LQR state feedback controller based on particle swarm optimization for IPMSM drive system

Paponpen, Kittithuch; Konghirun, Mongkol

doi:10.1109/iciea.2015.7334285

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…Particle Swarm Optimization (PSO) is used in the control to randomly initialise each particle's position and velocity when it is first produced (Paponpen & Konghirun, 2015). Each particle produced contains its position and velocity representing a candidate solution to the problem being solved.…”

Section: Particle Swarm Optimization a Particle Swarm Optimization (P...mentioning

confidence: 99%

Optimization of Field-Oriented Control for PMSM Motor

King,

Siang,

King

2023

IJMRA

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This research focuses on designing and simulating a sensorless control technique for optimising field-oriented control to achieve an optimization control applicable to water pressure regulation management. In the irrigation system, water pressure changes are determined by the number of activated emitters operating at different times. Unstable water pressure may reduce irrigation efficiency, leaving brown areas of the landscape not receiving adequate watering. Thus, the Field-oriented control technique is introduced in the paper to regulate the water pressure in the water pump controller using a permanent magnet synchronous motor (PMSM). The PSO-SFC field-oriented control technique for the PMSM was studied to improve motor efficiency and precision motor control over the torque and speed. The cascade-free state feedback controller (SFC) can achieve better dynamics and disturbance compensation in the controller. The SFC's tuning process has been achieved by using a linear-quadratic optimization method and PSO (particle swarm optimization) in the cascaded process. The process improves the coefficient of gain K produced by linear-quadratic optimization and PSO to obtain promising Q and R matrices in the adaptive system with improved output response in the simulation.

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Section: Particle Swarm Optimization a Particle Swarm Optimization (P...mentioning

confidence: 99%

Optimization of Field-Oriented Control for PMSM Motor

King,

Siang,

King

2023

IJMRA

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“…Recently, nature-inspired swarm-based optimization algorithms are increasingly applied to tuning of complex controllers in a motion control field [17][18][19][20]25]. In the case of state feedback controller, similarly to manual tuning, automatic selection can be done using direct selection, pole placement or linearquadratic optimization.…”

Section: Tuning Of State Feedback Controllermentioning

confidence: 99%

“…In such a case, optimization algorithm is utilized to find optimal values of weighting matrices needed for calculation of SFC. For example in [17], PSO is em-…”

Section: Introductionmentioning

confidence: 99%

“…In such a case, optimization algorithm is utilized to find optimal values of weighting matrices needed for calculation of SFC. For example in [17], PSO is em-ployed to obtain weighting matrices of linear-quadratic regulator (LQR) designed for IPMSM drive. The presented results demonstrate the feasibility of the proposed approach, however, the introduction of constraints on state variables (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Bulletin of the Polish Academy of Sciences: Technical Sciences

Tarczewski

Niewiara

Grzesiak³

2020

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This paper presents a state feedback controller (SFC) for position control of PMSM servo-drive. Firstly, a short review of the commonly used swarm-based optimization algorithms for tuning of SFC is presented. Then designing process of current control loop as well as of SFC with feedforward path is depicted. Next, coefficients of controller are tuned by using an artificial bee colony (ABC) optimization algorithm. Three of the most commonly applied tuning methods (i.e. linear-quadratic optimization, pole placement technique and direct selection of coefficients) are used and investigated in terms of positioning performance, disturbance compensation and robustness against plant parameter changes. Simulation analysis is supported by experimental tests conducted on laboratory stand with modern PMSM servo-drive.

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“…This controller presents good performance in disturbance rejection. Furthermore, in [23]- [26], the differential evolution algorithm (DEA), GA and PSO algorithms were employed to acquire the SFC gain matrix. In [24], the comparison between GA-based LQR and conventional LQR control method in doubly-fed induction generator system was presented.…”

Section: Introductionmentioning

confidence: 99%

State Feedback Control for a PM Hub Motor Based on Gray Wolf Optimization Algorithm

Sun

Lei

et al. 2020

IEEE Trans. Power Electron.

173

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This paper presents an optimal control strategy for a permanent-magnet synchronous hub motor (PMSHM) drive using the state feedback control method plus the grey wolf optimization (GWO) algorithm. First, the linearized PMSHM mathematical model is obtained by voltage feedforward compensation. Second, to acquire satisfactory dynamics of speed response and zero d-axis current, the discretized state space model of the PMSHM is augmented with the integral of rotor speed error and integral of d-axis current error. Then, the GWO algorithm is employed to acquire the weighting matrices Q and R in liner quadratic regulator optimization process. Moreover, a penalty term is introduced to the fitness index to suppress overshoots effectively. Finally, comparisons among the GWObased state feedback controller with and without the penalty term, the conventional state feedback controller, and the genetic algorithm enhanced PI controllers are conducted in both simulations and experiments. The comparison results show the superiority of the proposed state feedback controller with the penalty term in fast response.

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LQR state feedback controller based on particle swarm optimization for IPMSM drive system

Cited by 9 publications

References 11 publications

Optimization of Field-Oriented Control for PMSM Motor

Optimization of Field-Oriented Control for PMSM Motor

Bulletin of the Polish Academy of Sciences: Technical Sciences

State Feedback Control for a PM Hub Motor Based on Gray Wolf Optimization Algorithm

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