering the mutual position disturbances among the multiple motors, two network topologies are proposed for the multiple LSRMs control system. In order to improve the tracking precision, the motor position signals are used to compensate the tracking errors among the three motors. The encoders-to-controller and the controller-to-actuator time delays are modeled as a discrete-time Markov chain which denotes the whole random time delay step for the closed-loop control system. The stability conditions and the tracking controller design method for the networked LSRMs control system are proposed by using Lyapunov theory and the inequality techniques. Several groups of experimental results are presented to verify the effectiveness and practicability of the proposed method for the networked multiple LSRMs control system.

In this paper, the problem of fault tolerant tracking control for a linear time-invariant system subject to actuator faults and saturations is addressed. An auxiliary system is developed to ensure actuators behave within amplitude and rate limits under the influence of partial loss of control effectiveness. Based on the auxiliary system, a fault tolerant compensation controller is constructed to guarantee tracking errors to converge to a small region. Some relationships among tracking errors, command signals, actuator faults, amplitude and rate limits as well as controller parameters are comprehensively studied and explicitly illustrated with formulas. An example of rudder-roll damping control for a cruise keeping ship is included to illustrate the proposed procedures and their effectiveness.

This paper investigates the trajectory tracking control of the nonholonomic wheeled mobile robot (NWMR) using nonlinear model predictive control (NMPC). By defining the tracking error in the NWMR's local coordinate, the tracking problem is formulated into standard NMPC framework. However, the standard NMPC algorithm is computationally demanding so that it is difficult for the real-time implementation of the NMPC algorithm on NWMR systems. To overcome this limitation, a continuation/generalized minimal residual (c/GMRES) algorithm is adopted. To handle the actuator saturation for the NWMR systems, a modified c/GMRES method is developed based on the logarithmic barrier function so that inequality constraints in NMPC can be handled. Finally, two reference trajectories are given to evaluate the proposed algorithm. The simulation results show the efficiency and effectiveness of the proposed algorithm for the trajectory tracking problem.

This paper investigates the consensus problem for multiagent systems with time-varying delays. The bounded delays can be arbitrarily fast time-varying. The communication topology is assumed to be undirected and fixed. With general linear dynamics under average state feedback protocols, the consensus problem is then transformed into the robust control problem. Further, sufficient frequency domain criteria are established in terms of small gain theorem by analyzing the delay dependent gains for both continuous-time and discrete-time systems. The controller synthesis problems can be solved by applying the frequency domain design methods. Numerical examples are demonstrated to verify the effectiveness of the proposed approaches.

This paper investigates group synchronization for multiple interacting clusters of nonidentical systems that are linearly or nonlinearly coupled. By observing the structure of the coupling topology, a Lyapunov function-based approach is proposed to deal with the case of linear systems which are linearly coupled in the framework of directed topology. Such an analysis is then further extended to tackle the case of nonlinear systems in a similar framework. Moreover, the case of nonlinear systems which are nonlinearly coupled is also addressed, however, in the framework of undirected coupling topology. For all these cases, a consistent conclusion is made that group synchronization can be achieved if the coupling topology for each cluster satisfies certain connectivity condition and further, the intra-cluster coupling strengths are sufficiently strong. Both the lower bound for the intra-cluster coupling strength as well as the convergence rate are explicitly specified.

This paper investigates the collaborative tracking control for dual linear switched reluctance machines (LSRMs) over a communication network with random time delays. Considering the spatio-temporal constraint relationship of the dual LSRMs in complex industrial processes, the collaborative tracking control scheme is proposed based on the networked motion control method. The stability conditions and the controller design method for the networked dual LSRMs are obtained from the two motors relative position error by using Lyapunov theory and delay systems approach. Four different allocation schemes combined with two kinds of external control signals are applied onto the collaborative tracking control experiment platform of the dual LSRMs to validate the effectiveness of the proposed method. The maximum steady-state relative position error within 0.104 mm can be achieved under the constant absolute position reference input signal of 3 mm, and the maximum absolute relative position error within ±0.46 mm can be achieved under the sinusoidal reference of 8 mm amplitude and 0.2 Hz.

This paper investigates the flight control of a quadrotor subject to the model uncertainties and external disturbances. We propose a linear quadratic regulation (LQR) tracking algorithm. However, the designed LQR controller is hard to be implemented because of the existing noises in the measured states. A modified extended Kalman filter (EKF) is then designed for the online estimation of the position, velocity and motor dynamics by using the measured outputs. From the experimental testing results, it is shown that the proposed EKF-based LQR control method solves the tracking problem of the quadrotor with less tracking errors than only using the LQR method.

