A PI controller optimized with modified differential evolution algorithm for speed control of BLDC motor

The main contribution of this paper is to formulate a robust-adaptive and stable state-space speed control strategy for DC motors. The linear-quadratic-integral (LQI) controller is utilized as the baseline controller for optimal speed-regulation, accurate reference-tracking and elimination of steady-state fluctuations in the motor's response. To reject the influence of modelling errors, the LQI controller is augmented with a Lyapunov-based model reference adaptation system (MRAS) that adaptively modulates the controller gains while maintaining the asymptotic stability of the controller. To further enhance the system's robustness against parametric uncertainties, the adaptation gains of MRAS online gain-adjustment law are dynamically adjusted, after every sampling interval, using smooth hyperbolic functions of motor's speed-error. This modification significantly improves the system's response-speed and damping against oscillations, while ensuring its stability under all operating conditions. It dynamically re-configures the control-input trajectory to enhance the system's immunity against the detrimental effects of random faults occurring in practical motorized systems such as bounded impulsive-disturbances, modelling errors, and abrupt load-torque variations. The efficacy of the proposed control strategy is validated by conducting credible hardware-in-the-loop experiments on QNET 2.0 DC Motor Board. The experimental results successfully validate the superior tracking accuracy and disturbance-rejection capability of the proposed control strategy as compared to other controller variants benchmarked in this article.

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“…where ω ref is the reference speed of the motor, and ω is the actual speed of the motor. The state-vector is given by (5).…”

Section: System Descriptionmentioning

confidence: 99%

“…They are preferred due to their simple structure and reliable control yield. Despite their benefits, the integer-order PI controller lacks robustness to handle higher-order systems [4,5]. Furthermore, constituting a well-tuned PI controller is an ill-posed problem.…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions

et al. 2019

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“…[23] is a heuristic parallel algorithm based on population difference. Compared with other heuristic algorithms, DE algorithm has the advantages of lesser control parameters, fast convergence, good robustness and it has been widely used in [24] various engineering fields in recent years.…”

Section: Construction Of Response Surface Modelmentioning

confidence: 99%

Power Density Analysis and Optimization of SMPMSM Based on FEM, DE Algorithm and Response Surface Methodology

et al. 2019

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Surface-mounted permanent magnet synchronous motors (SMPMSM) with high power density and good speed regulation are widely used in industrial applications. In order to further improve its power density, this paper studied the relationship between the thickness of the stator yoke, the thickness of the rotor yoke, the relative magnet span of the motor and the motor power density using the finite element simulation method. On this basis, a response surface model between the three parameters and the power density of the motor was established. Based on this model and a differential evolution algorithm, the motor was optimized and the power density was improve; finally, the optimization results were verified using the finite element simulation method. In addition, the optimization results showed that, when other structure parameters remain unchanged, there is an optimal combination of parameters that can maximize the motor’s power density, including the thickness of the stator yoke, the thickness of the rotor yoke and relative magnet span of the motor.

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“…∂W jj (k) , ∂u k (k) ∂W jk (k) can be obtained by deriving W ij (k), W jj (k) and W jk (k) from formula (6).…”

Section: Design Of Q-drnn Controllermentioning

confidence: 99%

Q-learning optimized diagonal recurrent neural network control strategy for brushless direct current motors

Wang

et al. 2014

Advances in Mechanical Engineering

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In order to improve the working stability of brushless direct current motors (BLDCM), a diagonal recursive neural network (DRNN) control strategy based on Q-learning algorithm is proposed in this paper which is called as Q-DRNN. In Q-DRNN, DRNN iterates over the output variables through a unique recursive loop in the hidden layer, and its key weight is optimized to speed up the iteration. Moreover, an improved Q-learning algorithm is introduced to modify the weight momentum factor of DRNN, which makes DRNN have the ability of learning and online correction so as to make the BLDCM achieve better control effect. In MATLAB/Simulink environment, Q-DRNN is tested and compared with other popular control methods in terms of speed and torque response under different operating conditions, and the results show that Q-DRNN has better adaptive and anti-interference ability as well as stronger robustness.

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A PI controller optimized with modified differential evolution algorithm for speed control of BLDC motor

Cited by 52 publications

References 34 publications

Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions

Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions

Power Density Analysis and Optimization of SMPMSM Based on FEM, DE Algorithm and Response Surface Methodology

Q-learning optimized diagonal recurrent neural network control strategy for brushless direct current motors

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