Design and Genetic Algorithms Based Optimisation of Industrial Adaptive PID FLC System of Liquid Level

Abstract

The level control of the precarbonised solution in a soda ash production plant requires intelligent approaches that can tackle process complexity, nonlinearity and industrial environment impact. Therefore, model-free fuzzy logic controllers (FLC) with empirical tuning are suggested which are implemented in a general purpose programmable logic controller (PLC) and operate in real time control. Online adaptation improves the FLC parameters tuning. The aim of the present research is to optimise the adaptation strategy and the parameters of an adaptive PLC PID FLC using genetic algorithms (GA) and simulations for reducing both the system error and the control variance. The PID FLC is based on a PD FLC and a parallel integrator of the system error. A Sugeno model is used for adaptation of the PID FLC tuning parameters. Depending on the level it defines empirically via its input membership functions three linearisation zones and performs soft blending of the local for each zone PD FLC gains and integrator time-constants. Two adaptation strategies are suggested for online auto-tuning of the integrator time-constant only, and together with the PD FLC gain. The local parameters, in turn, are GA optimised. Simulations show that the best system performance is achieved by auto-tuning both PID FLC parameters with optimised local values.