Optimal fractional order PID controller for magnetic levitation system

Verma, Santosh Kumar; Yadav, Shekhar; Nagar, Shyam Krishna

doi:10.1109/natsys.2015.7489095

Cited by 18 publications

(6 citation statements)

References 11 publications

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“…Then the constancy of the system and dissolute responses are desired. The PID controller of PS and the structure of optimization using the EHO technique [17].…”

Section: Pid Controllermentioning

confidence: 99%

Application of FOPID Design for LFC Using Flower Pollination Algorithm for Three-Area Power System

Sambariya¹,

Nagar²,

Sharma³

2020

ujca

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In power system, frequency is one of the main factor which affects the A.C. system. In order to maintain the performance of the system, frequency need to be controlled on the generation part as well as load side. In this paper, fractional order PID (FOPID) controller is designed for the three-area load frequency control (LFC) in an interrelated power system (PS) in MATLAB simulation. The LFC of generating units have used in frequency control of the dissolute response in the power system. FOPID controller parameters are intended using the Flower Pollination Algorithm (FPA) for the minimize error. The prototype of generating units with FOPID controller is simulated in MATLAB/SIMULINK platform. In this study, simulation researches on a three-area LFC with different generating units are considered. The objective function is the minimization of the Integral Squared Error (ISE) for the optimum strategy of FOPID parameters. These results presented that the FOPID controller was vital to the parameter variations in the considered system. The simulation results specialized much better performance of FOPID controller for LFC in comparison with other FOPID controllers available in the literature. FPA-FOPID Controller in the system as compared to the existing ICA-FOPID and GA-FOPID controller in literature. The result shows that the FPA result outperforms as compared with other results.

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“…Then the constancy of the system and dissolute responses are desired. The PID controller of PS and the structure of optimization using the EHO technique [17].…”

Section: Pid Controllermentioning

confidence: 99%

Application of FOPID Design for LFC Using Flower Pollination Algorithm for Three-Area Power System

Sambariya¹,

Nagar²,

Sharma³

2020

ujca

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“…Naturally, by optimizing their parameters utilizing various approaches, PID controllers has been used in controlling MAGLEV systems as well [20][21][22][23][24][25][26][27]. Several researchers have developed different FOPID controllers to obtain stable levitating and reinforced trajectory tracking control of Maglev system [28][29][30][31][32][33][34][35]. Fuzzy control methods have also been employed in some studies on Maglev systems [36,37].…”

Section: Introductionmentioning

confidence: 99%

Design of a MAGLEV System with PID Based Fuzzy Control Using CS Algorithm

Ataşlar-Ayyıldız

Karahan

2020

Cybernetics and Information Technologies

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The main aim of this study consists of proposing a simple but effective and robust approach for PID type fuzzy controller (Fuzzy-PID) in order to improve the dynamics and stability of a magnetic ball levitation system. The design parameters of the proposed controller are optimally determined based on Cuckoo Search (CS) algorithm. During the optimization, a time domain objective function is used for minimizing the values of common step response characteristics for the optimal selection of the controller parameters. Robustness tests are performed to evaluate the performance of the proposed controller through extensive simulations under load disturbance, parametric variation and changes in references. Moreover, to show the advantage and compare the performance of the proposed controller, the PID and Fractional Order PID (FOPID) controllers tuned with CS are designed. The simulation results and comparisons with the CS based PID and FOPID controllers demonstrate that the CS based Fuzzy-PID controller has superior performance depending on small overshoot, short settling time, fast rise time and minimum steady state error. Compared with the PID and FOPID controller tuned with CS, the simulation results show that the proposed Fuzzy-PID controller tuned with CS outperforms in terms of the accuracy, robustness and the least control effort.

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“…However, the system must be linearized first which has an advantage in its simplicity of the design and implementation rather than a nonlinear controller. The linear controller has been proposed such as PID controller [16]. PID controller is applicable in controlling MLBS, but the system has to be linearized first, and the result of the linearization must not contain an integrator.…”

Section: Introductionmentioning

confidence: 99%

CDM Based Servo State Feedback Controller with Feedback Linearization for Magnetic Levitation Ball System

Ma’arif

Cahyadi

Wahyunggoro

2018

International Journal on Advanced Science, Engineering and Information Technology

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This paper explains the design of Servo State Feedback Controller and Feedback Linearization for Magnetic Levitation Ball System (MLBS). The system uses feedback linearization to change the nonlinear model of magnetic levitation ball system to the linear system. Servo state feedback controller controls the position of the ball. An integrator eliminates the steady state error in servo state feedback controller. The parameter of integral gain and state feedback gains is achieved from the concept of Coefficient Diagram Method (CDM). The CDM requires the controllable canonical form, because of that Matrix Transformation is needed. Hence, feedback linearization is applied first to the MLBS then converted to a controllable form by a transformation matrix. The simulation shows the system can follow the desired position and robust from the position disturbance. The uncertainty parameter of mass, inductance, and resistance of MLBS also being investigated in the simulation. Comparing CDM with another method such as Linear Quadratic Regulator (LQR) and Pole Placement, CDM can give better response, that is no overshoot but a quite fast response. The main advantage of CDM is it has a standard parameter to obtain controller's parameter hence it can avoid trial and error.

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Optimal fractional order PID controller for magnetic levitation system

Cited by 18 publications

References 11 publications

Application of FOPID Design for LFC Using Flower Pollination Algorithm for Three-Area Power System

Application of FOPID Design for LFC Using Flower Pollination Algorithm for Three-Area Power System

Design of a MAGLEV System with PID Based Fuzzy Control Using CS Algorithm

CDM Based Servo State Feedback Controller with Feedback Linearization for Magnetic Levitation Ball System

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