A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering

Volumn 216, Issue 3, 2002, Pages 265-273

  Zhu, Q M ^a   Warwick, K ^b  

^a UNIVERSITY OF THE WEST OF ENGLAND   (United Kingdom)

^b UNIVERSITY OF READING   (United Kingdom)

Author keywords

Complex dynamic plants; Neuro PID controller; Self tuning control

Indexed keywords

ALGORITHMS; BACKPROPAGATION; COMPUTER SIMULATION; CONTROL SYSTEM SYNTHESIS; ERRORS; INTELLIGENT CONTROL; NEURAL NETWORKS; NONLINEAR CONTROL SYSTEMS; SELF TUNING CONTROL SYSTEMS;

COMPLEX DYNAMIC PLANTS; LINEAR SUBMODEL; MINIMUM-VARIANCE SELF-TUNING CONTROL; NEURO PID CONTROLLER; RECURSIVE LEAST-SQUARES ALGORITHM;

THREE TERM CONTROL SYSTEMS;

EID: 0036299604     PISSN: 09596518     EISSN: None     Source Type: Journal    
DOI: 10.1243/095965102320005337     Document Type: Article

References (21)

1. Neural networks for control systems-a survey
2. Identification and control of dynamic systems using neural networks
3. Back-propagation neural networks for nonlinear self-tuning adaptive control
4. A critique of neural networks for discrete time linear control systems
5. Real time design of an adaptive non-linear predictive controller
6. Predictive control for nonlinear systems using neural networks
7. Neural network control of unknown nonlinear systems
8. Nonlinear control structures based on embedded neural system models
9. A neural network enhanced generalised minimum variance self tuning control for non-linear discrete-time systems
10. Adaptive control of a class of non-linear discrete-time systems using neural networks
11. Developing controller through a process of natural evolution
12. Design of a neural network enhanced auto-tuning PID controller