Nonlinear Adaptive Control of an Autonomous Ground Vehicle in Obstacle Rich Environment: Some Experimental Results

Abstract

Non-linear adaptive control of an Autonomous Ground Vehicle (AGV) is considered in this work. The adaptive control strategy has two main objectives: a) trajectory tracking in presence of actuator nonlinearity, b) navigation of the AGV in an obstacle rich environment. However, the proposed controller uses a conventional feedback controller in conjunction with an adaptive inverse dynamics strategy for the actuators. Two different models have been exploited namely Auto-Regressive Exogenous Model (ARX) and Non-Linear Auto-Regressive Exogenous Model (NARX) to identify the AGV's actuator inverse dynamics. The simulation results shows efficacy of the proposed indirect adaptive controllers in comparison to Inverse Dynamic Conventional Feedback Controller (IDCFB). Also, the experimental results in particular, show superiority of the proposed controller in terms of trajectory tracking while navigating an AGV in an obstacle rich environment.