The articles in this special issue present the latest developments and achievements in industrial cyber-physical systems (ICPSs). The papers in this issue cover key areas on architecture, design,enabling technologies, and applications of ICPSs. Additionally, they present emerging trends and visions of ICPSs for future investigations.

Modern smart grid, as a typical cyber-physical system (CPS), allows plug-in hybrid electric vehicles (PHEVs) to be a promising candidate for grid services. In this paper, by following the CPS design approach, we propose a novel framework for the local aggregator to estimate the charging status and solve for the charging control signals for PHEVs. The physical battery charging is executed by charging stalls, where charging information is processed in the embedded system and only the generated index information is transmitted to the aggregator via Internet. An aggregation model is developed for the entire cyberspace to inherently guarantee heterogeneous charging requirements, i.e., deadlines for charging. Furthermore, we develop a nonlinear model-predictive control (NMPC) scheme for the overnight valley-filling service. Both the aggregation model and control strategy are designed based on the PHEV population migration probabilities. From the CPS perspective, both the cyber and physical loads of this novel framework are extremely low. As part of this paper, we present a case study to verify the proposed approaches.

This paper proposes a hierarchical image-based visual servoing (IBVS) strategy for dynamic positioning of a fully actuated underwater vehicle. In the kinematic loop, the desired velocity is generated by a nonlinear model predictive controller, which optimizes a cost function of the predicted image trajectories under the constraints of visibility and velocity. A velocity reference model, representing the desired closed-loop vehicle dynamics, is integrated with an IBVS kinematic model to predict the future trajectories. In the dynamic velocity tracking loop, a neural-network-based model reference adaptive controller is designed to ensure the convergence of the velocity tracking error in the presence of uncertainties associated with vehicle dynamic parameters, water velocity, and thrust forces. Comparative simulations with different control and system configurations are performed to verify the effectiveness of the proposed scheme and to illustrate the influences of the prediction horizon, cost function, closed-loop vehicle dynamics, and predictive velocity reference model on the IBVS system performance.

This paper investigates the issue on how to utilize neighbor information in the distributed receding horizon control (RHC)-based consensus problem for first-order multiagent systems. The distributed RHC-based consensus problem is first formulated in a general framework in terms of using neighbor information. Based on the framework, a sufficient condition on utilizing neighbor information to ensure consensus is developed for the finite horizon case. For the infinite horizon case, a necessary and sufficient condition is proposed, and the best way of using neighbor information to achieve fastest convergence rate is also presented. It is shown that: 1) the way of utilizing neighbor information plays an important role in reaching consensus; 2) the parameter that ensures consensus is related with the network topology; and 3) the best convergence rate in consensus can be attained if the neighbor information is appropriately utilized. Simulation studies verify the proposed theoretical results.

This paper focuses on the robust output feedback H₂/H∞ control issue for a class of discrete-time networked control systems with uncertain parameters and external disturbance. Sensor-to-controller and controller-to-actuator packet dropouts and time delays are considered simultaneously. According to the stochastic characteristic of the packet dropouts and time delays, a model based on a Markov jump system framework is proposed to randomly compensate for the adverse effect of the two-channel packet dropouts and time delays. To analyze the robust stability of the resulting closed-loop system, a Lyapunov function is proposed, based on which sufficient conditions for the existence of the H₂/H∞ controller are derived in terms of linear matrix inequalities, ensuring robust stochastic stability as well as the prescribed H₂ and H∞ performance. Finally, an angular positioning system is exploited to demonstrate the effectiveness and applicability of the proposed design strategy.

This paper focuses on the filter design for nonuniformly sampled nonlinear systems which can be approximated by Takagi-Sugeno (T-S) fuzzy systems. The sampling periods of the measurements are time varying, and the nonuniform observations of the outputs are modeled by a homogenous Markov chain. A mode-dependent estimator with a fast sampling frequency is proposed such that the estimation can track the signal to be estimated with the nonuniformly sampled outputs. The nonlinear systems are discretized with the fast sampling period. By using an augmentation technique, the corresponding stochastic estimation error system is obtained. By studying the stochastic stability and the energy-to-peak performance of the estimation error system, we derive the linear-matrix-inequality-based sufficient conditions. The parameters of the mode-dependent estimator can be calculated by using the proposed iterative algorithm. Two examples are used to demonstrate the design procedure and the efficacy of the proposed design method.

