Prescribed Performance Neural Control of Strict-Feedback Systems via Disturbance Observers

Wei Xiang, Guangkui Xu, Fang Zhu and Chunzhi Yang

Complexity, 2020, vol. 2020, 1-12

Abstract:

This paper provides a disturbance observer-based prescribed performance control method for uncertain strict-feedback systems. To guarantee that the tracking error meets a design prescribed performance boundary (PPB) condition, an improved prescribed performance function is introduced. And radial basis function neural networks (RBFNNs) are used to approximate nonlinear functions, while second-order filters are employed to eliminate the “explosion-complexity” problem inherent in the existing method. Meanwhile, disturbance observers are constructed to estimate the compounded disturbance which includes time-varying disturbances and network construction errors. The stability of the whole closed-loop system is guaranteed via Lyapunov theory. Finally, comparative simulation results confirm that the proposed control method can achieve better tracking performance.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:8835512

DOI: 10.1155/2020/8835512

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