Research on Trajectory Tracking Control Method for Crawler Robot Based on Improved PSO Sliding Mode Disturbance...

To address the issues of low trajectory tracking accuracy and difficulties in tuning control parameters for crawler robots operating in uneven terrains, this paper proposes a trajectory tracking control method. The method is based on improved particle swarm optimization and sliding mode active disturbance rejection control (SPSO-SMADRC). Firstly, considering the influence of disturbances such as terrain undulations and soil inhomogeneity on trajectory deviation, the kinematic and dynamic models of the crawler robot are established. A vector field guidance approach is employed to transform the trajectory tracking task into a heading control problem. The heading angle is adaptively adjusted based on the position deviation and path curvature. A nonlinear extended state observer is introduced to estimate external disturbances. A velocity-based SMADRC controller is designed to dynamically regulate the robot's linear and angular velocities. This allows real-time correction of the robot's motion. To overcome the tendency of the standard particle swarm optimization (PSO) algorithm to fall into local optima during controller parameter tuning, a nonlinear dynamic adjustment strategy was adopted. This strategy adaptively adjusts the inertia weight and learning factors, enhancing the algorithm's global search capability. Comparative experiments were conducted using two types of curved trajectories: U-shaped and V-shaped paths. The experimental results show that, under the proposed SPSO-SMADRC method, the crawler robot achieved maximum position errors of 8.28 cm and 9.26 cm, average position errors of 1.41 cm and 2.94 cm, and maximum heading angle deviations of 0.56 rad and 0.87 rad. The standard deviations of the position errors were 3.19 and 4.28, respectively. Compared with conventional PSO-based SMADRC and standard SMADRC methods, the proposed approach improved the navigation tracking accuracy. In the U-shaped trajectory, the maximum position error was reduced by 19.22% and 38.21%, the average position error by 40.00% and 65.53%, and the heading angle error by 28.21% and 74.66%. In the V-shaped trajectory, the maximum position error was reduced by 17.39% and 38.95%, the average position error by 51.71% and 52.04%, and the heading angle error by 80.58% and 84.49%. These results demonstrate that the proposed SPSO-SMADRC method significantly enhances trajectory tracking performance and system robustness. It provides effective support for high-precision autonomous navigation of crawler robots in complex and unstructured environments.