This article addresses the output feedback control problem for a specific class of discrete-time fuzzy singularly perturbed systems subjected to nonuniform sampling and a round-robin protocol. An innovative method for modeling nonuniform sampling periods through nonhomogeneous sojourn probabilities is proposed, offering a more intuitive and adaptable framework for system design and analysis. The round-robin protocol is applied to nonuniformly sampled outputs, optimizing information transmission efficiency and enhancing overall system performance. To tackle potential limitations in state data acquisition, a token-dependent static output feedback controller is developed that addresses the complexities introduced by nonperiodic sampling and asynchronous premise variables. Sufficient conditions are derived to ensure stochastic stability of the closed-loop system. Finally, two simulation examples are presented to validate and demonstrate effectiveness of the theoretical approach.

This article proposes a novel reinforcement learning (RL)-based secure control policy for nonlinear Markov jump systems (MJSs) subject to false data injection attacks (FDIAs). First, the Takagi-Sugeno (T-S) fuzzy model is applied to describe the nonlinear MJS. A min-max strategy and an off-policy homotopic Q-learning (HQ) scheme are then introduced to design a secure control policy without requiring knowledge of the system dynamics. The proposed approach offers two main advantages: it does not require an initial stabilizing control gain, and it guarantees unbiased learning under persistently excited conditions. Furthermore, a rigorous stability analysis of the overall closed-loop system under FDIAs is presented. Finally, the effectiveness of the proposed approach is demonstrated using a tunnel diode circuit.

This article studies the robust optimal formation control problem of networked fixed-wing unmanned aerial vehicles (UAVs) under communication uncertainties and external disturbances. A learning-based observer–feedforward–feedback control framework is constructed. A resilient self-triggered (ST) observer is designed to estimate reference data while enabling intermittent communication under communication uncertainties. By integrating reference estimation with a backstepping technique, the cooperative formation control problem is reformulated as a robust optimal regulation problem. The robust optimal feedforward control law is learned via an off-policy reinforcement learning (RL) algorithm that exploits the collected internal system data and external disturbance inputs. The stability of the constructed closed-loop control system is guaranteed, and Zeno behavior in the ST rule is avoided. The effectiveness of the proposed approach is demonstrated through an experimental study of multiple fixed-wing UAVs.

This article addresses discrete-time Takagi–Sugeno fuzzy switching systems utilizing a dual-channel transmission framework combined with an innovative switching rule. A novel duration-based switching mechanism is proposed, capturing realistic multiple-distribution characteristics by employing a joint distribution function involving both the current mode and its duration. Unlike traditional semi-Markov-based transition probabilities, this method emphasizes sojourn probabilities, significantly reducing the parameter estimation complexity and computational burden. The developed mechanism supports generalized duration distributions, surpassing traditional single-distribution limitations and thus lowering conservatism in the analysis. To improve the modeling accuracy and practical applicability, mode-dependent fuzzy membership functions are adopted. Observer-based feedback control strategies are implemented to effectively estimate state variables, addressing practical challenges in state measurement. In addition, the dual-channel strategy is introduced to mitigate packet loss effects and enhance data transmission reliability. The effectiveness and superiority of the proposed method are demonstrated through simulations on a single-link robotic arm.

This paper studies the fault estimation problem for polynomial fuzzy systems with unmeasurable premise variables. Considering the limitations of existing methods that require the convergence of the original system, a novel augmentedstate observer is proposed for polynomial fuzzy systems. Unlike compensation-vector-based approaches, the proposed method addresses the singularity problem and eliminates the traditional linear growth assumptions on unmeasurable premise variables, while only existence of the corresponding upper bounds rather than knowledge of their specific values is assumed. Moreover, the proposed method enables fully mismatched design, thereby enhancing both design flexibility and computational efficiency. Finally, the effectiveness of the proposed method is demonstrated through a bridge crane system as a case study.

This study presents a nonlinear feedback regulator for three-dimensional overhead cranes that exploits composite outputs to deliver potent sway suppression. Dedicated barrier functions confine these composite signals within set limits. Owing to the simple structure, the control scheme maintains oscillation suppression under velocity and cable length uncertainties. We validate stability through a Lyapunov-based proof augmented by LaSalle’s invariance principle. Simulation results confirm that the controller achieves trolley positioning and effectively cancels oscillations under external disturbances.

