MR-SAS and electric power steering variable universe fuzzy PID integrated control

Cao, Zhaolong; Zheng, Shuai

doi:10.1007/s00521-017-3157-7

Cited by 19 publications

(12 citation statements)

References 14 publications

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“…By dynamically adjusting the size of the universe, densifying control can be achieved skillfully in another way. This is the principle of variable universe fuzzy control, which is highly suitable for complex excitation and control models [23]- [24]. It can be expressed as follows: Set the input of the controller as…”

Section: A Variable Universe Fuzzy Main Controllermentioning

confidence: 99%

Variable Universe Fuzzy Control of High-Speed Elevator Horizontal Vibration Based on Firefly Algorithm and Backpropagation Fuzzy Neural Network

Zhang

et al. 2021

IEEE Access

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Section: A Variable Universe Fuzzy Main Controllermentioning

confidence: 99%

Variable Universe Fuzzy Control of High-Speed Elevator Horizontal Vibration Based on Firefly Algorithm and Backpropagation Fuzzy Neural Network

Zhang

et al. 2021

IEEE Access

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“…There are currently three design methods: the functional contraction-expansion factor, the fuzzy reasoning contraction-expansion factor, and the error classification contraction-expansion factor. This paper chooses the calculation method based on the fuzzy inference type contraction-expansion factor, because the contraction-expansion factor calculation model based on fuzzy rules satisfies the monotonicity, duality, coordination, normality and zero avoidance of the contraction-expansion factor, and avoids the functional contraction-expansion factor's calculation model parameter selection [15,25]. At the same time, fuzzy rules are used to express the change law of the contraction-expansion factor to realize the online automatic adjustment of the contraction-expansion factor.…”

Section: Contraction-expansion Factor Adjustment Modulementioning

confidence: 99%

“…References [13,14] introduce the idea of a variable theory domain on the basis of a fuzzy PID control, which overcomes the shortcoming of the limited control rules of a fuzzy PID controller, further increases the controller's adaptive ability, and improves the dynamic characteristics of the control system. In addition, references [15][16][17] use variable universe fuzzy PID control in different applications, and the control effect is significant.…”

Section: Introductionmentioning

confidence: 99%

Master and Auxiliary Compound Control for Multi-Channel Confluent Water Supply Switching Control Based on Variable Universe Fuzzy PID

Zhao

Wang

et al. 2020

Applied Sciences

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During the multi-channel confluent water supply process, the pressure control of the main pipe is often held back by such problems as non-linearity, hysteresis and parameter uncertainty, its own unique load dynamic changes, channel switching disturbance and other system characteristics caused by the actual working conditions. Moreover, pressure fluctuations in the main pipe will lead to a reduction in the service life of fire-fighting equipment, an increase in the failure rate, and even an interruption of the fire-fighting water supply. Therefore, a master and auxiliary control strategy is proposed to stabilize the pressure change in the process of multi-channel concentrated water supply switching, by using variable universe fuzzy proportional integral derivative (PID) control as the main controller on the main pipe and traditional PID control as the subsidiary controller on the channel. The control strategy is verified by the co-simulation platforms of LabVIEW and AMESim. Simulation results show that the variable universe fuzzy PID control and the master and auxiliary compound control based on the variable universe fuzzy PID control have advantages in step response, tracking response and anti-interference, respectively. The parameters obtained in the co-simulation are used in the experimental system. The experimental results show that the maximum deviation rate of main pipe pressure can be reduced by about 10% compared with other control methods under different loads. In conclusion, the proposed control strategy has strong anti-interference ability, fast dynamic response speed, high stability and good peak shaving effect.

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“…However, adopting effective user-side load management control strategy to guide the charging and discharging behavior of electric vehicles can achieve the effect of orderly charging and discharging. [8][9][10] If we aim at changing electricity quantity and power supply, on the one hand, it is required to take measures to adjust the power demand during peak-load and valley-load periods, reduce the peak-valley difference rate, and reduce the capacity of new installed machines. On the other hand, on the premise of guaranteeing the same energy services, we should take effective measures to adjust the power consumption mode to reduce the power generation of power system, improve the energy utilization rate, and save social resources.…”

Section: Introductionmentioning

confidence: 99%

“…Electric vehicles have uncertainties in time and space. However, adopting effective user‐side load management control strategy to guide the charging and discharging behavior of electric vehicles can achieve the effect of orderly charging and discharging 8–10 . If we aim at changing electricity quantity and power supply, on the one hand, it is required to take measures to adjust the power demand during peak‐load and valley‐load periods, reduce the peak‐valley difference rate, and reduce the capacity of new installed machines.…”

Section: Introductionmentioning

confidence: 99%

An electric vehicle operation optimization method based on demand‐side management

Gan

Chui-yong

2019

Concurrency and Computation

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Against the background of energy restriction and environmental pollution, in recent years, new energy electric vehicles have developed rapidly with their advantages of energy saving and environmental protection. To realize the benign interaction between electric vehicle charging and switching power stations and power grid, and the demand-side management of electric vehicle charging and switching power stations, the Monte Carlo random sampling method is used to collect the battery-charged state of batteries in one day. Considering the constraints of charging and discharging times of charging stations, the operation model of charging and switching stations for electric vehicles is established, and the interior point optimization method is used to solve the model; thus, the optimal orderly charging and discharging strategy for electric vehicles can be obtained. Finally, simulation results show that the optimized charging mode can effectively reduce the peak-valley difference of the system load and improve the stability of the system compared with the stochastic charging mode. In addition, this mode of charging can effectively develop the economic potential of the electric-vehicle batteries without affecting the use needs of the owners, and it has good prospects for engineering popularization.

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MR-SAS and electric power steering variable universe fuzzy PID integrated control

Cited by 19 publications

References 14 publications

Variable Universe Fuzzy Control of High-Speed Elevator Horizontal Vibration Based on Firefly Algorithm and Backpropagation Fuzzy Neural Network

Variable Universe Fuzzy Control of High-Speed Elevator Horizontal Vibration Based on Firefly Algorithm and Backpropagation Fuzzy Neural Network

Master and Auxiliary Compound Control for Multi-Channel Confluent Water Supply Switching Control Based on Variable Universe Fuzzy PID

An electric vehicle operation optimization method based on demand‐side management

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