A self-evolving functional-linked wavelet neural network for control applications

• The proposed self-evolving functional-linked wavelet neural network (SFWNN) can vary its structure dynamically to keep the prescribed approximation accuracy with a simple computation.
• This paper presents an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) for a class of uncertain nonlinear systems.
• The proposed ASFWNC system is applied to a chaotic dynamic and a DC motor.
• Simulation and experimental results verify that the proposed ASFWNC system can achieve high-precision tracking performance.

The structure of a neural network is determined by time-consuming trial-and-error tuning procedure in advance for the reason that it is difficult to consider the balance between the neuron number and the desired performance. To attack this problem, a self-evolving functional-linked wavelet neural network (SFWNN) is proposed. Without the need for preliminary knowledge, a self-evolving approach demonstrates that the properties of generating and pruning the hidden neurons automatically. Then, an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) system which is composed of a neural controller and a supervisory compensator is proposed. The neural controller uses a SFWNN to online estimate an ideal controller and the supervisory compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. To investigate the capabilities of the proposed ASFWNC approach, it is applied to a chaotic system and a DC motor. The simulation and experimental results show that favorable control performance can be achieved by the proposed ASFWNC scheme.

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