Speed Control of DC Motor Using PID and FOPID Controllers Based on Differential Evolution and PSO

Idir, Abdelhakim; Kidouche, Madjid; Bensafia, Yassine; Khettab, Khatir; Tadjer, Sid Ahmed

doi:10.22266/ijies2018.0831.24

Cited by 30 publications

(25 citation statements)

References 15 publications

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“…The formula of continuous differ-integral operator (aD α t ) is defined as in Eq. (2) [8]. There are two commonly used definitions for general fractional differ-integral aD α t ÀÁ , which are used for realization of control problem algorithm:…”

Section: Fractional Order Calculusmentioning

confidence: 99%

A Hybrid Control Approach Based on the Combination of PID Control with LQR Optimal Control

Mohammed¹

2021

Control Based on PID Framework - The Mutual Promotion of Control and Identification for Complex Systems

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Proportional Integral Derivative (PID) is the most popular controller that is commonly used in wide industrial applications due to its simplicity to realize and performance characteristics. This technique can be successfully applied to control the behavior of single-input single-output (SISO) systems. Extending the using of PID controller for complex dynamical systems has attracted the attention of control engineers. In the last decade, hybrid control strategies are developed by researchers using conventional PID controllers with other controller techniques such as Linear Quadratic Regulator (LQR) controllers. The strategy of the hybrid controller is based on the idea that the parameters of the PID controller are calculated using gain elements of LQR optimal controller. This chapter focuses on design and simulation a hybrid LQR-PID controller used to stabilize elevation, pitch and travel axes of helicopter system. An improvement in the performance of the hybrid LQR-PID controller is achieved by using Genetic Algorithm (GA) which, is adopted to obtain best values of gain parameters for LQR-PID controller.

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Section: Fractional Order Calculusmentioning

confidence: 99%

A Hybrid Control Approach Based on the Combination of PID Control with LQR Optimal Control

Mohammed¹

2021

Control Based on PID Framework - The Mutual Promotion of Control and Identification for Complex Systems

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“…al [17] designed PID controllers for both a first-order process plus delay-time (FOPDT) model and a second-order process plus delaytime (SOPDT) model by proposing a novel fitness function which provides less overshoot and control input. Speed control of a DC motor was performed using PID controller whose parameters were tuned by PSO algorithm and the performance of the PID controller were compared to another PID controller tuned by differential evolution (DE) algorithm [18]. A comparative study for PID controller design with different algorithms was published in [19] where GA, DE, and PSO algorithms were used to tune the parameters of the PID controller.…”

Section: *Corresponding Authormentioning

confidence: 99%

Parameter effect analysis of particle swarm optimization algorithm in PID controller design

Ayas

Şahin

2019

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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PID controller has still been widely-used in industrial control applications because of its advantages such as functionality, simplicity, applicability, and easy of use. To obtain desired system response in these industrial control applications, parameters of the PID controller should be well tuned by using conventional tuning methods such as Ziegler-Nichols, Cohen-Coon, and Astrom-Hagglund or by means of meta-heuristic optimization algorithms which consider a fitness function including various parameters such as overshoot, settling time, or steady-state error during the optimization process. Particle swarm optimization (PSO) algorithm is often used to tune parameters of PID controller, and studies explaining the parameter tuning process of the PID controller are available in the literature. In this study, effects of PSO algorithm parameters, i.e. inertia weight, acceleration factors, and population size, on parameter tuning process of a PID controller for a second-order process plus delay-time (SOPDT) model are analyzed. To demonstrate these effects, control of a SOPDT model is performed by the tuned controller and system response, transient response characteristics, steady-state error, and error-based performance metrics obtained from system response are provided.

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“…where uk|k-1 and ŵk|k-1 are the predicted likely-hood function and the predicted state function, respectively, given by (14) where h(wk), Qk-1 and Rk are the control law model that is the neural actor, the last error covariance and error output covariance, respectively. wk-1 is the last updated state.…”

Section: The Critic Algorithmmentioning

confidence: 99%

“…(10) to Eq. (14). All parameters will be used in the neural critic algorithm to calculate the local successor state of the control system.…”

Section: The Critic Algorithmmentioning

confidence: 99%

“…Recently, the controller algorithms have been published to increase the performances such as a fuzzy controller [10], a Lyapunov gain PID (LGPID) algorithm [11], a neural network controller [12,13], and a fractional order PID (FOPID) algorithm [14]. One of the successful controllers is the neural network controller based on PID architecture.…”

Section: Introductionmentioning

confidence: 99%

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A Neural Network PID-Like Controller Using a Hybrid of Online Actor-Critic Reinforcement Algorithm with the Square Root Cubature Kalman Filter

Sento¹,

Kitjaidure²

2018

IJIES

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This paper presents a new model of the Neural Network PID-Like controller using an Actor-Critic reinforcement algorithm, called the Neural Network PID-Like controller using an Actor-Critic reinforcement algorithm (NNPID-AC). The proposed NNPID-AC controller is designed to develop the performances and the speed of calculation under the iterative learning algorithm. In the learning algorithm, the critic algorithm receives the reward value and control input to criticize the current state using the action-state value function approximation. Furthermore, instead of applying every available action to predict the local successor state, the algorithm only uses one-step estimation using the fifth degree spherical-radial cubature rule algorithm. To evaluate the proposed NNPID-AC controller, the robot arm MATLAB simulations have been implemented and provide the control system with the load and noise to prove the robustness and fault tolerance, respectively. From the results, the robot arm control system simulation under the control of the proposed NNPID-AC controller can potentially track the error and gives the best responses compared with the other conventional controller either with or without the load and the noise disturbance.

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Speed Control of DC Motor Using PID and FOPID Controllers Based on Differential Evolution and PSO

Cited by 30 publications

References 15 publications

A Hybrid Control Approach Based on the Combination of PID Control with LQR Optimal Control

A Hybrid Control Approach Based on the Combination of PID Control with LQR Optimal Control

Parameter effect analysis of particle swarm optimization algorithm in PID controller design

A Neural Network PID-Like Controller Using a Hybrid of Online Actor-Critic Reinforcement Algorithm with the Square Root Cubature Kalman Filter

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