Adaptive fuzzy control of DC motors using state and output feedback

Rigatos, Gerasimos

doi:10.1016/j.epsr.2009.06.007

Cited by 82 publications

(42 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The new results come to extend the method presented in [26][27][28][29][30]. By showing that the SI-engine model is a differentially flat one it becomes possible to transform it to the linear canonical form.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Flatness-Based Adaptive Fuzzy Control Of Spark-Ignited Engines

Rigatos

Siano

2014

Journal of Artificial Intelligence and Soft Computing Research

Self Cite

View full text Add to dashboard Cite

An adaptive fuzzy controller is designed for spark-ignited (SI) engines, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H ∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the SI-engine model into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H ∞ tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked through simulation experiments.

show abstract

Section: Introductionmentioning

confidence: 99%

“…It is possible to make the system's state vector x follow a given bounded reference trajectory x d . In the presence of model uncertainties and external disturbances, denoted by w d , successful tracking of the reference trajectory is provided by the H ∞ criterion [30], [17]: …”

Section: Nonlinear System Transformation Into the Brunovsky Formmentioning

confidence: 99%

Flatness-Based Adaptive Fuzzy Control Of Spark-Ignited Engines

Rigatos

Siano

2014

Journal of Artificial Intelligence and Soft Computing Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…(14) and Eq. (15) , one can also control the motor's speed ω, using control algorithms already applied to the control of DC motors [1].…”

Section: B Decoupling Of Speed-flux Dynamicsmentioning

confidence: 99%

“…Induction motors have been the most widely used machines in fixed-speed applications for reasons of cost, size, weight, reliability, ruggedness, simplicity, efficiency, and ease of manufacture. With the field-oriented method, the dynamic behavior of the induction motor is rather similar to that of a separately excited DC motor [1]. The possibility to reduce the number of sensors involved in the control of electric motors has been a subject of systematic research during the last years [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Differential flatness theory and Extended Kalman Filtering for sensorless control of induction motors

Rigatos¹,

Siano²

2012

Stochastic Systems Theory and its Applications (SSS)

View full text Add to dashboard Cite

Abstract-Diffenential flatness theory and Extended Kalman Filtering (EKF) are used for implementing a sensorless control scheme for induction motor drives. It is shown that the induction motor is a differentially flat system, since all state variables of the circuits describing the motor's dynamics can be expressed as functions of the flat output and its derivatives. The flat output consists of the angle of the rotor and of the the angle of the magnetic field. Flatness-based control for the complete (sixthorder) induction motor model is analyzed while the Extended Kalman Filter is proposed to estimate the state vector of the nonlinear electric motor using a limited number of sensors. The efficiency of the above mentioned Extended Kalman Filter-based control scheme, is tested through simulation experiments.

show abstract

“…Several authors, such as: PRAVADALIOGLU (2005), RIGATOS (2009) andOMAR et al (2011) list the advantages of the fuzzy control system as: they can be adjusted for different operating points of nonlinear systems, fit processes of multiple inputs, weight the influence of each variable on the sign of operation, and they are ease in describing, by linguistic rules, complex control models. RIGATOS (2009) reports that conventional controllers are unsuitable for the control of the performance of DC electric motors (DCEM), when operating with a change in its dynamics and its load. PRAVADALIOGLU (2005) developed a fuzzy proportional and integral (PI) control system in order to control a given type DCEM.…”

Section: Introductionmentioning

confidence: 99%

Electrically driven fertilizer applicator controlled by fuzzy logic

2014

View full text Add to dashboard Cite

The present study shows the development, simulation and actual implementation of a closed-loop controller based on fuzzy logic that is able to regulate and standardize the mass flow of a helical fertilizer applicator. The control algorithm was developed using MATLAB's Fuzzy Logic Toolbox. Both open and closed-loop simulations of the controller were performed in MATLAB's Simulink environment. The instantaneous deviation of the mass flow from the set point (SP), its derivative, the equipment´s translation velocity and acceleration were all used as input signals for the controller, whereas the voltage of the applicator's DC electric motor (DCEM) was driven by the controller as output signal. Calibration and validation of the rules and membership functions of the fuzzy logic were accomplished in the computer simulation phase, taking into account the system's response to SP changes. The mass flow variation coefficient, measured in experimental tests, ranged from 6.32 to 13.18%. The steady state error fell between -0.72 and 0.13g s -1 and the recorded average rise time of the system was 0.38 s. The implemented controller was able to both damp the oscillations in mass flow that are characteristic of helical fertilizer applicators, and to effectively respond to SP variations.KEYWORDS: fertilizer applicator, agricultural machinery, closed-loop control. DOSADOR DE FERTILIZANTE ACIONADO ELETRICAMENTE E CONTROLADOPOR LÓGICA FUZZY RESUMO: Este trabalho apresenta o desenvolvimento, a simulação e a implementação de um sistema de controle, em malha fechada, baseado em lógica fuzzy, para regular e uniformizar a vazão mássica aplicada por um dosador de fertilizante do tipo helicoidal. A estratégia de controle foi desenvolvida, utilizando-se da ferramenta "Fuzzy Logic Toolbox", do programa MATLAB. Foi simulado, em linguagem Simulink -MATLAB, o sistema operando em malha aberta e em malha fechada. A variável manipulada pelo controlador foi a tensão do motor elétrico de corrente contínua (MECC), e as suas variáveis de entrada foram: o erro e a variação do erro entre a vazão mássica atual aplicada e o valor de referência (VR), além da velocidade de deslocamento e a variação da velocidade de deslocamento do equipamento. A sintonia e o projeto das regras das funções de pertinência da lógica fuzzy foram realizados por meio de simulações, além de verificação de que o sistema foi capaz de responder às mudanças no VR. Nos testes experimentais, o coeficiente de variação da vazão mássica ficou entre 6,32 e 13,18%, o erro em regime estacionário variou de -0,72 a 0,13 g s -1 e o tempo de resposta médio foi de 0,38 s. O sistema de controle foi capaz de reduzir a característica oscilatória da vazão mássica aplicada pelo mecanismo dosador e foi eficiente em acompanhar as mudanças do VR.PALAVRAS CHAVE: adubadora, máquinas agrícolas, controle em malha-fechada.

show abstract

Adaptive fuzzy control of DC motors using state and output feedback

Cited by 82 publications

References 41 publications

Flatness-Based Adaptive Fuzzy Control Of Spark-Ignited Engines

Flatness-Based Adaptive Fuzzy Control Of Spark-Ignited Engines

Differential flatness theory and Extended Kalman Filtering for sensorless control of induction motors

Electrically driven fertilizer applicator controlled by fuzzy logic

Contact Info

Product

Resources

About