Enhancing Dual-Loop Pressure Control in Pneumatic Soft Robotics With a Comparison of Evolutionary Algorithms for PID & FOPID Controller Tuning

Abstract

The control of pneumatic soft robotics is challenging due to nonlinearites arising from many factors including pneumatic system components and material properties of the soft actuator. Manual methods for PID controller tuning are inadequate for the nonlinear and time-variant dynamics present in soft robotics. Affordable pneumatic components such as on/off valves cause discontinuities in flow rate, introducing nonlinearities and oscillatory fluctuations into the system. This study proposes a dual-loop control system: one for PID and Fractional-Order PID (FOPID) control of a solenoid valve that feeds air into the actuator, and another for PID control of the pump upstream of the valve. The PID and FOPD parameters are optimized using evolutionary algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). Simulations and real-world experiments are conducted to validate the optimized parameters. Our results demonstrate that the dual-loop hardware configuration reduces fluctuations from the valves compared with a single-loop control scheme. The experimental statistical analysis confirms that FOPID achieves the highest significant improvements in rise time (PSO) and peak time (GA, PSO), while PID performs better for overshoot (GA, PSO). These findings highlight the importance of selecting an appropriate optimization algorithm based on the specific control objective, as FOPID does not outperform PID in every metric across all methods.