Linear output-feedback-based semi-global stabilization for switched nonlinear time-delay systems

This paper focuses on the problem of semi-global output-feedback stabilization for a class of switched nonlinear time-delay systems in strict-feedback form. A switched state observer is first constructed, then switched linear output-feedback controllers for individual subsystems are designed. By skillfully constructing multiple Lyapunov–Krasovskii functionals and successfully solving several troublesome obstacles, such as time-varying delay and switching signals and nonlinearity in the design procedure, the switched linear output-feedback controllers designed can render the resulting closed-loop switched system semi-globally stabilizable under a class of switching signals with average dwell time. Furthermore, under some milder conditions on nonlinearities, the semi-global output-feedback stabilization problem for switched nonlinear time-delay systems is also studied. Simulation studies on two examples, which include a continuous stirred tank reactor, are carried out to demonstrate the effectiveness of the proposed approach.     

Adaptive fault-tolerant control for a class of nonlinear multi-agent systems with actuator faults

This paper considers the distributed adaptive fault-tolerant control problem for linear multi-agent systems with matched unknown nonlinear functions and actuator bias faults. By using fuzzy logic systems to approximate the unknown nonlinear function and constructing a local observer to estimate the states, an effective distributed adaptive fault-tolerant controller is developed. Furthermore, different from the traditional method to estimate the weight matrix, only the weight vector needs to be estimated by exchanging the order of weight vectors and fuzzy basis functions in the fuzzy logic systems. In contrast to the existing results, the assumption that the dimensions of input vector and output vector are equal is removed. In addition, it is proved that the proposed control protocol guarantees all signals in the closed-loop systems are bounded and all agents converge to the leader with bounded residual errors. Finally, simulation examples are given to illustrate the effectiveness of the proposed method.     

Consensus of first-order discrete-time multi-agent systems with time delays

This paper mainly considers the consensus for first-order discrete-time multi-agent systems w.r.t. two key parameters, the step size T and the delay τ. First, the consensus is recast into the concurrent stability for a series of trinomials. Then, for each associated trinomial, we derive a necessary and sufficient stability condition, based on proving the two invariance properties for the asymptotic behavior of the critical unitary roots. As a result, the exhaustive consensus region in the $T?\tau$ parameter space (i.e., the parameter set such that the multi-agent system reaches a consensus iff T and τ belong to that set) is determined. Furthermore, we show that the obtained result also applies to systems with diverse input delays, through an extra sufficient consensus condition. Finally, two illustrative examples are presented.     

Viable immersion and invariance control for a class of nonlinear systems and its application to aero-engines

This paper presents a novel approach to stabilize a class of nonlinear systems with state constraints. The motivation behind this study is the need to develop a stabilizing state feedback controller that does not require the knowledge of Lyapunov function and can regulate the states to the equilibrium while meeting the constraints. By using an integration of two relatively new tools: immersion and invariance (I&I) theory and viability theory, a sufficient condition for stability and stabilizability of a general nonlinear affine system with state constraints is derived; Then, the related results are exploited to stabilize a class of nonlinear system in feedback form and with state constraints represented by inequalities and the viable I&I stabilizing state feedback controller is obtained constructively. Further, an application to a nonlinear aero-engine model with the temperature constraint is given to illustrate the applicability and the effectiveness of the proposed method. Finally, a comparative simulation is presented, highlighting the advantages of the viable I&I controller.     

Sampled observer-based adaptive output feedback fault-tolerant control for a class of strict-feedback nonlinear systems

This paper studies the sampled outputs-based adaptive fault-tolerant control problem for a class of strict-feedback uncertain nonlinear systems, where the nonlinear functions are allowed to include the unmeasured system states. Within the framework, a sampled output observer is introduced to jointly estimate the system states and parameters. By combining the estimated states and the supervisory switching strategy, an adaptive fault-tolerant controller is designed to achieve the desirable tracking performance. By using Lyapunov stability theory, it is proved that all the signals of the closed-loop systems are bounded and the tracking error converges to an adjustable neighbourhood of the origin eventually both in the fault free and faulty cases. Especially, if the outputs are available all the time, the proposed output feedback fault-tolerant control method can ensure the tracking error satisfy the prescribed performance bounds regardless of the faults. Finally, two examples are used to illustrate the effectiveness of the proposed method.     

