Real-time adaptive super twisting algorithm based on PSO algorithm: application for an exoskeleton robot

• Published in: Robotica Vol. 42; no. 6; pp. 1816 - 1841
• Main Authors: Tiaiba, Hichame, Daachi, Mohamed El Hossine, Madani, Tarek
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.06.2024
• Subjects: Actuators; Adaptive algorithms; Adaptive systems; Algorithms; Control stability; Controllers; Dynamic models; Elbow; Exoskeletons; Neuromuscular electrical stimulation; Nonlinear control; Nonlinear systems; Parameters; Particle swarm optimization; Real time; Rehabilitation; Robot control; Robots; Robust control; Simulation; Sliding mode control; Trajectory control; Trajectory optimization; Twisting; Velocity; super twisting sliding mode control; online tuning; robotic rehabilitation; adaptive controller; particle swarm optimization; exoskeleton robot; real time
• DOI: 10.1017/S0263574724000547
• ISSN: 0263-5747

In this paper, an online adaptive super twisting sliding mode controller is proposed for a non-linear system. The adaptive controller has been designed in order to deal with the unknown dynamic uncertainties and give the best trajectory tracking. The adaptation is based on an optimal Particle Swarm Optimization (PSO) algorithm whose goal is online tuning the parameters through focusing on decreasing the objective function. The novelty of this study is online handling parameters setting in the conventional super twisting algorithm, bypass heavy offline calculation, and also avoid the instability and abrupt changing of the controller’s parameters for better actuators lifetime. This novel approach has been applied on an upper limb exoskeleton robot for arm rehabilitation. Despite the changes of the dynamic model of the system which defers from one patient to another due to the direct interactions between the wearer and the exoskeleton, this control technique preserves its robustness with respect to bounded external disturbances. The effectiveness of the proposed adaptive controller has been proved in simulation and then in real-time experiment with two human subjects. A comparison between the proposed approach and classic super twisting algorithm has been conducted. The obtained results show the performance and efficiency of the proposed controller.