This study proposes a nonlinear regulation controller for 3D overhead cranes, capable of achieving payload sway suppression under complex conditions. Utilizing a special form of barrier functions, constraints on the trolley motion and payload sway are derived. By analyzing the nonlinear terms, parameter estimation is incorporated to the control law, eliminating the need for prior knowledge of crane dynamics. To ensure smooth operation under varying transportation distances, a saturation function is employed to constrain the control torque generated by regulation errors. Within the Lyapunov framework, LaSalle’s invariance principle is invoked to demonstrate asymptotic convergence of the system states. Simulations validate the theoretical claims, including robustness under different transfer scenarios.

This study focuses on the learning-based asynchronous sliding mode control for switching systems, operating under a general switching rule and partially unknown probability information. A novel switching rule is constructed, governed by a joint probability distribution dependent on the current mode and its duration time, thereby overcoming the limitations of traditional Markov/semi-Markov models in terms of the difficulty in obtaining transition probabilities and computational complexity. Departing from traditional geometric distribution assumption, the proposed method follows more general duration distribution. Acknowledging the challenge of obtaining complete probability information in practical scenarios, partially unknown probability information is considered. In addition, a learning-based asynchronous sliding mode control law is developed, aimed at guiding state signals onto preset sliding regions and effectively reducing chattering induced by mode switchings. Finally, the efficacy and superiority of the developed theories are verified through both numerical and practical examples.

In this paper, the fault estimation and fault-tolerant control problems are addressed for a class of continuous-time switched affine systems with actuator faults and bounded disturbances. Two novel observer-based approaches are developed to address the fault estimation problem for switched affine systems. The first one refers to a dynamic proportional-integral observer design method, where the presented fault estimate constitute proportional and integral terms to enhance the accuracy of the fault estimation, the common assumption that the measurement output derivative needs to be measurable is eliminated. The second one is an intermediate variable observer, which relaxes the observer matching condition. The output estimation error feedback term is added to the intermediate variable observer to improve the estimation performance. Then, by introducing a switching multi-shifted-point-dependent Lyapunov functional, both a fault-tolerant controller and a new robust output-dependent switching law are jointly designed to compensate the fault effects in the closed-loop switched affine systems and to ensure the practical exponential stability of augmented system, where the convergence region consists of multiple regions and the center point is around some shifted points. The traditional switching quadratic Lyapunov function method is generalized by the designed method. A practical study of a DC-DC boost converter and a numerical example are provided to illustrate effectiveness and validity of the developed fault-tolerant control design method. Note to Practitioners—Power electronics are very common in practical systems, which are usually modeled as a class of switched affine systems. In real applications, faults inevitably occur, which may lead to undesirable behavior and damage to the system. Therefore, how to achieve better fault-tolerant control objectives to guarantee the normal operation of the system with faults is a hot topic. It is practically important to address the fault-tolerant control problem for switched affine systems, where actuator faults and bounded disturbances exist simultaneously. In addition, on account of the existence of affine terms, the controller synthesis of switched affine systems is more complicated than switched linear systems. Based on the special structure of switched affine systems, two kinds of novel fault observers are proposed, where the dynamic proportional-integral observer is proposed to improve the estimation accuracy and speed by utilizing the current output information, and the common supposition that the output derivative needs to be measurable is eliminated. Furthermore, to avoid the limitation of observer matching conditions, an improved intermediate variable observer is designed to estimate faults. Different from the conventional method, the intermediate variable parameters can be selected separately for the corresponding fault channel of each subsystem and the error feedback term of output estimation is added to the intermediate variable observer to enhance the estimation performance. In addition, it is challenging to design a fault-tolerant controller and an output-dependent switching law to guarantee that the augmented system is practically stable and robust to bounded disturbances. The results demonstrate that the designed fault-tolerant control scheme has a definitive practical value.

In this paper, the security control problem for a class of multi-tiered re-entrant manufacturing networks (RMNs) under cyber-attacks is investigated. First, a linear hyperbolic partial differential equation (PDE) continuum model is employed to model the dynamics of each single manufacturing line node and the overall RMN system is then characterized by a dual-layer coupling structure consisting of a production line network and a workshop network, both of which adhere to the global mass conservation law. Second, to mitigate the adverse effects of malicious cyber-attacks on RMNs, a node-dependent control approach and an edge-dependent control approach are developed, both of which guarantee the global exponential stability of the multi-tiered RMNs under cyber-attacks. Numerical simulations demonstrate the effectiveness of the proposed network architecture and control schemes.

