Design on Fuzzy Controller of DC Motors Speed Control Based on DSP

Zhang, Peng Jie; Guo, Ji; Jing, Hong; Ming, Hong

doi:10.4028/www.scientific.net/amr.722.357

Cited by 2 publications

(2 citation statements)

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“…Conventional proportional/integral/derivative (PID) controllers are usually used in industry due to their easy interpretation and implementation. There are also other possibilities that use such fuzzy, neural network or robust control [14,15,16,17].…”

Section: Creating Of Co-simulation Modelmentioning

confidence: 99%

Model Based Design of Fuel Pump Control

Andrš

Vetiska

Holub

et al. 2016

AMM

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This paper presents a co-simulation method to design of speed controller for turbojet fuel pump. Expected fuel pump is used for small turbine engine concept with reducer driven by free turbine. The amount of injected fuel into the combustion chamber is based on the speed of the fuel pump which is controlled by the engine control unit. The final flow of fuel into the combustion chamber is restricted by fuel bypass which constricts the return fuel according to pressure in the nozzles. This back fuel bypass has nonlinear and fixed characteristic determined by its structure. The only way how to control the amount of incoming fuel to the engine is the pump speed control. Effect of the bypass represents a variable component in the fuel pump load and from the view of the speed controller it is a disturbance variable. This paper describes the co-simulation model based on the use of MATLAB/Simulink and MSC Adams environment. This simulation uses interconnection of Simulink controller design and simplified model of the fuel pump dynamics in Adams (without hydraulic modelling).

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Section: Creating Of Co-simulation Modelmentioning

confidence: 99%

Model Based Design of Fuel Pump Control

Andrš

Vetiska

Holub

et al. 2016

AMM

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“…Fuzzy logic control is a powerful, intelligent control approach, primarily according to control of rules and the knowledge of professional staff and specialized skills is the key source of the rule. The fuzzy control can therefore attain the effect of simulating the automatic control mechanism [7][8][9][10]. However, the summarization of the rules of fuzzy control is challenging, and it is hard to tune online once the control rules are set, and also there is not enough capacity for the new rules [11].…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of Adaptive PID Controller Based on Fuzzy Q-Learning Algorithm for a BLDC Motor

Rr¹,

Nabiyev²,

Ss³

et al. 2020

Preprint

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Reinforcement learning (RL) is an extensively applied control method for the purpose of designing intelligent control systems to achieve high accuracy as well as better performance. In the present article, the PID controller is considered as the main control strategy for brushless DC (BLDC) motor speed control. For better performance, the fuzzy Q-learning (FQL) method as a reinforcement learning approach is proposed to adjust the PID coefficients. A comparison with the adaptive PID (APID) controller is also performed for the superiority of the proposed method, and the findings demonstrate the reduction of the error of the proposed method and elimination of the overshoot for controlling the motor speed. MATLAB/SIMULINK has been used for modeling, simulation, and control design of the BLDC motor.

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Design on Fuzzy Controller of DC Motors Speed Control Based on DSP

Cited by 2 publications

References 1 publication

Model Based Design of Fuel Pump Control

Model Based Design of Fuel Pump Control

Design and Simulation of Adaptive PID Controller Based on Fuzzy Q-Learning Algorithm for a BLDC Motor

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