Adaptive Event-Triggered Regulation Control of Nonholonomic Mobile Robots

Abstract

In this chapter, the design of adaptive-regulation control of mobile robots (MR) in the presence of uncertain MR dynamics with event-based feedback is provided. Two layer neural-networks (NN) are utilized to represent the uncertain dynamics of the MR which is subsequently employed to generate the control torque with event-sampled measurement update. By relaxing the perfect velocity tracking assumption, control torque is developed to minimize the velocity tracking errors, by explicitly taking into account the dynamics of the MR. The Lyapunov’s method is utilized to develop an event-sampling condition and to demonstrate the regulation error performance of the MR. At the end of the chapter, simulation results are given to verify our theoretical claims. © Springer Nature Singapore Pte Ltd. 2020.