Control of Mobile Robot Considering Actuator Dynamics with Uncertainties in the Kinematic and Dynamic Models

Abstract

In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a neural kinematic controller (NKC) and neural dynamic controller (NDC) is investigated, where the wheel actuator (e.g., dc motor) dynamics is integrated with mobile robot dynamics and kinematics so that the actuator input voltages are the control inputs, as well as both the kinematic and dynamic models contains parametric and/or nonparametric uncertainties. The proposed neural controller (PNC) is constituted of the NKC and the NDC, and were designed by use of a modelling technique of Gaussian radial basis function neural networks (RBFNNs). The NKC is applied to compensate the uncertainties in the kinematic parameters of the mobile robot. The NDC, based on the sliding mode theory, is applied to compensate the mobile robot dynamics, and parametric and/or nonparametric uncertainties. Also, the PNC are not dependent of the mobile robot kinematics and dynamics neither require the off-line training process. Stability analysis with basis on Lyapunov theory and numerical simulation is provided to show the effectiveness of the PNC.KeywordsMobile robottrajectory trackingactuator dynamicsuncertaintieskinematic controldynamic controlneural networksLyapunov theory