Hybrid Intelligent Control for a Transradial Prosthetic Wrist Using PID, Fuzzy Logic, and Particle Swarm Optimization

Resumo

This study presents the development of a hybrid intelligent control system for a transradial prosthetic wrist, integrating Proportional-Integral-Derivative (PID) control, Fuzzy Logic, and Particle Swarm Optimization (PSO). The prosthesis, based on a design developed by Labbio, incorporates a motor, a harmonic reducer, and a magnetorheological brake to enable precise torque control and smooth articulation. The proposed system addresses three distinct operational scenarios: abrupt motion, high-precision smooth movement, and a hybrid mode that alternates dynamically between the two.
A mathematical model of the wrist mechanism is first formulated to determine the effective torque output, incorporating the electrical inputs to both the motor and the brake, and adhering to the mechanical and control constraints of the device. Reference curves for torque are defined for each scenario to establish performance benchmarks.
A dynamic control framework is developed wherein PID controller parameters are automatically optimized using the PSO algorithm for each specific scenario. To enable real-time adaptability, a Fuzzy Logic system is implemented to identify the current movement regime and assign the corresponding PID coefficients. This integration enables the prosthesis to adapt its behavior dynamically in response to changes in user intent or task demands.
Simulation results demonstrate the system’s ability to deliver responsive and stable control across all operational conditions. The adaptive nature of the fuzzy-PSO-PID controller allows for smooth transitions between motion types, minimizing overshoot and delay while enhancing precision and user comfort. This approach exemplifies the synergy between classical control, soft computing, and bio-inspired optimization techniques, offering a robust solution for nonlinear, time-varying systems in biomedical applications.
This research contributes to the field of intelligent control systems by showcasing how hybrid methods can be applied effectively to prosthetic devices, expanding the frontiers of human-machine interaction and rehabilitative technologies.