|Adaptive Control Based on Extended Neural Network for SISO Uncertain Nonlinear Systems
Hao-guang Chen*, Yin-he Wang, and Li-li Zhang
International Journal of Control, Automation, and Systems, vol. 16, no. 1, pp.27-38, 2018
Abstract : "This paper proposes a novel adaptive control criterion for a class of single-input-single-output (SISO)
uncertain nonlinear systems by using extended neural networks (ENNs). Distinguished from the traditional neural
networks, our ENNs are composed of radial basis function neural networks (RBFNNs), scalers and saturators. And
these ENNs are used to approximate the uncertainties in the nonlinear systems. Based on the Lyapunov stability
theory and our ENNs, an adaptive control scheme is designed to guarantee that all the signals in the closed-loop
system are uniformly ultimately bounded (UUB). It is also worth pointing out that our control method makes the
construction of RBFNNs and the design of adaptive laws separated, which means only the outputs of ENNs and
one update law of the parameter in the scaler are to be adjusted. Thus, our control scheme can effectively reduce
the online computation burden of the adaptive parameters. Finally, simulation examples are given to verify the
effectiveness of our theoretical result."
"Adaptive control, extended neural networks (ENNs), scaler, saturator, uniformly ultimately bounded (UUB)."