Practically, the spatial power spectral density (PSD) of single or multiple incoherently distributed (ID) sources is often unknown, and the total spatial PSD is suitable to model the spatial distribution characteristic of signals if the number of multiple ID sources is also unknown. In this study, the Gaussian mixture model (GMM) is employed to characterise the total spatial PSD of multiple ID sources, and two algorithms are proposed to estimate the parameters of the GMM. The first one is the covariance fitting method for multiple ID sources with Gaussian PSD, and the other is the iterative expectation maximisation (EM) algorithm. Simulation studies demonstrate that the EM algorithm outperforms other methods in approximating the shape of the total spatial PSD, especially for small spatial spread.

This paper investigates the step tracking control problem for discrete-time nonlinear systems in a networked environment with a limited capacity. The nonlinear system is represented by a Takagi-Sugeno (T-S) fuzzy system, and a network-induced delay is incorporated in the modeling of the connection link. In order to compensate for the network link effects and eliminate the tracking error, we employ some techniques mainly used in the predictive control and the integral control. Moreover, a quadratic cost function which includes terms related to the performance of the system and the actuating capacity is used. We assume that the lumped network-induced delay lies within a known set, and that the occurrence probability for each element in the set is known a priori. Then, the delay information will be incorporated into the delay-dependent tracking controllers. The parameters for the tracking controller are derived by solving an optimization problem. A networked inverted pendulum is used to illustrate the efficacy of the proposed design method.

In this study, the authors deal with the mixed ℋ2/ℋ∞ filtering problem for continuous-time systems. The system model is subject to parameter variation and the parameters of the model vary slowly in the polytope. The parameters of the designed filter are dependent on variation, which is measured online. A new design approach is proposed by increasing the flexible dimensions in the solution space. An illustrative example shows the new features of the proposed design approach.

The problem of equalisation for communication channels with inter symbol interference (ISI) is investigated in this study. One practical yet challenging constraint for a channel with high transmission rate is incorporated into the modelling of the equalisation system: the communication channel is subject to uncertainties, which are assumed to be within a polytope with finite vertices. By using the augmentation method, the filtering error system of the equalisation problem is also characterised as a system with polytopic uncertainties. Sufficient conditions on the stability and the ℋ∞ performance for the filtering error system are obtained. A design method for the equaliser is proposed such that the filtering error system can achieve minimal ℋ∞ performance index even with the channel uncertainties. Two illustrative design examples demonstrate the design procedure and the effectiveness of the proposed method.

In this paper, we present a new design method for the${\cal H}_{\infty }$filtering of discrete-time Takagi–Sugeno (TS) fuzzy systems. The parameters of the filter are assumed to be linearly dependent on the normalized fuzzy weighting functions. By using an augmentation technique, the design parameters are incorporated into a filtering error system. In order to derive less-conservative results and reduce the filtering error, a new condition is established to ensure the${\cal H}_{\infty }$performance of the filtering error system. By introducing more slack matrices, the solution set of the filter parameters is extended. By using a partitioning technique, a design method for the${\cal H}_{\infty }$filter is proposed in terms of linear matrix inequalities (LMIs). An example demonstrates the improvement of the proposed design method over an existing approach.

Abstract This paper considers the discrete‐time${\cal H}_2$ output tracking control of wireless networked control systems (NCSs) where the time delays are modeled as Markov chains. Output tracking control can find many applications in industry. In order to reduce the conservativeness and achieve better performance, the designed state feedback controller is dependent on available sensor‐to‐controller and controller‐to‐actuator delays. Then, the formulated closed‐loop system is a special jump linear system governed by interdependent parameters of Markov chains and the condition for stochastic stability is proposed. By generalization of the${\cal H}_2$ norm definition, new relation of the${\cal H}_2$ norm for the special system is derived in terms of state space form. The condition of a set of linear matrix inequalities (LMIs) with nonconvex constraints is given to solve the${\cal H}_2$ output tracking control problem. Simulation examples are provided to illustrate the effectiveness of the method. Copyright © 2009 John Wiley & Sons, Ltd.

Crosstalk between multiple services transmitting through the same telephone cable is the primary limitation to digital subscriber line (DSL) services. From a spectrum management point of view, it is important to have an accurate map of all the services that generate crosstalk into a given pair. This paper on crosstalk identification is motivated by an important practical consideration: the signals constituting the crosstalk are transmitted at different rates in xDSL systems. Therefore, we here propose to use the "blocking technique," we derive blocked state-space models for multirate xDSL networks, and we set up the mapping relationship between available input and output data. Further, we use the least-squares principle to identify the crosstalk functions, and study the convergence rate and upper bound of the parameter-estimation error. Finally, we illustrate and verify the theoretical findings with simulation examples