Particle swarm optimization of an extended Kalman filter for speed and rotor flux estimation of an induction motor drive

Laamari, Yahia; Chafaa, Kheireddine; Athamena, Belkacem

doi:10.1007/s00202-014-0322-1

“…In our work, we suggest to use a recently proposed method for the adjusting and optimization of covariance matrices Q and G by using the PSO algorithm. 27 The PSO was developed by Kennedy and Eberhart in 1995. The main idea of the PSO algorithm was based on the simulation of simplified social models such as bird flocking or fish schooling.…”

Section: Pso Algorithmmentioning

confidence: 99%

“…The parameters to be optimized are the covariance matrices (Q and G). Compared with other references, Shi et al 17 have used GAs to optimize these matrices, same thing in the work of Laamari et al 27 where the authors have used particle swarm optimization (PSO). Both works have just solved the estimation problem.…”

Section: Introductionmentioning

confidence: 99%

Improved feedback linearization control based on PSO optimization of an extended Kalman filter

Medjghou

¹

,

Ghanai

²

,

Chafaa

³

2018

Optim Control Appl Methods

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A robust nonlinear controller based on an improved feedback linearization technique with state observer in presence of uncertainties and external disturbances is developed for a class of nonlinear systems. First, by combining classical feedback linearization approach with a robust control term and a fuzzy logic system, we design and study an efficient controller for such systems. Second, we propose an optimized extended Kalman filter for the observation of the states. The parameters to be optimized are the covariance matrices Q and G, which play an important role in the extended Kalman filter performances. This optimization is insured by the particle swarm optimization algorithm. The Lyapunov synthesis approach is used to prove the stability of the whole control loop. The proposed approach is simulated on a nonlinear inverted-pendulum system. Simulation results demonstrate the robustness and effectiveness of the proposed scheme and exhibit a more superior performance than its conventional counterpart.

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“…Therefore, to overcome this difficulty and to avoid trial-error method, authors in [27] have used genetic algorithms to optimize these matrices automatically. In our work, we suggest to use a recently proposed method for the adjusting and optimization of covariance matrices Q and R by using the PSO algorithm [28].…”

Section: Extended Kalman Filtermentioning

confidence: 99%

Untitled

2017

JESTR

0

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A robust nonlinear controller based on an improved feedback linearization technique with state observer is developed for a class of nonlinear systems with uncertainties and external disturbances. First, by combining classical feedback linearization approach with a discontinuous control and a fuzzy logic system, we design and study a robust controller for uncertain nonlinear systems. Second, we propose an optimized extended Kalman filter (EKF) for the observation of the states. The parameters to be optimized are the covariance matrices Q and R, which play an important role in the EKF performances. The particle swarm optimization algorithm insures this optimization. Lyapunov synthesis approach is used to prove the stability of the whole control loop. The proposed approach is applied on a two-link robot system under Matlab environment. Simulation results have confirmed the effectiveness of the proposed approach against uncertainties and external disturbances; and exhibited a more superior performance than the non-improved control actions.

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“…Fuzzy logic system is employed to improve control performance and to reduce chattering phenomenon in the sliding mode. (2) Second, an optimized EKF observer for system states estimation in which the optimization of EKF matrices (Q and R) is ensured by an alternative optimization method proposed in [28] which is an evolutionary algorithm inspired by social interactions, that relates to particle swarm optimization (PSO) algorithm. This paper is structured as follows.…”

Section: Introductionmentioning

confidence: 99%

A Robust Feedback Linearization Control Framework Using an Optimized Extended Kalman Filter

Medjghou¹,

Ghanai²,

Kheireddine³

2017

JESTR

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A robust nonlinear controller based on an improved feedback linearization technique with state observer is developed for a class of nonlinear systems with uncertainties and external disturbances. First, by combining classical feedback linearization approach with a discontinuous control and a fuzzy logic system, we design and study a robust controller for uncertain nonlinear systems. Second, we propose an optimized extended Kalman filter (EKF) for the observation of the states. The parameters to be optimized are the covariance matrices Q and R, which play an important role in the EKF performances. The particle swarm optimization algorithm insures this optimization. Lyapunov synthesis approach is used to prove the stability of the whole control loop. The proposed approach is applied on a two-link robot system under Matlab environment. Simulation results have confirmed the effectiveness of the proposed approach against uncertainties and external disturbances; and exhibited a more superior performance than the non-improved control actions.

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Particle swarm optimization of an extended Kalman filter for speed and rotor flux estimation of an induction motor drive

Cited by 38 publications

References 20 publications

Improved feedback linearization control based on PSO optimization of an extended Kalman filter

Improved feedback linearization control based on PSO optimization of an extended Kalman filter

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A Robust Feedback Linearization Control Framework Using an Optimized Extended Kalman Filter

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