Intelligent robust PI adaptive control strategy for speed control of EV(s)

Khooban, Mohammad Hassan; Almasi, Omid Naghash; Niknam, Taher; Shasadeghi, Mokhtar

doi:10.1049/iet-smt.2015.0190

Cited by 31 publications

(23 citation statements)

References 32 publications

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“…The nonlinear electric vehicle (EV) system is assumed a case study. The goal is to force an EV's speed to follow a desirable reference for both (structured and unstructured) uncertainties.The second order EV simulation results demonstrated the effectiveness of the proposed method with fast versus slow and large against small disturbances [9].…”

Section: Introductionmentioning

confidence: 76%

Helicopter Fractional Order Pid Controller Based the Technique of Bacterial Foraging

Sultan¹

2020

ETJ

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The theory of algorithm for improving bacterial feed is considered one of the important theories in advanced research and has been widely accepted for a global improvement algorithm to solve problems of control in improvement research in non-linear systems, which have become among the interests of researchers in this field .In our work, a new method for design of a bacteria foraging algorithm principle based on Proportional-integral-derivative controller (PID) for three degree of freedom (3DoF) helicopter system. This paper presents a method to stabilize pitch, roll and travel angle of the helicopter system using optimization technique based on bacterial foraging (BF) algorithm is used to tune the fractional order PID controller parameters by reducing the objective performance parameters. Transient and constant case responses are found to be appropriate. The result reveals that BF provides a good synthesis algorithm. In addition, BF-FOPID works better than BF-PID controller for the 3DoF helicopter model.

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Section: Introductionmentioning

confidence: 76%

Helicopter Fractional Order Pid Controller Based the Technique of Bacterial Foraging

Sultan¹

2020

ETJ

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“…What specifically make EVs powerful are the transmission and differential gears of the electric motors. A battery is the source of direct current (DC) power [4]. A DC motor benefits from two main advantages.…”

Section: Introductionmentioning

confidence: 99%

Model‐predictive control based on Takagi‐Sugeno fuzzy model for electrical vehicles delayed model

Khooban

Vafamand

Niknam

et al. 2017

IET Electric Power Applications

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Electric vehicles (EVs) play a significant role in different applications, such as commuter vehicles and short distance transport applications. This study presents a new structure of model-predictive control based on the Takagi-Sugeno fuzzy model, linear matrix inequalities, and a non-quadratic Lyapunov function for the speed control of EVs including time-delay states and parameter uncertainty. Experimental data, using the Federal Test Procedure (FTP-75), is applied to test the performance and robustness of the suggested controller in the presence of time-varying parameters. Besides, a comparison is made between the results of the suggested robust strategy and those obtained from some of the most recent studies on the same topic, to assess the efficiency of the suggested controller. Finally, the experimental results based on a TMS320F28335 DSP are performed on a direct current motor. Simulation and experimental results demonstrate the flawless performance of the suggested controller and the fast and accurate tracking of the EV speed to its set-point.

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“…Fuzzy control has been used to describe uncertain systems since it was first discovered by Professor Zadeh. Fuzzy control is used for the control of many industrial systems and has the advantage that it can be applied without accurate modeling [34][35][36][37][38]. The PI controller is the most widely used controller in the industrial field and has the advantage that the structure is simple, and thus the relationship between the gain and the control value is clear.…”

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confidence: 99%

Improvement of Energy Efficiency and Control Performance of Cooling System Fan Applied to Industry 4.0 Data Center

Kim

2019

Electronics

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This paper proposes a control method to improve the energy efficiency and performance of cooling fans used for cooling. In Industry 4.0, a large number of digital data are used, and a large number of data centers are created to handle these data. These data centers consist of information technology (IT) equipment, power systems, and cooling systems. The cooling system is essential to prevent failure and malfunction of the IT equipment, which consumes a considerable amount of energy. This paper proposes a method to reduce the energy used in such cooling systems and to improve the temperature control performance. This paper proposes an fuzzy proportional integral(FPI) controller that controls the input value of the proportional integral(PI) controller by the fuzzy controller according to the operation state, a VFPI (Variable Fuzzy Proportional Integral) controller that adjusts the gain value of the fuzzy controller, and a variable fuzzy proportion integration-variable limit (VFPI-VL) controller that adjusts the limit value of the fuzzy controller’s output value. These controllers control the fan applied to the cooling system and compare the energy consumed and temperature control performance. When the PI controller consumes 100% of the power consumed, the FPI is 50.5%, the VFPI controller is 44.3%, and the VFPI-VL is 32.6%. The power consumption is greatly reduced. In addition, the VFPI-VL controller is the lowest in temperature variation, which improves the energy efficiency and performance of the cooling system using a fan. The methods presented in this paper can not only be applied to fans for cooling, but also to variable speed systems for various purposes and improvement of performance and efficiency can be expected.

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Intelligent robust PI adaptive control strategy for speed control of EV(s)

Cited by 31 publications

References 32 publications

Helicopter Fractional Order Pid Controller Based the Technique of Bacterial Foraging

Helicopter Fractional Order Pid Controller Based the Technique of Bacterial Foraging

Model‐predictive control based on Takagi‐Sugeno fuzzy model for electrical vehicles delayed model

Improvement of Energy Efficiency and Control Performance of Cooling System Fan Applied to Industry 4.0 Data Center

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