R. Haber

Structure identification of nonlinear dynamic systems—a survey on input/output approaches

Abstract In the past several methods have been elaborated for the identification of nonlinear dynamic systems. Most of the methods assume that the structure of the system is given a priori. Therefore they are in reality parameter estimation algorithms and structure identification is thus usually performed by repeated parameter estimation. However in nonlinear system theory several methods are known to determine the structure of a system. In this paper structure identification of block-oriented (especially cascade) models, of semi-linear dynamic models with signal-dependent parameters and of nonlinear dynamic models being linear in the parameters will be considered. Different structure selection methods are summarized based on step and impulse tests, frequency response measurements, correlation analysis, repeated reproducible tests and normal operating data.

Paper: Adaptive load-frequency control of the hungarian power system

The paper deals with the modelling of the power stations and the interconnected power systems for the design of the load-frequency controller of the Hungarian power system. It presents an adaptive regulator which uses the a priori known information and satisfies the multi-objective character of the control. The elaborated control strategy performs the following objectives: •-It eliminates the effect of the area load fluctuations to the tie-line power. •-It guarantees the scheduled value of the exported/imported energy. •-It reduces the commands sent to power stations. •-It satisfies the requirement with minimum cost. The paper shows the real-time experiments with the implemented adaptive regulator which is presently applied for the load-frequency control of the Hungarian power system.

Nonlinearity Tests for Dynamic Processes

Abstract The paper deals with simple computations methods for the determination of nonlinearity of a system from input/output records. The so-called nonlinearity test should_ not require long computation time and has to be independent of the concrete nonlinear structure and the order of the linear dynamic part in the process, i.e. basically a nonparametric method is needed. The following methods are presented: time domain test, steady state test, output average value test, frequency method, linear spectral density method, linear correlation method, dispersion method, higher order autocorrelation method, nonlinear cross-correlation method and a method based on parameter estimation of a simple structure. The different procedures are shown at the determination of the linear or nonlinear feature of first-and second order linear, Hammerstein and Wiener models.

Identification and adaptive control of a glass furnace

The paper presents the modelling and control of a glass furnace. A portable process computer laboratory was applied. The computer was provided by a process control program and control tasks. Based on normal operating records experiments were designed and a model with three inputs and outputs of the furnace was elaborated. By means of identified models the glass level was successfully controlled by a self-tuning regulator.

Optimal Choice of Horizons for Predictive Control by Using Genetic Algorithms

Abstract For predictive control in industry often very long horizons for control error and manipulated signal are used because of the slow processes which take place in the petrochemical industry. In order to reduce the computational effort some commercial predictive control program packages offer the ability to reduce the number of points in both horizons but do not recommend how to select the points which have to be considered in the horizon of the control error and manipulated variable. In this work the authors introduce an optimal choice not only of the horizon lengths itself but also for the strategy of reducing the number of points in the horizons. A genetic optimization algorithm was used both for the search for the optimal length of the horizons and for the best allocation of the points in the horizons. The results of the optimization process where used to deduct a simple rule.

Application of Adaptive Control on Nonlinear Dynamic Processes - A Survey on Input-output Approaches

Abstract The paper summarizes adaptive control methods elaborated for nonlinear dynamic processes and real applications described in the reference. The classification of the nonlinear dynamic models is followed by the review on parameter estimation procedures. Here only features related to the nonlinear systems are dealt with. The presented concepts of control algorithms are partly linear ones (robust and adaptive control based on a linear process model), a few others use the exact model of the process (error-dependent and adaptive on-off regulators) and the other controllers can be designed on the knowledge of the process model (parameter-adaptive ones based on multi-models and “linear” models with signal-dependent parameters, one-step ahead deterministic and minimum variance stochastic regulators, etc.). The theoretical descriptions are followed by a review on practical applications and a comprehensive list of references.

Nonlinear Long-Range Predictive Control of a Distillation Pilot Plant

Abstract A distillation column separating ethanol-water mixture has been built at our department several years ago. The pilot plant is equipped with analogue instrumentation, manipulating board and alarm devices. It is connected to a microcomputer and a supervisory computer. The aim of the control is to keep the concentration of the products constant. Further on several physical quantities have to be controlled (feed flow rate, feed temperature, reflux flow rate, liquid level in the reboiler, etc.). The plant is highly nonlinear. Long-range predictive control algorithms based on nonparametric system description have been generalized to nonlinear systems characterized by Volterra series by Bars and Haber (1988). These algorithms have been applied successfully to some control loops of the distillation column. Initial experimental results are presented here.

Self-Tuning Load-Frequency Control of the Hungarian Power System

Abstract A combined regulator based on a self-tuning predictor of the area requirement and the RAFT control strategy for the ACE is described for the load-frequency control of the Hungarian Power System. Real-time experiments with the implemented adaptive regulator are also shown.

Robust Predictive PI Controller Based on First-Order Dead Time Model

Abstract Predictive control algorithms compute the manipulated variable minimizing a cost function considering expected future errors. PI control algorithms can be equipped with predictive properties. Simple predictive control algorithms are derived using approximation of an aperiodic process by a first-order model with dead time. Applying a noise model the robustness properties of the algorithm are enhanced considering plant-model mismatch. The noise filter is considered as a design parameter. Simulation examples demonstrate the behavior of the predictive PI algorithm and the robustifying effect of the noise filter.

A Microprocessor-Based Adaptive Composition Control System

Abstract A general multivariable model of the mill-silo system is given. A general multivariable adaptive control system is described. Using a special linearizing and decoupling possibility single variable adaptive control loops are then introduced. Finally the applied μP system and practical results are presented.