A Robust Iterative Learning Switching Controller for following Virtual Constraints: Application to a Hybrid Neuroprosthesis

作     者：Molazadeh, Vahidreza Sheng, Zhiyu Bao, Xuefeng Sharma, Nitin 

作者机构：Department of Mechanical Engineering and Materials Science University of Pittsburgh PittsburghPA15261 United States Department of Bioengineering at the University of Pittsburgh United States 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2019年第51卷第34期

页      面：28-33页

核心收录：

基　　金：Email:{vam50  zhs41  XUB3  nis62}@pitt.edu. This work was funded in This paper extends the switching control design in to findings hmosaeil:o{fvtaandhme5a0uconclusions thzohrs(4s)1 anXdUordBo3recommendationsn otninse6c2e}s@sapriiltyt.erdefuel.expressedcTththisevwioeinwrksthiswofastmaterialhefuNndateidonareianlpartinitdhinbygthse NationalaDndepcaorntmSciencecleunstionosfFoBoundationriroeecnogminmeeearnwidnardagtioannumbertsthexeprUes1646009.nsievdersinitythioAnsfmyPaiopinions ttetsribaulrgarhe. a mTuhlitsi-pDaOpeFr wexatleknindgs  tahdedsswaitlcehairnngincgopnrtroocledduesreig  nanind i[n1s3t]eatod SacritenbcyeNFaotuionndaaltioSnc.ienceFoundationawardnumber1646009.Anyopinions  afmtrualctik-iDngOFtimwea-lbkainsegd ajodidnstaanlegalerntirnagjecptroorcieeds urthe ealnodweinr-sltiemabd Sciencefindings Fandoundation.conclusions or recommendations expressed in this material are of tracking time-based joint angle trajectories  the lower-limb Cthoospeyorifgthhet ©au2t0ho1r8(s I)FaAnCd do not necessarily reflect the views of the National 28 CScienceopyrigFhoundation.t ©2018 IFAC 28of tracking time-based joint angle trajectories  the lower-limb 

主　　题：Controllers E learning Exoskeleton (Robotics) Functional electric stimulation Iterative methods Learning algorithms Neurosurgery Particle swarm optimization (PSO) Quadratic programming Sliding mode control Switching Two term control systems Functional electri cal stimulations Genetic particle swarm optimizations Iterative learning control Sequential quadratic programming Switching controllers Tracking performance Virtual constraints Walking exoskeleton 

摘      要：In this paper, a robust iterative learning switching controller that uses optimal virtual constraint is designed for a hybrid walking exoskeleton that uses functional electrical stimulation and a powered exoskeleton. The synthesis of iterative learning control with sliding-mode control improves tracking performance and accuracy. The motivation for designing this switching controller was to obtain joint torques either from functional electrical stimulation or electric motor. A generalized switching controller is utilized to switch based on the stimulated muscle fatigue state. For achieving stability in walking cycle, the controller is used to force the system to follow the designed virtual constraints. The combination of sequential quadratic programming and genetic-particle swarm optimization algorithm is used for deriving the virtual constraints. The effectiveness of the new iterative learning control for output tracking is verified in a simple model of walking (3-link) that has active actuation at the hip joints. © 2019