Adaptive disturbance attenuation for a class of uncertain nonlinear systems: A double-gain nonlinear observer method

This paper is concerned with the problem of adaptive disturbance attenuation for a class of nonlinear systems. The traditional adaptive methods are almost impossible to compensate the time-varying unknown disturbance by designing parameter adaptive laws without a priori knowledge about the bounds of external disturbances. To solve the problem, a new strategy is proposed by constructing an augmented system where the external disturbance is considered as another component of the augmented state vector. Based on this, a double-gain nonlinear observer is employed to estimate the state of the augmented nonlinear system. Further, an output feedback control strategy is designed, and it is proved that the proposed strategy ensures that all the signals are bounded and the tracking error exponentially converges to an adjustable compact set. Finally, an example is performed to demonstrate the validity of the proposed scheme.     

A data-driven covert attack strategy in the closed-loop cyber-physical systems

In this paper, a data-driven covert attack strategy is proposed for a class of closed-loop cyber-physical systems. Without the parameters of the system plant and the nominal controller, the attacker can only use the intercepted input and output data of the nominal system. The injected input attack signals are designed based on the subspace predictive control method, which can deviate the real outputs to the expected attack references in a future time horizon. Meanwhile, by injecting the designed output attack signals for compensation, the attack cannot be detected by the anomaly detector. The simulation results of an irrigation canal system illustrate the effect of the proposed strategy with satisfactory performances.     

Novel event-triggered filter design for nonlinear networked control systems

This paper investigates the problem of event-triggered filter design for nonlinear networked control systems (NCSs) in the framework of interval type-2 (IT2) fuzzy systems. A novel IT2 fuzzy filter for ensuring asymptotic stability and H_∞ performance of filtering error system is proposed, where the premise variables are different from those of the fuzzy model. Attention is focused on solving the problem of event-triggered filter design subject to parameter uncertainties, data quantization, and communication delay in a unified frame. It is shown that the proposed event-triggered filter design communication mechanism for IT2 fuzzy NCSs has the advantage of the existing event-triggered approaches to reduce the utilization of limited network resources and provides flexibility in balancing the tracking error and the utilization of network resources. Finally, simulation example is given to validate the advantages of the presented results.     

Periodic event-triggered resilient control for cyber-physical systems under denial-of-service attacks

This paper studies the problem of designing a resilient control strategy for cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. By constructing an H_∞ observer-based periodic event-triggered control (PETC) framework, the relationship between the event-triggering mechanism and the prediction error is obtained. Then, inspired by the maximum transmission interval, the input-to-state stability of the closed-loop system is proved. Compared with the existing methods, a Zeno-free periodic PETC scheme is designed for a continuous-time CPS with the external disturbance and measurement noise. In particular, the objective of maximizing the frequency and duration of the DoS attacks is achieved without losing robustness. Finally, two examples are given to verify the effectiveness of the proposed approach.     

Leaderless and leader-following consensus of linear multi-agent systems with distributed event-triggered estimators

This paper considers the event-triggered leaderless and leader-following consensus problems for linear multi-agent systems. By introducing event-triggered estimators, two novel control schemes are proposed. Different from the existing event-triggered controllers, which rely on the Fiedler eigenvalue of Laplacian matrix, the developed controllers only use the information from neighboring agents. Meanwhile, the adaptive trigger parameters are designed in the event-triggered mechanisms to improve the self-regulation ability of the event-triggered estimators. In addition, the leaderless consensus and the leader-following consensus can be achieved under the corresponding control protocols. Finally, two simulation examples are given to illustrate the validity of the proposed control protocols.     

Event-triggered decentralized output-feedback control for interconnected nonlinear systems with input quantization

In this paper, the event-triggered decentralized control problem for interconnected nonlinear systems with input quantization is investigated. A state observer is constructed to estimate the unmeasurable states, and the state-dependent interconnections are accommodated by presenting some smooth functions. Then by employing backstepping technique and neural networks (NNs) approximation capability, a novel decentralized output feedback control strategy and an event-triggered mechanism are designed simultaneously. It is proved through Lyapunov theory that the closed-loop system is stable and the tracking property of all subsystems is guaranteed. Finally, the effectiveness of the proposed scheme is illustrated by an example.     

A co-design methodology for cyber-physical systems under actuator fault and cyber attack

This paper investigates the controller design problem of cyber-physical systems (CPSs) to ensure the reliability and security when actuator faults in physical layers and attacks in cyber layers occur simultaneously. The actuator faults are time-varying, which cover bias fault, outage, loss of effectiveness and stuck. Besides that, some state-dependent cyber attacks are launched in control input commands and system measurement data channels, which may lead state information to the opposite direction. A novel co-design controller scheme is constructed by adopting a new Lyapunov function, Nussbaum-type function, and direct adaptive technique, which may further relax the requirements of actuator/sensor attacks information. It is proven that the states of the closed-loop system asymptotically converge to zero even if actuator faults, actuator attacks and sensor attack are time-varying and co-existing. Finally, simulation results are presented to show the effectiveness of the proposed control method.     

