Fuzzy Multiple Hidden Layer Recurrent Neural Control of Nonlinear System Using Terminal Sliding-Mode Controller

This study designs a fuzzy double hidden layer recurrent neural network (FDHLRNN) controller for a class of nonlinear systems using a terminal sliding-mode control (TSMC). The proposed FDHLRNN is a fully regulated network, which can be simply considered as a combination of a fuzzy neural network (FNN) and a radial basis function neural network (RBF NN) to improve the accuracy of a nonlinear approximation, so it has the advantages of these two neural networks. The main advantage of the proposed new FDHLRNN is that the output values of the FNN and DHLRNN are considered at the same time, and the outer layer feedback is added to increase the dynamic approximation ability. FDHLRNN was designed to approximate the nonlinear sliding-mode equivalent control term to reduce the switching gain. To ensure the best approximation capability and control performance, the proposed FDHLRNN using TSMC is applied for the second-order nonlinear model. Two simulation examples are implemented to verify that the proposed FDHLRNN has faster convergence speed and the FDHLRNN with TSMC has good dynamic property and robustness, and a hardware experimental study with an active power filter proves the feasibility of the method.