Achieving high-precision path tracking on uneven and complex terrain remains a significant challenge for mobile robots. The uneven changes in the ground and friction coefficients may introduce stochastic nonlinear disturbances, leading to degradation of the robot’s tracking performance, and even system instability. This paper aims to propose a robust predictive control strategy to address the performance degradation problem caused by such interference, with strong potential for applications in autonomous vehicles, robotic inspection and mobile robotics in uncertain environments.
This study models the interaction between the robot and the ground as a class of stochastic nonlinear functions. To ensure efficient implementation within embedded systems, the controller is synthesized by minimizing a regularized quadratic performance index. Furthermore, the stability of the closed-loop system is verified by Lyapunov stability analysis and linear matrix inequality (LMI) conditions.
Experiments demonstrate that the proposed method achieves high-precision tracking on marble, carpet and asphalt surfaces, exhibiting robust performance in environments with disturbances and uncertainties.
The control framework proposed in this study enhances the system’s robustness and computational efficiency by combining stochastic nonlinear modeling with regularization design, offering significant industrial application value.