Distributed fault detection for nonlinear multi-agent systems under fixed-time observer

This paper mainly investigates the fault detection problem for nonlinear multi-agent systems with actuator faults. For fault detection, a fixed-time observer is proposed by employing auxiliary variable received from neighbor agents. Then, with the aid of the observer, a residual vector is introduced by the auxiliary variable to detect the faults occurring on any followers, and each observer can estimate the whole state of followers. Moreover, the convergence time is dependent on the parameters of the designed observer and independent of initial condition of system state. Finally, the theoretical result is verified by a simulation example.     

Nuclear norm-based recursive subspace prediction of time-varying continuous-time stochastic systems via distribution theory

A method for nuclear norm-based recursive subspace prediction of time-varying continuous-time stochastic systems via distribution theory is proposed. The random distribution theory is adopted to describe the time-derivative of stochastic processes, which is the key to obtain the input–output algebraic equation. The low-rank matrix approximation of the input–output projection matrix is established by nuclear norm minimization instead of the singular value decomposition. Moreover, the optimization problem is deduced by the alternating direction method of multipliers. According to the angle rotation between past and present subspaces spanned by the extended observability matrices, the future signal subspace is predicted by the present subspace. Further, the system matrices are predicted and the corresponding system model is obtained. The results of simulation studies show the effectiveness of the presented method.     

Optimal synchronization controller design for complex dynamical networks with unknown system dynamics

In this paper, the optimal synchronization controller design problem for complex dynamical networks with unknown system internal dynamics is studied. A necessary and sufficient condition on the existence of the optimal control minimizing a quadratic performance index is given. The optimal control law consists of a feedback control and a compensated feedforward control, and the feedback control gain can be obtained by solving the well-known Algebraic Riccati Equation (ARE). Especially, in the presence of unknown system dynamics, a novel adaptive iterative algorithm using the information of system states and inputs is proposed to solve the ARE to get the optimal feedback control gain. Finally, a simulation example shows the effectiveness of the theoretical results.     

Dynamic output feedback H∞ control for fractional-order linear uncertain systems with actuator faults

This paper is concerned with the problem of robust fault-tolerant H_∞ dynamic output feedback control for fractional-order linear uncertain systems with the order satisfying 0 < α < 1 in the presence of actuator faults. A new linear matrix inequality (LMI) formulation corresponding to the H_∞ norm of fractional-order linear systems is proposed. Based on the new formulation and by introducing a new linearizing change of variables, sufficient conditions for robust fault-tolerant H_∞ dynamic output feedback controller designs are derived in term of LMIs. Furthermore, the proposed controller not only enables the system to keep robust stabilization, but also achieves a better H_∞ performance compared with the existing methods. Numerical examples are given to illustrate the design procedure and its effectiveness.     

General value iteration based single network approach for constrained optimal controller design of partially-unknown continuous-time nonlinear systems

In this paper, a novel iterative approximate dynamic programming scheme is proposed by introducing the learning mechanism of value iteration (VI) to solve the constrained optimal control problem for CT affine nonlinear systems with utilizing only one neural network. The idea is to show the feasibility of introducing the VI learning mechanism to solve for the constrained optimal control problem from a theoretical point of view, and thus the initial admissible control can be avoided compared with most existing works based on policy iteration (PI). Meanwhile, the initial condition of the proposed VI based method can be more general than the traditional VI method which requires the initial value function to be a zero function. A general analytical method is proposed to demonstrate the convergence property. To simplify the architecture, only one critic neural network is adopted to approximate the iterative value function while implementing the proposed method. At last, two simulation examples are proposed to validate the theoretical results.     

Multi-cluster distributed optimization via random sleep strategy

In this paper, the distributed optimization problem over multi-cluster networks is considered. Different from the existing works, this paper studies the optimization algorithm under uncoordinated step sizes. More specifically, by combining a random sleep strategy and the round-robin communication among clusters, a new hierarchical algorithm is developed to solve the considered problem. In the proposed algorithm, the random sleep strategy enables each agent to independently choose either performing the projected subgradient descent, or keeping the previous estimate by a Bernoulli decision, based on which the step size of each agent is selected as an uncoordinated form that only relates to the independent Bernoulli decision variable. Technically, by introducing a key definition on the algorithm history, it is proven that the estimates of the proposed algorithm can converge to the optimal solution even with the uncoordinated step sizes. In addition, we also study the convergence performance of the proposed algorithm with simpler constant step sizes. In this case, it is proven that the random sleep strategy can efficiently improve the convergence accuracy of the algorithm. Finally, the theoretical findings are verified via simulation examples.