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

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence and Optimization
• Autonomous Navigation

Background:

• Crawler robots face challenges in trajectory tracking accuracy and parameter tuning, especially in uneven terrains.
• Disturbances from terrain undulations and soil inhomogeneity significantly impact robot trajectory deviation.
• Existing methods like standard sliding mode active disturbance rejection control (SMADRC) can struggle with parameter optimization and local optima.

Purpose of the Study:

• To develop an advanced trajectory tracking control method for crawler robots operating in challenging environments.
• To improve the global search capability of particle swarm optimization (PSO) for controller parameter tuning.
• To enhance the navigation accuracy and robustness of crawler robots through an improved control strategy.

Main Methods:

• Established kinematic and dynamic models for crawler robots, incorporating terrain disturbance factors.
• Employed a vector field guidance approach to convert trajectory tracking into a heading control problem.
• Designed a nonlinear extended state observer for disturbance estimation and a velocity-based SMADRC controller for real-time motion regulation.
• Introduced a nonlinear dynamic adjustment strategy for inertia weight and learning factors in PSO to improve global search capability (SPSO-SMADRC).

Main Results:

• The SPSO-SMADRC method achieved significantly reduced position and heading angle errors compared to conventional methods on U-shaped and V-shaped trajectories.
• Maximum position errors were as low as 8.28 cm and 9.26 cm, with average errors of 1.41 cm and 2.94 cm.
• Navigation tracking accuracy was improved, with reductions in maximum position error by up to 38.21% and average position error by up to 65.53%.

Conclusions:

• The proposed SPSO-SMADRC method offers superior trajectory tracking performance and enhanced system robustness for crawler robots.
• This advanced control strategy provides effective support for high-precision autonomous navigation in complex, unstructured terrains.
• The improved PSO algorithm overcomes limitations of standard PSO, leading to more effective controller parameter tuning.