In this article, the fixed-time formation-containment tracking (FXFCT) problem is addressed for heterogeneous multi-agent systems (HMASs) under a directed interaction topology. Agents are divided into a reference leader, providing a trajectory for the entire HMASs; formation-leaders, achieving the desired formation by following this trajectory; and followers. Novel distributed fixed-time observers are developed for the formation-leaders and followers with the directed interaction topology, respectively. Distributed fixed-time control protocols are then proposed for the formation-leaders and followers using the coordinate transformation methods, eliminating the restrictive but commonly adopted full-row rank assumption of agent input matrices. It is shown that under the proposed control protocols, the concerned FXFCT problem can be solved. Simulations verify the effectiveness of the obtained theoretical results. Note to Practitioners - Fixed-time cooperative control of HMASs, e.g., autonomous aerial vehicles (AAVs) and autonomous ground vehicles (AGVs), enables rapid execution of complex collective tasks, efficient resource allocation, and improved operational efficiency. Such capabilities are particularly relevant to applications in logistics, energy management, and smart agriculture, where time-critical coordination delivers significant social and economic benefits. As a unified cooperative control framework, the formation-containment tracking (FCT) problem encompasses several classical problems, such as consensus tracking, formation tracking, and containment control. Solving the FCT problem therefore provides a comprehensive solution that addresses multiple coordination objectives simultaneously, making it particularly relevant for complex real-world missions that require hierarchical coordination in HMASs. This paper addresses the FXFCT problem for HMASs under a directed interaction topology. We develop novel distributed fixed-time protocols that integrate distributed fixed-time observers and controllers for both formation-leaders and followers. For practitioners, a key practical advantage lies in an explicit, a priori computable upper bound on the convergence time that is independent of initial conditions, enabling predictable, deadline-aware mission planning. In addition, directed communication and the removal of restrictive full-row rank assumptions on agent dynamics alleviate communication overhead and improve practical applicability to heterogeneous platforms. © 2026 IEEE.

In this study, an improved jump model is proposed for the Roesser-type 2-D Markov jump systems (MJSs). We use two independent Markov chains that propagate along the horizontal and vertical directions, respectively, to characterize the switching of system dynamics in those two directions. Compared with the conventional jump model, which uses only one Markov chain to characterize the switching of system dynamics in both directions, the newly proposed 2-D jump model shows better modeling capabilities for real-world applications with abrupt changes while inherently avoiding the mode ambiguity phenomenon. Based on the proposed jump model, we then propose a dual-mode-dependent state feedback control law to stabilize the concerned 2-D MJS. A sufficient criterion, whose feasibility is enhanced via a dual-mode-dependent Lyapunov functional technique, is obtained to ensure the asymptotic mean square stability and $H_{\infty }$ disturbance attenuation level of the resulting closed-loop system. Subsequently, resorting to a novel nonconservative separation principle, two equivalent conditions with one of them in the form of linear matrix inequalities (LMIs) are developed. Finally, a convex optimization algorithm which is formulated by the obtained LMIs is proposed to design the control law. An example of the Darboux equation with Markov switching parameters is presented to validate the effectiveness of the obtained results.

This article presents a double-channel (sensor-to-controller channel and controller-to-actuator channel) event triggered control method for nonlinear interconnected systems subject to sensor and actuator faults via the backstepping technique. It should be emphasized that the utilization of triggering mechanism at the sensor side poses a challenge to the design of backstepping control, as it leads to nondifferentiable virtual control signals due to the discontinuous nature of the state/output signals received at the controller side. In contrast to existing methods, the proposed event triggering mechanism eliminates the need for computing virtual control signals at the sensor side before transmitting them to the controller side. By establishing the relationships of the corresponding variables in two communication scenarios (namely, without and with event triggering) and introducing dynamic filtering technique, the problem of nondifferentiable virtual control signals in backstepping design is solved. We present a numerical case study to validate the effectiveness and advantages of the proposed decentralized event triggered control approach.

This brief presents a novel optimal control (OC) approach based on $\mathcal {Q}$ -learning to address robust control challenges for uncertain nonlinear systems subject to mismatched perturbations. Unlike conventional methodologies that solve the robust control problem directly, our approach reformulates the problem by minimizing a value function that integrates perturbation information. The $\mathcal {Q}$ -function is subsequently constructed by coupling the optimal value function with the Hamiltonian function. To estimate the parameters of the $\mathcal {Q}$ -function, an integral reinforcement learning (IRL) technique is employed to develop a critic neural network (NN). Leveraging this parameterized $\mathcal {Q}$ -function, we derive a model-free OC solution that generalizes the model-based formulation. Furthermore, using Lyapunov's direct method, the resulting closed-loop system is guaranteed to have uniform ultimate bounded stability. A case study is presented to showcase the effectiveness and applicability of the proposed approach.

This article investigates the problem of distributed optimization over multiagent networks with the global objective being the sum of a set of possibly nonconvex functions. Based on recent developments in distributed average tracking as well as distributed extremum seeking, a distributed bounded gradient-free optimization algorithm is proposed. It is shown that the proposed scheme is able to solve nonconvex optimization problems with arbitrary prescribed accuracy. The relationship between the optimization error and control parameters is established with the error bound’s explicit dependence on the bounds of agents’ control inputs, which clearly demonstrates a tradeoff between the optimization error and input bound. An illustrative example is included to validate the effectiveness of proposed scheme.

In this article, we investigate the global regulation problem for a class of feedforward nonlinear systems. Notably, the systems under consideration allow unknown input-output-dependent nonlinear growth rates, which has not been considered in existing works. A novel logic-based switching (LBS) gain approach is proposed to counteract the system uncertainties and nonlinearities. The key idea of the proposed approach is that whenever the Lyapunov function does not decrease as desired, the switching mechanism is activated so that a new gain is adopted. Furthermore, a tanh-type speed-regulation function is embedded into the switching mechanism for the first time to improve the convergence speed and transient performance. Then, a switching adaptive output feedback (SAOF) controller, which is of concise form and low complexity, is proposed based on the developed switching mechanism. It is shown that the objective of global regulation is achieved with faster convergence speed and better transient performance under the proposed controller. Moreover, it is also shown that the proposed control approach can be extended with the enhanced switching mechanism to deal with feedforward nonlinear systems with external disturbances. Finally, numerical examples are presented to demonstrate the effectiveness and advantages of our approach in comparison with the existing approaches.

The semi-global stabilization problem of discrete-time systems subject to infinite distributed input delays and actuator saturations is investigated in this article. This article develops two low-gain feedback control laws for two types of systems, respectively. It is shown that the resulting system is semi-globally exponentially stabilized. Our results include those existing results on systems subject to only input saturations and systems subject to bounded delays and input saturations as special cases. Compared with existing results on infinite delays and actuator saturations, this article develops a more accurate scaling utilizing a more general framework. Furthermore, a novel converse Lyapunov theorem for discrete-time linear infinite-delayed systems and a novel stability analysis theorem for perturbed discrete-time linear infinite-delayed systems are developed to handle the nonlinearity induced by saturations. Finally, this article provides two numerical examples to illustrate the effectiveness of the developed theorems.

This article deals with the leader-following consensus problem of multiple uncertain Euler-Lagrange systems with unknown nonlinear dynamics. By introducing a dynamic compensator for each agent, a fully distributed control strategy is developed based on the fuzzy approximation approach, which is independent of any priori global information associated with the communication topology. Meanwhile, a distributed event-triggering mechanism (ETM) is designed such that each agent broadcasts its states only when an event occurs. It is shown that with the proposed ETM, the leader-following consensus is achieved with aperiodic intermittent communication and Zeno behavior is excluded by contradiction. Moreover, the consensus tracking errors converge to small sets around the origin. Finally, an example is provided to illustrate the effectiveness of the obtained theoretical results.

In this article, the finite-frequency fault detection filter design problem is investigated for a class of networked nonlinear systems subject to medium access constraints. The nonlinear system is modeled by Takagi–Sugeno (T–S) fuzzy affine dynamic models, and only one node that includes partial measured information can gain access to the shared transmission medium according to the allocated access probability. Within the stochastic$\mathscr {H}_{-}/\mathscr {H}_{\infty }$filtering framework, an admissible filter is designed such that, in the finite-frequency domain, the filtering error system is stochastically stable and the fault can be detected using partially available measurements. First, by integrating with the$\mathscr{S}$-procedure, the generalized Kalman–Yakubovič–Popov lemma is further developed to obtain sufficient conditions guaranteeing the desired finite-frequency performance of the filtering error system. Then, by applying piecewise quadratic Lyapunov functions, Projection lemma, and some convexification techniques, the filter design approach is proposed for the constrained networked nonlinear system. It is shown that the filter design problem can be addressed by solving a set of linear matrix inequalities. Simulation studies are finally given to illustrate the effectiveness of the proposed design approach.

This article investigates the adaptive optimal control problem for networked discrete-time nonlinear systems with stochastic packet dropouts in both controller-to-actuator and sensor-to-controller channels. A Bernoulli model-based Hamilton-Jacobi-Bellman (BHJB) equation is first developed to deal with the corresponding nonadaptive optimal control problem with known system dynamics and probability models of packet dropouts. The solvability of the nonadaptive optimal control problem is analyzed, and the stability and optimality of the resulting closed-loop system are proven. Two reinforcement learning (RL)-based policy iteration (PI) and value iteration (VI) algorithms are further developed to obtain the solution to the BHJB equation, and their convergence analysis is also provided. Furthermore, in the absence of a priori knowledge of partial system dynamics and probabilities of packet dropouts, two more online RL-based PI and VI algorithms are developed by using critic-actor approximators and packet dropout probability estimator. It is shown that the concerned adaptive optimal control problem can be solved by the proposed online RL-based PI and VI algorithms. Finally, simulation studies of a single-link manipulator are provided to illustrate the effectiveness of the proposed approaches.

The stabilization problem of two categories of discrete-time linear time-varying (LTV) systems subject to unbounded distributed input delays is investigated in this article. A truncated predictor feedback law is first built for a category of systems under some common assumptions. Then, under some weakened assumptions, a predictor-type feedback law is developed for the other category of more general systems. The global exponential stability of the closed-loop systems is proved. Furthermore, the result on the truncated predictor feedback control law includes many existing results on LTV systems subject to bounded input delays and linear time-invariant (LTI) systems subject to unbounded input delays as special cases. Finally, the results of simulations validate the effectiveness of the developed control laws.

In this article, the cooperative output regulation problem of heterogeneous linear multiagent systems under jointly connected digraphs is addressed. The event-triggered control protocols based on state feedback and output feedback are proposed, respectively. It is shown that the output tracking errors of the resulting closed-loop control systems converge to 0 exponentially via the proposed protocols. One of the key advantages of the proposed event-triggering mechanism is that the information transmissions induced by event triggerings and topology switchings are independent, and data transmissions among agents are thus reduced. Furthermore, an explicit minimum interevent time is provided for all the agents so that the Zeno-behavior is excluded strictly. Finally, three numerical examples are provided to verify the effectiveness of the proposed protocols.

This article studies the tracking problem of a class of heterogeneous linear minimum-phase discrete-time multiagent systems (MASs) with unknown agent parameters in the presence of bounded disturbances. By introducing a distributed adaptive observer and a reference model, a novel framework is proposed to convert the complicated cooperative tracking problem of unknown heterogeneous MASs into a cooperative tracking problem of the reference models to the leader and a local robust model reference adaptive control problem. It is shown that under the adaptive controller designed based on the proposed framework, the tracking errors between the outputs of all the agents and the output of the leader converge to a residual set. It is also shown that the tracking errors will converge to zero asymptotically when the disturbances are absent. Compared with the existing related works, our main contribution is that the proposed framework could deal with the unknown MASs with arbitrary individual relative degrees and do not rely on any global graph information. Finally, the effectiveness of the proposed controller is illustrated by an example.

