Level Control of Quadruple Tank System Based on Adaptive Inverse Evolutionary Neural Controller

This article proposes an adaptive inverse evolutionary neural (AIEN) controller for liquid level control of the quadruple tank system. Firstly, an inverse evolutionary neural model (IEN) that is utilized for offline identifying a dynamics of quadruple tank system, provides a feed-forward control signal from the reference liquid level. In which, the evolutionary neural model is a 3-layers neural network that is optimized by a hybrid method of modified differential evolution and backpropagation algorithm. Then, a hybrid feedforward and PID feedback control is realized to eliminate the steady-state error. Finally, to solve an uncertainty and disturbance characteristic, an adaptive law is proposed to adopt online in its operation. Simulation and real-time control experimental results demonstrated the feasibility and effectiveness of the proposed approach for the quadruple-tank system.