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| Received:June 22, 2011Revised:February 26, 2012 |
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| Learning from NN output feedback control of nonlinear systems in Brunovsky canonical form |
| Wei ZENG,Cong WANG |
| (School of Mechanical & Automotive Engineering, South China University of Technology; School of Physics and Mechanical & Electrical Engineering, Longyan University;School of Automation Science and Engineering, South China University of Technology) |
| Abstract: |
| In this paper, we investigate the learning issue in the adaptive neural network (NN) output feedback control of nonlinear systems in Brunovsky canonical form with unknown affine term. With only output measurements, a high-gain observer (HGO) is employed to estimate the derivatives of the system output which may be associated with the generation of peaking phenomenon. The adverse effect of peaking on learning and its elimination strategies are analyzed. When the gain of HGO is chosen too high, it may cause the failure of learning from the unknown closed-loop system dynamics. Hence, the gain of HGO is not chosen too high to relieve peaking and guarantee the accuracy of the estimated system states. Then, learning from the unknown closed-loop system dynamics can be achieved. When repeating the same or similar control tasks, a neural learning controller is presented which can effectively recall and reuse the learned knowledge to guarantee the output tracking performance. Finally, simulation results demonstrate the effectiveness of the proposed scheme. |
| Key words: Deterministic learning High-gain observer Peaking phenomenon Adaptive neural network Output feedback control Learning control |