In this article, a disturbance-observer-based adaptive fuzzy tracking control scheme is proposed for a medium-scale unmanned helicopter of six degrees of freedom in the presence of system uncertainties, flight boundary constraints, and external disturbances. A flight boundary protection algorithm is proposed to ensure its flight trajectory within the given safety range. A fuzzy logic system is utilized to estimate the system uncertainties and a nonlinear disturbance observer is adopted to handle the unknown compound terms of the external disturbances and the estimation errors resulting from the fuzzy logic system. An inverse optimal control approach is then used to avoid solving the Hamilton–Jacobi–Bellman equation in minimizing a cost function in the attitude loop. It is shown via the Lyapunov method that the desired safe tracking performance of the position loop and attitude loop of the controlled unmanned helicopter can be achieved. Simulations are provided to illustrate the effectiveness of the proposed control scheme.

This article proposes a novel fixed-time converging proximal neurodynamic network (FXPNN) via a proximal operator to deal with equilibrium problems (EPs). A distinctive feature of the proposed FXPNN is its better transient performance in comparison to most existing proximal neurodynamic networks. It is shown that the FXPNN converges to the solution of the corresponding EP in fixed-time under some mild conditions. It is also shown that the settling time of the FXPNN is independent of initial conditions and the fixed-time interval can be prescribed, unlike existing results with asymptotical or exponential convergence. Moreover, the proposed FXPNN is applied to solve composition optimization problems (COPs),$l_{1}$-regularized least-squares problems, mixed variational inequalities (MVIs), and variational inequalities (VIs). It is further shown, in the case of solving COPs, that the fixed-time convergence can be established via the Polyak–Lojasiewicz condition, which is a relaxation of the more demanding convexity condition. Finally, numerical examples are presented to validate the effectiveness and advantages of the proposed neurodynamic network.

This article considers the dynamic event-triggered$\mathcal {H}_{\infty }$filtering problem for networked control systems (NCSs) under multiple cyber-attacks. By taking into account both denial-of-service (DoS) and deception attacks (DAs), a novel dynamic event-triggering mechanism (DETM) and the corresponding event-triggered filter are presented. The proposed DETM further reduces data transmissions and also guarantees the exclusion of Zeno behavior. By resorting to a piecewise Lyapunov functional (PLF), sufficient conditions are derived to ensure that the resulting filtering error system is globally stochastically exponentially stable with the$\mathcal {H}_{\infty }$performance in the presence of both DoS and DAs. A simulation example of a tunnel diode circuit system is displayed to illustrate the validity of the proposed event-triggered filter under multiple cyber-attacks.

In this article, the output containment control problem of heterogeneous linear multiagent systems (MASs) with unbounded distributed transmission delays is considered. A key technical lemma is first established to show that the distributed observers under unbounded distributed transmission delays can be utilized to estimate the convex combination of the states of multiple leaders. Then, a novel distributed output feedback control law is proposed based on those distributed observers. It is shown that under our proposed control law, the output of each follower converges to the convex hull spanned by the outputs of the leaders. In addition, our results include relevant results on output containment control of MASs with bounded distributed or constant transmission delays as special cases. Finally, the effectiveness of the proposed control law is demonstrated by a simulation example.

This article investigates the cooperative output regulation problem for heterogeneous nonlinear multiagent systems subject to disturbances and quantization. The agent dynamics are modeled by the well-known Takagi-Sugeno fuzzy systems. Distributed reference generators are first devised to estimate the state of the exosystem under directed fixed and switching communication graphs, respectively. Then, distributed fuzzy cooperative controllers are designed for individual agents. Via the Lyapunov technique, sufficient conditions are obtained to guarantee the output synchronization of the resulting closed-loop multiagent system. Finally, the viability of proposed design approaches is demonstrated by an example of multiple single-link robot arms.

This article investigates the cooperative output regulation problem for discrete-time heterogeneous multiagent Markov jump systems. Two cases are studied: 1) output regulation quadratic control in the case where the exosystem is accessible to all agents and 2) cooperative output regulation quadratic control in the case where only a part of agents can directly communicate with the exosystem. The hidden Markov models are employed to describe the asynchronous modes of the agents and their corresponding controllers. Via the jumping regulator equation, asynchronous control laws are constructed and the algorithms to obtain control parameters are presented in terms of linear matrix inequalities. For the first case, the optimal synchronous/mode-dependent control law, which is a special case of the asynchronous control protocol, is also given via the stochastic dynamic programming approach. Finally, an example is given to illustrate the effectiveness of the proposed approaches.

This article proposes a novel fixed-time converging forward-backward-forward neurodynamic network (FXFNN) to deal with mixed variational inequalities (MVIs). A distinctive feature of the FXFNN is its fast and fixed-time convergence, in contrast to conventional forward-backward-forward neurodynamic network and projected neurodynamic network. It is shown that the solution of the proposed FXFNN exists uniquely and converges to the unique solution of the corresponding MVIs in fixed time under some mild conditions. It is also shown that the fixed-time convergence result obtained for the FXFNN is independent of initial conditions, unlike most of the existing asymptotical and exponential convergence results. Furthermore, the proposed FXFNN is applied in solving sparse recovery problems, variational inequalities, nonlinear complementarity problems, and min-max problems. Finally, numerical and experimental examples are presented to validate the effectiveness of the proposed neurodynamic network.

This article investigates the leader-following consensus problem of heterogeneous linear multiagent systems under switching and directed topologies. It is assumed that the communication between agents suffers from time-varying delays and only the neighboring agents of the leader are able to get access to the information of the leader agent, including its agent matrices. A key technical lemma on the input to state stability of time-delayed systems is first established with which the main results of this article can be obtained. An adaptive distributed observer, taking into consideration of communication time delays, is proposed for each follower to estimate the leader's system matrices and its state. Then, a distributed controller based on this adaptive observer is developed. We show that the resulting closed-loop multiagent system achieves the leader-following output consensus. Two examples are finally given to illustrate the effectiveness of the proposed controller.

This article addresses the event-triggered adaptive fuzzy output-feedback control problem for a class of nonstrict-feedback nonlinear systems with asymmetric and time-varying output constraints, as well as unknown nonlinear functions. By designing a linear observer to estimate the unmeasurable states, a novel event-triggered adaptive fuzzy output-feedback control scheme is proposed. The barrier Lyapunov function (BLF) and the error transformation technique are used to handle the output constraint under a completely unknown initial tracking condition. It is shown that with the proposed control scheme, all the solutions of the closed-loop system are semiglobally bounded, and the tracking error converges to a small set near zero, while the output constraint is satisfied within a predetermined finite time, even when the constraint condition is violated initially. Moreover, with the proposed event-triggering mechanism (ETM), the Zeno behavior can be strictly ruled out. An example is finally provided to demonstrate the effectiveness of the proposed control method.

This article investigates the event-triggered output consensus problem for a class of unknown heterogeneous discrete-time linear multiagent systems in the presence of unmodeled dynamics. The agents have individual nominal dynamics with unknown parameters, and the unmodeled dynamics are in the form of multiplicative perturbations. A novel design framework is developed based on an event-triggered internal reference model and a distributed model reference adaptive controller. To deal with the heterogeneity of the multiagent system, the event-triggered internal reference model is designed to generate a virtual reference signal for each agent with a dynamic event-triggering mechanism being adopted to reduce the communication burden between neighboring agents. To handle the unknown parameters and unmodeled dynamics, the robust model reference adaptive controller is then designed to follow the generated virtual reference signal. It is shown that if the unmodeled dynamics satisfy certain conditions, then the boundedness of all the signals and variables in the closed-loop system and convergence of consensus errors to a residual set are guaranteed. Moreover, the consensus errors will converge to zero asymptotically in the absence of unmodeled dynamics. Compared with existing related works, the proposed framework is able to deal with the agents with individual unknown nominal dynamics and unmodeled dynamics. Moreover, the proposed framework is fully distributed in the sense that no knowledge of any global information is needed. Finally, the performance of the proposed method is validated by examples.

This article addresses the cooperative output tracking problem of a class of linear minimum-phase multiagent systems, where the agent dynamics are unknown and heterogeneous. A distributed event-triggered model reference adaptive control strategy is developed. It is shown that under the proposed event-triggered control strategy, the outputs of all the agents synchronize to the output of the leader asymptotically. It is also shown that Zeno behavior can be excluded with the proposed novel event triggering mechanism. In addition, the proposed adaptive control strategy is fully distributed in the sense that no prior knowledge of some global information, such as the eigenvalues of the associated Laplacian matrix and the number of the agents is required. Finally, an example is given to demonstrate the effectiveness of the proposed control strategy.

This article addresses the dynamic positioning control problem of a nonlinear unmanned marine vehicle (UMV) system subject to network communication constraints and deny-of-service (DoS) attack, where the dynamics of UMV are described by a Takagi-Sugeno (T-S) fuzzy system (TSFS). In order to save limited communication resource, a new intelligent event-triggering mechanism is proposed, in which the event triggering threshold is optimized by a Q -learning algorithm. Then, a switched system approach is proposed to deal with the aperiodic DoS attack occurring in the communication channels. With a proper piecewise Lyapunov function, some sufficient conditions for global exponential stability (GES) of the closed-loop nonlinear UMV system are derived, and the corresponding observer and controller gains are designed via solving a set of matrix inequalities. A benchmark nonlinear UMV system is adopted as an example in simulation, and the simulation results validate the effectiveness of the proposed control method.

This article is concerned with the finite-frequency$\mathcal {H}_{-}/\mathcal {H}_{\infty }$memory fault detection filtering problem for discrete-time Takagi–Sugeno fuzzy affine systems with norm-bounded uncertainties. The objective is to design a piecewise affine memory filter by using system historical information such that the resulting closed-loop filtering error system is asymptotically stable with the prescribed finite-frequency$\mathcal {H}_{-}/\mathcal {H}_{\infty }$performance. Based on the generalized Kalman–Yakubovič–Popov lemma combined with the celebrated$\mathcal {S}$-procedure, new sufficient conditions for the fuzzy affine filtering error system to have the finite-frequency$\mathcal {H}_{-}/\mathcal {H}_{\infty }$performance are given at first. By further using piecewise fuzzy quadratic Lyapunov functions and Projection lemma, the filtering analysis results for the filtering error system to be asymptotically stable with the prescribed finite-frequency$\mathcal {H}_{-}/\mathcal {H}_{\infty }$performance are obtained. Then, the filtering synthesis is carried out with the aid of matrix inequality convexification techniques, and the synthesis results are described in terms of linear matrix inequalities. It is further shown that a better filtering performance can be achieved by using more system historical information. Finally, simulation is provided to verify the effectiveness of the proposed approach.

This article investigates the leader-following output consensus problem of heterogeneous linear multiagent systems, where the nonidentical followers are subject to norm bounded parameter uncertainties. Two novel distributed event-triggered consensus strategies based on state feedback and output feedback, respectively, are proposed to solve the leader-following output consensus problem. A dynamic event-triggering mechanism which involves internal state variables is proposed to determine the next triggering instant. It should be noted that the controllers can be designed without the knowledge of some global graph information, such as the eigenvalues of the Laplacian matrix associated with the corresponding communication graph and the number of agents. In addition, the interevent time can be prolonged and the Zeno behavior can be excluded with the proposed dynamic triggering mechanism. Finally, an example is presented to illustrate the effectiveness of the proposed controllers.

In this article, the event-triggered and self-triggered leader-following output-feedback control problems are investigated for a class of high-order stochastic nonlinear multiagent systems (MASs) under an undirected graph. First, using the high-gain method, the observer is designed to estimate the unmeasured state variables of the given nonlinear system. Then, by introducing an internal dynamic variable, a distributed Zeno-free dynamic event-triggered controller is constructed. Compared with the static event-triggering results, the interevent time of the proposed dynamic event-triggering mechanism is shown to be prolonged and, thus, the advantages of the event-triggered control approaches can be enhanced. Further, to avoid continuously monitoring the states, a Zeno-free self-triggering mechanism is given. It is shown that the expectations of the output tracking errors converge to an arbitrarily small set if the diffusion terms are different for all agents, and that the expectations of all state tracking errors converge to an arbitrarily small set if the diffusion terms are the same for all agents. Finally, simulation studies are given to illustrate the effectiveness of the proposed methods.

