Trajectory Tracking Control of a Six-Degree-of-Freedom Manipulator Based on EAVOA-LADRC

In the trajectory tracking control of the manipulator, since it is difficult to establish an accurate system model for the manipulator, the use of Linear Active Disturbance Rejection Control (LADRC), although it does not rely on an accurate system model, is difficult to obtain the optimal parameters in practical applications, which affects the control accuracy. Therefore, a linear self-imposed perturbation parameter tuning method based on the Enhance African Vulture Optimization Algorithm (EAVOA) is proposed. In order to solve the problem that the AVOA is unbalanced between the exploration stage and the development stage, and is prone to fall into the local optimal solution, the Kent Chaos initialization, Cauchy inverse learning, optimization of the F computation, and introduction of inhibition coefficients are proposed as the improvement strategies. Through experimental comparison, the improved algorithm has faster convergence speed and better optimal solution, and has smaller joint angle error in the trajectory tracking control of six-degree-of-freedom manipulator based on LADRC.