In this paper, several relations between pole placement (PP) controller and unconstrained predictive controller are investigated. The placement of poles at certain positions leads to various types of time optimal control and has a direct relation to the choice of minimum control and output horizons for stabilising MPC. On the other hand, a predictive control method for linear systems is derived...

Problems of the system identification using orthonormal functions are discussed and algorithms of computing parameters of the discrete time state-space model of the plant based on the generalized orthonormal functions and the Laguerre functions are derived. The adaptive LQ regulator and the predictive controller based on the Laguerre function model are also presented. The stability and the robu...

The present paper deals with a decoupled multimodel predictive control based on multi-observer for the control of discrete-time nonlinear systems with time-varying delay. For each local model, a controller based on partial predictor/observer is synthesized. A switching algorithm is established to yield the adequate partial controller ensuring the closed-loop desired performances. Simulation res...

Sufficient linear matrix inequalities (LMI) conditions for regularization of discrete-time singular systems are given. Then a new class of regularizing stabilizing controllers is discussed. The proposed controllers are the sum of predictive and memoryless state feedbacks. The predictive controller aims to regularizing the singular system while the memoryless state feedback is designed to stabil...

The aim of this article is to present the Iterative Model Predictive Controller, inmpc, as a good candidate to control chemical batch reactors. The proposed control approach is derived from a model-based predictive control formulation which takes advantage of the repetitive nature of batch processes. The proposed controller combines the good qualities of Model Predictive Control (mpc) with the ...

Genetic algorithm based neuro-predictive controller design of a non-linear dynamic system is described. The genetic algorithm represents an optimisation procedure, where the cost function to be minimized comprises the closed-loop simulation of the control process and a selected performance index evaluation. Using this aproach the neural model of the process has been optimised from point of view...