This article is concerned with the event-triggered output feedback control problem for a class of switched nonlinear strict-feedback systems subject to asymmetric input saturation. The nonlinear terms are assumed to be bounded by a continuous function of the output multiplied by unmeasured states. The hyperbolic tangent function is employed to process the error caused by the event-triggered scheme, and an indicator function of the saturation degree is used to analyze the influence generated by the asymmetric input saturation. By adopting the common Lyapunov function method and the dynamic gain control design approach, a new design procedure based on a reduced-order observer is proposed to construct an output feedback controller. It is proved by the Lyapunov analysis that the proposed event-triggered control scheme can ensure that all the signals of the closed-loop system are globally bounded. Furthermore, the output can be converged to a bounded region around the origin, and this region can be tuned to be small by adjusting the design parameters. Different from typical existing results on the switched nonlinear strict-feedback systems, the celebrated backstepping method is not employed in this article. The continuous stirred tank reactor is finally used to demonstrate the effectiveness of the proposed control scheme.

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

This article studies the problem of finite-frequency static output feedback (SOF) \mathscr H∞controller design for discrete-time Roesser-type two-dimensional (2-D) nonlinear systems based on Takagi-Sugeno (T-S) fuzzy models. The 2-D Roesser nonlinear systems are described by T-S fuzzy models with parameter uncertainties. The objective is to design a SOF controller guaranteeing the asymptotic stability of the resulting closed-loop system with finite frequency \mathscr H∞performance. Via a system state-input augmentation technique, the closed-loop system is formulated in a descriptor form. Then, based on fuzzy Lyapunov functions and some elegant convexification procedures, the SOF controller design approach is proposed. It is shown that the controller gains can be obtained by solving a set of linear matrix inequalities. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

This article devotes to investigating the issue of fuzzy adaptive control for a class of strict-feedback nonlinear systems with nonaffine nonlinear faults. The computational complexity is reduced by adopting the dynamic surface control technique. Under the framework of finite-time stability, a novel fault-tolerant control strategy is designed so that the closed-loop system is semiglobally practically finite-time stable, and the tracking error converges to a small residual set in a finite time. Finally, simulation studies for an electromechanical system are shown to verify the feasibility of the presented approach.

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

This article proposes a high-order disturbance observer (HODO) and dynamic surface control (DSC) technique-based adaptive fuzzy control scheme for nonlinear systems subjected to input saturation and external time-varying disturbances. First, based on a Sigmoid function, the saturation input is tackled by utilizing a well-defined nonlinear smooth function. Furthermore, HODO and fuzzy logic systems are used to estimate the external disturbances and to handle the lumped unknown functions, respectively. Then, by using the backstepping method and DSC technique, a HODO-based adaptive fuzzy tracking control scheme is proposed for nonlinear systems with saturation nonlinearity, uncertainties, and external disturbances. The Lyapunov analysis method is used to prove that all signals in the entire system are semiglobally uniformly ultimately bounded (SGUUB). In addition, the tracking error converges to a compact set with a tunable error bound determined by some design parameters. Finally, a numerical simulation of two-stage chemical reactor shows the effectiveness of the developed tracking control strategy.

In this paper, we investigate the distributed output-feedback tracking problem of nonlinear multiagent systems (MASs) where nonlinear functions of agent dynamics depend on unmeasurable states. Most existing results on output-feedback control of nonlinear MASs are concerned with nonlinear agent dynamics in output canonical form in which the nonlinear functions of agent dynamics only depend on the measurable output. When the nonlinear functions of agent dynamics depend on unmeasurable states, the problem remains open due to the difficulties in constructing distributed observers. When the leader is only available to a small portion of followers in the directed communication topology, we establish relationship between the network topology and the design of the corresponding distributed reduced order observers. Furthermore, by developing a new distributed high-gain homogeneous domination design method, distributed observers, and controllers are designed to guarantee that the errors between the followers' outputs and the leader's output can be made arbitrarily small while keeping all the states of closed-loop system bounded. Finally, a numerical example is employed to illustrate the effectiveness of the proposed control scheme.

This article studies the robust output regulation problem (RORP) for a class of uncertain linear systems with unknown exosystems via event-triggered control (ETC). Compared with existing related works, the system matrix of the exosystem under consideration is allowed to contain unknown parameters. Based on an adaptive internal model, an ETC strategy composed of an event-triggered adaptive output feedback control law and an output-based event-triggering mechanism is proposed such that the RORP is solved. It is shown that under the proposed ETC strategy, the tracking error converges to zero asymptotically in spite of the unknown exosystem, and meanwhile the occurrence of Zeno behavior is prevented. Finally, two examples are provided to illustrate the effectiveness of the proposed control strategy.

This paper investigates the leader-follower robust H∞consensus of heterogeneous multiagent systems with denial of service attack, where different attack intensities are considered. A switched system model is introduced to model such an attack phenomenon. Then, sufficient conditions to guarantee the solvability of the robust output consensus problem are obtained, and the quantitative relationship between the consensus performance and attack parameter is established. It is shown that the consensus protocol design problem can be transformed into two static output feedback (SOF) control problems. It is also shown that the SOF controller gains can be determined by solving some linear matrix inequalities without the knowledge of the probability information of each attack. Finally, the effectiveness of the control protocol is demonstrated by a simulation study.

This paper studies the cooperative control problem of multiple nonlinear benchmark systems perturbed by second-order moment processes. The nonlinear benchmark system consists of a moving car and a rolling ball in oscillating surroundings. When the leader is only accessible to a small part of the followers in a directed graph, a new vectorial backstepping method is proposed for the design of distributed cooperative control laws. By using stochastic analysis techniques and algebra graph theory, it is shown that the cooperative control problem under consideration is solvable. Specifically, the errors between the followers' outputs and the leader's output can be made arbitrarily small while keeping all states of the closed-loop system bounded in probability. Finally, the effectiveness of the proposed control scheme is demonstrated through a simulation example.

This paper investigates the consensus problem of linear multi-agent systems subject to external disturbances via distributed event-triggered adaptive control. First, a distributed event-triggered adaptive output feedback control strategy is proposed for each agent. It is shown that under this control strategy, the consensus problem can be solved for any connected undirected communication graph in a fully distributed manner without using any global information. Then a distributed self-triggered adaptive output feedback control strategy is designed with which continuous monitoring of the measurement error is no longer needed. It is further shown that for the proposed event-triggered and self-triggered control strategies, no agent will exhibit Zeno behavior. Finally, the effectiveness of the proposed two control strategies is illustrated on a group of two-mass-spring systems.

This paper investigates the cooperative output regulation problem for heterogeneous linear multiagent systems under fixed communication graphs via event-triggered control. A fully distributed event-triggered dynamic output feedback control law is proposed based on the feedforward design approach. At the same time, a fully distributed dynamic event-triggering mechanism is designed so that each agent can determine when to broadcast its information to its neighbors. Compared with existing related results, both the control law and the event-triggering mechanism in this paper are independent of any global information. It is shown that with the proposed dynamic event-triggered control strategy, the cooperative output regulation problem can be solved in a fully distributed manner by intermittent communication. Moreover, Zeno behavior can be strictly ruled out for each agent. Finally, the effectiveness of the proposed dynamic event-triggered control strategy is validated by a numerical example.

This paper tackles the problem of piecewise affine memory filtering design for the discrete-time norm-bounded uncertain Takagi-Sugeno fuzzy affine systems. The objective is to design an admissible filter using past output measurements of the system, guaranteeing the asymptotic stability of the filtering error system with a given [Formula: see text] performance index. Based on the piecewise fuzzy Lyapunov functions and the projection lemma, a new sufficient condition for [Formula: see text] filtering performance analysis is first derived, and then the filter synthesis is carried out. It is shown that the filter gains can be obtained by solving a set of linear matrix inequalities. In addition, it is also shown that the filtering performance can be improved with the increasing number of past output measurements used in the filtering design. Finally, two examples are presented to show the advantages and effectiveness of the proposed approach.

This article addresses the finite-time adaptive fuzzy control problem for a class of nonstrict-feedback uncertain nonlinear systems via an event-triggered strategy. A novel design scheme, consisting of finite-time adaptive fuzzy controller and event-triggering mechanism (ETM), is proposed to decrease the number of data transmission and the number of control actuation updates. With the proposed event-triggered adaptive fuzzy control scheme, all the solutions of the resulting closed-loop system are guaranteed to be semi-globally bounded within finite time. Moreover, the feasibility of the proposed ETM is verified by excluding Zeno behavior. In contrast to existing results on similar problems, the restrictions on nonlinearities are relaxed and the more general uncertain nonlinear systems are considered. Finally, an example is provided to illustrate our theoretical results.

This paper studies the finite-time H∞control problem of T-S fuzzy systems subject to external disturbances. A novel fuzzy control approach is developed on the basis of the control Lyapunov function technique and the finite-time Lyapunov theorem so that the closed-loop fuzzy system is finite-time stable with guaranteed H∞performance. It is shown that the proposed control approach does not require the existence of a Lyapunov function before design of the fuzzy controller in comparison with many existing control approaches to finite-time control of nonlinear systems via the technique of control Lyapunov functions, and thus, the finite-time controller and the corresponding Lyapunov function can be designed simultaneously. The procedures on how to obtain the fuzzy controller are provided in terms of linear matrix inequalities. Moreover, an upper bound on the time taken for state trajectories to arrive at their desired targets is estimated. The effectiveness of the proposed finite-time H∞control approach is finally illustrated via numerical simulations.

For a class of stochastic nonlinear systems in pure-feedback form with dead-zone input and multiple time-varying delays, a novel neural network (NN)-based adaptive control approach is presented in this paper through the use of backstepping approach and dynamic surface technique. By choosing proper Lyapunov-Krasovskii functionals, utilizing the characteristic of hyperbolic tangent functions and adopting the function separation technique, difficulties of controller design that introduced by the time-varying delays can be dealt with properly. Moreover, all unknown nonlinear functions are lumped together and approximated by the NN. Additionally, any information over the boundedness of dead-zone parameters is not needed in the process of controller design. The control scheme proposed in this paper ensures the boundedness in probability of all signals in the closed-loop system, besides, excellent performance of arbitrarily small tracking error will be achieved by selecting control parameters appropriately. At last, two numerical simulation examples are provided to verify the validity of the designed algorithm.

This paper investigates the quantized consensus problem of general linear multiagent systems (MASs) by event-triggered control. A novel event-triggered distributed control protocol is proposed based on a new dynamic quantizer. Compared with periodical sampling for most existing works on quantized consensus, the proposed event-triggered control approach can significantly save communication and thus energy resource. It is shown that with the proposed distributed control protocol quantized consensus of the concerned MAS can be achieved, and the Zeno behavior and continuous monitoring of the neighbors' states are avoided. Moreover, it is shown that the required number of the quantization levels of the new quantizer remains small even if the number of the agents in the MAS is large. Three simulation examples are given to illustrate the effectiveness of the proposed consensus protocol.

In this paper, we study the adaptive fuzzy output feedback control problem for a class of nonstrict-feedback nonlinear systems with output constraint. The case of asymmetric and time-varying output constraint together with unmeasurable states is considered. By integrating the barrier Lyapunov function (BLF) approach and the fuzzy approximation technique, we show that the output constrained adaptive fuzzy output feedback control problem for a class of uncertain nonlinear systems can be solved. Finally, the effectiveness of the main result is demonstrated by simulation studies of a one-link manipulator system.

In this paper, the robust output consensus problem for a class of heterogeneous linear multiagent systems (MASs) in presence of aperiodic sampling and random deny-of-service (DoS) attack is investigated. A novel distributed output-feedback control strategy is proposed so that the controlled MAS achieves the objective of output consensus in spite of aperiodic sampling and DoS attack. By assuming that the sampling process is nonuniform and the consecutive attack duration is upper bounded, the closed-loop control system is first described as a discrete-time switched stochastic delay system. Some sufficient conditions are then obtained for the solvability of the secure consensus problem. Furthermore, a constructive design procedure for the proposed controller is then presented. Finally, a simulation example is introduced to illustrate the effectiveness of controller design.

This paper considers a robust adaptive fuzzy control problem for a class of uncertain nonlinear systems via an event-triggered control strategy. Fuzzy logic systems are used to approximate the unknown nonlinear functions in the nonlinear system. A novel robust adaptive control scheme together with a novel event-triggering mechanism (ETM) is proposed to reduce communication burden. It should be noted that both the control signal and the adaptive parameters are updated only at the triggering time instants in the proposed scheme, which further saves the system energy and resources. It is shown that with the proposed event-triggered robust adaptive control scheme, all the signals in the closed-loop system are guaranteed to be semiglobally bounded, and the output of the system converges to a small neighborhood of the origin. Moreover, with the proposed ETM, Zeno behavior can be strictly excluded. Finally, a one-link manipulator system is used to demonstrate the effectiveness of the proposed control scheme.

