A Design of Neural-Net Based Self-Tuning PID Controllers

Recently, neural network techniques have widely used in designing adaptive and learning controllers for nonlinear systems. However, it costs a lot of time for learning of the neural network included in the control system. Furthermore, the physical meaning of neural networks constructed as a result, is not obvious. In this paper, a design scheme of self-tuning PID controllers is proposed, which has a structure of fusing self-tuning and neural network techniques. The newly proposed scheme enables us to adjust PID gains quickly.

[1]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[2]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[3]  Madan M. Gupta,et al.  Neuro-Control Systems: Theory and Applications , 1993 .

[4]  Sigeru Omatu,et al.  Self-tuning PID control by neural-networks , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[5]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[6]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[7]  K. Narendra,et al.  Bounded error adaptive control , 1980, 1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[8]  Marzuki Khalid,et al.  MIMO furnace control with neural networks , 1993, IEEE Trans. Control. Syst. Technol..

[9]  Judith E. Dayhoff,et al.  Neural Network Architectures: An Introduction , 1989 .

[10]  Richard S. Sutton,et al.  Neural networks for control , 1990 .