Neuroadaptive global sliding mode control for nonlinear systems and its application

Abstract

One type of adaptive controller on the basis of an original dual-loop recursive fuzzy neural network (DRFNN) with two closed loop structures for a kind of nonlinear dynamic systems is designed in this paper. Though the conventional FNN could approximate arbitrary smooth functions, the inner network parameters need to be set up in advance. Usually, the setting of the base width and central vector are lack of theoretical guidance to some degree and need to be debugged many times. However, the initial values of the center vector and the base width can be arbitrarily set and stable to optimal ones in the light of adaptive mechanism of the proposed dual recursive FNN. Besides, the dynamic dual recursive FNN possesses the ability of storing more beneficial information through constructing signal back loops, meanwhile accuracy of the approximation is higher than the traditional FNN. Furthermore, the learning speed and detection accuracy can be improved due to the fact that expert knowledge is a priori knowledge in the network structure by including if-then rules of this FNN. To validate effectiveness of the developed scheme, simulations and three suits of experiments are carried out on a set of active power filter to demonstrate that this presented global sliding mode control using DRFNN can achieve expected performance. Eventually, some comparisons among FNN and the proposed DRFNN are implemented to suggest the dual recursive FNN can obtain more superior properties.

Predictive cloud control for nonlinear multiagent systems based on extended state observer and quantizers

“…Remark In References 26 and 35, MPC and neural networks are used for different nonlinear NCSs. However, due to the real‐time computation online, the computational effort is higher by using these two approaches.…”