Title :
Predictive control based on feedforward neural network for strong nonlinear system
Author :
Han, Min ; Guo, Wei ; Wang, Jincheng
Author_Institution :
Sch. of Electron. & Inf. Eng., Dalian Univ. of Technol., China
fDate :
July 31 2005-Aug. 4 2005
Abstract :
The paper presents a generalized predictive control (GFC) algorithm based on feedforward neural network to control nonlinear system. In recent years, approximate linearization theory via feedback is used to control nonlinear system, but robustness can not be guaranteed. Considering neural network can accomplish nonlinear mapping from input to output, feedforward neural network is chosen as a nonlinear model of process. Based on such model, GPC is applied to control a second-order nonlinear system. To test the performance of system utilized such control algorithm, different experiments are made. Simulation results demonstrate that the performance of the system controlled by the proposed algorithm is good, and that system essentially responds in the desired manner. It is also demonstrated that the GPC based on neural network is provided with good adaptation and robustness.
Keywords :
approximation theory; feedback; feedforward neural nets; linearisation techniques; neurocontrollers; nonlinear control systems; predictive control; approximate linearization theory; feedback; feedforward neural network; generalized predictive control; strong nonlinear system; Control systems; Feedforward neural networks; Linear approximation; Linear feedback control systems; Neural networks; Neurofeedback; Nonlinear control systems; Nonlinear systems; Prediction algorithms; Predictive control;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1556254