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Neural Adaptive Compensation Control for a Class of MIMO Uncertain Nonlinear Systems with Actuator Failures

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Abstract

A neural adaptive compensation control scheme for a class of multi-input multi-ouput (MIMO) uncertain nonlinear systems with actuator failures is proposed based on prescribed performance bound (PPB) transient performance which characterizes the convergence rate and maximum overshoot of the tracking error. RBF neural networks are used to approximate the error of plant model, the control law proposed can guarantee the asymptotic output tracking and closed-loop signal bounds. The control scheme is applied to a twin otter aircraft longitudinal nonlinear dynamics model in the presence of unknown failures. Simulation results demonstrate the effectiveness of the proposed method.

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Acknowledgements

This work is supported by the National Nature Science Foundation of China under Grants 61074063 and National University Foundational Research Funds of NUAA under grants NZ2012007, NS2013032.

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Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Shaojie Zhang, Xiangwei Qiu & Chunsheng Liu

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  1. Shaojie Zhang
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  2. Xiangwei Qiu
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  3. Chunsheng Liu
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Correspondence to Shaojie Zhang.

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Zhang, S., Qiu, X. & Liu, C. Neural Adaptive Compensation Control for a Class of MIMO Uncertain Nonlinear Systems with Actuator Failures. Circuits Syst Signal Process 33, 1971–1984 (2014). https://doi.org/10.1007/s00034-013-9716-y

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  • DOI: https://doi.org/10.1007/s00034-013-9716-y

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