This article presents a robust optimal consensus control strategy for switched nonlinear multi-agent systems (MASs). The robust control problem with unmatched uncertainties is first reformulated as an optimal control framework through the introduction of augmented control inputs and a tailored cost function. To construct a distributed integral sliding-mode control (ISMC), a novel measurement error term is designed to enforce finite-time convergence of the consensus error to the origin. An event-based mechanism is incorporated to minimize controller execution, thereby optimizing computational and communication resource utilization. A concurrent learning approach is proposed to update neural network (NN) weights, where the value function is approximated by a critic NN. This method circumvents the constraints of initial admissible control and persistent excitation (PE) conditions inherent in traditional adaptive dynamic programming (ADP) designs. Theoretical analysis demonstrates that the developed event-triggered ADP controller guarantees system robustness for nonlinear MASs and ensures the uniform ultimate boundedness (UUB) of critic NN weight estimation errors. Finally, simulation results are presented to validate the effectiveness of the proposed control methodology.

The design of stabilizing controllers for general nonlinear systems remains a challenging task due to their inherent complexities and nonconvexities. In this paper, we consider the problem of designing an asymptotically stable controller of a nonlinear dynamic system. We begin by framing the problem as an inverse optimal control problem, aiming to design a pair of cost functions that ensure asymptotic stability for the nonlinear model predictive control closed-loop system. By leveraging the relaxed dynamic programming inequality, a machine learning based algorithm is proposed to learn the cost functions. Finally, we demonstrate the effectiveness of the proposed method through illustrative examples.