Optimal Control Gains Optimization for Mobile Robot Considering Dynamic Constraints

Traditional kinematic control of mobile robots primarily regulates position and orientation, often neglecting dynamic factors such as acceleration and torque, making it suitable for low-speed operations. However, to enhance safety and efficiency, it is crucial to consider dynamic constraints in the robot's control system, including maximum velocity and angular velocity. Existing kinematic control methods typically fail to incorporate these dynamic limitations. This paper proposes a novel method for optimizing control gains for mobile robots, factoring in dynamic constraints. We introduce optimal control concepts to kinematic and dynamic controllers, employing a genetic algorithm to identify optimal control gains. Furthermore, we leverage a neural network with robust interpolation capabilities to select control gains for arbitrary initial poses effectively. The trained neural network accurately predicts control gains across various initial conditions, as simulation results confirm. The performance of the proposed neural network controller for diverse mobile robot postures is nearly equivalent to that of a controller using optimization gains derived from a genetic algorithm. In experiments with various robot postures, the maximum performance error time recorded was 0.44 seconds, reflecting a delay of 3.2% in arrival time. This approach enables mobile robots to reach target destinations with improved stability and performance, addressing the limitations inherent in traditional kinematic control methods.