An IMC-PID controller with Particle Swarm Optimization algorithm for MSBR core power control

Zeng, Wenjie; Zhu, Weicong; Hui, Tianyu; Chen, Lezhi; Xie, Jinsen; Yu, T.

doi:10.1016/j.nucengdes.2020.110513

Cited by 39 publications

(14 citation statements)

References 10 publications

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“…is can effectively improve the algorithm's global search capability [9]. Zeng et al proposed a cloud genetic algorithm by using cloud generator to replace the traditional crossover and mutation operators in genetic algorithm, which has achieved good results in function optimization [10]. Kumari et al combining genetic algorithms with cloud models offers a cloud-based evolutionary algorithm that effectively solves the problem of genetic algorithms and easily facilitates local optimization and early convergence [11].…”

Section: Literature Reviewmentioning

confidence: 99%

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An Improved Particle Swarm Optimization Algorithm and Its Application to the Extreme Value Optimization Problem of Multivariable Function

Min

2022

Computational Intelligence and Neuroscience

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It is proposed to improve the study of particle optimization and its application in order to solve the problem of inefficiency and lack of local optimization skills in the use of particle herd optimization. Firstly, the basic principle, mathematical description, algorithm parameters, and flow of the original (Particle Swarm Optimization, PSO) algorithm are introduced, and then the standard PSO algorithm is introduced; thirdly, over the last 10 years, four types of improvements have been proposed through the study of improved particle algorithms. The improved algorithm is applied to the extreme value optimization problem of multivariable function. The simulation results show that the basic (Cloud Particle Swarm Optimization, CPSO) algorithm within 500 generations has not reached convergence for 8 times, 6 times, 4 times, and 5 times, respectively. In the case of convergence, the average number of steps is much higher than ICPSO, and the improved algorithm converges completely. In terms of time performance, the convergence time of ICPSO is much better than that of CPSO algorithm. Therefore, the improved particle optimization algorithm ensures the effectiveness of the improvement measures, such as small optimization algebra, fast merging speed, high efficiency, and good population diversity.

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Section: Literature Reviewmentioning

confidence: 99%

“…Let the size of the particle swarm be N, the fitness value of the particle X i in the tth iteration is f i , and the average fitness value of the particle is equations ( 8)∼ (10):…”

Section: Basic Particle Swarm Optimization (Cpso)mentioning

confidence: 99%

An Improved Particle Swarm Optimization Algorithm and Its Application to the Extreme Value Optimization Problem of Multivariable Function

Min

2022

Computational Intelligence and Neuroscience

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“…This choice does not have a specific rule but heavily depends on the expert knowledge of the designer. To solve this difficulty, optimization algorithms have been proposed, such as particle swarm optimization [23,24]. Compared with mathematical algorithms and other heuristic optimization techniques, PSO is simple, easy to implement, robust to control parameters, and has high computational efficiency.…”

Section: -Introductionmentioning

confidence: 99%

PSO based Hybrid PID-FLC Sugeno Control for Excitation System of Large Synchronous Motor

et al. 2022

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This paper proposes a hybrid control system integrating a PID controller and a fuzzy logic controller, using the particle swarm optimization (PSO) algorithm to optimize control parameters. The control object is an excitation system for a large synchronous motor, which is widely used in large power transmission systems. In practice, the change in load and excitation source can affect the operating mode of the motor. Therefore, a hybrid controller is designed to stabilize the power factor, resulting in better working performance. In the control algorithm, a PID controller is initially designed using PSO to optimize the control coefficients. The FLC-Sugeno control is then integrated with the PID, in which PSO is utilized to optimize membership functions. Numerical simulation results demonstrate the advantages of the proposed approach. Doi: 10.28991/ESJ-2022-06-02-01 Full Text: PDF

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“…In the past, the combination of PID and advanced control methods and PID parameter optimization are always the research hotspots in the field of control engineering. On the one hand, some nonlinear PID control strategies, such as fractional PID [16], [17], neural network PID [18], [19], neural fuzzy PID [20], [21], and particle swarm optimization PID [22], [23] have been proposed. On the other hand, it is particularly important to adjust the PID controller parameters appropriately, using methods such as the well-known Ziegler -Nichols (ZN) tuning rule [24], H 2 /H ∞ robust optimization design [25], internal model control (IMC) principle [26], and other parameter adjustment methods [27], [28].…”

Section: Introductionmentioning

confidence: 99%

Extended State Observer-Based IMC-PID Tracking Control of PMLSM Servo Systems

et al. 2021

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Servo systems driven by a permanent magnet linear synchronous motor (PMLSM) are often affected by uncertain disturbances, such as magnetic resistance, friction, and external disturbances, which increase tracking errors and reduce motion accuracy. Traditional control methods may have difficulty to achieve satisfactory control performance in terms of tracking accuracy and disturbance rejection. In this paper, we propose an internal model control PID method based on a model linear extended state observer, which is termed as IMC-PID-MLESO method. With this method, the nominal model parameters of the PMLSM servo system are obtained via system identification. The model linear extended state observer (MLESO), with known parameter information, is designed to improve the estimation accuracy for the system states and total unknown uncertainties. As the uncertainties are compensated in the feedback control law, the PMLSM servo system model is transformed into a known nominal model. Based on the nominal model, the IMC-PID feedback controller is designed to ensure satisfactory performance on disturbance rejection and tracking error reduction. Simulation and experimental results show that the IMC-PID-MLESO method can effectively improve the position tracking accuracy of the PMLSM servo system and rapidly suppress the system disturbance.INDEX TERMS Disturbance suppression, internal model control, permanent magnet linear synchronous motor, tracking accuracy, uncertainties.

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An IMC-PID controller with Particle Swarm Optimization algorithm for MSBR core power control

Cited by 39 publications

References 10 publications

An Improved Particle Swarm Optimization Algorithm and Its Application to the Extreme Value Optimization Problem of Multivariable Function

An Improved Particle Swarm Optimization Algorithm and Its Application to the Extreme Value Optimization Problem of Multivariable Function

PSO based Hybrid PID-FLC Sugeno Control for Excitation System of Large Synchronous Motor

Extended State Observer-Based IMC-PID Tracking Control of PMLSM Servo Systems

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