Optimized control of skid steering mobile robot with slip conditions

Abstract

Application of different optimization techniques for nonlinear controller parameters of a skid steering mobile robot (SSMR) with its inherited slip characteristics is crucial in saving designer's time and effort. In this paper, two computational optimization techniques, particle swarm optimization (PSO) and genetic algorithms (GA), are applied, evaluated and compared to optimize the nonlinear controller parameters of an SSMR moving in a plane. The SSMR controller is designed for tracking a reference robot with the same kinematics. For the purpose of simulation, SSMR kinematics is extended to include slip effects. Simulation programs for both optimization techniques are implemented and the optimized controller parameters are obtained. The system response is examined with the optimized parameters for tracking different trajectories in the presence of different types of disturbances and slip coefficients. Simulation results show better performance of PSO tuning based algorithm than GA one, especially in terms of Mean Square Error (MSE) performance index and computational time.