Predictor-Based Adaptive Output Feedback Control: Application to Functional Electrical Stimulation of a Human Arm Model

被引:2
作者
Nguyen, Chuong H. [1 ]
Leonessa, Alexander [1 ]
机构
[1] Virginia Tech, Dept Mech Engn, Dynam Syst Modeling & Control Ctr, Blacksburg, VA 24060 USA
来源
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME | 2016年 / 138卷 / 11期
基金
美国国家科学基金会;
关键词
unknown model; uncertain nonlinear dynamics; predictor; output feedback control; adaptive control; high relative degree; Thelen model; Millard model; DYNAMIC SURFACE CONTROL; NONLINEAR-SYSTEMS; SLIDING MODE; FES; ADJUSTMENT;
D O I
10.1115/1.4033863
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A simulation study to control the motion of a human arm using muscle excitations as inputs is presented to validate a recently developed adaptive output feedback controller for a class of unknown multi-input multi-output (MIMO) systems. The main contribution of this paper is to extend the results of Nguyen and Leonessa (2014, "Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Linear Systems," ASME Paper No. DSCC2014-6214; 2014, "Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Linear Systems: Experimental Results," ASME Paper No. DSCC2014-6217; and 2015, "Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Systems: Experimental Results," American Control Conference, pp. 3515-3521) by combining a recently developed fast adaptation technique and a new controller structure to derive a simple approach for a class of high relative degree uncertain systems. Specifically, the presented control approach relies on three components: a predictor, a reference model, and a controller. The predictor is designed to predict the systems output for any admissible control input. A full state feedback control law is then derived to control the predictor output to approach the reference system. The control law avoids the recursive step-by-step design of backstepping and remains simple regardless of the system relative degree. Ultimately, the control objective of driving the actual system output to track the desired trajectory is achieved by showing that the system output, the predictor output, and the reference system trajectories all converge to each other. Thelen and Millard musculotendon models (Thelen, D. G., 2003, "Adjustment of Muscle Mechanics Model Parameters to Simulate Dynamic Contractions in Older Adults," ASME J. Biomech. Eng., 125(1), pp. 70-77; Millard, M, Uchida, T, Seth, A, and Delp, Scott L., 2013, "Flexing Computational Muscle: Modeling and Simulation of Musculotendon Dynamics," ASME J. Biomech. Eng., 135(2), p. 021005) are used to validate the proposed controller fast tracking performance and robustness.
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页数:12
相关论文
共 35 条
[1]  
Alibeji N, 2015, P AMER CONTR CONF, P631, DOI 10.1109/ACC.2015.7170806
[2]  
[Anonymous], DSCC20146214 ASME
[3]  
[Anonymous], INT C CONTR DYN SYST
[4]  
[Anonymous], ADV INTELLIGENT AUTO
[5]  
[Anonymous], IEEE AER C
[6]  
[Anonymous], 2002, NONLINEAR SYSTEMS
[7]  
[Anonymous], 1995, NONLINEAR ADAPTIVE C
[8]  
[Anonymous], IECON 41 ANN C IEEE
[9]  
[Anonymous], 2 INT C CONTR INSTR
[10]  
[Anonymous], ARM26 MODELS