Parameters Tuning Approach for Proportion Integration Differentiation Controller of Magnetorheological Fluids Brake Based on Improved Fruit Fly Optimization Algorithm

Liu, Xinhua; Shi, Yao; Xu, Jing

doi:10.3390/sym9070109

Cited by 24 publications

(12 citation statements)

References 22 publications

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“…erefore, when using the VMD algorithm to deal with bearing fault signals, multiple IMF components will be obtained. According to the definition of marginal spectrum entropy in equation (18), if the IMF component contains more noise components, its corresponding MSE value is larger. Conversely, if the IMF component mainly contains the periodic impact component of bearing failure, the marginal spectrum entropy of VMD is very small.…”

Section: Proposed Improved Algorithm Frameworkmentioning

confidence: 99%

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Fault Diagnosis of Variable Load Bearing Based on Quantum Chaotic Fruit Fly VMD and Variational RVM

Zhou

et al. 2019

Shock and Vibration

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Under normal circumstances, bearings generally run under variable loading conditions. Under such conditions, the vibration signals of the bearing malfunctions are often nonstationary signals, which are difficult to process effectively. In order to accurately and effectively diagnose the failure types and damage degree of bearings under variable load conditions, an intelligent diagnostic model based on the variational mode decomposition (VMD) of quantum chaotic fruit fly optimization algorithm (QCFOA) and a multiclassification variational relevance vector machine (VRVM) is proposed. First, the key parameters of the VMD are selected using the QCFOA. Secondly, the known bearing fault signal is decomposed by the optimized VMD, and the center frequency and marginal spectral entropy (MSE) of each natural modal component are extracted to construct two-dimensional MSE. Then, the probit model is used to replace the logistic model, and a simpler and more practical multiclassification model is constructed. The two-dimensional MSE of each intrinsic modal component is used as a learning sample for VRVM. Finally, the bearing fault data under 1 hp load are taken as training samples, and the bearing fault data under two loads of 0 hp and 3 hp are used as test samples to verify the effectiveness of the intelligent diagnosis model. The experimental results show that the intelligent fault diagnosis method proposed in this paper can accurately diagnose the type of fault and the degree of damage and has high robustness.

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Section: Proposed Improved Algorithm Frameworkmentioning

confidence: 99%

“…is algorithm is a new swarm intelligence algorithm based on the bionics principle of fruit fly foraging behavior. It is applied in many fields [16][17][18]. However, its convergence accuracy is very sensitive to the initial value.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Variable Load Bearing Based on Quantum Chaotic Fruit Fly VMD and Variational RVM

Zhou

et al. 2019

Shock and Vibration

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“…Like Monte Carlo For the tracking and adaptive cruise control of an intelligent vehicle, the proportion integration differentiation (PID) control is a control loop mechanism employing feedback that is widely used in industrial control systems and a variety of other applications requiring continuously modulated control. It has been the classic type of controller since the mid-20th century and will continue as the most often used industrial control scheme, due to its remarkable effectiveness, simple implementation, and broad applicability [6,7]. However, the parameter setting processing of conventional PID controllers is very complicated and the results are hard to be satisfactory, which might cause a great waste of manpower, material resources, and equipment.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Approach to Design Practical Adaptive Control for a Small-Scale Intelligent Vehicle

Yang

et al. 2019

Symmetry

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Reinforcement learning (RL) based techniques have been employed for the tracking and adaptive cruise control of a small-scale vehicle with the aim to transfer the obtained knowledge to a full-scale intelligent vehicle in the near future. Unlike most other control techniques, the purpose of this study is to seek a practical method that enables the vehicle, in the real environment and in real time, to learn the control behavior on its own while adapting to the changing circumstances. In this context, it is necessary to design an algorithm that symmetrically considers both time efficiency and accuracy. Meanwhile, in order to realize adaptive cruise control specifically, a set of symmetrical control actions consisting of steering angle and vehicle speed needs to be optimized simultaneously. In this paper, firstly, the experimental setup of the small-scale intelligent vehicle is introduced. Subsequently, three model-free RL algorithm are conducted to develop and finally form the strategy to keep the vehicle within its lanes at constant and top velocity. Furthermore, a model-based RL strategy is compared that incorporates learning from real experience and planning from simulated experience. Finally, a Q-learning based adaptive cruise control strategy is intermixed to the existing tracking control architecture to allow the vehicle slow-down in the curve and accelerate on straightaways. The experimental results show that the Q-learning and Sarsa (λ) algorithms can achieve a better tracking behavior than the conventional Sarsa, and Q-learning outperform Sarsa (λ) in terms of computational complexity. The Dyna-Q method performs similarly with the Sarsa (λ) algorithms, but with a significant reduction of computational time. Compared with a fine-tuned proportion integration differentiation (PID) controller, the good-balanced Q-learning is seen to perform better and it can also be easily applied to control problems with over one control actions.

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“…The improved fruit fly optimization algorithm (IFOA) is an optimization of the FOA with increased global search capability for optimizing the PID parameters. However, its control accuracy is still not ideal (Liu et al, 2017). To solve this problem, this paper develops a new method based on particle swarm optimization and the improved fruit fly optimization algorithm (PSO-IFOA) to optimize the PID parameters for the vibration control of semi-active seat suspension and shows that it possesses better dynamic response characteristics and control accuracy compared with FOA, PSO, and IFOA.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Vibration Attenuation Performance of a Magnetorheological Damper-Based Semi-active Seat Suspension Using Artificial Intelligence

Liu

Wang

et al. 2019

Front. Mater.

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This paper aims to improve control performance for a magnetorheological damper (MRD)-based semi-active seat suspension system. The vibration of the suspension is isolated by controlling the stiffness of the MRD using a proportion integration differentiation (PID) controller. A new intelligent method for optimizing the PID parameters is proposed in this work. This new method appropriately incorporates particle swarm optimization (PSO) into the PID-parameter searching processing of an improved fruit fly optimization algorithm (IFOA). Thus, the PSO-IFOA method possesses better optimization ability than IFOA and is able to find a globally optimal PID-parameter set. The performance of the PID controller optimized by the proposed PSO-IFOA for attenuating the vibration of the MRD suspension was evaluated using a numerical model and an experimental platform. The results of both simulation and experimental analysis demonstrate that the proposed PSO-IFOA is able to optimize the PID parameters for controlling the MRD semi-active seat suspension. The control performance of the PSO-IFOA-based PID is superior to that of individual PSO-, FOA-, or IFOA-based methods.

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Parameters Tuning Approach for Proportion Integration Differentiation Controller of Magnetorheological Fluids Brake Based on Improved Fruit Fly Optimization Algorithm

Cited by 24 publications

References 22 publications

Fault Diagnosis of Variable Load Bearing Based on Quantum Chaotic Fruit Fly VMD and Variational RVM

Fault Diagnosis of Variable Load Bearing Based on Quantum Chaotic Fruit Fly VMD and Variational RVM

Reinforcement Learning Approach to Design Practical Adaptive Control for a Small-Scale Intelligent Vehicle

Optimizing Vibration Attenuation Performance of a Magnetorheological Damper-Based Semi-active Seat Suspension Using Artificial Intelligence

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