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Model Predictive Control with Optimal Modelling for Pneumatic Artificial Muscle in Rehabilitation Robotics: Confirmation of Validity Though Preliminary Testing
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Model Predictive Control with Optimal Modelling for Pneumatic Artificial Muscle in Rehabilitation Robotics: Confirmation of Validity Though Preliminary Testing
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Model Predictive Control with Optimal Modelling for Pneumatic Artificial Muscle in Rehabilitation Robotics: Confirmation of Validity Though Preliminary Testing
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Journal Article
Model Predictive Control with Optimal Modelling for Pneumatic Artificial Muscle in Rehabilitation Robotics: Confirmation of Validity Though Preliminary Testing
2025
Overview
This paper presents a model predictive controller (MPC) based on dynamic models generated using the Particle Swarm Optimisation method for accurate motion control of a pneumatic artificial muscle (PAM) for application in rehabilitation robotics. The physical compliance and lightweight nature of PAMs make them desirable for use in the field but also introduce nonlinear dynamic properties which are difficult to accurately model and control. As well as the MPC, three other control systems were examined for a comparative study: a particle-swarm optimised proportional-integral-derivative controller (PSO-PID), an iterative learning controller (ILC), and classical PID control. A series of different waveforms were used as setpoints for each controller, including addition of external loading and simulated disturbance, for a system consisting of a single PAM. Based on the displacement error measured for each experiment, the PID controller performed worst with the largest error values and an issue with oscillating about the setpoint. PSO-PID performed better but still poorly compared with the other intelligent controllers, as well as still exhibiting oscillation, which is undesirable in any human–robot interaction as it can heavily impact the comfort and safety of the system. ILC performed well with rapid convergence to steady-state and low-error values, as well as mitigation of loads and disturbance; however, it performed poorly under changing frequency of input. MPC generally performed the best of the controllers tested here, with the lowest error values and a rapid response to changes in setpoint, as well as no required learning period due to the predictive algorithm.
