A new non-linear multi-variable multiple-controller incorporating a neural network learning sub-model

Citation

Zayed AS & Hussain A (2004) A new non-linear multi-variable multiple-controller incorporating a neural network learning sub-model. In: Smith L, Hussain A & Aleksander I (eds.) Brain Inspired Cognitive Systems 2004. BICS2004: Brain Inspired Cognitive Systems 2004, Stirling, Scotland, 29.08.2004-01.09.2004. Stirling, Scotland: University of Stirling, pp. 23-24. http://www.cs.stir.ac.uk/~lss/BICS2004/CD/bookofabstracts.pdf

Abstract
A new non-linear multi-variable multiple -controller incorporating a neural network learning sub-model is proposed. The unknown multivariable non-linear plant is represented by an equivalent stochastic model consisting of a linear timevarying sub-model plus a non-linear neural-network based learning sub-model. The proposed multiple controller methodology provides the designer with a choice of using either a conventional Proportional- Integral-Derivative (PID) self-tuning controller, a PID based pole -placement controller, or a newly proposed PID based pole -zero placement controller through simple switching. The novel PID based pole-zero placement controller employs an adaptive mechanism, which ensures that the closed loop poles and zeros are located at their pre-specified positions. The switching decision between the different non-linear fixed structure controllers can be done either manually or by using Stochastic Learning Automata. Simulation results using a non-linear Multiple Input Multiple Output (MIMO) plant model demonstrate the effectiveness of the proposed multiple controller, with respect to tracking setpoint changes. The aim is to achieve a desired speed of response, whilst penalizing excessive control action, for application to non-minimum phase and unstable systems.