When a manipulator moves in a highly dynamic scenario with a large range of rapid motion, the coupling disturbances between the manipulator and the UAV in the aerial manipulator system (AMS) become very strong, which directly affects the ability of the AMS to perform aerial manipulation and even poses a threat to the safety of the system. The aim of this article is to address the strong coupling disturbance problem in the AMS through precise coupling disturbance modeling and compensation. First, considering the rapid changes in the center of mass (CoM) and the moment of inertia (MoI) of the system under a highly dynamic scenario, this article delves into the generation mechanism of the coupling disturbances and models them based on the variable inertia parameters. The proposed precise coupling disturbance model (CDM) makes good use of the state information of the system, which enables one to achieve accurate estimation of the coupling disturbances without the aid of external force and torque sensors. With the proposed model, the strong coupling disturbances in the AMS are compensated in a feedforward way during the controller design process. An indoor AMS experimental platform is developed for validation purposes. The experiments and simulation are conducted in a highly dynamic scenario, involving rapid movements of the manipulator across a large range. The experimental and simulation results demonstrate the effectiveness and advantages of the proposed method for suppressing the strong coupling disturbances.

This article studies the input-to-state stability (ISS) problem of networked control systems (NCSs) subject to both Denial-of-Service (DoS) and unknown deception attacks (DAs). A neural network (NN)-based resilient event-triggered control protocol (RETCP) is first presented to mitigate resource constraints and the adverse effects of cyber attacks, where the NN technology is leveraged to neutralize and approximate the malicious data injected by unknown DAs. Then, we develop a new predictor to compensate for lost signals of NCSs during the DoS threats, so that the NCSs can further tolerate more unfavorable DoS and unknown DAs. It is shown that the resulting NCSs with the designed novel NN-based controller can achieve ISS under the complex attacks. Finally, experimental evaluations are conducted for an uncrewed ground vehicle (UGV) to verify efficacy of the proposed intelligent control protocols.

In this article, we focus on solving the problem of online multiplayer differential games (MDGs) of Markov jump systems (MJSs) using a reinforcement learning (RL) method. We consider MDGs of MJSs from the following two scenarios. In the first scenario, we propose a distributed minmax strategy, where each player can derive their optimal control policy from distributed game algebraic Riccati equations (DGAREs) without prior knowledge of the policies adopted by other players, distinguishing it from existing RL algorithms. We design a novel online distributed RL algorithm to approximate the solution of DGAREs without completely knowing system dynamics and initial admissible control policy. The second scenario involves applying Nash strategy to address MDGs of MJSs. Different from existing synchronous RL algorithm, we propose a novel online asynchronous RL algorithm that employs asynchronous iterative calculations for both policy evaluation and policy improvement, incorporating the latest information into the iterative process. The convergence of the designed RL algorithms is rigorously analyzed. Finally, two inverted pendulum system applications validate the effectiveness of the proposed methods.

This article focuses on the fault reconstruction and bumpless transfer fault-tolerant (FT) control problems for switched linear systems with magnitude-bounded disturbances and actuator faults in continuous-time domain. A new learning-based robust unknown input observer (UIO), not requiring fault differentiability and completely decoupled disturbances, is developed to accomplish fault reconstruction and state estimation. The fault reconstruction value is updated by one iteration learning on the timeline, i.e., the fault at the current moment is reconstructed by learning historical information from the previous moment. Based on the obtained estimation information, an efficient bumpless transfer FT controller is designed to counteract the fault effects and suppress the control bumps. The bumpless transfer constraint is guaranteed via a new inequality transformation method, which improves the anti-disturbance capability of the controller and also decreases the switching bumps. The solvability conditions for the bumpless transfer controller and learning-based UIO are developed under the condition of average dwell time switching. Finally, an application of the inverted pendulum controlled by a direct current motor is presented to reveal the effectiveness and applicability of the developed methods.

The fuzzy adaptive state and output quantization bilateral cooperative control problem for nonlinear multiagent systems (NMASs) is studied. Since the considered system is nonlinear, fuzzy logic system (FLS) is applied to approximate the unknown nonlinear function, and a fuzzy state observer is constructed because the state cannot be measured. A second-order command filter is used to solve the complex problem of calculating the time derivative of the virtual control function, and a uniform quantizer is used for fuzzy adaptive inversion design in the process of controller design. Ultimately, the effectiveness of the proposed control method is verified by a series of simulation experiments and research results.

