Sensors 2012, 12(5), 6117-6128; https://doi.org/10.3390/s120506117
Self-Learning Variable Structure Control for a Class of Sensor-Actuator Systems
1
Key Lab of Visual Media Processing and Transmission, Shenzhen Institute of Information Technology, Shenzhen 518029, Guangdong, China
2
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA
3
Department of Computer Science, University of Massachusetts, Amherst, MA 01003, USA
4
School of Mechatronics and Information, Yiwu Industrial and Commercial College, Yiwu 322000, Zhejiang, China
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These authors contributed equally to this work.
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Received: 4 April 2012 / Revised: 16 April 2012 / Accepted: 29 April 2012 / Published: 10 May 2012
(This article belongs to the Section Physical Sensors)
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Abstract
Variable structure strategy is widely used for the control of sensor-actuator systems modeled by Euler-Lagrange equations. However, accurate knowledge on the model structure and model parameters are often required for the control design. In this paper, we consider model-free variable structure control of a class of sensor-actuator systems, where only the online input and output of the system are available while the mathematic model of the system is unknown. The problem is formulated from an optimal control perspective and the implicit form of the control law are analytically obtained by using the principle of optimality. The control law and the optimal cost function are explicitly solved iteratively. Simulations demonstrate the effectiveness and the efficiency of the proposed method. View Full-TextKeywords:
sensor-actuator system; principle of optimality; Bellman equation; variable structure control; self-learning
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Chen, S.; Li, S.; Liu, B.; Lou, Y.; Liang, Y. Self-Learning Variable Structure Control for a Class of Sensor-Actuator Systems. Sensors 2012, 12, 6117-6128.