Data-driven techniques for direct adaptive control: the lazy and the fuzzy approaches

Abstract

This paper presents an approach to modeling and controlling discrete-time non-linear dynamical system on the basis of a finite amount of input/output observations. The controller consists of a multiple-step-ahead direct adaptive controller which, at each time step, first performs a forward simulation of the closed-loop system and then makes an adaptation of the parameters of the controller. This procedure requires a sufficiently accurate model of the process in order to meet the control requirements. Takagi–Sugeno fuzzy systems and Lazy Learning are two approaches which have been proposed in control literature as effective ways of identifying a plant. This paper compares these two approaches in two main configurations: (i) when the number of observations is fixed and (ii) when new observations are collected on-line after each control action. Simulation examples of the control of the manifold pressure of a car engine are given.