Adaptive Dynamic Event-Based Robust Control for Multiple Networked Euler–Lagrange Systems

This article develops an event-based adaptive robust control scheme for multiple networked Euler–Lagrange systems with a dynamic leader, addressing some key challenges such as parameter uncertainties, unknown perturbations, inherent nonlinearities, and limited resources, for the practical applications of networked robotics and autonomous systems. To reduce the communication network burden and the computational resources consumption, an adaptive dynamic triggering strategy is developed. In addition, to estimate the inaccurate states, a nested adaptive sliding-mode estimator is proposed. Then, a fully distributed adaptive dynamic event-based time-varying sliding-mode control strategy is developed based on the designed triggering scheme and estimator, without requiring any global information. This strategy reduces the effect of large initial errors on the varying gain during adaptation, and compensates for the influences of inherent nonlinearities, unknown external perturbations, and parameter uncertainties, making it feasible for practical implementation. Moreover, Lyapunov stability theory is used to guarantee the asymptotic convergence of the closed-loop networked systems. Finally, hardware experiments are conducted using multiple quadrotors to validate the effectiveness of the proposed control scheme in multiagent coordination tasks.