International Journal of Robust and Nonlinear Control最新文献

In this article, we present an innovative approach for controlling nonlinear switched systems (NSSs) with strict feedback utilizing adaptive neural networks (ANNs). Our methodology encompasses several facets, addressing key challenges inherent to these systems. To commence, we tackle the constrained nature of NSSs with strict feedback by designing a barrier Lyapunov function. This function ensures that all states within the switched systems remain within prescribed constraints. Additionally, we harness neural networks (NNs) to approximate the unknown nonlinear functions inherent to the system. Furthermore, we deploy an ANN state observer to estimate unmeasurable states. Our approach then proceeds to develop a cost function for the subsystem. Building upon this, we apply the Hamiltonian–Jacobi–Bellman (HJB) solution in conjunction with observer and behavior critic architectures, all rooted in backstepping control (BC) principles. This integration yields both a virtual optimal controller and a real optimal controller. Furthermore, we introduce a novel ANN event-triggered control (ETC) strategy tailored explicitly for strictly feedback systems. This strategy proves highly effective in reducing the utilization of communication resources and eliminating the occurrence of Zeno behavior. Our analysis provides formal proof that all states within the closed-loop system exhibit half-leaf consistency and are ultimately bounded, regardless of arbitrary switching conditions. Finally, we substantiate the efficacy and viability of our control scheme through comprehensive numerical simulations.

The primary focus of this research paper is to explore the realm of dynamic learning in sampled-data strict-feedback nonlinear systems (SFNSs) by leveraging the capabilities of radial basis function (RBF) neural networks (NNs) under the framework of adaptive control. First, the exact discrete-time model of the continuous-time system is expressed as an Euler strict-feedback model with a sampling approximation error. We provide the consistency condition that establishes the relationship between the exact model and the Euler model with meticulous detail. Meanwhile, a novel lemma is derived to show the stability condition of a digital first-order filter. To address the non-causality issues of SFNSs with sampling approximation error and the input data dimension explosion of NNs, the auxiliary digital first-order filter and backstepping technology are combined to propose an adaptive neural dynamic surface control (ANDSC) scheme. Such a scheme avoids the $��n�$-step time delays associated with the existing NN updating laws derived by the common $��n�$-step predictor technology. A rigorous recursion method is employed to provide a comprehensive verification of the stability, guaranteeing its overall performance and dependability. Following that, the NN weight error systems are systematically decomposed into a sequence of linear time-varying subsystems, allowing for a more detailed analysis and understanding. In order to ensure the recurrent nature of the input variables, a recursive design is employed, thereby satisfying the partial persistent excitation condition specifically designed for the RBF NNs. Meanwhile, it can verify that the NN estimated weights converge to their ideal values. Compared with the common $��n�$-step predictor technology, there is no need to redesign the learning rules due to the designed NN weight updating laws without time delays. Subsequently, after capturing and storing the convergence weights, a novel neural learning dynamic surface control (NLDSC) scheme is specifically formulated by leveraging the acquired knowledge. The introduced methodology reduces computational complexity and facilitates practical implementation. Finally, empirical evidence obtained from simulation experiments validates the efficacy and viability of the proposed methodology.

The issue of velocity-free practical predefined-time (PPT) attitude coordination control (ACC) for multiple rigid spacecraft under directed topology and subject to bounded external disturbances is investigated in the article. To gain precise estimates of the spacecraft's velocities together with external disturbances, PPT extended-state observers are presented by virtue of a time-varying function technique. Next, distributed PPT state observers are designed to estimate the leader's attitude which is accessible to only some of the followers. One advantage of the designed distributed observers is that the information required to transmit among neighbor spacecraft is only the estimated attitudes, which results in a reduction of the communication burden. Then, a distributed velocity-free PPT ACC law is put forward in terms of the backstepping approach and the PPT observers. The present control protocol can ensure that the settling time is bounded by a predefined time with no reliance on any other controller parameters or initial conditions. Finally, the efficiency of the developed ACC scheme is illustrated by numerical simulation examples, and it is shown that the designed control law possesses one further salient advantage of required reduced magnitudes of control torques to reach accurate ACC performance.