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

This paper investigates the adaptive output regulation problem for heterogeneous linear multiagent systems under randomly switching communication topologies. The switching mechanism is governed by a time-homogeneous Markov process, whose states correspond to all possible communication topologies among agents. A novel distributed adaptive cooperative controller is presented, where the dynamic compensators are utilized to estimate the exogenous signal for all the agents in mean square sense. The distributed control law is based upon the local information of agents, without using the global information of the communication topologies. Finally, illustrative examples are put forward to demonstrate the effectiveness of the given control scheme.

In this paper, we will investigate the problem of finite frequency memory fixed-order output feedback controller design for Takagi–Sugeno (T–S) fuzzy affine systems. It is assumed that the disturbances reside in a finite frequency range, i.e., the low, middle, or high frequency range. The objective is to design a memory piecewise affine (PWA) controller by using past output measurements to guarantee the asymptotic stability of the resulting closed-loop system with a prescribed finite frequency $\mathscr H_{\infty }$ performance. Via the system state-input augmentation, a novel descriptor system approach is proposed to facilitate the controller design. All the design conditions are formulated in the form of linear matrix inequalities. It is also proven that the $\mathscr H_{\infty }$ performance can be improved with the memory control strategy. Finally, simulation studies are presented to show the effectiveness of the proposed design method.

In this paper, an antisynchronization problem is considered for an array of linearly coupled reaction-diffusion neural networks with cooperative-competitive interactions and time-varying coupling delays. The interaction topology among the neural nodes is modeled by a multilayer signed graph. The state evolution of a neuron in each layer of the coupled neural network is described by a reaction-diffusion equation (RDE) with Dirichlet boundary conditions. Then, the collective dynamics of the multilayer neural network are modeled by coupled RDEs with both spatial diffusion coupling and state coupling. An edge-based adaptive antisynchronization strategy is proposed for each neural node to achieve antisynchronization by using only local information of neighboring nodes. Furthermore, when the activation functions of the neural nodes are unknown, a linearly parameterized adaptive antisynchronization strategy is also proposed. The convergence of the antisynchronization errors of the nodes is analyzed by using a Lyapunov-Krasovskii functional method and a structural balance condition. Finally, some numerical simulations are presented to demonstrate the effectiveness of the proposed antisynchronization strategies.

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

This paper investigates exponential stabilization of continuous-time dynamic systems (CDSs) via event-based impulsive control (EIC) approaches, where the impulsive instants are determined by certain state-dependent triggering condition. The global exponential stability criteria via EIC are derived for nonlinear and linear CDSs, respectively. It is also shown that there is no Zeno-behavior for the concerned closed loop control system. In addition, the developed event-based impulsive scheme is applied to the synchronization problem of master and slave memristive neural networks. Furthermore, a self-triggered impulsive control scheme is developed to avoid continuous communication between the master system and slave system. Finally, two numerical simulation examples are presented to illustrate the effectiveness of the proposed event-based impulsive controllers.

This paper addresses the output consensus problem of heterogeneous linear multi-agent systems. We first propose a novel distributed event-triggered control scheme. It is shown that, with the proposed control scheme, the output consensus problem can be solved if two matrix equations are satisfied. Then, we further propose a novel self-triggered control scheme, with which continuous monitoring is avoided. By introducing a fixed timer into both event- and self-triggered control schemes, Zeno behavior can be ruled out for each agent. The effectiveness of the event- and self-triggered control schemes is illustrated by an example.

This paper studies the adaptive finite-time stabilization problem for a class of nonlinear systems described by Takagi-Sugeno (T-S) fuzzy dynamic models with parametric uncertainties. A novel adaptive state feedback control scheme for the T-S fuzzy systems is proposed, and the scheme is developed based on finite-time Lyapunov theorem and adaptive backstepping-like method. Augmented dynamics are introduced in the design of finite-time stabilization controllers to construct suitable finite-time Lyapunov functions. It is shown that finite-time convergence of the closed-loop adaptive control system can be achieved, and the potential controller singularity problem caused by the augmented dynamics can be avoided. In addition, constructive procedures to obtain such an adaptive finite-time controller are given. Convergence time as a transient performance specification is also taken into account, and a finite upper bound on the convergence time is estimated. Finally, two numerical examples are provided to illustrate the effectiveness and practicality of the proposed adaptive control approach.

This paper studies the consensus problem of heterogeneous linear multiagent systems with arbitrarily large constant, time-varying, or distributed communication delays. Novel distributed dynamic controllers are proposed for such multiagent systems with fixed and switching directed communication topologies, respectively. It is shown that the controlled heterogeneous linear multiagent system can reach consensus for arbitrarily large constant, time-varying, and distributed communication delays under some sufficient conditions. Simulation examples are provided to demonstrate the effectiveness of the proposed controllers.

This paper considers the finite-time stabilization problem for a class of nonlinear systems that can be described by Takagi-Sugeno (T-S) fuzzy models. We propose a novel finite-time switching fuzzy control scheme for T-S fuzzy models, and the scheme is based on the Lyapunov stability theory and the control Lyapunov function technique. It is shown that the finite-time fuzzy controller and the quadratic control Lyapunov function can be obtained at the same time by solving a set of linear matrix inequalities, which can be easily facilitated by available software packages. It is also shown that the potential control law singularity can be avoided with the proposed control scheme. Unlike many existing approaches to finite-time stabilization of general nonlinear systems, the proposed approach does not require the restrictive assumption on the existence of a control Lyapunov function before the corresponding control law is constructed. Furthermore, a finite upper bound on the settling time is estimated, which indicates that within the settling time, the system trajectory would arrive and stay at the origin thereafter. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed approach.

The memristor has been extensively studied in electrical engineering and biological sciences as a means to compactly implement the synaptic function in neural networks. The cellular neural network (CNN) is one of the most implementable artificial neural network models and capable of massively parallel analog processing. In this paper, a novel memristive multilayer CNN (Mm-CNN) model is presented along with its performance analysis and applications. In this new CNN design, the memristor crossbar circuit acts as the synapse, which realizes one signed synaptic weight with a pair of memristors and performs the synaptic weighting compactly and linearly. Moreover, the complex weighted summation is executed in an efficient way with a proper design of Mm-CNN cell circuits. The proposed Mm-CNN has several merits, such as compactness, nonvolatility, versatility, and programmability of synaptic weights. Its performance in several image processing applications is illustrated through simulations.

In this talk distributed even triggered control algorithms will be presented for output consensus of heterogeneous multi agent systems with general linear dynamics, with the objective to reduce the number of controller updates and communication exchanges. It is shown that the output consensus problem can be solved by the proposed event triggered control algorithms if a necessary and sufficient condition is satisfied. Then a self triggered control scheme is also developed, where continuous monitoring of measurement errors can be avoided. The feasibility of both proposed control schemes is discussed by excluding Zeno behavior. It is also shown that agents are able to achieve output consensus with significant reduction of the number of triggering events, controller updates and communication transmission. As a result, energy can be saved and the lifespan of the agents can be prolonged. A numerical example is given to illustrate the effectiveness of the proposed control schemes.

By the combination of the adaptive backstepping design with the dynamic surface control technique, an novel adaptive neural control approach is investigated for a class of pure-feedback stochastic nonlinear systems with multiple unknown time-varying delays. To overcome the design difficulty arising from the non-affine structure of pure-feedback stochastic systems, the mean value theorem is exploited. The design difficulties due to multiple unknown time-varying delay functions are overcome by using the function separation technique, the appropriate Lyapunov-Krasovskii functionals and the desirable property of hyperbolic tangent functions. The radial-basis-function (RBF) neural networks are utilized to approximate the unknown nonlinear functions. It is shown that the proposed control approach can guarantee that all signals of the closed-loop system are bounded in probability, and the tracking errors can be made arbitrarily small in probability by choosing suitable design parameters. Finally, simulation example is provided to demonstrate the effectiveness of the proposed control scheme.

This paper studies the consensus problem of multi-agent systems with general linear dynamics. We propose a novel event-triggered control scheme with some desirable features, namely, distributed, asynchronous, and independent. It is shown that consensus of the controlled multi-agent system can be reached asymptotically. The feasibility of the event-triggered strategy is further verified by the exclusion of both singular triggering and Zeno behavior. Moreover, a self-triggered algorithm is developed, where the next triggering time instant for each agent is determined based on its local information at the previous triggering time instant. Continuous monitoring of measurement errors is thus avoided. The effectiveness of the proposed control schemes is demonstrated by two examples.

This paper investigates the problems of universal fuzzy model and universal fuzzy controller for discrete-time nonaffine nonlinear systems (NNSs). It is shown that a kind of generalized T-S fuzzy model is the universal fuzzy model for discrete-time NNSs satisfying a sufficient condition. The results on universal fuzzy controllers are presented for two classes of discrete-time stabilizable NNSs. Constructive procedures are provided to construct the model reference fuzzy controllers. The simulation example of an inverted pendulum is presented to illustrate the effectiveness and advantages of the proposed method. These results significantly extend the approach for potential applications in solving complex engineering problems.

This paper investigates the output consensus problem of heterogeneous discrete-time multiagent systems with individual agents subject to structural uncertainties and different disturbances. A novel distributed control law based on internal reference models is first presented for output consensus of heterogeneous discrete-time multiagent systems without structural uncertainties, where internal reference models embedded in controllers are designed with the objective of reducing communication costs. Then based on the distributed internal reference models and the well-known internal model principle, a distributed control law is further presented for output consensus of heterogeneous discrete-time multiagent systems with structural uncertainties. It is shown in both cases that the consensus trajectory of the internal reference models determines the output trajectories of agents. Finally, numerical simulation results are provided to illustrate the effectiveness of the proposed control schemes.

This paper addresses the sliding mode control problem for a class of Takagi-Sugeno fuzzy systems with matched uncertainties. Different from the conventional memoryless sliding surface, a memory-based sliding surface is proposed which consists of not only the current state but also the delayed state. Both robust and adaptive fuzzy sliding mode controllers are designed based on the proposed memory-based sliding surface. It is shown that the sliding surface can be reached and the closed-loop control system is asymptotically stable. Furthermore, to reduce the chattering, some continuous sliding mode controllers are also presented. Finally, the ball and beam system is used to illustrate the advantages and effectiveness of the proposed approaches. It can be seen that, with the proposed control approaches, not only can the stability be guaranteed, but also its transient performance can be improved significantly.

A multiconsensus problem of multiagent networks is solved in this paper, where multiconsensus refers to that the states of multiple agents in each subnetwork asymptotically converge to an individual consistent value when there exist information exchanges among subnetworks. A distributed impulsive protocol is proposed to achieve multiconsensus of second-order multiagent networks in terms of three categories: 1) stationary multiconsensus; 2) the first dynamic multiconsensus; and 3) the second dynamic multiconsensus. This impulsive protocol utilizes only sampled position data and is implemented at sampling instants. For those three categories of multiconsensus, the control parameters in the impulsive protocol are designed, respectively. Moreover, necessary and sufficient conditions are derived, under which each multiconsensus can be reached asymptotically. Several simulations are finally provided to demonstrate the effectiveness of the obtained theoretical results.

This paper is concerned with the problem of adaptive neural control for a class of uncertain or ill-defined nonaffine nonlinear systems. Using a self-organizing radial basis function neural network (RBFNN), a direct self-constructing neural controller (DSNC) is designed so that unknown nonlinearities can be approximated and the closed-loop system is stable. The key features of the proposed DSNC design scheme can be summarized as follows. First, different from the existing results in literature, a self-organizing RBFNN with adaptive threshold is constructed online for DSNC to improve the control performance. Second, the control law and adaptive law for the weights of RBFNN are established so that the closed-loop system is stable in the term of Lyapunov stability theory. Third, the tracking error is guaranteed to uniformly asymptotically converge to zero with the aid of an additional robustifying control term. An example is finally given to demonstrate the design procedure and the performance of the proposed method. Simulation results reveal the effectiveness of the proposed method.

