Cascade Sliding Control for Trajectory Tracking of a Nonholonomic Mobile Robot with Adaptive Neural Compensator

Abstract

A design of sliding mode controllers (SMC) with adaptive capacity is presented. This control technique is formed by two cascaded SMC controllers, one of them having an adaptive neural compensator (ANC); both are put on a WMR (wheeled mobile robot). The mobile robot is divided into a kinematics and a dynamics structure; the first SMC controller acts only on the kinematic structure and the SMC with neural adaptive compensator on the other one. The dynamic SMC was designed applying an inverse dynamic controller and using the model dynamics of the WMR. The adaptive neural compensation (ANC) was used in order to reduce the control error caused by the dynamics variations but it conveys a residual approximation error, so a sliding part was designed to cancel such error. This technique allows achieving the control objective despite parameter variations and external disturbances that take place in the dynamics; on the other hand, the ANC can adjust its neural parameters to reduce the dynamics variations of the WMR and thus improve the trajectory tracking control. Problems of convergence and stability are treated and design rules based on Lyapunov's theorem are given.

Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique

“…Several years ago, the work of many researchers resulted in the development of various controllers for robots and mechatronic systems [1][2][3][4][5][6]. ese algorithms were the first in the literature whose stability was proved using different theories [7][8][9] or using finite-time stability [10][11][12][13].…”

Robust Switched Tracking Control for Wheeled Mobile Robots Considering the Actuators and Drivers

“…Also, they proposed a controller based on neural networks and a terminal sliding mode control for friction estimation and trajectory tracking of the robot. Capraro et al in [53] proposed a control technique based on two-cascaded sliding mode controls which are combined with a feedback linearization controller and adaptive neural compensation, respectively. Chen et al in [54] developed a nonlinear robust tracking control based on a feedback linearization controller and a robust compensator that also considers the unmodeled dynamics and modeling uncertainties.…”

“…The previous literature shows that, in general, the control design for the trajectory tracking task in DDWMRs has been tackled from five directions linked to the kinematics/dynamics of the mechanical structure: (1) by considering only the kinematics of the mechanical structure [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37], (2) by taking into account the kinematics of the mechanical structure and the dynamics of the actuators [38,39,40], (3) by using the kinematic model of the mechanical structure along with the dynamics of the actuators and power stage [5,6,7], (4) by using only the dynamics of the mechanical structure [41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59], and (5) by considering the dynamics of the mechanical structure and the actuators [60,61]. Considering the aforementioned perspectives, the present paper is particularly motivated by (3), that is, when the mathematical models of the three subsystems composing a DDWMR are used in control design.…”