The study focuses on the problem of switching event-triggered protocol control for fuzzy singularly perturbed systems under random sampling. The non-uniform sampling of the model is characterized by introducing a random variable obeying the Markov process. Nextly, to reduce the network transmission burden, a novel switching event-triggered protocol is proposed, which can dynamically adjust the triggering parameters based on the time interval between the current sampling instant and the previous sampling instant. Meanwhile, a switching fuzzy event triggered controller is devised, by jointly triggering state information. Additionally, a set of sufficient conditions is derived to ensure the finite-time stability of the closed-loop system. The effectiveness and advantages of the proposed methodology are validated through both a numerical simulation and a practical example, demonstrating its feasibility and superiority.

This study focuses on TSSDC for Takagi-Sugeno (T-S) fuzzy reaction-diffusion neural networks (NNs) under random sampling and network-induced delays. Fuzzy reaction-diffusion NNs extend traditional NNs by incorporating spatial dynamics through reaction-diffusion processes, offering improved modeling capabilities for complex systems. However, the combination of reaction-diffusion terms and fuzzy modeling increases the complexity of dynamic analysis, particularly in synchronization control. To address these challenges, a novel random TSSDC (RTSSDC) framework is developed. The proposed approach integrates random sampling across both temporal and spatial dimensions with an innovative random event-triggered communication scheme to increase resource utilization efficiency and accommodate real-world network conditions. A unified closed-loop model is established by introducing a packet loss scheduling strategy to handle data disorder caused by significant transmission delays. The framework incorporates switching gains to provide additional flexibility and robustness. Ultimately, numerical simulations are conducted to validate the superior synchronization performance and efficient resource utilization under random sampling and network-induced delays.

In this article, robustH∞synchronization problem for a class of networked re-entrant manufacturing systems (RMSs) is investigated by utilizing state feedback control and distributed adaptive state feedback control approaches. Different from the isolated single re-entrant manufacturing line, a networked RMS with three-tiered architecture is presented, which contains the production line, the production workshop and the workshop network. Based on the mass conservation law, the dynamics of the production line and the production workshop are established by a first-order linear hyperbolic PDE and a first-order semi-linear hyperbolic PDE, respectively. On one hand, in view of communication delays and external disturbances that might exist in the system, a delayed state feedback controller is constructed to address robustH∞synchronization of the networked RMSs. On the other hand, considering the uncertainty with coupling gain and the unavailability of global information, a distributed cooperative controller with the edge-dependent adaptive gain is further developed to ensure robustH∞synchronization of the networked RMSs. Numerical simulations validate the effectiveness of both proposed control schemes.

This article focuses on the co-design of fault detection (FD) and time-varying formation control for a nonlinear multiagent system (MAS) over a signed switching digraph. The interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy model is utilized to represent the nonlinearities and parameter uncertainties, while a Markov process describes a signed digraph indicating possible environmental changes. To further handle the co-design problem over a signed digraph, the equivalence between FD with time-varying formation control and FD with a bipartite time-varying formation protocol is first established. Then, the sufficient conditions of stochastic stability are derived based on a mode-dependent Lyapunov function. It can be proven that the formation error is uniformly ultimately bounded, and the FD performance complies with a dissipative index. Finally, simulations of two-link robotic arm systems are performed to validate the effectiveness and feasibility of the proposed approach.

This paper studies an underwater inhalation-type cleaning robot designed to remove floating garbage. The dynamic model of the underwater cleaning robot is established to show the relationship between the speed of the propeller and the motion state, where the added mass and the viscous resistance coefficient during translational motion are obtained by the empirical formulas, and the added moment of inertia and the resistance moment coefficient are determined using Fluent software. Then a cascade PID motion control strategy with a position outer loop and a speed inner loop is proposed for this inhalation-type garbage cleaning robot to improve control performance. Finally, the effectiveness of the obtained results is demonstrated by simulations in MATLAB/Simulink.

This paper proposes a nonlinear feedback control for overhead cranes that offer satisfactory performance by taking advantages of only a composite output. Particularly, the construction of a barrier function keeps the composite output between predefined boundary values, which can enhance the safety of the system. Nonetheless, the controller with simple structure ensures the stabilization of the payload despite the presence of parametric uncertainties. To substantiate the stability proof, two analytical methodologies are employed: the Lyapunov technique and LaSalle’s invariance principle. The simulation evidences efficient positioning and oscillation elimination of the controller for various uncertain parameters, large initial errors and external disturbances, without tuning the gains of each term.

