Concurrent Learning-Based Neuroadaptive Robust Tracking Control of Wheeled Mobile Robot: An Event-Triggered Design

In this article, an event-based neuroadaptive robust tracking controller for a perturbed and networked differential drive mobile robot (DMR) is designed with concurrent learning. A radial basis function neural network (RBFNN), which approximates an unknown perturbation, is used to design an adaptive sliding mode controller. The RBFNN weights and sliding mode controller parameters are estimated online using an adaptive tuning law to ensure performance with reduced chattering. To improve the convergence of RBFNN weight estimation error, a concurrent learning-based adaptive law is derived, which uses measured online and recorded data. Furthermore, a suitable triggering condition is designed to achieve a reduced number of control computations while minimizing network resources without sacrificing the stability of the sampled data closed-loop control system. A finite sampling frequency is guaranteed for the designed triggering condition by establishing a positive lower bound on the inter-event execution time, which is equivalent to the Zeno-free behavior of the system. Finally, the proposed event-based neuroadaptive robust controller is implemented on a practical system (Q-bot 2e) to show the effectiveness of the proposed design.