Data-driven performance improvement of control systems for three-tank systems

Abstract

This paper proposes the data-driven performance improvement of low-cost control systems (CSs) for vertical three-tank systems. The MIMO CSs dedicated to two tanks of the three-tank systems consist of two SISO control loops with separately tuned PI controllers. The Modulus Optimum method is applied to initially tune the PI controllers. Optimization problems are defined on the basis of an original objective function which depends on the controller tuning parameters and is expressed as the sum of squared output errors multiplied by variable weights. The performance improvement is achieved by a new convergent Iterative Feedback Tuning (IFT) algorithm which aims the parameter tuning of PI controllers by the experiment-based solving of the optimization problems. The convergence is ensured by the formulation of the parameter update laws in the IFT algorithm as a nonlinear dynamical feedback system in the parameter space and iteration domain and by setting the step sizes to fulfill inequality-type convergence conditions derived from Popov's hyperstability theory. The experimental results for a laboratory vertical three-tank system show the convincing CS performance improvement by few experiments.