This paper investigates the robust intelligent control problem of multi-line re-entrant manufacturing plants. The control system is designed with a hierarchical architecture, where a nonlinear stochastic hyperbolic partial differential equation (PDE) is used to describe the system dynamics and a robust controller is designed to exponentially drive the manufacturing plants to a desired operation mode with steady feeding and production rates. The developed robust control scheme is shown to be practically implementable through convex optimization techniques. Numerical experiments are presented to demonstrate the feasibility and advantages of the proposed approach.Note to Practitioners—The motivation of this work originates from the need to develop an intelligent robust control strategy for a class of practical complex re-entrant manufacturing plants, for instance, the semiconductor wafer factory and the chemical production lines with numerous process procedures. Discrete-model-based algorithms have been extensively employed in this field due to their excellent convenience and great accuracy. However, when dealing with coupled multi-line re-entrant manufacturing plants with nonlinearities, traditional discrete-model-based methods lack rigorous theoretical analysis and, more importantly, suffer from the curse of dimensionality in many cases. To equip the re-entrant manufacturing plant with a desired operation mode that enjoys significant robustness against stochastic noises, we propose a continuum-model-based intelligent robust control strategy. The proposed method is practically useful in the sense that it can be conveniently applied to various industrial scenarios with re-entrant characteristics and the control design problem can be well solved via available convex optimization algorithms.

This article delves into the distributed resilient output containment control of heterogeneous multiagent systems against composite attacks, including Denial-of-Service (DoS) attacks, false-data injection (FDI) attacks, camouflage attacks, and actuation attacks. Inspired by digital twin technology, a twin layer (TL) with higher security and privacy is employed to decouple the above problem into two tasks: 1) defense protocols against DoS attacks on TL and 2) defense protocols against actuation attacks on the cyber-physical layer (CPL). Initially, considering modeling errors of leader dynamics, distributed observers are introduced to reconstruct the leader dynamics for each follower on TL under DoS attacks. Subsequently, distributed estimators are utilized to estimate follower states based on the reconstructed leader dynamics on the TL. Then, decentralized solvers are designed to calculate the output regulator equations on CPL by using the reconstructed leader dynamics. Simultaneously, decentralized adaptive attack-resilient control schemes are proposed to resist unbounded actuation attacks on the CPL. Furthermore, the aforementioned control protocols are applied to demonstrate that the followers can achieve uniformly ultimately bounded (UUB) convergence, with the upper bound of the UUB convergence being explicitly determined. Finally, we present a simulation example and an experiment to show the effectiveness of the proposed control scheme.

This article is concerned with the switched control of hybrid terrestrial and aerial quadrotors (HyTAQs) via stochastic hybrid fuzzy system methodology, in which the terrestrial and aerial mode switching is subject to a Markov process with lower-bounded sojourn time. For the first time, the bimodal nonlinear attitude dynamics of HyTAQs is analyzed and modeled based on the Takagi-Sugeno (T-S) fuzzy model, and switched fuzzy controllers are developed to stabilize the hybrid fuzzy system. The characteristic of state dimension switching caused by ground contact is modeled via the singular system presentation with mode-dependent singularity matrices, based on which numerically testable criteria of stability and stabilization in the stochastic sense are derived. Compared with the previous control approaches based on Markov jump systems, the proposed one is able to describe the deterministic dwelling duration in practice and integrate multiple subsystems with algebraic equations of different dimensions, while achieving lower conservatism. Illustrative examples are provided to demonstrate the effectiveness and potential of the designed variable-dimension fuzzy controllers.

This study examines the state estimation problem for uncertain descriptor systems subject to unknown dynamics. An integration of interval type-2 fuzzy set (IT2-FS) and cerebellar model articulation controller (CMAC) neural network, called the IT2-FCMAC approximator, is introduced to approximate the unknown dynamics and is incorporated into a sliding-mode descriptor observer. Then, its learning problem is cast into a robust control framework subject to discrete-time nonlinear systems, and a robust$\mathcal {H}_{\infty }$control-based learning algorithm is proposed. Besides, an adaptive compensator is introduced to mitigate the impact of approximation error. An IT2-FCMAC-based adaptive sliding-mode observer is developed and the calculation of observer gain and learning parameters is solved by several linear matrix inequalities (LMIs). The proposed scheme is applied in estimating the state of charge (SOC) of lithium-ion batteries, showcasing its exceptional performance.

This article is concentrated on the load frequency control for interconnected multiarea power systems (IMAPSs) with nonhomogeneous sojourn probabilities (NSPs) and cyber-attacks. A generalized framework of NSPs is formulated to describe the dynamic behavior of IMAPSs. To govern variations of sojourn probabilities, a deterministic switching signal is introduced using the average dwell-time technique. Essentially, different from the existing protocols, an improved event-triggered protocol that is relevant to the dynamic quantizer parameter is presented, thereby increasing triggering intervals. Furthermore, both denial-of-service and deception attacks, which obey Bernoulli distributions, are considered during information transmission. By virtue of the Lyapunov theory, the mean-square exponential stability of the considered systems is established. Finally, the effectiveness of the obtained results is verified via a numerical example.

