Autotuning Controller for Motion Control System Based on Intelligent Neural Network and Relay Feedback Approach

Nguyen, Giap Hoang; Shin, Jin-Ho; Kim, Won‐Ho

doi:10.1109/tmech.2014.2344692

Cited by 17 publications

(8 citation statements)

References 11 publications

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“…In this section, the environment detection strategy is introduced. For most DMOEAs [16][17][18][19][23][24][25][26][27], they usually do not take into account the type and extent of environmental change, and there are only few studies [22] on which kinds of changes happen in the environment and their effects on the predicted population. In our change detection mechanism, if the environment has changed, the types of change are estimated simultaneously.…”

Section: Environment Detection Strategymentioning

confidence: 99%

“…ere are two challenges that often encounter when dealing with DMOPs [22]: one is to handle the conflicts in multiple objectives, while the other is to track their dynamism caused by the time-varied objective functions and constraints. During the recent decades, different approaches have been proposed to solve DMOPs, such as the co-evolutionary approaches [23][24][25], the decomposition-based approaches [16], the prediction-based approaches [14,17,18], and some other self-learning mechanisms such as artificial neural network [26,27].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Multiobjective Optimization with Multiple Response Strategies Based on Linear Environment Detection

Shen

Liu

et al. 2020

Complexity

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Dynamic multiobjective optimization problems (DMOPs) bring more challenges for multiobjective evolutionary algorithm (MOEA) due to its time-varying characteristic. To handle this kind of DMOPs, this paper presents a dynamic MOEA with multiple response strategies based on linear environment detection, called DMOEA-LEM. In this approach, different types of environmental changes are estimated and then the corresponding response strategies are activated to generate an efficient initial population for the new environment. DMOEA-LEM not only detects whether the environmental changes but also estimates the types of linear changes so that different prediction models can be selected to initialize the population when the environmental changes. To study the performance of DMOEA-LEM, a large number of test DMOPs are adopted and the experiments validate the advantages of our algorithm when compared to three state-of-the-art dynamic MOEAs.

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Section: Environment Detection Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Multiobjective Optimization with Multiple Response Strategies Based on Linear Environment Detection

Shen

Liu

et al. 2020

Complexity

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“…This work couldn't nullify the error. Giap Hoang Nguyen et al, [2] have propounded an auto-tuning of the PID controller based on the RBF neural network and relay feedback approach. They were able to achieve a frequency of 117 Hz.…”

Section: Literature Surveymentioning

confidence: 99%

Efficient VLSI Implementation of the C-MANTEC Conn Algorithm by Using PID Controllers

Caleb¹,

Kannan²

2017

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Through the research on the existing C-MANTEC neural network and PID control technology, this paper presents an improved C-MANTEC algorithm based on PID control system. The combining of the artificial neural networks with conventional PID control helps in exploring their respective advantages to forming the intelligent PID control. From UCI Repository cancer dataset, the developed system is tested. The results show that the scheme can not only improve the speed of the algorithm in the training process but also improve the generalization capability of the network, which further enhances the performance of PID controllers. The overall power consumed is also reduced to a greater extent.

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“…e neural network can approximate continuous functions closely, so there has been much research on the use of the neural network for model-free control. By training the neural network online or offline, the performance of the control system can be improved, and finally, a satisfactory control effect can be achieved [15][16][17][18][19][20]. Nowadays, neural network control is promisingly used in magnetic levitation ball system.…”

Section: Introductionmentioning

confidence: 99%

Position Control of Magnetic Levitation Ball Based on an Improved Adagrad Algorithm and Deep Neural Network Feedforward Compensation Control

Wei

Huang

2020

Mathematical Problems in Engineering

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To control the position of the magnetic levitation ball more accurately, this paper proposes a deep neural network feedforward compensation controller based on an improved Adagrad optimization algorithm. The control structure of the controller consists of a deep neural network identifier, a deep neural network feedforward compensator, and a PID controller. First, the dynamic inverse model of the magnetic levitation ball is established by the deep neural network identifier which is trained online based on the improved Adagrad algorithm, and the trained network parameters are dynamically copied to the deep neural network feedforward compensator. Then, the position control of the magnetic levitation ball system is realized by the output of the feedforward compensator and the PID controller. Simulations and experiments illustrate that the accuracy of the deep network feedforward compensation control based on an improved Adagrad algorithm is higher, and its control system shows good dynamic and static performance and robustness to some extent.

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Autotuning Controller for Motion Control System Based on Intelligent Neural Network and Relay Feedback Approach

Cited by 17 publications

References 11 publications

Dynamic Multiobjective Optimization with Multiple Response Strategies Based on Linear Environment Detection

Dynamic Multiobjective Optimization with Multiple Response Strategies Based on Linear Environment Detection

Efficient VLSI Implementation of the C-MANTEC Conn Algorithm by Using PID Controllers

Position Control of Magnetic Levitation Ball Based on an Improved Adagrad Algorithm and Deep Neural Network Feedforward Compensation Control

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