Finite-Level Quantized Iterative Learning Control for a Class of Fuzzy Cellular Neural Networks Based on Encoding-Decoding Mechanism

This paper addresses the problem of finite-level uniform quantized iterative learning control for a class of fuzzy cellular neural networks employing an encoding-decoding scheme. The proposed approach involves encoding the fuzzy cellular neural network system state signal into uncomplicated data, which is subsequently broadcasted to the controller. The encoded data is then utilized to update the system input, while the decoder estimates the sender’s state. To achieve consensus tracking convergence and alleviate communication burden, one will introduce a uniform quantizer to quantity the information and establish an iterative learning protocol using the estimated tracking error. The effectiveness of the proposed protocol is demonstrated through simulation results.