This article investigates the noncooperative game of multiple clusters composed by double-integrator agents subject to set constraints. In particular, by decomposing each cluster cost function into multiple individual ones and allocating them to respective agents, the generalized Nash equilibrium (GNE) associated with the upper cluster layer is sought by the lower agent layer in terms of a hierarchical structure. A particular agent in each cluster is appointed as the messenger that is specialized in interacting with adjacent messengers from other clusters. It is first shown that the concerned seeking objective is achieved provided that the agents from the same cluster reach a consensus on their corresponding component of the GNE. Motivated by this consensus idea, a distributed seeking strategy through the intracluster interaction is first developed, which is applicable to the case where each agent has access to the information of the messengers from other clusters. Based on this result, we next consider a relaxed case where the messenger information is absent for each agent and the inter-cluster information interaction is only allowed via their messengers. Another distributed seeking strategy resorting to a distributed observer is developed via both the inter- and intra-cluster interactions. It is shown that both the developed distributed seeking strategies are capable of seeking out the concerned GNE. As extensions to the double-integrator model, we further consider two evolved models, i.e., Euler–Lagrange model and multi-integrator model. Two modified distributed seeking strategies are developed for achieving the concerned NE seeking objective. Finally, the effectiveness of the developed distributed seeking strategies is validated by an application.

This article studies the bipartite tracking control problem of distributed nonlinear multiagent systems with input quantization, external disturbances, and actuator faults. We use the radial basis function (RBF) neural networks (NNs) to model unknown nonlinearities. Due to the fact that the upper bounds of disturbances and the number of actuator faults are unknown, an intermediate control law is designed based on a backstepping strategy, where a compensation term is introduced to eliminate external disturbances and actuator faults. Meanwhile, a novel smooth function is incorporated into the real distributed controller to reduce the effect of quantization on the virtual controller. The proposed distributed controller not only realizes the bipartite tracking control but also ensures that all signals are bounded in the closed-loop systems and the outputs of all followers converge to a neighborhood of the leader output. Finally, simulation results demonstrate the effectiveness of the proposed control algorithm.

The aim of this article is to establish generic stability conditions for switched nonlinear systems with mode-dependent average dwell-time (MDADT) switching rules via Takagi–Sugeno (T–S) fuzzy modeling, which cover all unstable modes, all stable modes, and partially unstable modes as special cases. Different from traditional multiple and multiple discontinuous Lyapunov function (MDLF) methods, the proposed novel switching-signal-based multiple discontinuous Lyapunov function (SMDLF) approach divides each mode-running interval dynamically according to the actual switching of the system. The developed approach can not only yield less conservative bounds on the MDADT but can also handle all unstable subsystems, which cannot be done by means of the traditional MDLF. A class of SMDLF is constructed for continuous-time switched T–S fuzzy systems, and relaxed stability conditions are presented for the system with both stable and unstable subsystems, using a larger switching signal space. Then, novel stability conditions are deduced for all stable and unstable subsystems in terms of slow and fast MDADT switching signals, respectively. Finally, comparative simulation examples are provided to verify the advantages and effectiveness of the proposed approach.

This article addresses the robust fixed-time stabilization control problem for generic linear systems with both matched and mismatched disturbances. A new observer-based fixed-time control technique is proposed to solve this robust stabilization problem, provided that the system matrix pair$(A,B)$is controllable. The ultimate boundedness of the closed-loop system in the presence of mismatched disturbances is proven. An upper bound of the convergence time is provided, which is irrelevant to initial conditions. Finally, a simulation example is presented to show the efficiency of the proposed control design method.

This paper provides a systematic method to easily design robust, smooth and effective control laws of PD, PID and (PD2)-type for a marine surface vehicle (MSV). The proposed control laws allow one to force, with ideal or real actuators, a MSV with uncertain parameters and subject to disturbances to follow any navigation trajectory, having a bounded second or third derivative, with a tracking error norm as small as desired, a good transient phase, and feasible control signals. Some practical guidelines to easily design the proposed controllers are given. Finally, the provided control approach is illustrated and validated in cases of berthing a MSV in a busy port, accurately and in a minimum time, and maintaining a MSV in a predetermined position without using anchoring systems, also in presence of measurement noises.

