Optimal Controller Design for a Mobile Robot Using Genetic Algorithm and Adaptive PID Controller

This paper addresses the challenge of enabling a mobile robot to reach target coordinates and posture in minimal time by tackling the complexities of kinematic optimal control problems. Determining optimal control gains is analytically challenging, especially when incorporating dynamic models. The mobile robot’s control system comprises two controllers: a kinematic controller, optimized using genetic algorithms and cubic polynomial interpolation, to generate the optimal trajectory, and a dynamic controller using an adaptive PID approach to ensure stable and robust performance despite variations in system parameters like the robot’s weight. The proposed method demonstrates superior performance over existing methods, particularly under dynamic changes in robot characteristics. Simulation results show that the kinematic controller generates optimal trajectories more effectively than traditional techniques, while the adaptive PID controller maintains optimal performance despite disturbances and mass variations. In experiments with various disturbances, the proposed method consistently reached target coordinates with minimal timing discrepancies, outperforming comparative methods. Additionally, it significantly reduces spatial constraints and learning time. These findings contribute to the practical design of mobile robot controllers, ensuring efficient and stable operation, especially in scenarios requiring rapid derivation of optimal gains. Future work should focus on integrating kinematic and dynamic controllers into intelligent systems for mobile robots with arbitrary positions and postures, further enhancing autonomy and adaptability.