PSO-based optimized neural network PID control approach for a four wheeled omnidirectional mobile robot

Al-Jodah, Ammar; Abbas, Saad Jabbar; Hasan, Alaq F.; Humaidi, Amjad J.; Al-Obaidi, Abdulkareem Sh. Mahdi; Al-Qassar, Arif A.; Hassan, Raaed F.

doi:10.1556/1848.2022.00420

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…In addition, the PID controller shows good robustness and stability if it operates within linear range. The elements of PID controller consists of Proportional, Integral and Derivative gains and its transfer function is given by [17]:…”

Section: Pid Controller Designmentioning

confidence: 99%

Optimal Tuning of PID-Controlled Magnetic Bearing System for Tracking Control of Pump Impeller in Artificial Heart

Noaman¹,

Gatea²,

Humaidi³

et al. 2023

JESA

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In this work, using optimal PID control for magnetic bearing in artificial heart pump, two magnetic bearings used to suspend the impeller rotor, the small air gap, high speed of rotor that important think to keep the life of the human that uses Artificial Heart Ventricle, the Artificial Heart Ventricle it the is the full-actuated system the state-space model developed for the control, choosing the value of parameter control very important, the performance of output depending on this parameter. This study presents an optimization algorithm based on PSO (particle swarm optimization) to optimize performance of Proportional Integral Derivative (PID) controller to magnetically hanging the rotary pump impeller of Artificial Heart Ventricle (AHV). The optimal controller's terms are obtained by minimization of fitness function which is defined based on the index Root Mean Square of Error (RMSE). The optimal values of control elements lead to optimal PID controller which results in optimal tracking performance of PID controlled bearing system. The numerical simulation has been conducted to verify the effectiveness of proposed controller. The results showed that the optimal controller could stabilize the impeller within small deviations in displacement and angular position.

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Section: Pid Controller Designmentioning

confidence: 99%

Optimal Tuning of PID-Controlled Magnetic Bearing System for Tracking Control of Pump Impeller in Artificial Heart

Noaman¹,

Gatea²,

Humaidi³

et al. 2023

JESA

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“…There are two different classification algorithms for control systems built using neural networks. Training the network to carry out the necessary activities is required for both designs [23].…”

Section: Fractional Order Pid Neural Networkmentioning

confidence: 99%

FOPID Neural Network Controller Design for Nonlinear CSTR System

2023

IJIES

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In this paper, four control structures are provided for continuous ctirred-tank reactor (CSTR) system's fractional/integer order proportional integral derivative neural network controllers. The revised neural network weights and the controller's parameters are optimized using the optimization technique called ant colony optimization (ACO). The proposed controllers' resistance to changes in the initial state, outside disturbances, and parameter modifications is also tested. The fractional order proportional integral derivative neural network controllers provide the best assurance and also enhance the system's robustness to changes in the initial state, external disturbances, and parameter variations, according to the results of MATLAB code.The fractional order proportional integral derivative neural network controller1(FOPIDNNC1) is the best structure among all those with the minimum cost function equal to 0.011588 for the set-point variations, 0.015325 for uncertainty parameter, 0.018274 for disturbances rejection, and the best structure fractional order proportional integral derivative neural network controller3 (FOPIDNNC3) among all those with the minimum cost function equal to 0.008733 for tracking.

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“…It is important to keep in mind that creating and training a PID neural network can be difficult and calls for knowledge of both control theory and neural networks. Additionally, the quantity and quality of the training data may affect the network's performance, and the complexity of the network may raise the processing needs of the control system [18][19][20]. Therefore the IP system is proposed to be controlled by four structures of PIDNN controllers.…”

Section: Pid Neural Networkmentioning

confidence: 99%

PID Neural Controller Design for Nonlinear Inverted Pendulum System

2023

IJIES

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The inverted pendulum (IP) system, is a highly coupled, complex, nonlinear system in which the performance of the system is adversely affected by parameter uncertainty and outside disturbances. Therefore, these complications must be managed by the controllers created for such systems. The primary objective of this work is to develop four control structures, including integer order proportional integral derivative neural network controllers for inverted pendulums that deal with trajectory tracking issues. Proportional-integral-derivative neural network structure1 (PIDNNS1), proportional-integral-derivative neural network structure2 (PIDNNS2), proportional-integral-derivative neural network structure3 (PIDNNS3), and proportional-integral-derivative neural network structure4 (PIDNNS4) are the controller structures for inverted pendulum (IP) system . The ant colony optimization (ACO) is a metaheuristic optimization method that is offered to optimize. the controllers' settings while minimizing the cost function. The proposed controllers' resilience to outside disturbances and parameter uncertainty is also tested. The results using MATLAB code demonstrate that the PIDNNS4 controller, which best has a reduced cost function equal to (1.177494), (1.273627), (1.209761) for trajectory tracking, parameters uncertainty, and disturbances rejection for the inverted pendulum (IP) system. and the best controller for stabilization with a low-cost function is the PIDNNS1 controller (1.280839).

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PSO-based optimized neural network PID control approach for a four wheeled omnidirectional mobile robot

Cited by 8 publications

References 29 publications

Optimal Tuning of PID-Controlled Magnetic Bearing System for Tracking Control of Pump Impeller in Artificial Heart

Optimal Tuning of PID-Controlled Magnetic Bearing System for Tracking Control of Pump Impeller in Artificial Heart

FOPID Neural Network Controller Design for Nonlinear CSTR System

PID Neural Controller Design for Nonlinear Inverted Pendulum System

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