This paper designs a predefined-time convergent continuous control algorithm using a linear time-varying control input to stabilize a permanent magnet synchronous motor system in three cases: disturbance-free, in presence of a deterministic disturbance satisfying a Lipschitz condition, and in presence of both a stochastic white noise and a deterministic disturbance satisfying a Lipschitz condition. Numerical simulations are provided for a permanent magnet synchronous motor system to validate the obtained theoretical results. The simulation results demonstrate that the employed values of the predefined-time convergent control inputs are applicable in practice.

In this paper, the H∞stabilization problem is studied for discrete-time semi-Markov jump singularly perturbed systems (SMJSPSs) with repeated scalar nonlinearities. As the exact statistical information of the sojourn time or the mode transition is difficult to obtain, the case with only partial semi-Markov kernel information available is considered. Furthermore, introducing an external disturbance or nonlinearity into the analysis of discrete-time semi-Markov jump systems (DTSMJSs) meets critical obstacles, since the relation between the system state vectors at two nonadjacent instants is difficult to determine. To address this issue, the variation trend of the Lyapunov function for a semi-Markov jump sequence is analyzed in detail. Subsequently, criteria of mean-square exponential stability (MSES) for DTSMJSs are established for the first time based on the Lyapunov stability theory. By virtue of the criteria obtained and the cone complementary linearization algorithm, a controller ensuring MSES and H∞performance for discrete-time nonlinear SMJSPSs is constructed. Finally, the effectiveness and applicability of the proposed method are validated by simulation examples including an inverted pendulum model.

In this article, finite-time$\mathcal {H}_{\infty }$control is studied for a kind of continuous-time-switched Takagi–Sugeno (T–S) fuzzy systems with mode-dependent average dwell-time (MDADT) switching. The dynamic event-triggered mechanism (ETM) is utilized to monitor the data transmission from the system plant to the controller, which more efficiently reduces the amount of transmitted data than the conventional static one. First, it is demonstrated that the adopted dynamic ETM can avoid the Zeno behavior, and also yield a larger minimal interexecution time compared with the static one. Then, an improved criterion of finite-time$\mathcal {H}_{\infty }$performance is introduced by utilizing a novel Lyapunov-like function with an internal dynamic variable. Based on this criterion, a dynamic event-triggered controller is designed together with a switching signal subject to the MDADT property. Finally, the validity, and virtues of the proposed control scheme are verified by two simulation examples.

This article studies the bipartite containment control problem for nonlinear multiagent systems (MASs) with input quantization over a signed digraph. The design objective is to provide an appropriate distributed protocol such that the followers converge to a convex hull containing each leader's trajectory as well as its opposite trajectory different in sign. Based on a nonlinear decomposition approach of input quantization, an event-triggered control scheme is developed via backstepping technique. A fuzzy observer is constructed to estimate unmeasurable states. Moreover, the bipartite containment control scheme for nonlinear MASs is designed. It is demonstrated that all signals in the closed-loop system are semiglobally uniformly ultimately bounded and Zeno behavior is excluded. Finally, a simulation example is given to verify the validity of the designed method.

This paper presents an adaptive fixed-time convergent continuous controller designed to solve a stock management problem with the objective to drive stock and supply chain levels at the reference values, subject to loss rate disturbances whose bounds are unknown. The only measurable state of the supply chain is the inventory retailer stock level, whereas the supply line inventory level should be estimated. The designed controller includes a fixed-time convergent differentiator, an adaptive fixed-time convergent disturbance observer, and a fixed-time convergent regulator. The adaptive fixed-time convergent observer is used to estimate a disturbance without excessively increasing the controller gains. The controller design is validated in a case study of stock management. The calculated upper estimate for the total settling (convergence) time and the obtained simulation results confirm the fixed-time convergence and the robustness of the designed controller.

This paper provides an approach to design robust smooth controllers that allow a plant belonging to a broad class of nonlinear uncertain systems, with possible real actuators and subject to bounded or rate-bounded disturbances, to track a sufficiently smooth reference signal with an error norm smaller than a prescribed value. The proposed control laws are based on the concept of majorant systems and allow one to establish asymptotic bounds for the tracking error and its first and second derivatives. The proposed controller design is based on two parameters: the first is related to the minimum eigenvalue of an appropriate matrix, which the practical stability depends on, and the second is determined by the desired maximum norm of the tracking error and its convergence velocity. If the trajectories to be tracked are not sufficiently smooth, suitable filtering laws are proposed to facilitate implementation of the control laws and reduce the control magnitude, especially during the transient phase. The obtained theoretical results are validated in two case studies. The first one presents a tracking control design for an industrial robot, both in the joint space and workspace, with and without real actuators or velocity measurement noise. The second one deals with tracking control design for a complex uncertain nonlinear system.

