Sliding Mode Backstepping Control of Excavator Bucket Trajectory Synovial in Particle Swarm Optimization Algorithm and Neural Network Disturbance Observer

Abstract

As a representative of multi-functional engineering machinery, the excavator is irreplaceable in the field of engineering construction. To autonomously control the excavator bucket, it is essential to control the position of the bucket hydraulic cylinder. As a consequence of the problem of position tracking control spawned from external disturbance and other factors in the self-mining servo system of excavators, a strategy of sliding mode backstepping control based on the particle swarm optimization algorithm and neural network disturbance observer (PSO-NNDO-SMBC) was recommended accordingly. Meanwhile, the complex disturbance was estimated online and compensated for by the system control input by the universal approximation property of the neural network disturbance observer (NNDO). Afterwards, the uncertainty of control parameters was optimized by the particle swarm optimization algorithm (PSO) and was fed back to the controller parameter input end. Afterwards, a co-simulation model of MATLAB/Simulink (MATLAB2023b) and AMESim (Simcenter Amesim 2304) was established for simulation analysis, and a test bench was set up for comparison and verification. As proven by the experimental results, PSO-NNDO-SMBC possessed strong anti-interference ability. In contrast to the sliding mode backstepping control based on the particle swarm optimization algorithm (PSO-SMBC), the maximum displacement tracking error was lowered by 50.5%. Furthermore, in comparison with the Proportional-Integral-Derivative (PID), the maximum displacement tracking error was decreased by 75.2%, which tremendously optimized the control accuracy of excavator bucket displacement tracking.

Keywords

• excavator bucket
• electro-hydraulic servo system
• particle swarm optimization
• neural network disturbance observer
• sliding mode backstepping control