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Research Papers

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

[+] Author and Article Information
Chuong H. Nguyen

Department of Mechanical Engineering,
Dynamic Systems Modeling and Control Center,
Virginia Tech,
Blacksburg, VA 24060
e-mail: chuong98@vt.edu

Alexander Leonessa

Department of Mechanical Engineering,
Dynamic Systems Modeling and Control Center,
Virginia Tech,
Blacksburg, VA 24060
e-mail: aleoness@vt.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 7, 2015; final manuscript received June 8, 2016; published online August 11, 2016. Assoc. Editor: Xiaopeng Zhao.

J. Dyn. Sys., Meas., Control 138(11), 111014 (Aug 11, 2016) (12 pages) Paper No: DS-15-1622; doi: 10.1115/1.4033863 History: Received December 07, 2015; Revised June 08, 2016

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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References

Figures

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Fig. 1

Controller structure for SISO systems

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Fig. 2

The Arm26 model with three active muscles: TRIlat, TRImed, and BRA

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Fig. 5

The time evolution of outputs ym(t), ŷ(t), and y(t) and its closed-look in the time period [290 300(s)] for r(t)=(π/180) (15 tan h(3 sin(5t))+70)

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Fig. 6

The time evolution of outputs ym(t), ŷ(t), and y(t) conducted with Millard model and its closed-look in the time period [0 7(s)] for r(t)=(π/180) (−20 tan h(3 sin(5t))+100)

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Fig. 7

The tracking performance using the sliding mode control (Fig. 5, [15]) and the proposed controller for r(t)=(π/180) (−35 cos(πt))+70)

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Fig. 3

The time evolution of outputs ym(t), y¯m(t), ŷ(t), and y(t), the excitation signals ei(t), and the adaptive parameters for r(t)=(π/180) (25 sin(t)+90)

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Fig. 4

The time evolution of outputs ym(t), y¯m(t), ŷ(t), and y(t)and the excitation signals ei(t) for r(t)=(π/180) (−20 tan h(4 cos(2t)+30)

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