In this paper we develop convex delay-dependent conditions in terms of linear matrix inequalities (LMIs) for the synthesis of fuzzy stabilizing feedback controllers. The condition is developed from a novel Lyapunov-Krasovskii fuzzy function. We consider that the T-S fuzzy model represents the nonlinear system only inside a region of validity. Because of this, we determine a domain of stability inside the region of validity, such that the trajectories of the nonlinear system in closed-loop starting from this domain converge asymptotically to origin. The domain of stability is characterized through a Cartesian product of two sets, where the first one is used to treat the initial state vector at the sample k = 0, and the second set is used to treat the delayed state vectors and the difference between two sampling of the delayed state vectors. We also develop a convex optimization problem to compute the gains of the fuzzy controllers to maximize the domain of stability. Finally, we show an example to demonstrate the developed conditions.

This paper considers the global robust output regulation problem of nonlinear output feedback systems in the case that both exosystem and high-frequency gain sign are unknown. The unavailability of these information poses challenges in the control law design. To solve the output regulation problem, a feedback controller is proposed by integrating the internal model principle, certainty equivalence adaptive control scheme and the Nussbaum gain technique.

In this paper, a spiking growing algorithm (SGA) is proposed for optimizing the structure of radial basis function (RBF) neural network. Inspired by the synchronous behavior of spiking neurons, the spiking strength (ss) of the hidden neurons is defined as the criteria of SGA, which investigates a new way to simulate the connections between hidden and output neurons of RBF neural network. This SGA-based RBF (SGA-RBF) neural network can self-organize the hidden neurons online, to achieve the appropriate network efficiency. Meanwhile, to ensure the accuracy of SGA-RBF neural network, the structure-adjusting and parameters-training phases are performed simultaneously. Simulation results demonstrate that the proposed method can obtain a higher precision in comparison with some other existing methods.

This paper presents a novel mathematical model for the TiO2thin-film memristor device discovered by Hewlett-Packard (HP) labs. Our proposed model considers the boundary conditions and the nonlinear ionic drift effects by using a piecewise linear window function. Four adjustable parameters associated with the window function enable the model to capture complex dynamics of a physical HP memristor. Furthermore, we realize synaptic connections by utilizing the proposed memristor model and provide an implementation scheme for a small-world multilayer neural network. Simulation results are presented to validate the mathematical model and the performance of the neural network in nonlinear function approximation.

In this paper, a novel dynamic sliding mode control scheme is proposed for a class of uncertain stochastic nonlinear time-delay systems represented by Takagi-Sugeno fuzzy models. The key advantage of the proposed scheme is that two very restrictive assumptions in most existing sliding mode control approaches for stochastic fuzzy systems have been removed. It is shown that the closed-loop control system trajectories can be driven onto the sliding surface in finite time almost certainly. It is also shown that the stochastic stability of the resulting sliding motion can be guaranteed in terms of linear matrix inequalities; moreover, the sliding-mode controller can be obtained simultaneously. Simulation results illustrating the advantages and effectiveness of the proposed approaches are also provided.

In this paper, the universal integral sliding-mode controller problem for the general stochastic nonlinear systems modeled by Itô type stochastic differential equations is investigated. One of the main contributions is that a novel dynamic integral sliding mode control (DISMC) scheme is developed for stochastic nonlinear systems based on their stochastic T-S fuzzy approximation models. The key advantage of the proposed DISMC scheme is that two very restrictive assumptions in most existing ISMC approaches to stochastic fuzzy systems have been removed. Based on the stochastic Lyapunov theory, it is shown that the closed-loop control system trajectories are kept on the integral sliding surface almost surely since the initial time, and moreover, the stochastic stability of the sliding motion can be guaranteed in terms of linear matrix inequalities. Another main contribution is that the results of universal fuzzy integral sliding-mode controllers for two classes of stochastic nonlinear systems, along with constructive procedures to obtain the universal fuzzy integral sliding-mode controllers, are provided, respectively. Simulation results from an inverted pendulum example are presented to illustrate the advantages and effectiveness of the proposed approaches.

In this paper, a novel dynamic integral sliding-mode control (ISMC) scheme is proposed for a class of uncertain stochastic nonlinear time-delay systems represented by Takagi-Sugeno fuzzy models. The key advantage of the proposed scheme is that two very restrictive assumptions in most existing ISMC approaches for stochastic fuzzy systems have been removed. It is shown that the closed-loop control system trajectories are kept on the integral sliding surface almost surely since the initial time. It is also shown that the stochastic stability of the resulting sliding motion can be guaranteed in terms of linear matrix inequalities, and moreover, the sliding-mode controller can be obtained simultaneously. Simulation results from an inverted pendulum example illustrating the advantages and effectiveness of the proposed approaches are provided in the end.

This paper addresses the sliding-mode control (SMC) design problem for a class of uncertain nonlinear systems that can be represented by Takagi-Sugeno (T-S) fuzzy models. We propose a novel dynamic sliding-mode control scheme for T-S fuzzy models, aiming to eliminate the restrictive assumption that all subsystems share a common input matrix, which is required in most existing fuzzy SMC approaches. Sufficient conditions for the reachability of the sliding surface and asymptotic stability of the sliding motion are formulated in the form of linear matrix inequalities. Finally, simulation results that illustrate the advantages and effectiveness of the proposed approaches are provided.

This paper addresses the universal fuzzy integral sliding-mode controllers' problem for continuous-time multi-input multi-output nonlinear systems based on Takagi-Sugeno (T-S) fuzzy models. By using the approximation capability of T-S fuzzy models, the nonlinear systems are expressed by uncertain T-S fuzzy models with norm-bounded approximation errors. A novel fuzzy dynamic integral sliding-mode control (DISMC) scheme is then developed for the nonlinear systems based on their T-S fuzzy approximation models. One of the key features of the new DISMC scheme is that the restrictive assumption that all local linear systems share a common input matrix, which is required in most existing fuzzy integral sliding-mode control (ISMC) approaches, is removed. Furthermore, the results of universal fuzzy ISMCs for two classes of nonlinear systems, along with constructive procedures to obtain the universal fuzzy ISMCs, are provided, respectively. Finally, the advantages and effectiveness of the proposed approaches are illustrated via a numerical example.

In this paper, robust model predictive control for uncertain discrete-time Takagi-Sugeno (T-S) fuzzy systems with input constraints and persistent disturbances is considered. The robust positively invariant set for T-S fuzzy systems is investigated. Based on this result, computation of the terminal constraint set is proposed, which is of crucial importance in the robust predictive controller design. A zero-step predictive controller is discussed first, which has a time-varying terminal constraint set. The recursive feasibility and input-to-state stability can be ensured. Then, a novel controller with N-step prediction is further proposed, which can be used to deal with the case of fixed terminal constraint set. The implementation of the N-step controller involves both online and offline computations. It is shown that a sequence of approximating robust one-step sets can be computed offline. Then, bisection searches are carried out online, as well as a constrained convex optimization problem. The N-step controller guarantees that the system state can be steered to the terminal constraint set in less than N-steps, if the initial state lies in a specific region. Simulation results are finally presented to show the effectiveness of the proposed controllers.

We address the problem of subchannel and transmission power allocation in orthogonal frequency division multiple access relay networks with an aim to maximize the sum rate and maintain proportional rate fairness among users. Because the formulated problem is a mixed-integer nonlinear optimization problem with an extremely high computational complexity, we propose a low-complexity suboptimal algorithm, which is a two-step separated subchannel and power allocation algorithm. In the first step, subchannels are allocated to each user, whereas in the second step, the optimal power allocation is carried out on the basis of the given subchannel allocation and the nonlinear interval Gauss–Seidel method. Simulation results have demonstrated that the proposed algorithm can achieve a good trade-off between the efficiency and the fairness compared with two other existing relevant algorithms. In particular, the proposed algorithm can always achieve 100% fairness under various conditions. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, the sliding mode control (SMC) design problem for uncertain T-S fuzzy systems is investigated. It is noted that most existing fuzzy SMC approaches rely on a very restrictive assumption that all subsystems of the T-S fuzzy systems have the same input matrix. Aiming to remove this assumption, we propose a novel dynamic sliding mode control (DSMC) scheme for a class of uncertain T-S fuzzy systems. It is shown that the sliding surface can be reached in finite time and the asymptotic stability of the sliding motion can be guaranteed if a set of linear matrix inequalities are feasible. Simulation results from two numerical examples illustrating the effectiveness and advantages of the proposed approaches are also provided.

This paper studies the global asymptotic stability problem of a class of reaction–diffusion neural networks with time-varying delays. To overcome the difficulty caused by the partial differential term, a novel Lyapunov–Krasovskii functional is proposed, and a partial differential equation technique together with a linear operator approach are also applied to obtain the delay-dependent stability criteria, which are less conservative than the existing results. Finally, simulation examples are given to verify and illustrate the theoretical analysis.

This paper investigates the universal fuzzy model and universal fuzzy controller problems for stochastic nonaffine nonlinear systems. The underlying mechanism of stochastic fuzzy logic is first discussed, and a stochastic generalized fuzzy model with new stochastic fuzzy rule base is then given. Based on their function approximation capability, these kinds of stochastic generalized fuzzy models are shown to be universal fuzzy models for stochastic nonaffine nonlinear systems under some sufficient conditions. An approach to stabilization controller design for stochastic nonaffine nonlinear systems is then developed through their stochastic generalized Takagi-Sugeno (T-S) fuzzy approximation models. Then, the results of universal fuzzy controllers for two classes of stochastic nonlinear systems, along with constructive procedures to obtain the universal fuzzy controllers, are also provided, respectively. Finally, a numerical example is presented to illustrate the effectiveness of the proposed approach.

This paper investigates the problem of robust H∞output feedback control for a class of continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems with parametric uncertainties and input constraints. The objective is to design a suitable constrained piecewise affine static output feedback controller, guaranteeing the asymptotic stability of the resulting closed-loop fuzzy control system with a prescribed H∞disturbance attenuation level. Based on a smooth piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexification techniques, some new results are developed for static output feedback controller synthesis of the underlying continuous-time T-S fuzzy affine systems. It is shown that the controller gains can be obtained by solving a set of linear matrix inequalities (LMIs). Finally, three examples are provided to illustrate the effectiveness of the proposed methods.

This paper investigates the universal fuzzy models problem and universal fuzzy controllers problem for discrete-time general nonlinear systems based on a class of generalized T-S fuzzy models. The generalized T-S fuzzy models, which are shown to be universal function approximators, are also proved to be universal fuzzy models for non-affine nonlinear systems under some sufficient conditions. The results of static and dynamic universal fuzzy controllers for two classes of nonlinear systems are then given, respectively, and constructive procedures to obtain the universal fuzzy controllers are also provided.

This paper addresses the coordinated tracking control problem for multi-agent systems with general linear dynamics and a leader whose control input might be nonzero and not available to any follower. Based on the relative states of neighboring agents, two distributed continuous controllers with, respectively, static and adaptive coupling gains, are designed, under which the tracking error of each follower is uniformly ultimately bounded, if the communication graph among the followers is undirected, the leader has directed paths to all followers, and the leader's control input is bounded. A sufficient condition for the existence of the distributed controllers is that each agent is stabilizable.

This paper studies the problem of robust H∞output feedback control for a class of continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems with parametric uncertainties and input constraints. Based on a smooth piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexification techniques, some new results are developed for static output feedback H∞controller synthesis of the underlying continuous-time T-S fuzzy affine systems. It is shown that the controller gains can be obtained by solving a set of linear matrix inequalities. Finally, a simulation example is provided to illustrate the application of the proposed methods.

This paper is concerned with the problem of robust${\mathscr H}_{\infty }$output feedback control for a class of continuous-time Takagi–Sugeno (T–S) fuzzy affine dynamic systems using quantized measurements. The objective is to design a suitable observer-based dynamic output feedback controller that guarantees the global stability of the resulting closed-loop fuzzy system with a prescribed${\mathscr H}_{\infty }$disturbance attenuation level. Based on common/piecewise quadratic Lyapunov functions combined with S-procedure and some matrix inequality convexification techniques, some new results are developed to the controller synthesis for the underlying continuous-time T–S fuzzy affine systems with unmeasurable premise variables. All the solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, two simulation examples are provided to illustrate the advantages of the proposed approaches.