This paper studies the fault detection problem for a class of discrete-time switched linear systems under dwell-time (DT) constraints, using the parity space-based approach. The DT-dependent Lyapunov function is employed to investigate the asymptotic stability with less conservatism and to solve the constant l2-gain performance analysis problem, and its advantage is verified compared to the time-independent Lyapunov function approach. The corresponding switching residual generation is made to carry out the desired fault detection in the framework of parity space-based model. Then, by means of solving a generalized eigenvalue-eigenvector problem, the parity space matrices design is implemented. A quantitative relationship is established between the optimization performance and the choice of the parity space order. Two numerical examples are utilized to demonstrate effectiveness of the developed fault detection approach, including an application to switched RLC circuits.

This paper presents a comprehensive approach, which allows one to design in a unified way robust smooth proportional-integral-derivative (PID) or proportional-second order derivative (PD2)-type control laws for broad classes of uncertain nonlinear multi-input multi-output (MIMO) systems, including mechatronic and transportation ones, subject to bounded disturbances and noises. The proposed controllers are used to track a reference signal with a bounded third derivative, yielding a tracking error norm less than a prescribed value. The proposed control laws are simple to design and implement and have various engineering applications. Two cases studies are considered: the first one concerns mechatronic processes, and the second one deals with a transportation/assembling robot.

This paper focuses on the problem of L2-L∞dynamic output feedback controller (DOFC) design for nonlinear switched systems with nonlinear perturbations in the Takagi- Sugeno fuzzy framework. First, the average dwell time approach is used to stabilize a nonlinear switched system exponentially under an arbitrary switching law. Then, based on the technique of piecewise Lyapunov functions, a fuzzy-rule-dependent DOFC is designed to ensure that the overall closed-loop system is exponentially stable with a weighted L2-L∞performance level (γ, α). The solvability condition for the desired DOFC is derived using a linearization technique. It is shown that the controller parameters can be obtained as solutions to a set of strict linear matrix inequalities that are numerically solvable with available standard software. Finally, two simulation examples illustrate effectiveness of the developed technique, including cognitive-radio systems.

This paper studies the observer-based adaptive fuzzy tracking control problem for a general class of multi-input-single-output nonstrict-feedback systems subject to unmeasured states and actuator failures. For actuator failures, both cases of lock-in-place and loss of effectiveness are synchronously considered. To handle the unknown nonlinear functions, fuzzy logic systems are employed. By constructing a fuzzy observer and a serial-parallel estimation model, the unmeasured states are estimated and the accuracy of approximating the unknown functions is improved. Moreover, taking into account the prediction error between the fuzzy observer and the serial-parallel estimation model, a novel composite fuzzy output-feedback control scheme is developed. Unlike some existing control schemes for systems with actuator failures, the developed control scheme allows one to avoid the problem of “explosion of complexity” and improve the approximation performance. It is proved that all signals in the system are bounded and the tracking error converges to a small neighborhood of the origin by choosing appropriate parameters. Finally, the effectiveness of the proposed method is confirmed via simulation examples with actuator failures.

In this paper, the problem of reliable filter design with strict dissipativity has been investigated for a class of discrete-time T-S fuzzy time-delay systems. Our attention is focused on the design of a reliable filter to ensure a strictly dissipative performance for the filtering error system. Based on the reciprocally convex approach, firstly, a sufficient condition of reliable dissipativity analysis is proposed for T-S fuzzy systems with time-varying delays and sensor failures. Then, a reliable filter with strict dissipativity is designed by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, numerical examples are provided to illustrate the effectiveness of the developed techniques.

This paper presents the optimal linear-quadratic-Poisson (LQP) controller for stochastic linear systems with Poisson noises and unknown parameters. The optimal controller equations are obtained using the separation principle, whose applicability to the considered problem is substantiated. Performance of the obtained optimal LQP controller is verified in the illustrative example against the LQP controller that is optimal for linear systems with known parameters; the designed LQP controller is also compared with the conventional linear-quadratic-Gaussian (LQG) controller available for stochastic linear systems with Gaussian noises and unknown parameters. Simulation graphs demonstrating overall performance and computational accuracy of the designed LQP controller for stochastic linear systems with Poisson noises and unknown parameters are included.