We propose an approach to tune the Computed Torque Control (CTC) plus robust PID optimal parameters for trajectory tracking of a delta-type parallel robot using a hybrid optimization algorithm of Particle Swarm Optimization (PSO) and Differential evolution (DE).
This approach differs from the previous research results in finding a robust PID controller parameter based on a conventional gradient-based optimization algorithm and applying it to process control.
First, in this paper, a robust PID controller parameter tuning problem coupled with a CTC approach, which satisfies various characteristics simultaneously, such as robustness and disturbance rejection characteristics of the closed-loop system, is reduced to a nonlinear optimization problem with nonlinear constraints, by considering the nonlinear dynamics model of the delta-type parallel robot.
Second, we designed a robust PID controller by finding the global optimization solution of the above-mentioned nonlinear optimization problem using PSO-DE hybrid optimization algorithm to find the global optimization solution by setting in detail the design characteristics for trajectory tracking of the delta-type parallel robot and maintaining the diversity of the swarm based on the evolution of the cognitive experience.
Simulation results and experimental results show that the designed robust PID controller has robustness and disturbance rejection characteristics when applied to trajectory tracking control of delta-type parallel robot.
Detailed results were published in the journal "Robotica" (Volume 40, Number 1, (2023), pp. 1-20) under the title of "Robust PID Optimal Tuning of Delta Parallel Robot based on PSO-DE Hybrid Optimization Algorithm." (https://doi.org/10.1017/S0263574722001606).
