Research And Application Of Predictive Control For Nonlinear Systems Based On Universal Learning Network

Intelligent predictive control aim at complex system control using an intelligent model combined with typical predictive control algorithm to constitute a class of intelligent predictive control system, which makes up the defects of traditional control algorithms, such as low accuracy, only adaptive to linear System, lack of self-learning and self-organization functions, and tso on. Intelligent predictive control can handle non-linear, multi-objective, constraints and other anomalies. Therefore, intelligent predictive control is one of the hot spots in current researches for predictive control. The research relates to the identification and the predictive control of nonlinear system based on a new type of neural network that is Universal Learning Network (ULN).Universal Learning Networks consist of a number of nodes and branches for inter-connecting the nodes and each pair of nodes can be connected to each other by multiple branches with arbitrary time delays. With all these structural characteristics, it can be used in modeling the highly complicated nonlinear system. Firstly a detail introduction of the structure and learning algorithm of Universal Learning Network was given in the paper, and then the network was used to model a robot arm's measured signal system, through simulation we can see that universal learning network with multiple branches is better than the single-branch neural network. In the following part of the paper, both the universal learning network and the conventional recurrent network which is Elman have been used to identify the CSTR system, the simulation results further validate that the universal learning network has higher accuracy than the Elman network when they are used in identification, and furthermore, the network structure is more simple and compact.Based on the good identification ability of Universal Learning Network in complex system, Universal Learning Network was proposed to be used in the predictive control of the nonlinear system, Universal Learning Network was used to build a Universal Learning Network predictor (ULNP) to predict the output value on the next moment, and then BP neural network controller (NNC) was used to realize the model-based predictive control. The modeling and the on-line recursive learning algorithm of the predictor and the controller are explicated in detail. Simulation results show that the proposed control algorithm can give good tracking performance for an illustrative nonlinear system.