Predictor Based Adaptive Output Feedback Control: Application to Functional Electrical Stimulation of a Human Arm ModelSource: Journal of Dynamic Systems, Measurement, and Control:;2016:;volume( 138 ):;issue: 011::page 111014DOI: 10.1115/1.4033863Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: 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 multiinput multioutput (MIMO) systems. The main contribution of this paper is to extend the results of Nguyen and Leonessa (2014, “Adaptive PredictorBased Output Feedback Control for a Class of Unknown MIMO Linear Systems,†ASME Paper No. DSCC20146214; 2014, “Adaptive PredictorBased Output Feedback Control for a Class of Unknown MIMO Linear Systems: Experimental Results,†ASME Paper No. DSCC20146217; and 2015, “Adaptive PredictorBased 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 stepbystep 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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contributor author | Nguyen, Chuong H. | |
contributor author | Leonessa, Alexander | |
date accessioned | 2017-05-09T01:27:19Z | |
date available | 2017-05-09T01:27:19Z | |
date issued | 2016 | |
identifier issn | 0022-0434 | |
identifier other | ds_138_11_111014.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/160761 | |
description abstract | 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 multiinput multioutput (MIMO) systems. The main contribution of this paper is to extend the results of Nguyen and Leonessa (2014, “Adaptive PredictorBased Output Feedback Control for a Class of Unknown MIMO Linear Systems,†ASME Paper No. DSCC20146214; 2014, “Adaptive PredictorBased Output Feedback Control for a Class of Unknown MIMO Linear Systems: Experimental Results,†ASME Paper No. DSCC20146217; and 2015, “Adaptive PredictorBased 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 stepbystep 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Predictor Based Adaptive Output Feedback Control: Application to Functional Electrical Stimulation of a Human Arm Model | |
type | Journal Paper | |
journal volume | 138 | |
journal issue | 11 | |
journal title | Journal of Dynamic Systems, Measurement, and Control | |
identifier doi | 10.1115/1.4033863 | |
journal fristpage | 111014 | |
journal lastpage | 111014 | |
identifier eissn | 1528-9028 | |
tree | Journal of Dynamic Systems, Measurement, and Control:;2016:;volume( 138 ):;issue: 011 | |
contenttype | Fulltext |