This paper presents a novel approach to control general nonlinear systems based on Takagi-Sugeno (T-S) fuzzy dynamic models. It is first shown that a general nonlinear system can be approximated by a generalized T-S fuzzy model to any degree of accuracy on any compact set. It is then shown that the stabilization problem of the general nonlinear system can be solved as a robust stabilization problem of the developed T-S fuzzy system with the approximation errors as the uncertainty term. Based on a piecewise quadratic Lyapunov function, the robust semiglobal stabilization and H∞ control of the general nonlinear system are formulated in the form of linear matrix inequalities. Simulation results are provided to illustrate the effectiveness of the proposed approaches.

In this paper, the problem of generalized H2filtering is concerned for a class of discrete-time T-S fuzzy systems with measurement quantization and packet loss. The quantized measurements are transmitted to the filter via an imperfect communication channel, where the phenomenon of packet loss can be encountered. A random binary process is utilized to describe the packet dropouts, while the quantization errors are treated as sector bound uncertainties. Attention is focused on the design of generalized H2piecewise filter such that the filtering error system is stochastically stable and preserves a guaranteed generalized H2performance. The developed filter gains can be obtained by solving a set of linear matrix inequalities. Finally, an illustrative example is provided to show the effectiveness of the proposed method.

A novel approach to control of general nonlinear system based on T-S fuzzy model is presented in this paper. Firstly, it is shown that a general nonlinear system can be approximated by a generalized T-S fuzzy model to arbitrary degree of accuracy on any compact set. And the basic idea of the proposed approach is to stabilize the general nonlinear system by solving a robust stabilization problem of the developed T-S fuzzy system with the approximation errors as the uncertainty term. Then using a piecewise Lyapunov function, robust semi-global stabilization and generalized H2control of the general nonlinear system are formulated in the form of linear matrix inequalities. Finally, an example is provided to demonstrate the effectiveness of the proposed approaches.

This paper investigates the problem of robustH∞state estimation for a class of multichannel networked nonlinear systems with multiple packet dropouts. The nonlinear plant is represented by Takagi-Sugeno (T-S) fuzzy-affine dynamic models with norm-bounded uncertainties, and stochastic variables with general probability distributions are adopted to characterize the data missing phenomenon in output channels. The objective is to design an admissible state estimator guaranteeing the stochastic stability of the resulting estimation-error system with a prescribedH∞disturbance attenuation level. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the estimator implementation with state-space partition may not be synchronized with the state trajectories of the plant. Based on a piecewise-quadratic Lyapunov function combined with S -procedure and some matrix-inequality-convexifying techniques, two different approaches are developed to robust filtering design for the underlying T-S fuzzy-affine systems with unreliable communication links. All the solutions to the problem are formulated in the form of linear-matrix inequalities (LMIs). Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This paper investigates the problem of robust $\hbox{\scr{H}}_{\infty }$ output-feedback control for a class of networked nonlinear systems with multiple packet dropouts. The nonlinear plant is represented by Takagi–Sugeno (T–S) fuzzy affine dynamic models with norm-bounded uncertainties, and stochastic variables that satisfy the Bernoulli random binary distribution are adopted to characterize the data-missing phenomenon. The objective is to design an admissible output-feedback controller that guarantees the stochastic stability of the resulting closed-loop system with a prescribed $\hbox{\scr{H}}_{\infty }$ disturbance attenuation level. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the controller implementation with state-space partition may not be synchronous with the state trajectories of the plant. Based on a piecewise quadratic Lyapunov function combined with an S-procedure and some matrix inequality convexifying techniques, two different approaches to robust output-feedback controller design are developed for the underlying T–S fuzzy affine systems with unreliable communication links. The solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This paper addresses the issue of piecewise integral sliding-mode control (ISMC) for the Takagi-Sugeno (T-S) fuzzy systems. ISMC is chosen to stabilize the T-S fuzzy system because of its superior capability in treating uncertainties. Individual integral sliding surfaces are designed in different operating regions of the T-S fuzzy system. Conditions on the existence of the sliding mode in the associated region are given. The chattering phenomenon around region boundaries is analyzed, and the prevention of such chattering is discussed. An illustrative example is finally given to show the efficiency of the proposed method.

This paper addresses piecewise sliding-mode control for Takagi-Sugeno (T-S) fuzzy models. A novel sliding-mode control (SMC) design approach is developed which is based on individual sliding surface in each local region of the T-S fuzzy systems. Conditions of existence of sliding mode in the associated region are given. The chattering effect around region boundaries is analyzed, and prevention of such chattering is discussed. Two illustrative examples are finally given to illustrate the effectiveness and performance of the proposed controller.

This paper investigates the problem of H∞filtering for a class of nonlinear discrete-time systems with measurement quantization and packet dropouts. Each output is transmitted via an independent communication channel, and the phenomenon of packet dropouts in transmission is governed by an individual random binary distribution, while the quantization errors are treated as sector-bound uncertainties. Based on a piecewise-Lyapunov function, an approach to the design of H∞-piecewise filter is pro posed such that the filtering-error system is stochastically stable with a guaranteed H∞performance. Some slack matrices are introduced to facilitate the filter design procedure by eliminating the coupling between the Lyapunov matrices and the system matrices. The filter parameters can be obtained by solving a set of linear matrix inequalities (LMIs), which are numerically tractable with commercially available software. Finally, two illustrative examples are provided to show the effectiveness of the proposed method.

This brief studies the global exponential stability of the equilibrium point of discrete-time delayed Hopfield neural networks (DHNNs) with impulse effects by using difference inequalities. We shall consider the stabilizing effects of impulses when the corresponding impulse-free DHNN is even not asymptotically stable. The obtained results characterize the aggregated effects of impulses and deviation of the impulse-free DHNN from its equilibrium point on the exponential stability of the whole system. It is shown that, because of effects of impulses, the impulsive discrete-time DHNN may be exponentially stable even if the evolution of impulse-free component deviates from its equilibrium point exponentially.

In this paper, an adaptive fuzzy backstepping dynamic surface control (DSC) approach is developed for a class of multiple-input-multiple-output nonlinear systems with immeasurable states. Using fuzzy-logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is designed to estimate the immeasurable states. By combining adaptive-backstepping technique and DSC technique, an adaptive fuzzy output-feedback backstepping-control approach is developed. The proposed control method not only overcomes the problem of "explosion of complexity" inherent in the backstepping-design methods but also overcomes the problem of unavailable state measurements. It is proved that all the signals of the closed-loop adaptive-control system are semiglobally uniformly ultimately bounded, and the tracking errors converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approach.

Consensus strategies find extensive applications in coordination of robot groups and decision making of agents. Since balanced graph plays an important role in the average consensus problem for directed communication networks, this work explores the conditions and algorithms for the digraph balancing problem. It has been proved that a directed graph can be balanced if and only if the null space of its incidence matrix contains positive vectors. Then two weight balance algorithms have been proposed, and the conditions for obtaining a unique balanced solution have been investigated. This work has also pointed out the relationship between the weight balance problem and the features of the corresponding underlying Markov chain. Finally, two numerical examples are presented to verify the proposed algorithms.

Robust adaptive-fuzzy-tracking control of a class of uncertain multi-input/multi-output nonlinear systems with coupled interconnections is considered in this paper. Takagi–Sugeno (T–S) fuzzy systems are used to approximate the unknown system functions. A novel adaptive-control scheme is developed on the basis of the so-called “dynamic-surface control” and “minimal-learning parameters” techniques. The proposed scheme has following two key features. First, the number of parameters updated online for each subsystem is reduced to one, and both problems of “curse of dimension” for high-dimensional systems and “explosion of complexity” inherent in the conventional backstepping methods are circumvented. Second, the potential controller-singularity problem in some of the existing adaptive-control schemes with feedback-linearization techniques is overcome. It is shown via Lyapunov theory that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation results via two examples are presented to demonstrate the effectiveness and advantages of the proposed scheme.

This paper investigates the problem of robust H∞output-feedback control for a class of nonlinear systems under unreliable communication links. The nonlinear plant is represented by a Takagi-Sugeno (T-S) uncertain fuzzy model, and the communication links between the plant and controller are assumed to be imperfect, i.e., data-packet dropouts occur intermittently, which is often the case in a network environment. Stochastic variables that satisfy the Bernoulli random-binary distribution are adopted to characterize the data-missing phenomenon, and the attention is focused on the design of a piecewise static-output-feedback (SOF) controller such that the closed-loop system is stochastically stable with a guaranteed H∞performance. Based on a piecewise Lyapunov function combined with some novel convexifying techniques, the solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, simulation examples are also provided to illustrate the effectiveness of the proposed approaches.

A robust adaptive tracking control approach is presented for a class of strict-feedback single-input-single-output nonlinear systems. By employing radial-basis-function neural networks to account for system uncertainties, the proposed scheme is developed by combining "dynamic surface control" and "minimal learning parameter" techniques. The key features of the algorithm are that, first, the problem of "explosion of complexity" inherent in the conventional backstepping method is avoided, second, the number of parameters updated online for each subsystem is reduced to 2, and, third, the possible controller singularity problem in the approximation-based adaptive control schemes with feedback linearization technique is removed. These features result in a much simpler adaptive control algorithm, which is convenient to implement in applications. In addition, it is shown via input-to-state stability theory and small gain approach that all signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, three simulation examples are used to demonstrate the effectiveness of the proposed scheme.

This paper investigates delay-dependent$\hbox{H}_{\bm\infty }$filter design problems for discrete-time fuzzy systems with time-varying delays. First, a novel delay-dependent piecewise Lyapunov–Krasovskii functional (DDPLKF) is proposed in which both the upper bound of delays and the delay interval are considered. Based on this DDPLKF, the delay-dependent stability criteria for discrete-time systems with constant or time-varying delays are obtained, respectively. Then, delay-dependent full-order and reduced-order$\hbox{H}_{\bm\infty }$filter design approaches are proposed. The filter parameters can be obtained by solving a set of linear matrix inequalities (LMIs). Simulation examples are also given to illustrate the performance of the proposed approaches. It is shown that our approaches are less conservative and that the corresponding$\hbox{H}_{\bm\infty }$filters can achieve better performance than the existing approaches.

This paper investigates the problem of delay-dependent robustHinfinfiltering design for a class of uncertain discrete-time state-delayed Takagi-Sugeno (T-S) fuzzy systems. The state delay is assumed to be time-varying and of an interval-like type, which means that both the lower and upper bounds of the time-varying delay are available. The parameter uncertainties are assumed to have a structured linear fractional form. Based on a novel fuzzy-basis-dependent Lyapunov-Krasovskii functional combined with Finsler's lemma and an improved free-weighting matrix technique for delay-dependent criteria, a new sufficient condition for robustHinfinperformance analysis is first derived, and then, the filter synthesis is developed. It is shown that by using a simple linearization technique incorporating a bounding inequality, a unified framework can be developed such that both the full-order and reduced-order filters can be obtained by solving a set of linear matrix inequalities (LMIs), which are numerically efficient with commercially available software. Finally, simulation examples are provided to illustrate the advantages and less conservatism of the proposed approach.

This paper is concerned with the robustHinfincontrol problem for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with time delays and uncertain parameters. The time delay is assumed to be infinitely distributed in the discrete-time domain, and the uncertain parameters are norm-bounded. By using the linear matrix inequality (LMI) technique, sufficient conditions are derived for ensuring the exponential stability as well as theHinfinperformance for the closed-loop fuzzy control system. It is also shown that the controller gain can be characterized in terms of the solution to a set of LMIs, which can be easily solved by using standard software packages. A simulation example is exploited in order to illustrate the effectiveness of the proposed design procedures.

The solid oxide fuel cell (SOFC) is widely accepted for clean and distributed power generation use, but critical operation problems often occur when the stand-alone fuel cell is directly connected to the electricity grid or the dc electric user. In order to address these problems, in this paper, a data-driven fuzzy modeling method is employed to identify the dynamic model of an integrated SOFC/capacitor system. A novel offset-free input-to-state stable fuzzy predictive controller is developed based on the obtained fuzzy model. Both the rapid power load following and safe SOFC operation requirements are taken into account in the design of the closed-loop control system. Simulations are also given to demonstrate the load following control performance of the proposed fuzzy predictive control strategy for the SOFC/capacitor power system.

