An optimal fuzzy-theoretic setting of adaptive robust control design for a lower limb exoskeleton robot system

• Published in: Mechanical systems and signal processing Vol. 141; p. 106706
• Main Authors: Yang, Siyang, Han, Jiang, Xia, Lian, Chen, Ye-Hwa
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 01.07.2020; Elsevier BV
• Subjects: Adaptive control; Adaptive robust control; Algorithms; Computer simulation; Disturbances; Exoskeleton robot; Exoskeletons; Fuzzy control; Fuzzy set theory; Fuzzy sets; Human motion; Limbs; Optimal control; Optimization; Parameter uncertainty; Performance indices; Rehabilitation; Robots; Robust control; Training; Uncertainties and disturbances; Fuzzy set theory; Uncertainties and disturbances; Exoskeleton robot; Adaptive robust control
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2020.106706

•Transform the gait tracking control into servo trajectory constraint control.•Deterministic robust control performance has been ensured by the proposed methodology.•The bounds of uncertainties and disturbances are described by fuzzy compact sets.•The existence of optimal gain can be demonstrated strictly in mathematics. Aim to recover the motor function of impaired lower limbs of stroke patients, in this paper, we propose a novel adaptive robust control with a fuzzy optimal gain design approach for an effective rehabilitation training equipment, i.e. the 2DOF lower limb exoskeleton robot system (LLERs). Our proposed control method includes two parts, the first part is a novel adaptive robust control, as a bottom line, it will guarantee the uniform boundedness and uniform ultimate boundedness regardless of uncertainties and disturbances. The second part is a novel optimization method for control gain parameter, since the uncertainties and disturbances will arise in the practical 2DOF LLERs inevitably, we adopt the fuzzy set theory to describe these uncertainties and disturbances, and the bounds of these uncertainties and disturbances are characterized by membership functions. Based on such descriptions and several subsequent fuzzy operations, a fuzzy performance index which contains average fuzzy system performances and control costs will be constructed to seek an optimal control gain for the adaptive robust control put forward, in addition, the existence of optimal control gain has been also verified theoretically. Eventually, the simulation results presented have demonstrated the effectiveness of our proposed algorithm on rehabilitation training of lower limbs.