Comparative Analysis of Evolutionary Algorithms for PID Controller Optimization in Pneumatic Soft Robotic Systems: A Simulation and Experimental Study

The control of soft elastomeric actuators via pneumatic systems presents challenges due to system nonlinearities and oscillatory behavior. The control of soft robotics is an underexplored field compared to the control of traditional robotics. This study explores evolutionary algorithms for auto-tuning Proportional-Integral-Derivative (PID) controllers in pneumatic soft robotics. Four optimization algorithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Surrogate-Based Optimization (SBO), were employed to optimize PID parameters for pneumatic pressure control. Each algorithm was tested with four objective functions: Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), Integral Square Error (ISE), and Integral Time Square Error (ITSE). Both simulated and experimental studies are conducted to evaluate these algorithms using a pneumatic system designed with affordable on/off valves controlled by Pulse Width Modulation (PWM). Key performance metrics were analyzed: rise time, settling time, overshoot, peak value, and peak time. Results indicate that PSO and GA offer faster response times and moderate overshoot, while SA provides minimal overshoot at the cost of slower response times. These findings can significantly contribute to the practical control of pneumatic systems in soft robotics, offering insights for future optimization and application development.