LQR optimized BP neural network PI controller for speed control of brushless DC motor

Wang, Tingting; Wang, Hongzhi; Hu, Huangshui; Wang, Chuhang

doi:10.1177/1687814020968980

Cited by 11 publications

(8 citation statements)

References 32 publications

(40 reference statements)

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“…According to Eq. ( 6), the internal structure of BLDCM can be described as an equivalent circuit diagram [33] given in Fig. 1.…”

Section: System Modelsmentioning

confidence: 99%

An adaptive fuzzy PID controller for speed control of brushless direct current motor

Wang

et al. 2022

SN Appl. Sci.

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Aiming at the problems of the poor adaptive ability in the current control methods for brushless DC motor, an adaptive fuzzy proportional integral derivative controller (AFPID) is proposed to realize the better control performance of speed for brushless DC motor. AFPID includes a conventional PID controller (C-PID) and PI + PD architectures with a configurable fuzzy logic controller (C-PID-FLC). The FLC in C-PID-FLC consists of two fuzzy inference engines, one is used to self-tune the parameters for PI control, the other is for the scaling factor self-tuning of PI control parameters. The PD structure in C-PID-FLC is mainly to reduce oscillation, overcome overshoot and speed up system response while effectively eliminating static errors. When the system reaches a certain stable state of rotation, AFPID adjusts the C-PID controller ground on the speed error, which saves control costs under the premise of ensuring control performance. AFPID adaptively realizes the respective advantages of C-PID and C-PID-FLC. Compared with other control methods, the merits of the proposed controller are highlighted. The results show that the AFPID controller has a better control performance regardless of changes in load disturbance and parameter variations. And through the change of mechanical parameters of brushless DC motor, the sensitivity of AFPID is analyzed.

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“…According to Eq. ( 6), the internal structure of BLDCM can be described as an equivalent circuit diagram [33] given in Fig. 1.…”

Section: System Modelsmentioning

confidence: 99%

An adaptive fuzzy PID controller for speed control of brushless direct current motor

Wang

et al. 2022

SN Appl. Sci.

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“…One of them is comparison of current response under varying load condition. 53 In Medjghou et al 54 authors investigate presence of uncertainties and disturbances in object in combination with the feedback linearization control and they have concluded that FBL is unable to ensure good performances 54 for the closed-loop system. There are many ways to overcome this problem, for example improve this control by adding a robust control term (discontinuous control).…”

Section: Design Of the Control Lawmentioning

confidence: 99%

Control of a DC motor using feedback linearization and gray wolf optimization algorithm

Vesović

Jovanović

Trišović

2022

Advances in Mechanical Engineering

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The aim of this study is to investigate nonlinear DC motor behavior and to control velocity as output variable. The linear model is designed, but as it is experimentally verified that it does not describe the system well enough it is replaced by the nonlinear one. System’s model has been obtained taking into account Coulomb and viscous friction in the firmly nonlinear environment. In the frame of the paper the dynamical analysis of the nonlinear feedback linearizing control is carried out. Furthermore, a metaheuristic optimization algorithm is set up for finding the coefficient of the proportional-integral feedback linearizing controller. For this purpose Gray wolf optimization technique is used. Moreover, after the introduction of the control law, analysis of the pole placement and stability of the system is establish. Optimized nonlinear control signal has been applied to the real object with simulated white noise and step signal as disturbances. Finally, for several desired output signals, responses with and without disruption are compared to illustrate the approach proposed in the paper. Experimental results obtained on the given system are provided and they verify nonlinear control robustness.

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“…To solve this problem, a variety of intelligent BLDCM control technologies have been proposed to obtain satisfactory performance. Among these control strategies, integer-order PID controller is a classical feedback controller, which has been applied in BLDCM control system due to its advantages such as simple structure and flexible parameter adjustment rules [10][11][12][13][14][15][16][17][18]. However, along with the increasing growth of science and technology, the demand for the controller's performance is continuously improving too.…”

Section: Introductionmentioning

confidence: 99%

“…However, along with the increasing growth of science and technology, the demand for the controller's performance is continuously improving too. Thus, a series of PID optimisation ways have been put forward [11][12][13][14][15] in the last decades. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Lion swarm optimisation‐based tuning method for generalised predictive fractional‐order PI to control the speed of brushless direct current motor

Wang

et al. 2022

IET Electric Power Appl

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In order to enhance the speed tracking performance of brushless direct current motor (BLDCM), a new generalised predictive fractional‐order PI (GPFOPI) control algorithm ground on lion swarm optimisation (LSO‐GPFOPI) is proposed in this paper. First, GPFOPI combines robust generalised predictive control with scalable fractional‐order PI, whose optimal control law is obtained through the model parameters and the predicted speed reference values. Next, the lion swarm algorithm is utilised to optimally tune the three parameters KP,KI,λ ${K}_{P},{K}_{I},\lambda $ of GPFOPI, so as to make them be optimised. Finally, the optimal control signal is input into the system of BLDCM to control its speed. To verify the effectiveness of LSO‐GPFOPI, its performance is compared with other algorithms. The results show that LSO‐GPFOPI has better response speed, speed tracking ability, anti‐interference ability, robustness, and self‐adaptability than other algorithms. Moreover, an experimental platform of the BLDCM drive system is built to verify the feasibility of the LSO‐GPOPI algorithm, and the test results prove that LSO‐GPOPI can guarantee the smooth and accurate operation of BLDCM.

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LQR optimized BP neural network PI controller for speed control of brushless DC motor

Cited by 11 publications

References 32 publications

An adaptive fuzzy PID controller for speed control of brushless direct current motor

An adaptive fuzzy PID controller for speed control of brushless direct current motor

Control of a DC motor using feedback linearization and gray wolf optimization algorithm

Lion swarm optimisation‐based tuning method for generalised predictive fractional‐order PI to control the speed of brushless direct current motor

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