A low-complexity state feedback fault-tolerant control scheme guaranteeing prescribed tracking performance is proposed for a family of uncertain nonlinear systems with unknown control directions. Contrary to the current state-of-the-art, novel error transformation functions and new update laws related to performance functions are introduced to the control design such that no compensators or approximation structures are needed, in spite of actuation faults, component faults, and unknown nonlinearities. The proposed method is verified via a simulation on an inverted pendulum.

This paper is concerned with the input-to-state stabilizing control problem for cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS) attacks. Under the data update policy with bounded update interval, a new control scheme that discards the outdated information is proposed, and the stability analysis of CPSs under DoS attacks is transformed into analyzing the stability of the system under a switched controller with the help of a class of linear matrix inequalities (LMIs). Then, inspired by the techniques for switched systems, sufficient conditions on the duration and frequency of the DoS attacks, under which the stability of the closed-loop systems is still guaranteed, are proposed. Compared with the existing method for the single-channel case, the considered multiple-channel case is more challenging, and the proposed LMI-based method is more flexible.

In this paper, for linear leader-follower networks with multiple heterogeneous actuator faults, including partial loss of effectiveness fault and actuator bias fault, a cooperative fault-tolerant control (CFTC) approach is developed. Assume that the interaction network topology among all nodes is a switching directed graph. To address the difficulty of designing the distributed compensation control laws under the time-varying asymmetrical network structure, a novel distributed-reference-observer-based fault-tolerant tracking control approach is established, under which the global tracking errors are proved to be asymptotically convergent in the presence of actuator failures. First, by constructing a group of distributed reference observers based on neighborhood state information, all followers can estimate the leader's state trajectories directly. Second, a decentralized adaptive fault-tolerant tracking controller via local estimation is designed to achieve the global synchronization. Furthermore, the reliable coordination problem under switching directed topology with intermittent communications is solved by utilizing the presented CFTC approach. Finally, the effectiveness of the proposed coordination control protocol is illustrated by its applications to a networked aircraft system.

Cyber-physical systems (CPSs) are naturally highly interconnected and complexly nonlinear. This paper investigates the problem of decentralized adaptive output feedback control for CPSs subject to intermittent denial-of-service (DoS) attacks. The considered CPSs are modeled as a class of nonlinear uncertain strict-feedback interconnected systems. When a DoS attack is active, all the state variables become unavailable and standard backstepping cannot be applied. To overcome this difficulty, a switching-type adaptive state estimator is constructed. Based on an improved average dwell time method incorporated by frequency and duration properties of DoS attacks, convex design conditions of controller parameters are derived in term of solving a set of linear matrix inequalities. The proposed controller guarantees that all closed-loop signals remain bounded, while the error signals converge to a small neighborhood of the origin. As an illustrative example, the proposed control scheme is applied to a power network system.

This paper focuses on the problem of output tracking with prescribed transient and steady-state performance for strict-feedback systems with unknown nonlinear functions and unmatched disturbances. In lieu of Nussbaum gain techniques, parameter estimation algorithms and switching control strategies, a continuous static low-complexity control solution is provided by means of a novel combination of smooth orientation functions and error transformation functions. The proposed method possesses inherent robustness against model uncertainties, disturbances, and virtual control signal derivatives, thus eliminating the needs to introduce extra robust control schemes and compute analytic derivatives. Comparative simulation results further illustrate the above theoretical findings.

This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN weights are aperiodically updated only when the event-triggered condition is violated. A positive lower bound on the minimum intersample time is guaranteed to avoid accumulation point. The closed-loop stability of the resulting nonlinear impulsive dynamical system is rigorously proved via Lyapunov analysis under an adaptive event sampling condition. In comparing with the traditional adaptive backstepping design with a fixed sample period, the event-triggered method samples the state and updates the NN weights only when it is necessary. Therefore, the number of transmissions can be significantly reduced. Finally, two simulation examples are presented to show the effectiveness of the proposed control method.