Zero-Error Consensus Tracking With Preassignable Convergence for Nonaffine Multiagent Systems

Sliding Mode Control Based on Reinforcement Learning for T-S Fuzzy Fractional-Order Multiagent System With Time-Varying Delays

This article researches the sliding mode control (SMC) for fuzzy fractional-order multiagent system (FOMAS) subject to time-varying delays over directed networks based on reinforcement learning (RL), α ∈ (0,1) . First, since there is information communication between an agent and another agent, a new distributed control policy ξi(t) is introduced so that the sharing of signals is implemented through RL, whose propose is to minimize the error variables with learning. Then, different from the existed papers studying normal fuzzy MASs, a new stability basis of fuzzy FOMASs with time-varying delay terms is presented to guarantee that the states of each agent eventually converge to the smallest possible domain of 0 using Lyapunov-Krasovskii functionals, free weight matrix, and linear matrix inequality (LMI). Furthermore, in order to provide appropriate parameters for SMC, the RL algorithm is combined with SMC strategy, and the constraints on the initial conditions of the control input ui(t) are eliminated, so that the sliding motion satisfy the reachable condition within a finite time. Finally, to illustrate that the proposed protocol is valid, the results of the simulation and numerical examples are presented.

Prescribed-time containment control of multi-agent systems subject to collision avoidance and connectivity maintenance

In this paper, the problem of prescribed-time containment control for a second-order multiple leader-follower systems (MLFSs) is studied, in where both collision avoidance and connectivity maintenance of the agents are considered. Firstly, an effective exponential potential field function (EAPF) with constraints based on the estimated distance is designed to achieve collision resistance and connectivity preservation of the agents at a prescribed-time. Secondly, an estimator-based distributed control protocol is proposed, which drives the agents to achieve containment control in a cooperative manner at a prescribed-time. Furthermore, a novel distributed control protocol containing a collision avoidance term and a containment control term is addressed as well, which enables all followers to complete collision avoidance and connectivity maintenance in any prescribed-time and enter the leaders' convex packet. Finally, the stability of the system is technically analyzed by using Lyapunov theory, and the effectiveness of the presented strategies is verified by several simulations.

Global Prescribed Performance Control for Strict Feedback Systems Pursuing Uncertain Target

In this work, an online solution for reconstructing and predicting the uncertain target trajectory in real-time is proposed based on general regression neural network (GRNN). On this basis, an adaptive tracking control scheme guaranteeing prescribed performance is suggested for a class of strict-feedback systems with unknown control directions. In contrast to existing trajectory reconstruction methods, the one presented in this note does not require prior modeling of the uncertain target or offline training. Contrary to most current state-of-the-art prescribed performance control (PPC) technology, a novel time-varying scaling function and its corresponding translation function are introduced such that no strict constraints on initial conditions are needed, that is, global stability is achieved. The proposed control scheme allows the output of the system to chase the predicted value of the uncertain target, and the tracking error converges to a prescribed small set within a preassigned time, despite unmatched uncertainties and unknown control directions. The benefits of the proposed control scheme are confirmed by numerical simulations.

H∞-based control of multi-agent systems: Time-delayed signals, unknown leader states and switching graph topologies

The paper investigates a leader-following scheme for nonlinear multi-agent systems (MASs). The network of agents involves time-delay, unknown leader’s states, external perturbations, and switching graph topologies. Two distributed protocols including a consensus protocol and an observer are utilized to reconstruct the unavailable states of the leader in a network of agents. The H_∞-based stability conditions for estimation and consensus problems are obtained in the framework of linear-matrix inequalities (LMIs) and the Lyapunov-Krasovskii approach. It is ensured that each agent achieves the leader-following agreement asymptotically. Moreover, the robustness of the control policy concerning a gain perturbation is guaranteed. Simulation results are performed to assess the suggested schemes. It is shown that the suggested approach gives a remarkable accuracy in the consensus problem and leader’s states estimation in the presence of time-varying gain perturbations, time-delay, switching topology and disturbances. The H_∞ and LMIs conditions are well satisfied and the error trajectories are well converged to the origin.

Globally Exponentially Stable Tracking Control of Self-Restructuring Nonlinear Systems

This article proposes a neural networks (NNs)-based tracking control approach for a class of uncertain high-order self-restructuring nonaffine dynamic systems. Unlike most existing NN-based works that normally ignore the precondition on the functionality and reliability of NN unit and thus can only ensure semiglobal stability, the proposed method explicitly addresses the issue of reliable in-loop operation of NN approximation-based control unit, resulting in a safeguarded NN-based control solution capable of ensuring globally stable tracking. Furthermore, the control method proposed guarantees exponentially globally stable tracking for systems with self-restructuring nonlinearities and uncertainties, distinguishing itself from those that only yield uniformly ultimately bounded (UUB) regulation/tracking results for nonlinear systems with fixed structures. All of these features are achieved by the proposed strategy consisting of two cooperative control units: 1) safeguard control and 2) NN-based control. The role of the safeguard control is to force the states (starting from any initial condition) to enter a stable region, so that the NN-based control can be activated trustworthily and safely. It is such cooperation of the two units that not only ensures the tracking error entering the stable region first within a prespecified finite time but also guarantees the tracking error converging to zero exponentially thereafter, resulting in global zero-error tracking. Both the theoretical analysis and numerical simulation authenticate the effectiveness of the proposed method.