Stable Nonlinear Position Control Law for Mobile Robot Using Genetic Algorithm and Neural Network
In this paper we proposed a new stable control algorithm for mobile robot trajectory tracking. The stability conditions are guaranteed by Lyapunov theory. The control parameters of backstepping algorithm are adjusted using genetic algorithm. Some of them are represented by unknown functions which are generated by neural network. The performance of the proposed controller is investigated using a kinematic model of a nonholonomic mobile robot. The efficient position tracking performance was obtained but the velocities were very high at the start of the motion. In order to avoid this, we proposed the extension of backstepping position controller by adding a new control law, which provided lower velocity servo inputs. Simulation results show the good quality of both velocity and position tracking capabilities of a mobile robot.