Evolutionary Computing Control Strategy of Nonholonomic Robots with Ordinary Differential Equation Kinematics Model

This paper introduces an Evolutionary Computing Control Strategy (ECCS) for the motion control of nonholonomic robots, and integrates an ordinary differential equation (ODE)-based kinematics model with a nonlinear model predictive control (NMPC) strategy and a particle-based evolutionary computing (PEC) algorithm. The ECCS addresses the key challenges of traditional NMPC controllers, such as their tendency to fall into local optima when solving nonlinear optimization problems, by leveraging the global optimization capabilities of evolutionary computation. Experiment results on the MATLAB Simulink platform demonstrate that the proposed ECCS significantly improves motion control accuracy and reduces control errors compared to linearized MPC (LMPC) strategies. Specifically, the ECCS reduces the maximum error by 90.6% and 94.5%, the mean square error by 67.8% and 92.6%, and the root mean square error by 43.5% and 70.3% in velocity control and steering angle control, respectively. Furthermore, experiments are separately implemented on the CarSim platform and the physical environment to verify the availability of the proposed ECCS. Furthermore, experiments are separately implemented on the CarSim platform and the physical environment to verify the availability of the proposed ECCS. These results validate the effectiveness of embedding ODE kinematics into the evolutionary computing framework for robust and efficient motion control of nonholonomic robots.