This paper focuses on a detection-based resilient control issue for cyber-physical systems (CPSs) subject to false data injection (FDI) attacks, where FDI attacks occur in the communication channels from the sensor-to-controller and controller-to-actuator. Firstly, to reduce the adverse impacts of FDI attacks on estimation performance, an unbiased estimator is constructed by tackling the equality-constrained optimization problem. Then, an effective attack detection mechanism is devised by introducing a pseudo-innovation sequence to formulate the detection function, which can successfully detect two-channel FDI attacks. Based on these detection results, a resilient controller combining linear quadratic Gaussian and $��{�H�}_{�\infty �}�$ controllers is provided to guarantee the mean-square asymptotic stability of CPSs with $��{�H�}_{�\infty �}�$ performance. Finally, the validity of the proposed resilient control approach is demonstrated by a simulation involving satellite control system.

This article presents a discrete-time robust model reference adaptive controller and adaptive sigmoid super-twisting sliding mode (RMRAC-ASSTSM) and its stability analysis using Lyapunov stability theory. This control structure is robust to matched and unmatched dynamics. In addition, the chattering phenomenon tends to be suppressed in the steady state due to adaptive super-twisting sliding mode action using a sigmoid function. The performance of the proposed control structure is corroborated with simulation results, considering a second-order non-minimum phase unstable plant, where it can be seen that RMRAC-ASSTSM regulation errors converge to a finite residual set when the plant presents unmodeled dynamics. A comparison is also presented with a similar RMRAC-based control structure with an adaptive super-twisting sliding mode implemented with a sign function.

This article investigates the exponential $��{�H�}_{�\infty �}�$ control issue of bidirectional associative memory neural network (BAMNN) with unbounded time-varying delays. A bounded real lemma (BRL) is first established via a direct method, which is on the basis of the solutions of BAMNN. Second, based on the obtained BRL, the state feedback controller is designed to guarantee the global exponential stability of the resulting closed-loop BAMNN with an $��{�H�}_{�\infty �}�$ performance index. Since no Lyapunov–Krasovskii functionals is constructed in the proposed method, the computation burden and complexity are reduced. Lastly, the effectiveness of the theoretical results is illustrated through two numerical examples.

The traditional Nussbaum-type functions encounter challenges when extended to systems with multiple unknown control directions, as the coupling between the Nussbaum-type function and Nussbaum-type gain may lead to mutual elimination. To address this issue, a novel Nussbaum-type function is introduced to handle multiple unknown control directions for stochastic systems. The stability of the proposed Nussbaum-based design can be guaranteed using the Lyapunov analysis. To conserve computational resources, a static composite event-triggered mechanism is integrated with intermittent feedback in the Nussbaum-based design. In contrast to conventional event-triggering mechanisms that depend solely on control signals, the presented design incorporates tracking errors into the event-triggering conditions. Moreover, to further alleviate computational burdens, we further develop a dynamic composite event-triggered mechanism. With the overall design scheme, bounded tracking errors and reduced computational burden can be ensured. Simulation examples validate the efficacy of the proposed adaptive Nussbaum composite event-based robust control design.

Time-varying group formation tracking control problems for multi-agent systems are investigated based on distributed multi-sensor multi-target filtering with intermittent observations. First, in order to estimate the states of multiple targets under the phenomenon of intermittent observations accurately, a distributed multi-sensor multi-target filtering algorithm is proposed based on cubature Kalman filtering. Second, a time-varying group formation tracking protocol is designed for multi-agent systems by using the state estimations obtained from the filtering algorithm and the neighboring interaction. The protocol enables multi-agent systems to form time-varying subformations and track multiple targets in the same subgroups, respectively. Third, the boundedness of the error covariance matrices is proved under the condition that the observation probability is higher than the minimum threshold. Then the estimation errors of the filtering algorithm are proved to be stochastically bounded by introducing a stochastic process. Furthermore, the boundedness of the group formation tracking errors is proved. Finally, a numerical example is used to verify the performance of the proposed algorithm and protocol.

In this article, the consensus problem for disturbed multi-agent systems (MASs) under independent denial-of-service (DoS) attacks is investigated. The concerned attacks can jam different channels independently, resulting in a time-varying and unknown network topology. In this situation, a dynamic event-triggered control scheme with a hybrid communication strategy is presented to schedule information interaction over the network, and a distributed prediction-based control algorithm is proposed to ameliorate the resilience. The system stability is proved in present of DoS attacks by introducing the decay rates of Lyapunov functions associated with different connectivity modes. In comparison with the most existing MASs studies under DoS attacks, the triggering mechanism that possibly subject to the malicious attacks executed at the triggering times is protected by the proposed randomized transmission protocol, and the tolerable attack intensities are quantified. Finally, simulations are presented to substantiate that the proposed strategy is effective.