In this paper, a novel anti-windup dynamic output compensator is developed to deal with the robustHinfinoutput feedback control problem of nonlinear processes with amplitude and rate actuator saturations and external disturbances. Via fuzzy modeling of nonlinear systems, the proposed piecewise fuzzy anti-windup dynamic output feedback controller is designed based on piecewise quadratic Lyapunov functions. It is shown that with sector conditions, robust output feedback stabilization of an input-constrained nonlinear process can be formulated as a convex optimization problem subject to linear matrix inequalities. Simulation study on a strongly nonlinear continuously stirred tank reactor (CSTR) benchmark plant is given to show the performance of the proposed anti-windup dynamic compensator.

In this paper, we present a synchronization approach to trajectory tracking of multiple mobile robots while maintaining time-varying formations. The main idea is to control each robot to track its desired trajectory while synchronizing its motion with those of other robots to keep relative kinematics relationships, as required by the formation. First, we pose the formation-control problem as a synchronization control problem and identify the synchronization control goal according to the formation requirement. The formation error is measured by the position synchronization error, which is defined based on the established robot network. Second, we develop a synchronous controller for each robot's translation to guarantee that both position and synchronization errors approach zero asymptotically. The rotary controller is also designed to ensure that the robot is always oriented toward its desired position. Both translational and rotary controls are supported by a centralized high-level planer for task monitoring and robot global localization. Finally, we perform simulations and experiments to demonstrate the effectiveness of the proposed synchronization control approach in the formation control tasks.

The output regulation problem is discussed for a class of discrete-time T-S fuzzy systems under periodic disturbances generated form the so-called exosystems. With the assumption that the subsystem in each rule is of the controllable canonical form, the regulation equations are solvable if and only if the poles of the exosystem are different from those of the fuzzy system. By exploiting the structural information encoded in the fuzzy rules, a piecewise state feedback control law can then be constructed to achieve asymptotic rejecting and/or tracking of the unwanted disturbances or the desired trajectory.

A robust adaptive tracking control problem is discussed for a class of strict-feedback uncertain nonlinear systems. Takagi-Sugeno type fuzzy logic systems are used to approximate the uncertainties. A unified and systematic procedure is developed to derive a novel robust adaptive tracking controller by use of the input-to-state stability (ISS) and by combining the dynamic surface control(DSC)-based backstepping technique and generalized small gain approach. The key features of the algorithm are that, firstly, the problem of “explosion of complexity” inherent in the conventional backstepping method is circumvented, secondly, the number of parameters updated on line for each subsystem is reduced dramatically to 2. These features result in a much simpler algorithm, which is convenient to realize in application. In addition, it is shown that all closed-loop signals are semi-global uniformly ultimately bounded(SGUUB). Finally, simulation results via an application example of a pendulum system with motor is used to demonstrate the effectiveness and performance of the proposed scheme.

This paper investigates the problem of delay-dependent robust H℞filtering design for a class of uncertain discrete-time state-delayed T-S fuzzy systems. The state delay is assumed to be time-varying and of an interval-like type, which means that both the lower and upper bounds of the time-varying delay are available. The parameter uncertainties are assumed to have a structured linear fractional form. Based on a novel delay and fuzzy-basis-dependent Lyapunov-Krasovskii functional combined with Finsler’s Lemma, a new sufficient condition for robust H℞performance analysis is firstly derived and then the filter synthesis is developed. It is shown that by using a new linearization technique incorporating a bounding inequality, a unified framework can be developed such that both the full-order and reduced-order filters can be obtained by solving a set of linear matrix inequalities, which are numerically efficient with commercially available software. Finally, a numerical example is provided to illustrate the advantages and less conservatism of the proposed approach.

Solid oxide fuel cell (SOFC) is widely accepted for clean and distributed power generation use, but critical operation problems often occur when stand-alone fuel cell is directly connected to the electricity grid or the DC electric user. In order to address these problems, in this paper a data-driven fuzzy identification method is applied to the dynamic modeling of an integrated SOFC and capacitor system. And the identified fuzzy SOFC model is employed to develop a novel constrained feedforward generalized predictive controller. Both the rapid power load following and safe SOFC operation requirements are taken into account in the design of the closed-loop control system. Simulations are also given to demonstrate the load following control performance of the proposed fuzzy predictive control strategy for the SOFC/Capacitor power system.

This paper revisits the problem of delay-dependent robust Hinfinfiltering design for a class of continuous-time polytopic linear systems with a time-varying state delay. Based on a delay and parameter-dependent Lyapunov-Krasovskii functional combined with Projection Lemma, a new sufficient condition for robust Hinfinperformance analysis is firstly derived and then the filter synthesis is developed by using a novel matrix linearization technique. It is shown that the desired filters can be constructed by solving a set of linear matrix inequalities. Finally, a numerical example is given to show the effectiveness and less conservatism of the proposed method in comparison with the existing approaches.

This paper presents two filter design methods for discrete time fuzzy dynamic systems based on a piecewise quadratic Lyapunov function. It is shown that the resulting filtering error system is globally stable with guaranteed Hinfinor generalized H2performance and the filter gains can be obtained by solving a set of linear matrix inequalities. Two simulation examples are also given to illustrate the performance of the proposed approaches.

The robust state estimation problem for a class of uncertain neural networks with time-varying delay is studied in this paper. The parameter uncertainties are assumed to be norm bounded. Based on a new bounding technique, a sufficient condition is presented to guarantee the existence of the desired state estimator for the uncertain delayed neural networks. The criterion is dependent on the size of the time-varying delay and on the size of the time derivative of the time-varying delay. It is shown that the design of the robust state estimator for such neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. Finally, two simulation examples are given to demonstrate the effectiveness of the developed approach.

This paper presents a novel recurrent neural network for solving nonlinear optimization problems with inequality constraints. Under the condition that the Hessian matrix of the associated Lagrangian function is positive semidefinite, it is shown that the proposed neural network is stable at a Karush-Kuhn-Tucker point in the sense of Lyapunov and its output trajectory is globally convergent to a minimum solution. Compared with variety of the existing projection neural networks, including their extensions and modification, for solving such nonlinearly constrained optimization problems, it is shown that the proposed neural network can solve constrained convex optimization problems and a class of constrained nonconvex optimization problems and there is no restriction on the initial point. Simulation results show the effectiveness of the proposed neural network in solving nonlinearly constrained optimization problems.

This paper formulates and studies a model of hybrid impulsive and switching Hopfield neural networks (NNs). Using switching Lyapunov functions and a generalized Halanay inequality, some general criteria, which characterize the impulse and switching effects in aggregated form, for asymptotic and exponential stability of such NNs with arbitrary and conditioned impulsive switching are established. Several numerical examples are given for illustration and interpretation of the theoretical results.

This paper is concerned with stability analysis and H(infinity) decentralized control of discrete-time fuzzy large-scale systems based on piecewise Lyapunov functions. The fuzzy large-scale systems consist of J interconnected discrete-time Takagi-Sugeno (T-S) fuzzy subsystems, and the stability analysis is based on Lyapunov functions that are piecewise quadratic. It is shown that the stability of the discrete-time fuzzy large-scale systems can be established if a piecewise quadratic Lyapunov function can be constructed, and moreover, the function can be obtained by solving a set of linear matrix inequalities (LMIs) that are numerically feasible. The H(infinity) controllers are also designed by solving a set of LMIs based on these powerful piecewise quadratic Lyapunov functions. It is demonstrated via numerical examples that the stability and controller synthesis results based on the piecewise quadratic Lyapunov functions are less conservative than those based on the common quadratic Lyapunov functions.

This paper investigates delay-dependent H∞filter design problems for discrete-time fuzzy systems with time-varying delays. Firstly, a novel delay-dependent piecewise Lyapunov-Krasovskii functional (DDPLKF) is proposed, in which both the upper bound of delays and the delay interval are considered. Based on this DDPLKF, the delay-dependent stability criteria for discrete-time systems with time-varying delays are obtained. Then, delay-dependent H∞filter design approaches are proposed. The filter parameters can be obtained by solving a set of linear matrix inequalities (LMIs). A simulation example is also given to illustrate the FL filter design procedures and the performance.

In this paper, a fuzzy affine model, which is more capable of representing strongly nonlinear dynamics, is used for predictive controller design. Based on piecewise quadratic Lyapunov functions, the proposed fuzzy affine model predictive control approach can ensure both the closed-loop system stability and the satisfactory transient control performance even under input and state constraints. With the help of partitioned degenerate ellipsoids and S-procedure, the large terminal invariant set of a fuzzy affine system can be achieved offline by solving a convex semi-definite programming problem subject to some linear matrix inequalities, rather than the non-convex bilinear matrix inequalities as in conventional fuzzy affine model based control. Then with the associated terminal cost, the resulting online open-loop predictive control approach can be formulated as a standard quadratic programming problem, which is readily solvable. Simulation results have demonstrated the performance of the proposed approach.

This paper proposes two novel stable fuzzy model predictive controllers based on piecewise Lyapunov functions and the min-max optimization of a quasi-worst case infinite horizon objective function. The main idea is to design state feedback control laws that minimize the worst case objective function based on fuzzy model prediction, and thus to obtain the optimal transient control performance, which is of great importance in industrial process control. Moreover, in both of these predictive controllers, piecewise Lyapunov functions have been used in order to reduce the conservatism of those existent predictive controllers based on common Lyapunov functions. It is shown that the asymptotic stability of the resulting closed-loop discrete-time fuzzy predictive control systems can be established by solving a set of linear matrix inequalities. Moreover, the controller designs of the closed-loop control systems with desired decay rate and input constraints are also considered. Simulations on a numerical example and a highly nonlinear benchmark system are presented to demonstrate the performance of the proposed fuzzy predictive controllers.

In this paper, a novel stable observer-based integral model predictive controller using piecewise Lyapunov functions is proposed for constrained nonlinear systems. The main idea is to design integrator based state feedback control laws that minimize the worst-case objective function based on fuzzy model prediction, and then to design observer based output feedback controller. It is expected that satisfactory transient control performance without any steady-state offset can be achieved. The asymptotic stability of the resulting closed-loop predictive control system is established by solving a set of linear matrix inequalities. Simulations on a highly nonlinear benchmark system are finally presented to demonstrate the tracking performance of the proposed output feedback fuzzy predictive controllers.

This paper investigates the asymptotically stability and robust stability for time-delay Hamiltonian systems. First, the asymptotical stability of two classes of time-delay Hamiltonian systems is studied and some sufficient conditions are derived based on the Lyapunov-Krasovskii (L-K) approach. Then, the robust stability is investigated for polytypic uncertain time-delay Hamiltonian systems, which possesses time-invariant uncertainties belonging to some convex bounded polytypic domain. Finally, several illustrative examples are studied to support the new results proposed in this paper

We consider the distributed random access algorithms for wireless ad hoc networks in which each node needs to tune its persistent probability so as to optimize its own the total throughput. First, we present an asynchronous algorithm for updating persistent probabilities and prices to avoid collision using local coordination. By casting this algorithm as a best response in a cooperative game, we characterize its convergence analytically. We further model that each node attempts to maximize a selfish local payoff function. We characterize the Nash equilibrium (NE) of the non-cooperative game and prove the convergence of a best response algorithm to the unique NE. Then we study the energy efficient throughput maximization problem when the wireless nodes are constrained by their battery power. Despite the inherent difficulty of non-separability of the constraint set, we propose a distributed primal-based algorithm. Its convergence is studied numerically

This paper concerns with the delay-dependent piecewise control problem for a class of time-delay T-S fuzzy systems based on a novel delay-dependent piecewise Lyapunov-Krasovskii functional (DPLKF). A new partition approach for the premise variable space of the fuzzy model is presented to facilitate the piecewise feedback controller design. It is then shown that the global stabilization can be established for the closed-loop system if a DPLKF can be constructed by solving a set of linear matrix inequalities. Meanwhile, the piecewise controller gains can be obtained. Application to chaos control is given to demonstrate the effectiveness and advantage of the proposed method

The output trajectory convergence of an extended projection neural network was developed under the positive definiteness condition of the Jacobian matrix of nonlinear mapping. This note offers several new convergence results. The state trajectory convergence and the output trajectory convergence of the extended projection neural network are obtained under the positive semidefiniteness condition of the Jacobian matrix. Comparison and illustrative examples demonstrate applied significance of these new results.

