期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 27, 期 12, 页码 2564-2576出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2015.2496622
关键词
Adaptive neural control (ANC); dynamic learning; high-gain observer; predefined performance; strict-feedback systems
类别
资金
- National Natural Science Foundation of China [61374119, 61473121, 61573112]
- Australian Research Council [LP140100471, DP140102180]
- National Science Fund for Distinguished Young Scholars [61225014]
- Guangdong Natural Science Foundation [2014A030312005]
- 111 Project [B12018]
- Science and Technology New Star of Zhujiang
- Fundamental Research Funds for the Central Universities
This paper focuses on dynamic learning from neural control for a class of nonlinear strict-feedback systems with predefined tracking performance attributes. To reduce the number of neural network (NN) approximators used and make the convergence of neural weights verified easily, state variables are introduced to transform the state-feedback control of the original strict-feedback systems into the output-feedback control of the system in the normal form. Then, using the output error transformation based on performance functions, the constrained tracking control problem of the normal systems is transformed into the stabilization problem of an equivalent unconstrained one. By combining the backstepping method, a high-gain observer with radial basis function (RBF) NNs, a novel adaptive neural control (ANC) scheme is proposed to guarantee the predefined tracking error performance as well as the ultimate boundedness of all other closed-loop signals. In particular, only one NN is employed to approximate the lumped unknown system dynamics during the controller design. Under the satisfaction of the partial persistent excitation condition for RBF NNs, the proposed stable ANC scheme is shown to be capable of achieving knowledge acquisition, expression, and storage of unknown system dynamics. The stored knowledge is reused to develop a neural learning controller for improving the control performance of the closed-loop system. When the initial condition satisfies the predefined performance, the proposed neural learning control can still guarantee the predefined tracking performance. Simulation results on a third-order one-link robot are given to show the effectiveness of the proposed method.
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