Speed control of DC motor using PID controller based on artificial intelligence techniques

Elsrogy, Walaa M.; Fkirin, M. A.; Hassan, Mohamed

doi:10.1109/codit.2013.6689543

Cited by 60 publications

(23 citation statements)

References 2 publications

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“…Genetic Algorithms (GAs) are an optimization process that follows the process of natural evolution [15]. It is inspired by the process of selection and the mechanics of genetics [16].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm

2019

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Temperature is one of the exigent parameters that needs to be controlled in today's industries. Importantly this temperature control should be precise and fast. As the conventional controllers are not optimally tuned, the controller used for controlling the temperature of the electric furnace does not exhibit better performance. Its rise time and settling time is too large as well as it has a sizable amount of overshoot. This paper presents a Genetic Algorithm based PID controller to overcome the low precision, long rise time and settling time of the controller. In this algorithm, Integral of Absolute Error is taken as the object function for minimizing the error. Using this function, the algorithm engenders the optimum value of the gain parameters (K p , K i , K d) for the PID controller. It shows better control over the conventional controllers. As the overshoot, settling time, and rise time are substantially improved, it provides sharp and prompt control over the temperature. This precise and instant control of temperature has a great impact on the food and medicine industries. As the temperature could be controlled precisely and instantly, we can avoid the change/degradation of the physical properties of the materials that are under process.

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“…Genetic Algorithms (GAs) are an optimization process that follows the process of natural evolution [15]. It is inspired by the process of selection and the mechanics of genetics [16].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm

2019

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“…The difference in motors' speed creates a rolling and yawing moment, which hugely affects the performance and maneuver of the UAV. According to [4], [5], a PID controller are developed to maintain the same rotating speed on both motors. Fig.…”

Section: Propulsion Designmentioning

confidence: 99%

Sounding Rocket Booster Design, Manufacture and Testing

Hoang¹,

Lin²,

Chao³

2018

IJMMM

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The Unmanned Aerial Vehicles (UAVs) are widely applied in daily life with different purposes recently. This UAV was designed to fulfill a specific mission: launch a sounding rocket at high altitude. The specifications of the UAV are 3.2 meters wingspan, maximum takeoff weight (MTOW) is 25 (kg) (10 (kg) payload included), service ceiling is 800 meter and operating range is 2 km. Computational Fluid Dynamics (CFD) software (XLFR5, CFX5) is used for design process. UAV structure are made from foam, fiber glass and epoxy glue instead of balsa wood as traditional method. This thesis proposes a procedure to build the UAV from initial ideas to preliminary designs, CAD designs, then manufacture and lastly flight test and verification.

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“…Fuzzy Neural Model Reference controller has used in [2] to control the speed of SEDCM, the results obtained presented a good performance and robust response in load variations, this work uses Fuzzy Neural Model Reference controller and MRAC method, while our present work uses fuzzy and ANFIS techniques to implement the speed control for the DC motor. To select the parameters of the PID controllers, Elsrogy et al in [3] used genetic algorithm (GA) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for controlling the speed of DC motor. A Comparison study has presented in [4] between fuzzy and ANFIS controllers that control the speed of SEDCM, the results indicate that ANFIS is better than fuzzy controller.…”

Section: Introductionmentioning

confidence: 99%

Speed Reference Tracking for Separately Excited DC Motor Based ANFIS and Hysteresis Current Control Techniques

Mahmood¹

2018

ETJ

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In this work, a closed loop control system has designed to control the speed of separately excited direct current motor (SEDCM) using Fuzzy (Mamdani and Sugeno) and an Adaptive Neuro-Fuzzy techniques (ANFIS). The action of these control techniques is to produce the reference armature current that has fed to the hysterics current controller (HCC) that produces the required gating signal to a Buck chopper .Different load conditions has been applied to the motor to obtain many mode of operation, the speed held constant at the required references values using both fuzzy (Mamdani-type and Sugenotype) and ANFIS techniques. The results has collected and compered with a classical PID controller using MATLAB/Simulink. Step response for the speed has drawn and the control parameters for this response have evaluated. According to the results, the Mamdani fuzzy controller technique is better than as compared with the other controllers. There are many applications for this plant such as production process that need to fill or Packaging any product or used in the autopilot channels. The new goal for this proposed system is to get robust speed controllers that track the speed at any mode of operation using three artificial intelligent techniques.

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Speed control of DC motor using PID controller based on artificial intelligence techniques

Cited by 60 publications

References 2 publications

Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm

Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm

Sounding Rocket Booster Design, Manufacture and Testing

Speed Reference Tracking for Separately Excited DC Motor Based ANFIS and Hysteresis Current Control Techniques

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