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| DOI： |
| Received:July 21, 2010Revised:March 22, 2011 |
| 基金项目:This research was partly supported by the National Science Foundation (No.0901491). |
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| Asymptotic tracking by a reinforcement learning-based adaptive critic controller |
| Shubhendu BHASIN,Nitin SHARMA,Parag PATRE,Warren DIXON |
| (Department of Mechanical and Aerospace Engineering, University of Florida;Department of Physiology, University of Alberta;NASA Langley Research Center) |
| Abstract: |
| Adaptive critic (AC) based controllers are typically discrete and/or yield a uniformly ultimately bounded stability result because of the presence of disturbances and unknown approximation errors. A continuous-time AC controller is developed that yields asymptotic tracking of a class of uncertain nonlinear systems with bounded disturbances. The proposed AC-based controller consists of two neural networks (NNs) – an action NN, also called the actor, which approximates the plant dynamics and generates appropriate control actions; and a critic NN, which evaluates the performance of the actor based on some performance index. The reinforcement signal from the critic is used to develop a composite weight tuning law for the action NN based on Lyapunov stability analysis. A recently developed robust feedback technique, robust integral of the sign of the error (RISE), is used in conjunction with the feedforward action neural network to yield a semiglobal asymptotic result. Experimental results are provided that illustrate the performance of the developed controller. |
| Key words: Adaptive critic Reinforcement learning Neural network-based control |
