A Robust Fuzzy Fractional Order PID Design Based On Multi-Objective Optimization For Rehabilitation Device Control

Abstract

In this context, Fuzzy Fractional Order Proportional Integral Derivative (FOPID-FLC) controllers are emerged as efficient approaches due to their flexibility and ability to handle nonlinearities and uncertainties. This paper proposes the use of a FOPID-FLC controller for a two-degree-of-freedom (2-DOF) lower limb exoskeleton. Our proposal is based on an enhanced control approach that combines fuzzy logic advantages and fractional calculus benefits. Contrary to popular existing methods, that use the FLC to tune the FOPID parameters, the FLC in this work is used to generate the system torque depending on patient morphology. Indeed, our fundamental contribution is to design and implement an enhanced FOPID-FLC that achieves an adequate optimal control based on system rules composed of optimal torques and input data. The fractional calculus is approximated using successive first order filters. Next, a multi-objective optimization is established for the tuning of each FOPID parameters. Finally, the FLC is used to adjust the torque depending on the kid's age. The effectiveness of the proposed controller in various scenarios is validated based on numerical simulations. Extensive analyses prove that the FOPID-FLC outperforms the FOPID with a 90\% of improvement in terms of error performance indices and 20\% of improvement for the control action. Moreover, the controller exhibits improved robustness against uncertainties and disturbances encountered in rehabilitation environments.

RETRACTED: Modeling and Analysis Adaptive-Fuzzy FOPID System With Genetic Algorithm Tuned FOPID Speed Control Of PMSM For Electric Vehicle System

“…It highlighted how well fuzzy -based controllers worked in contrast to the pid algorithms. When designing controllers for many types of systems, the fuzzy technique offers a systematic process that frequently produces greater performance relative to the PID controller [7]. The evoluation control methodology that mimics human thought and behavior by utilizing multivalent fuzzy logic and applying natural selection, crossover inheritance, mutation, and other concepts from evolutionary biology, which is founded on Darwin's theory of evolution.…”