Based on a novel delay-dependent piecewise Lyapunov–Krasovskii functional (DPLKF), this paper presents delay-dependent stability analysis and synthesis methods for discrete-time Takagi–Sugeno (T–S) fuzzy systems with time delays. It is shown that the stability and stabilization with some required performance can be established for the closed loop control system if there exists a DPLKF and that the DPLKF and the corresponding controller can be obtained by solving a set of linear matrix inequalities (LMIs). New algorithms have also been developed to obtain the maximum value of the allowable constant delay and the suboptimal performance upper bound. An example is finally presented to demonstrate the efficiency and advantage of the proposed methods.

This paper presents an observer based H/sub /spl infin// output feedback synthesis method for discrete time fuzzy dynamic systems based on a piecewise Lyapunov function. The basic idea of the approach is to design an observer based piecewise linear output feedback control law to guarantee the global stability with H/sub /spl infin// performance of the resulting closed-loop fuzzy control systems. It is shown that the controller parameters can be obtained by solving a set of linear matrix inequalities (LMIs) that are numerically feasible with commercially available software. Application to control chaotic systems is given to illustrate the effectiveness and advantages of the proposed method.

This work presents an H/sub /spl infin// controller design method for fuzzy dynamic systems based on techniques of piecewise smooth Lyapunov functions and bilinear matrix inequalities. It is shown that a piecewise continuous Lyapunov function can be used to establish the global stability with H/sub /spl infin// performance of the resulting closed-loop fuzzy control systems and the control laws can be obtained by solving a set of bilinear matrix inequalities (BMIs). Two examples are given to illustrate the application of the proposed methods.

This paper considers the problem of robust exponential epsilon-regulation for a class of multi-input nonlinear systems with uncertainties. The uncertainties appear not only in the feedback channel but also in the control channel. Under some mild assumptions, an adaptive neural network control scheme is developed such that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded and, under the control scheme with initial data starting in some compact set, the states of the closed-loop system is guaranteed to exponentially converge to an arbitrarily specified epsilon-neighborhood about the origin. The important contributions of the present work are that a new exponential uniformly ultimately bounded performance is proposed and that the design parameters and initial condition set can be determined easily. The development generalizes and improves earlier results for the single-input case.

This paper proposes a primal-dual neural network with a one-layer structure for online resolution of constrained kinematic redundancy in robot motion control. Unlike the Lagrangian network, the proposed neural network can handle physical constraints, such as joint limits and joint velocity limits. Compared with the existing primal-dual neural network, the proposed neural network has a low complexity for implementation. Compared with the existing dual neural network, the proposed neural network has no computation of matrix inversion. More importantly, the proposed neural network is theoretically proved to have not only a finite time convergence, but also an exponential convergence rate without any additional assumption. Simulation results show that the proposed neural network has a faster convergence rate than the dual neural network in effectively tracking for the motion control of kinematically redundant manipulators.

This paper introduces some robust stability conditions for a class of time delay systems whose uncertainties are assumed to be time-invariant and belong to convex bounded polytopic domain. Both single polyhedral and double polyhedral Lyapunov functionals are defined according to the form of Lyapunov-Krasovskii functional. And based on these functionals, the new results of robust stability analysis can reduce the conservatism of quadratic stability. All the proposed stability conditions are expressed in terms of linear matrix inequality (LMI). Two numerical examples are given to illustrate the advantages of the stability conditions based on the double polyhedral Lyapunov functional.

This article presents H∞, controller analysis and design methods for piecewise discrete time linear systems based on a piecewise smooth Lyapunov function. It is shown that the H∞, controller analysis or the synthesis problem can be casted as a convex optimaization problem, and the controller can be obtained by solving a set of linear matrix inequalities that is numerically feasible with commercially available software. Simulation examples are also given to illustrate the advantage of the proposed approaches.

This paper deals with chained form systems with strongly nonlinear disturbances and drift terms. The objective is to design robust nonlinear output feedback laws such that the closed-loop systems are globally exponentially stable. The systematic strategy combines the input-state-scaling technique with the so-called backstepping procedure.

In this paper we consider a dual-gradient optimization flow control scheme. In an earlier work it was shown that such algorithms converge in a delay free case. We present the sufficient condition under which the stability can be global focusing on the scenario of a single flow and bottleneck link with delay. We first show that this synchronous algorithm is convergent in general network topology without delay. Then we provide a result that even with delays, the queue length increasing at the router is bounded. The upper bound grows with increase in the number of flows as well as the maximum source sending rate and the maximum round trip delay. The upper bound decreases as the link departing rate and stepsize increase.

This paper presents a stability analysis method for discrete-time Takagi-Sugeno fuzzy dynamic systems based on a piecewise smooth Lyapunov function. It is shown that the stability of the fuzzy dynamic system can be established if a piecewise Lyapunov function can be constructed, and moreover, the function can be obtained by solving a set of linear matrix inequalities that is numerically feasible with commercially available software. It is also demonstrated via numerical examples that the stability result based on the piecewise quadratic Lyapunov functions is less conservative than that based on the common quadratic Lyapunov functions.

This paper presents a controller design method for fuzzy dynamic systems based on a piecewise smooth Lyapunov function. The basic idea of the proposed approach is to construct controllers for the fuzzy dynamic systems in such a way that a piecewise continuous Lyapunov function can be used to establish the global stability with Hinfinity performance of the resulting closed loop fuzzy control systems. It is shown that the control law can be obtained by solving a set of Linear Matrix Inequalities (LMI) that is numerically feasible with commercially available software. An example is given to illustrate the application of the proposed method.

This paper presents a new H infinity controller design method for the discrete time fuzzy systems based on the piecewise Lyapunov functions. The basic idea of the proposed approach is to construct the controller for the fuzzy systems in such a way that a discrete time piecewise Lyapunov function can be used to establish the global stability with H infinity-disturbance attenuation performance of the resulting close loop fuzzy control systems. It is shown that the control laws can be obtained by solving a set of linear matrix inequalities (LMIs) that is numerically tractable with commercially available software. Numerical example is given to demonstrate the advantage of the proposed method.

In this paper, a robust adaptive tracking control problem is discussed for a general class of strict-feedback uncertain nonlinear systems. The systems may possess a wide class of uncertainties referred to as unstructured uncertainties, which are not linearly parameterized and do not have any prior knowledge of the bounding functions. The Takagi-Sugeno type fuzzy logic systems are used to approximate the uncertainties. A unified and systematic procedure is employed to derive two kinds of novel robust adaptive tracking controllers by use of the input-to-state stability (ISS) and by combining the backstepping technique and generalized small gain approach. One is the robust adaptive fuzzy tracking controller (RAFTC) for the system without input gain uncertainty. The other is the robust adaptive fuzzy sliding tracking controller (RAFSTC) for the system with input gain uncertainty. Both algorithms have two advantages, those are, semi-global uniform ultimate boundedness of adaptive control system in the presence of unstructured uncertainties and the adaptive mechanism with minimal learning parameterizations. Four application examples, including a pendulum system with motor, a one-link robot, a ship roll stabilization with actuator and a single-link manipulator with flexible joint, are used to demonstrate the effectiveness and performance of proposed schemes.

This paper presents a controller design method for fuzzy dynamic systems based on techniques of piecewise smooth Lyapunov functions and bilinear matrix inequalities. The basic idea of the proposed approaches is to construct the controller for the fuzzy dynamic systems in such a way that a piecewise continuous Lyapunov function can be used to establish the global stability of the resulting closed loop fuzzy control systems. It is shown that the control law can be obtained by solving a set of Bilinear Matrix Inequalities (BMI). An example is given to illustrate the application of the proposed method.

This paper presents a kind of controller synthesis method for fuzzy dynamic systems based on a piecewise smooth Lyapunov function. The basic idea of the proposed approach is to construct controllers for the fuzzy dynamic systems in such a way that a piecewise continuous Lyapunov function can be used to establish the global stability with H/sub /spl infin// performance of the resulting closed loop fuzzy control systems. It is shown that the control laws can be obtained by solving a set of linear matrix inequalities that is numerically feasible with commercially available software. An example is given to illustrate the application of the proposed methods.

This paper points out some errors existing in the aforementioned paper. Unfortunately, it seems that no immediate correction can be done. The authors reply is provided.

This paper presents a generalized H/sub 2/ controller synthesis method for the Takagi-Sugeno fuzzy dynamic systems based on a piecewise smooth Lyapunov function. The basic idea of the proposed approach is to construct a controller for the fuzzy dynamic systems in such a way that a piecewise continuous Lyapunov function can be used to establish the global stability with generalized H/sub 2/ performance of the resulting closed loop fuzzy control systems. It is shown that the control law can be obtained by solving a set of linear matrix inequalities. An example is given to illustrate the application of the proposed method.

This paper discusses adaptive control for a class of fuzzy dynamic models. The adaptive control law is first designed in each local region and then constructed in the global domain. It is shown that the resulting fuzzy adaptive control system is globally stable. Robustness issues of the adaptive control system are also addressed. A simulation example is given for demonstration of the application of the approach.

A new kind of dynamical fuzzy model is proposed to represent discrete-time complex systems which include both linguistic information and system uncertainties. A new stability analysis and control system design approach is then developed for this kind of dynamical fuzzy model. Furthermore, a constructive algorithm is developed to obtain the H(infinity) feedback control law. An example is given to illustrate the application of the method.

This paper presents dynamic output feedback controller design for fuzzy dynamic systems. Three kinds of controller design methods are proposed based on a smooth Lyapunov function or a piecewise smooth Lyapunov function. The controller design involves solving a set of linear matrix inequalities (LMI's) and the control laws are numerically tractable via LMI techniques. The global stability of the closed-loop fuzzy control system is also established.

A discrete-time fuzzy control system which is composed of a dynamic fuzzy model and a fuzzy-state feedback controller is proposed. Stability of the fuzzy control system is discussed and two sufficient conditions to guarantee the stability of the system are given in terms of uncertain linear system theory. An algorithm is developed to check the stability condition. The controller design method is divided into two procedures, one is to get the state feedback matrix by linear system theory in every local rule map; the other is to determine conditions of global stability by using a nonlinear analysis method. Two examples are used to show the design method.

An adaptive fuzzy system implemented within the framework of neural network is proposed. The integration of the fuzzy system into a neural network enables the new fuzzy system to have learning and adaptive capabilities. The proposed fuzzy neural network can locate its rules and optimize its membership functions by competitive learning, Kalman filter algorithm and extended Kalman filter algorithms. A key feature of the new architecture is that a high dimensional fuzzy system can be implemented with fewer number of rules than the Takagi-Sugeno fuzzy systems. A number of simulations are presented to demonstrate the performance of the proposed system including modeling nonlinear function, operator's control of chemical plant, stock prices and bioreactor (multioutput dynamical system).

The paper considers tracking control of robots in joint space. A new control algorithm is proposed based on the well known computed torque method and a compensating controller. The compensating controller is realized by using a switch type structure and an RBF neural network. It is shown that stability of the closed loop system and better tracking performance can be established based on Lyapunov theory. Simulation results are also provided to support our analysis.

A discrete-time fuzzy control system which is composed of a dynamic fuzzy model and a fuzzy state feedback controller is proposed. Stability of the fuzzy control system is discussed and a sufficient condition to guarantee the stability of the system is given in terms of uncertain linear system theory. The results in this paper improve our previous stability results. An example is used to show the proposed method.

Adaptive control of robotic manipulators in task space coordinates is considered in this paper. A new composite adaptive control law, which uses the prediction error and tracking error to drive parameter estimation, is developed based on sliding mode and a general Lyapunov-like concept. It is shown that global stability and convergence can be achieved for the adaptive control algorithm. The algorithm has the advantage that inverse of Jacobian matrix and the bounded inverse of the estimated inertia matrix are not required. The algorithm is further modified so as to achieve robustness to bounded disturbances. A simulation example is provided to demonstrate the performance of the proposed algorithm.>