Simon Oblak

An approach to predictive control of multivariable time-delayed plant: stability and design issues.

In this paper, a new method of multivariable predictive control is presented. The main advantage of a predictive approach is that multivariable plants with time delays can be easily handled. The proposed control algorithm also introduces a compact and simple design in the case of higher-order and nonminimal phase plants, but it is limited to open-loop stable plants. The algorithm of the proposed multivariable predictive control is developed, designed, and implemented on an air-conditioned system. The stability of the proposed control law is discussed.

Fuzzy-model-based hybrid predictive control

In this paper we present a method of hybrid predictive control (HPC) based on a fuzzy model. The identification methodology for a nonlinear system with discrete state-space variables based on combining fuzzy clustering and principal component analysis is proposed. The fuzzy model is used for HPC design, where the optimization problem is solved by the use of genetic algorithms (GAs). An illustrative experiment on a hybrid tank system is conducted to demonstrate the benefits of the proposed approach.

On applying interval fuzzy model to fault detection and isolation for nonlinear input-output systems with uncertain parameters

This paper presents an application of the interval fuzzy model (INFUMO) in fault detection and isolation for a class of processes with uncertain interval-type parameters. Confidence data bands for the process input-output pairs are approximated using a fuzzy model with interval parameters. The approximation, based on linear programming, employs linfin-norm as the modelling error measure. Arbitrary sets of identification input signals can be used due to the application of low-pass filtering when obtaining the confidence bands. Using a combination of INFUMOs makes it possible to devise a fault-isolation scheme based on the given incidence matrix. Simulation results of a fault detection and isolation for a two-tank system are provided, which illustrate the relevance of the proposed FDI method

If approximating nonlinear areas, then consider fuzzy systems

The fuzzy system is a knowledge-based system consisting of linguistic if-then rules. This article will focus on the Takagi-Sugeno (TS) type models. A novel approach of INFUMO has been applied in fault detection. The INFUMO was derived using the linfin-norm function approximation. The majority of papers focus on the construction of the so-called residual generator, a comparator of the process and the process-model output that creates a residual signal

A Comparison of Fuzzy and CPWL Approximations in the Continuous-time Nonlinear Model-predictive Control of Time-delayed Wiener-type Systems

This paper deals with a novel method of continuous-time model-predictive control for nonlinear time-delayed systems. The problems relating to time delays are solved by incorporating the Smith-predictor scheme in a control-law derivation. A nonlinear-mapping approximation, employing either continuous piece-wise linear functions or a fuzzy system, is also an integral part of the control scheme, and thus removes the need for output-function invertibility. An illustrative experiment is conducted to compare the control quality in both approaches when tackling a time-delayed Wiener-type system control.

Continuous-Time Wiener-Model Predictive Control of a pH Process

This paper deals with a novel formulation of continuous-time model-predictive control for nonlinear systems. A nonlinear-mapping approximation, employing a fuzzy system, is also an integral part of the control scheme, and thus removes the need for output-function invertibility. Analytical formulation of the control law makes it possible to use the method in practice, especially in chemical industry. An illustrative experiment is conducted to compare the proposed approach with a method of nonlinear robust Hinfin control of a pH neutralization process.

Hybrid Predictive Control based on Fuzzy Model

In the paper, the hybrid predictive control based on a fuzzy model is presented. The identification methodology for a nonlinear system with discrete state-space variables by combining fuzzy clustering and principal component analysis is proposed. The fuzzy model is used for hybrid predictive control design where the optimization problem is solved by the use of genetic algorithms. An illustrative experiment on a hybrid tank system is conducted to present the benefits of the proposed approach.

Nonlinear Model-predictive Control of Wiener-type Systems in Continuous-time Domain Using a Fuzzy--system Function Approximation

This paper presents a method of continuous-time model predictive control for nonlinear Wiener-type systems. The systems output nonlinear mapping is approximated by the means of fuzzy systems, and is inherently incorporated in the predictive control scheme; thus, inverting the output function is not necessary. The predictive control law is derived in an analytical form to avoid the problems with non-convex optimization in each time step. A simulation experiment is conducted to present the control quality in the case of a pH-neutralization process with a heavily nonlinear output mapping.