Neural Network Based Tuning Algorithm for MPID Control

Mansour, Tamer; Konno, Atsushi; Uchiyama, Masaru

doi:10.5772/16058

Cited by 3 publications

(7 citation statements)

References 17 publications

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“…If the absolute stability of the closed loop is assured, the designer closes the loop and tests the performance of the resulting closed loop system. Because of the neglected loading effects among the components, nonlinearities, distributed parameters, and so on, which were not taken into consideration in the original design work (Mansour, 2011), the actual performance of the prototype system will probably differ from the theoretical predictions (Rodić, 2009). Thus the first design may not satisfy all the requirements on performance.…”

Section: Design and Tuning Rules Of Pid Controllersmentioning

confidence: 99%

“…However the process of selecting the controller parameters to meet given performance specifications is known as controller tuning. The controller tuning greatly affects the control system properties, such as robustness to disturbances and noise, performance and robustness to delays (Mansour, 2011). For example, figure 2 shows a block diagram of a simple cascade PID control of a plant.…”

Section: Design and Tuning Rules Of Pid Controllersmentioning

confidence: 99%

“…Because of the simplicity and robustness, PID controllers are frequently used controllers in industries (Mansour, 2011). Parameter adjustment of PID controllers is an old challenge in the field of control system design.…”

Section: Genetic Algorithms Applications To Control System Designmentioning

confidence: 99%

“…Figure 20 represents the closed loop system with genetic controller, which is designed according to the algorithm proposed in this chapter. The disturbance can affect plants and no countermeasure can be applied, as the controller can only counteract after the changes at the system output (Bartoszewicz, 2011) and (Mansour, 2011).…”

Section: Robust Stability Using Genetic Pid Controllermentioning

confidence: 99%

“…Some of methods have been proposed to select the PID coefficients, but they are not completely systematic methods and result in a poorly tuned controller that needs some trail and error. So far, finding new methods to automatically select PID parameters was interest of researches (Ho, 2003), (Almeida et al, 2005), (Nasri et al, 2007), and (Mansour, 2011). However in this chapter, the selection of optimal PID controller coefficients are introduced and applied based on genetic algorithms technique.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Enhancing Control Systems Response Using Genetic PID Controllers

Al-Rawi¹

2012

Genetic Algorithms in Applications

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Section: Design and Tuning Rules Of Pid Controllersmentioning

confidence: 99%

Section: Design and Tuning Rules Of Pid Controllersmentioning

confidence: 99%

Section: Genetic Algorithms Applications To Control System Designmentioning

confidence: 99%

Section: Robust Stability Using Genetic Pid Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enhancing Control Systems Response Using Genetic PID Controllers

Al-Rawi¹

2012

Genetic Algorithms in Applications

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Speed control of DC motor using conventional and adaptive PID controllers

Al-Bargothi

Qaryouti

Jaber

2019

IJEECS

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<p>Proportional Integral Derivative (PID) controllers are <a href="http://www.thesaurus.com/browse/extensively">extensively</a> used in practical industries to control the speed of DC Motors. The single weakness of PID controllers is their sensitivity to variation in parameters and operating conditions; thus, tuning the controller gains to adapt with these variations presents a practical challenge. In this paper, an adaptive mechanism that utilizes a Recursive Least Square (RLS) algorithm, with rate limiters, is implemented to perform an online self-adjusting of each of the PID gains in order to achieve Adaptive PID (APID) controller that will accommodate to system variations. MATLAB/ Simulink software is used to implement and simulate APID control of a Chopper-Fed DC motor. A conventional PID control system is also designed and simulated to obtain results that can be used to judge the performance of the APID controller. Results proved that the APID controller forced the motor speed to track the reference input with <a href="http://www.thesaurus.com/browse/insignificant">insignificant</a> tracking error, and also managed to attain the motor speed at its desired value, regardless of the load changes inflected on the motor. This enhances both transient and steady-state speed responses.</p>

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Optimization of PID Controller Parameter using the Geometric Mean Optimizer

Abdellatif,

Issa,

Ziedan

2024

IJACSA

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The PID controller is a crucial element in numerous engineering applications. However, a significant challenge with PID lies in selecting optimal parameter values. Conventional methods need extra tunning and may not yield the best performance. In this study, a recently introduced metaheuristic algorithm, Geometric Mean Optimizer (GMO), is employed to identify the most suitable PID parameter values. In conventional methods, a fixed empirical equations are applied to select parameter values of PID. In GMO, there is a wide search space to select the optimal parameter values of PID based on an objective function. The objective function that the GMO seeks to minimize is the Integral of Absolute Error (IAE). GMO is chosen for its effectiveness in balancing exploration and exploitation of the search space, as well as its robustness and scalability. GMO is tested in the context of optimizing PID parameters for an engineering application: DC motor regulations. The results demonstrated GMO's superiority over comparable algorithms.

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Neural Network Based Tuning Algorithm for MPID Control

Cited by 3 publications

References 17 publications

Enhancing Control Systems Response Using Genetic PID Controllers

Enhancing Control Systems Response Using Genetic PID Controllers

Speed control of DC motor using conventional and adaptive PID controllers

Optimization of PID Controller Parameter using the Geometric Mean Optimizer

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