Adaptive output feedback control of general MIMO systems using multirate sampling and its application to a cart–crane system

Mizumoto, Ikuro; Chen, Tongwen; Ohdaira, Satoshi; Kumon, Makoto; Iwai, Zenta

doi:10.1016/j.automatica.2007.04.017

Cited by 80 publications

(32 citation statements)

References 22 publications

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“…Modelarea sistemelor MIMO se bazeazã pe aproximare ºi liniarizare, dupã cum se specificã în [5] ºi [6]. Strategiile de control pentru sistemele MIMO utilizate de-a lungul anilor includ o gamã largã de tehnici, de la controlul inteligent, cum ar fi strategiile hibride de control logicã-fuzzy-reþea-neuronalã [7], la controlul adaptiv, unele asigurând stabilitate la nivel global prin scheme de control bazate pe liniarizare [8], altele recurgând la eºantionãri diferite pentru a rezolva problema numãrului diferit de intrãri ºi ieºiri [9] sau chiar la sistemele de control fuzzy adaptive [10].…”

Section: Introductionunclassified

“…Modeling MIMO systems has been based on approximation and linearization, as found in [5] and [6]. Control strategies for MIMO systems employed over the years include a wide range of techniques, from intelligent control, such as hybrid fuzzy-logic-neural-network control strategies [7], to adaptive control, some ensuring global stability through control schemes based on linearization [8], others resorting to multirate sampling in order to resolve the different number of inputs and outputs issue [9], or even taking on fuzzy-adaptive-based control schemes [10].…”

Section: Privire Generalã Asupra Strategiei De Control Propusementioning

confidence: 99%

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Parameters Optimization of PID Controllers for Leather Manufacturing Process by Using Genetic Algorithms

Hanchevici¹,

Guta²

2015

LFJ

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ABSTRACT. In this paper is proposed a control strategy for leather manufacturing process. This control strategy makes use of conventional PID control algorithms in combination with an evolutionary optimization technique, namely genetic algorithms. In order to find the parameters for the PID controllers, different performance indexes are provided to the genetic algorithms. In this study the proposed control strategy is used to control one process parameter, namely the pH value. Simulation results are presented. The results show performance improvement which proves that the proposed control strategy is able to make the closed-loop system behave as expected according to different given performance indexes. KEY WORDS: PID, genetic algorithms, leather manufacturing process OPTIMIZAREA PARAMETRILOR REGULATOARELOR PID PENTRU PROCESUL DE FABRICARE A PIELII UTILIZÂND ALGORITMI GENETICIREZUMAT. În aceastã lucrare se propune o strategie de control pentru procesul de fabricare a pielii. Aceastã strategie de control utilizeazã algoritmi de control PID convenþionali în combinaþie cu o tehnicã de optimizare evolutivã, ºi anume algoritmi genetici. Pentru a determina parametrii pentru regulatoarele PID, algoritmilor genetici li se acordã diferiþi indici de performanþã. În acest studiu, strategia de control propusã este utilizatã pentru a controla un parametru de proces, ºi anume valoarea pH-ului. Sunt prezentate rezultatele de simulare. Rezultatele aratã îmbunãtãþirea performanþei, ceea ce demonstreazã cã strategia de control propusã este capabilã sã inducã sistemului în buclã închisã un comportament dorit în funcþie de diferiþii indici de performanþã stabiliþi. CUVINTE CHEIE: PID, algoritmi genetici, procesul de fabricare a pielii L'OPTIMISATION DES PARAMÈTRES DE CONTRÔLEURS PID POUR LE PROCÉDÉ DE FABRICATION DU CUIR EN UTILISANT DES ALGORITHMES GÉNÉTIQUESRÉSUMÉ. Cet article propose une stratégie de contrôle pour le processus de fabrication du cuir. Cette stratégie de contrôle utilise des algorithmes de contrôle PID classiques en combinaison avec la technique d'optimisation évolutive, à savoir les algorithmes génétiques. Pour déterminer les paramètres pour les contrôleurs PID, on fourni de différents indicateurs de performance aux algorithmes génétiques. Dans cette étude, la stratégie de contrôle proposée est utilisée pour contrôler un paramètre de processus, à savoir le pH. Les résultats de simulation sont présentés. Les résultats montrent une amélioration des performances, ce qui démontre que la stratégie proposée est capable de rendre le système de contrôle en boucle fermée se comporter comme prévu en fonction de divers indicateurs de performance établis. MOTS-CLÉS: PID, algorithmes génétiques, processus de fabrication du cuir 229Revista de Pielarie Incaltaminte 15 (2015) 4 INTRODUCERERegulatorul PID este cel mai utilizat ºi apreciat regulator (controler), fiind prezent în aplicaþii industriale de mulþi ani ºi dovedindu-ºi fiabilitatea în tot acest timp. Pentru sistemele liniare SISO (single input single output), sarcina de ...

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Section: Introductionunclassified

Section: Privire Generalã Asupra Strategiei De Control Propusementioning

confidence: 99%

Parameters Optimization of PID Controllers for Leather Manufacturing Process by Using Genetic Algorithms

Hanchevici¹,

Guta²

2015

LFJ

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“…In the past two decades, multirate systems have been increasingly studied for model identification and control design [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Recursive state-space identification of non-uniformly sampled-data systems using QR decomposition

Hou

Liu

Wang

2014

Proceeding of the 11th World Congress on Intelligent Control and Automation

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-A recursive least-squares (LS ) state-space identification method based on the QR decomposition is proposed for non-uniformly sampled-data systems. Both cases of measuring all states and only the output(s) are considered for model identification. For the case of state measurement, a QR decomposition-based recursive LS (QRD-RLS ) identification algorithm is given to estimate the state matrices. For the case of only output measurement, another identification algorithm is developed by combining the QRD-RLS approach with a hierarchical identification strategy. Both algorithms can guarantee fast convergence rate with low computation complexity. An illustrative example is shown to demonstrate the effectiveness of the proposed methods.

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“…Various kinds of crane control techniques have been applied from classical methods such as linear control [1], nonlinear control [2,5,6], optimal approach [7], adaptive algorithms [8,9] to modern techniques such as fuzzy logic [3,4,10], neural network [11], command shaping [12], and so on.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Control of Flexible Two-Dimensional Overhead Cranes

Nguyễn¹,

Duong²

2018

Adaptive Robust Control Systems

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Considering gantry cable as an elastic string having a distributed mass, we constitute a dynamic model for coupled flexural overhead cranes by using the extended Hamilton principle. Two kinds of nonlinear controllers are proposed based on the Lyapunov stability and its improved version entitled barrier Lyapunov candidate to maintain payload motion in a certain defined range. With such a continuously distributed model, the finite difference method is utilized to numerically simulate the control system. The results show that the controllers work well and the crane system is stabilized.

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Adaptive output feedback control of general MIMO systems using multirate sampling and its application to a cart–crane system

Cited by 80 publications

References 22 publications

Parameters Optimization of PID Controllers for Leather Manufacturing Process by Using Genetic Algorithms

Parameters Optimization of PID Controllers for Leather Manufacturing Process by Using Genetic Algorithms

Recursive state-space identification of non-uniformly sampled-data systems using QR decomposition

Nonlinear Control of Flexible Two-Dimensional Overhead Cranes

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