Adaptive minimum variance control of a DC motor

Zidane, Zohra; Lafkih, Mustapha Ait; Ramzi, Mohamed; Abounada, Abdelouahed

doi:10.1109/med.2010.5547628

Cited by 4 publications

(2 citation statements)

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“…The technical literature shows that this type of relation between the excitation voltage (considered as the plant input variable) and the terminal voltage (as plant output variable) can be enough accurately described by a 4th order linear mathematical with time-varying parameters [12,22,24,25]. This reduced order (4th) is based on several simplifying assumptions regarding the electrical phenomenology, which does not affect the accuracy of the model near an operating point [12,15,26]. For maximum accuracy of the simulation results, the complete 7th order nonlinear model of the controlled process was used to test the proposed control strategy.…”

Section: Design Of the Minimum Variance Control Lawmentioning

confidence: 99%

Minimum-Variance Control System with Variable Control Penalty Factor

et al. 2020

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The present paper proposes (as the main contribution) an additional self-tuning mechanism for an adaptive minimum-variance control system, whose main goal is to extend its functionality for a large value range of unmeasurable perturbations which disturb the controlled process. Through the standard design procedure, a minimum variance controller uses by default an internal self-tuning mechanism based on the process parameter estimates. However, the main parameter which overwhelmingly influences the control performance is the control penalty factor ( ρ ) . This parameter weights the term that describes the control variance in a criterion function whose minimization is the starting point of the control law design. The classical minimum-variance control involves an off-line tuning of this parameter, its value being set as constant throughout the entire operating regime. Based on the measurement of the process output error, the contribution of the proposed strategy consists in a real-time tuning of the control penalty factor, to ensure the stability of the control system, even under conditions of high disturbances. The proposed tuning mechanism adjusts this parameter by implementing a bipositional switching strategy based on a sharp hysteresis loop. Therefore, instead of the standard solution that involves a constant value of the control penalty factor ρ (a priori computed and set), this paper proposes a dual value for this controller parameter. The main objective is to allow the controlled process to operate in a stable fashion even in more strongly disturbed regimes (regimes where the control system becomes unstable and is usually switched off for safety reasons). To validate the proposed strategy, an induction generator integrated into a wind energy conversion system was considered as controlled plant. Operating under the action of strong disturbances (wind gusts, electrical load variations), the extension of safe operating range (thus avoiding the system disengagement) is an important goal of such a control system.

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Section: Design Of the Minimum Variance Control Lawmentioning

confidence: 99%

Minimum-Variance Control System with Variable Control Penalty Factor

et al. 2020

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“…The classical structure for an adaptive control system based on a minimum variance controller is well known in technical literature and is presented in figure 2. The structure is also detailed in other papers [3], [5], [6], [9], [10].…”

Section: Considerations On Minimun Variance Adaptive Control Strmentioning

confidence: 99%

Above Modelling and Adaptive Control of a Double Winded Induction Generator

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Filip

Proştean

et al. 2018

2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI)

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The paper deals with the modelling and control of a double winded induction generator. The controlled process is an induction generator with distinct excitation winding. At the generator's terminal is connected a load (electrical consumer). There are presented the results obtained by using a minimum variance adaptive control system. The main goal of the control structure is to keep the generator output (terminal voltage) constant by controlling the excitation voltage from the distinct winding. The study cases in the paper are for the validation of the reduced order model of induction generator (5 th order model) used only to design the adaptive controller. There is also validated the control structure. There were considered variations of the mechanical torque.I.

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