International Journal of Robust and Nonlinear Control最新文献

This paper studies the problem of event-triggered adaptive neural network (NN) control for constrained stochastic multi-agent systems (MASs) subject to actuator failures (AFs) and time delay. In the controller design process, the NN is exploited to approximate the unknown terms of the bias fault and unknown nonlinearities. Meanwhile, the AFs are successfully addressed by designing appropriate parameter adaptation laws and intermediate variables. Besides, to overcome the impact of state constraints (SCs) on the system, a suitable barrier Lyapunov function (BLF) is designed. Then, based on the backstepping technique and Lyapunov stability theory, a distributed event-triggered adaptive controller is constructed to guarantee the consensus tracking with a certain error range, all closed-loop system signals are bounded, and Zeno behavior (ZB) is excluded. Finally, the simulation results demonstrate the effectiveness of the presented method.

The control problem of robust output tracking with prescribed performance for a class of nonlinear systems is considered in this paper to reject both matched and unmatched perturbations. A novel integral sliding hyperplane is studied, which guarantees that the output tracking performance satisfies the prescribed requirements from the beginning. Then, based on the equivalent control concept, the chattering issue is addressed by the proposed adaptation algorithm of the control gain. And when the adaptive gain is employed, the reachability and existence of the sliding mode are analyzed. Moreover, the proposed adaptive sliding mode controller is augmented with the dynamic surface control approach to compensate for the unmatched perturbations. Finally, using the proposed methods, the output tracking performance is tested by several numerical simulations for a robotic manipulators.

In this paper, generalized predefined/prescribed-time stability theory is first put forward, where i) the powers of the upper bound of the derivative of Lyapunov function are no longer confined to $��1�-�\frac{�1�}{�\mu �}�$ and $��1�+�\frac{�1�}{�\mu �}�$ with $��\mu �>�1�$; ii) the upper bound of the settling-time function is simplified and directly deduced by Euler's reflection formula. In addition, the upper bound of the settling-time function is still irrelevant to the initial states, and can be arbitrarily tuned in advance. As an application, a predefined/prescribed-time algorithm is proposed for solving the nonconvex-nonconcave min-max optimization issue. Under the two-sided Polyak-Lojasiewicz (PL) inequality and the Lipschitz continuous gradient, a new dynamical system is established to ensure that the solution of the dynamic system can reach the saddle point of the min-max optimization issue in a predefined/prescribed time. Two examples are applied to validate the predefined/prescribed-time algorithm of nonconvex-nonconcave min-max optimization.

This article focuses on the prescribed-time attitude bipartite consensus tracking control problem of multi-unmanned aerial vehicle (UAV) systems under directed graphs. Currently, in the field of bipartite consensus tracking control, algorithms based on finite-time stability and fixed-time stability have been developed. However, existing schemes exhibit significant limitations: finite-time stability algorithms rely on the system's initial states for convergence time, resulting in insufficient temporal controllability. Fixed-time stability algorithms involve complex coupling among convergence time, system parameters, and control gains, which prevents arbitrary presetting of the convergence moment. This paper introduces the prescribed-time stability theory, whose core advantage is the ability to preset the convergence time. To achieve this goal, a novel hybrid distributed observer is first designed to ensure accurate estimation of the leader's states within the prescribed-time. Based on the observer output, a prescribed-time sliding mode surface (SMS) and the corresponding tracking control law are constructed to drive the multi-UAV system to achieve attitude bipartite consensus tracking within the prescribed-time. The proposed scheme not only guarantees the tracking performance within the prescribed time but also avoids the numerical implementation challenges caused by infinite time-varying gains in traditional prescribed-time control. Finally, the effectiveness and feasibility of the proposed method are verified through rigorous theoretical analysis and multiple sets of numerical simulations.

This study centrally investigates the resolution of event-triggered $��H�\infty �$ control problem in renewable energy network power systems, especially in the context of time delays. First, an adaptive event-triggered strategy (AETS) is presented. The strategy aims to optimize utilization of a limited communication network. Second, to minimize the trigger rate while maintaining optimal control performance, the used error signal is the difference between the recently transmitted packet and the average of the current sampled packet and the recently transmitted packet. Then, the $��H�\infty �$ stability analysis of power systems is formulated through the employment of the Lyapunov–Krasovskii functional (LKF). Utilizing the matrix decoupling technique, a criterion is developed to simultaneously compute both the trigger matrix and the controller gain. Finally, two simulation examples confirm the efficacy and superiority of the presented strategy.

Random data dropouts, arising from bandwidth limitations or network congestion, significantly affect the tracking performance of iterative learning control (ILC) schemes for networked control systems. To address this challenge, we propose a state feedback-based ILC scheme for a class of discrete-time multi-input-multi-output (MIMO) nonlinear systems with a feedforward term, operating in environments prone to random packet loss. More specifically, the realizability of the desired trajectory of the considered nonlinear system is first studied. When the feedforward matrix is full-row rank, we show that any given desired trajectory is realizable. For strictly full-column rank case, we give a necessary and sufficient condition that guarantees the realizability of the given desired trajectory. Then, using the contraction mapping method combined with matrix transformation and decomposition techniques, we investigate the almost sure convergence of tracking errors. For the full-row rank case, we show that the spectral radius condition is necessary and sufficient for the tracking errors to converge to zero almost surely. For the strictly full-column rank case, we illustrate that the spectral radius condition can ensure the output sequence converges to any realizable desired trajectory almost surely. Meanwhile, we show how to design the state feedback for better tracking performance. Finally, a numerical example is presented to demonstrate the effectiveness of the findings.

This paper investigates the leaderless consensus problem of high-order multi-agent systems (MASs) under a general directed graph via a fully distributed event-triggered output feedback control strategy. First, a fully distributed event-triggered state compensator with a static control gain is constructed, by which the information of the neighbors is asynchronously and intermittently obtained. Since only the output information can be used, an event-triggered output feedback control strategy is designed without using any global information of MAS, with which leaderless consensus is guaranteed asymptotically, and the state of each agent is dynamically evolving instead of converging to some stationary point. The Zeno behavior is strictly ruled out in the event-triggered communication. The effectiveness is finally verified by some simulation examples.