Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Computer-controlled systems: theory and design (2nd ed.)
Computer-controlled systems: theory and design (2nd ed.)
Fuzzy adaptive control of a certain class of SISO discrete-time processes
Fuzzy Sets and Systems
Lazy learning meets the recursive least squares algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
The local paradigm for modeling and control: from neuro-fuzzy to lazy learning
Fuzzy Sets and Systems - Special issue on formal methods for fuzzy modeling and control
Adaptive Control: The Model Reference Approach
Adaptive Control: The Model Reference Approach
Artificial Intelligence Review - Special issue on lazy learning
| Hi-index | 0.00 |
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.