Abstract
Considering the unknown dynamics of the multiple-input-multiple-output strict-feedback nonlinear systems, this paper proposed the neural composite learning control using the online recorded data. The control structure follows the back-stepping scheme, while neural networks (NNs) are employed for uncertainty approximation. Through the error dynamics analysis, the critical prediction error is constructed to indicate the performance of intelligent learning over the interval. Furthermore, the novel composite learning law is proposed for NN weights update. The stability of the closed-loop system is analyzed via the Lyapunov approach and the signals are guaranteed to be bounded. Through simulation test of two-input and two-output systems, the proposed controller can achieve better tracking performance, while with the composite learning algorithm, the NNs can efficiently approximate nonlinear functions. Similar conclusions are obtained on the control of the hypersonic reentry vehicle.
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