International Journal of Robust and Nonlinear Control最新文献

Focusing on the actuator fault and output constraints of a nonlinear strict-feedback system, a neural network-based optimized fixed-time controller is investigated in this paper. An optimized backstepping framework is adopted for controller design, where a critic–actor architecture is integrated to progressively approximate the optimal control policy. And the adaptive laws are developed to compensate for the disturbance and actuator failure. To address the output constraints, a nonlinear function related to these constraints is directly incorporated into the controller, thereby simplifying the computations. Furthermore, the fixed-time stability of the closed-loop system and each subsystem is analyzed, along with the fulfillment of output constraints. Lastly, the efficiency and correctness of the proposed algorithm are confirmed through two numerical simulations.

In this article, we investigate the tracking control issue for a kind of constrained nonlinear cyber-physical systems (CPSs) with exogenous perturbations, which are subjected to venomous actuator attacks. By designing a new orientation function and error transformation function, a novel low-complexity tracking control scheme is ulteriorly developed. The proposed control architecture owns inherent intrusion-tolerant capacity and robustness, which is capable of settling exogenous disturbances and actuator attacks in unison. Distinct from most existing related studies, our proposed approach demands no other knowledge about model uncertainty except for the boundedness of exogenous interferences, actuator attacks and local Lipschitz conditions of nonlinearities. Besides, although there is a lack of prior knowledge about control directions, the proposed control architecture does not involve Nussbaum gain technologies, parameter estimation methods or logic switching mechanisms. Eventually, the effectiveness and superiority of the proposed control framework are illustrated through a representative application example.

The Hammerstein parameters varying system (HPVS) with output noises is studied for dynamic processes in the paper. It has a cascaded structure of Hammerstein models, and its parameters are dependent on a time-varying variable known as the scheduling variable, while also considering non-Gaussian output noises. We present a robust kernel-based global identification method (RKGIM) by using kernel methods and expectation maximization variational inference (EMVI) algorithm. Firstly, a Gaussian process (GP) with a radial basis kernel is considered to model the dependence of HPVS's parameters on scheduling variables, and a noise-like term is introduced for numerical reasons. Their union designs a prior distribution for the system's noise-free output, providing a possible description of the HPVS's output on scheduling variables. Then, to ensure the robustness of the identification, the measurement noise is described as a parametric Student's t distribution rather than the traditional Gaussian distribution. Furthermore, in the EMVI framework, the E-step estimates the posterior estimations of the noise parameters and noise-free output by using VI, while the M-step estimates the hyperparameters that determine the aforementioned kernel by maximizing the likelihood. Finally, the effectiveness of the proposed method is demonstrated by simulation experiments.

Under the dual constraints of preset time limits and coupled nonlinear dynamics, it is extremely challenging to achieve cooperative control and strong robustness guarantees for multi-agent systems. This paper focuses on the fixed-time formation control for nonholonomic wheeled mobile robots (NWMRs) via time-base generator (TBG). To begin with, distributed fixed-time formation controllers are developed to eliminate the dependence of traditional controllers on initial values. Subsequently, TBG incorporates time-dependent functions to design control strategies with either asymptotic or non-asymptotic characteristics, ensuring system convergence within a fixed time. Limiting the magnitude of control inputs not only safeguards actuators but also improves the system's robustness to external disturbances and model uncertainties. Additionally, a distributed observer based on a TBG is designed to enable each follower to estimate the state of the virtual leader within a fixed time. The observers of the followers can operate independently, eliminating the need for a global clock or precise synchronization, thereby simplifying the system design. Eventually, the effectiveness and superiority of the controller are verified by simulation. Compared with the traditional fixed-time consensus protocol, the proposed protocol reduces the amplitude of the initial linear velocity by 32.1% and the angular velocity by 32.2%. The settlement time can be preset offline, and the estimation of the settlement time is significantly more accurate.

In this article, we present a novel adaptive resilient control strategy for a class of uncertain strict-feedback nonlinear cyber-physical systems (CPSs) suffering false data injection (FDI) and actuator faults. In contrast to most studies on nonlinear systems, the unknown state-dependent gains are considered during system modeling. By formulating convergence-region-related piecewise functions, we construct a novel Lyapunov candidate. Then, an adaptive resilient controller, which is proved to guarantee both prescribed performance and finite-time stability of this system, is subsequently developed using the dynamic surface control (DSC) technique. Specifically, Nussbaum functions and fuzzy logic systems (FLSs) are introduced to address the adverse effects induced by FDI and actuator faults. Through rigorous analysis, it is demonstrated that all signals within the closed-loop system are semi-globally bounded, and the state errors can be constrained within any predefined negative exponential function under appropriate parameters. In the end, the feasibility of this control strategy is verified through a simulation based on wing rock dynamics.

In this article, the robust stabilization problem of linear delay systems with parametric uncertainties is investigated. First, by means of the state transition matrix and the structured singular value, a sufficient condition for the robust stability of linear delay systems is proposed for robust controller design. Then, based on this proposed sufficient condition, the robust control problem is transformed into a constrained optimization problem and the feedback gain matrices can be obtained by solving this optimization problem. Finally, numerical examples are given to illustrate the effectiveness of the proposed algorithm.

In this article, we investigate the problem of consensus of multi-agent systems in the presence of input saturation constraints and sampled data control. An improved low-gain feedback strategy is proposed for achieving semi-global consensus while mitigating the adverse effects of saturation. Furthermore, a distributed dynamic saturation reconstruction protocol is designed to tackle the global consensus problem in systems with asymmetric heterogeneous actuator saturation. The results in these two approaches reveal a novel aspect by providing an explicit upper bound for the sampling periods of heterogeneous saturated agents, posing difficulties to consensus analysis schemes. These findings, as shown in simulations, contribute to advancing the understanding and implementation of consensus in multi-agent systems.