Neural-networks-based Adaptive Control for an Uncertain Nonlinear System with Asymptotic Stability
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This paper proposes a neural-networks(NN)-based adaptive controller for an uncertain nonlinear system with asymptotic stability. While the satisfactory performance of the NN-based adaptive controller is validated well in various uncertain nonlinear systems, the stability is commonly restricted to the uniformly ultimate boundedness(UUB). To improve the UUB of the NN-based adaptive control to the asymptotically stability(AS) with continuous control, the existing NN-based adaptive controller is augmented with a robust-integral-signum-error (RISE) feedback term, and overall closed-loop stability is rigorously analyzed by modifying the typical stability analysis for the RISE feedback control. To demonstrate the effectiveness of the proposed controller, numerical simulations for a fault tolerant flight control with a nonlinear F-16 aircraft model are performed.
KeywordsAdaptive control asymptotic stability fault tolerant flight control neural network RISE feedback
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- M. M. Polycarpou and P. A. Ioannou, “Identification and control of nonlinear systems using neural networks models: Design and stability analysis,” Univ. Southern California, Los Angeles, CA, Tech. Rep. 91-09-01, 1991.Google Scholar
- Y. Li and S. Tong, “Adaptive neural networks prescribed performance control design for switched interconnected uncertain nonlinear systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. PP, no. 99, pp. 1–10. DOI:10.1109/TNNLS.2017.2712698.Google Scholar
- E. A. Morelli, “Global nonlinear parametric modeling with application to F-16 aerodynamics,” Proceeding of American Control Conference, pp. 997–1001, 1998.Google Scholar