Tuning of optimal fractional-order<i>PID</i>controller using an artificial bee colony algorithm

Kesarkar, Ameya Anil; Selvaganesan, N.

doi:10.1080/21642583.2014.987480

Cited by 68 publications

(36 citation statements)

References 16 publications

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“…PID controller tuned at an exacting operating point will not offer a rewarding response when, there subsists divergence in the method operating series [2]. Therefore, soft computing supported PID controller tuning is extensively anticipated by the researchers throughout the previous few decades [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Flower Pollination Optimization Based PID Controller Tuning Scheme With Robust Stabilization of Gain Scheduled CSTR

Sudha

Subbulekshmi

2020

1790-5044

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In the area of chemical and control engineering, Continuous Stirred Tank Reactor (CSTR) has been deliberated as a most important topic in temperature process control and it is a extremely nonlinear process, which displays stability only in definite regions and unsteadiness in other regions. In this CSTR, Proportional Integral Derivative (PID) controller has been used to minimize the Integral SquareError (ISE) by adjustment of control parameters like k_p,k_i and k_d.But, this conventional controller has high computational complexity, so to reduce this, the optimization based tool has been designed for PID and it was conceptually simple.Earlier, the Genetic Algorithm (GA) optimization has been focused, but it was not guarantee for good fitness and global optimal solution. So to overcome this, here, Flower Pollination based Global Optimization Algorithm (FPGOA) with Gain scheduling based PID controller is proposed for CSTR. When a plant is exposing to big changes in its operational state, the Gain scheduling has been used. The FPGOA is used for tuning the parameters of PID controller for each region to reduce the ISE. The fuzzy gain scheduler has been combined with multiple local linear PID controllers to check the stability of loop for entire regions with various levels of temperatures. This scheduling is an exceptional form of fuzzy control, which customs linguistic rules and fuzzy perceptive to establish the controller factor transition strategy for the dynamic plant dependent on high changes in its operational state to improve the optimization. MATLAB results demonstrate that the feasibility of using the proposed PID controller compared than existing controller, and it shows the FPGOA attained better results, due to the effectual fitness and global optimal solution for the dynamical nonlinear control of CSTR. The response curves of these systems has been generated and compared with existing schemes.

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Section: Introductionmentioning

confidence: 99%

“…as shown in Eqns(1) and(2)also and in Eqn(3). Linearization of the non-linear phase is performed in the , operating point.…”

mentioning

confidence: 99%

Flower Pollination Optimization Based PID Controller Tuning Scheme With Robust Stabilization of Gain Scheduled CSTR

Sudha

Subbulekshmi

2020

1790-5044

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“…It was observed that POS tuned PID controller improves ride comfort and vehicle stability. Kesarkar and Selvaganesan (2015) designed fractional order PID controller using artificial bee colony algorithm with objective functions such as integral absolute error, integral square error, and integral time absolute error implemented to a multi-modal complex optimization problem. The authors observed that the results were promising as compared to the conventional PID method.…”

Section: Introductionmentioning

confidence: 99%

GA-based multi-objective optimization of active nonlinear quarter car suspension system—PID and fuzzy logic control

Nagarkar¹,

Bhalerao²,

Patil³

et al. 2018

Int J Mech Mater Eng

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Background: The primary function of a suspension system is to isolate the vehicle body from road irregularities thus providing the ride comfort and to support the vehicle and provide stability. The suspension system has to perform conflicting requirements; hence, a passive suspension system is replaced by the active suspension system which can supply force to the system. Active suspension supplies energy to respond dynamically and achieve relative motion between body and wheel and thus improves the performance of suspension system. Methods: This study presents modelling and control optimization of a nonlinear quarter car suspension system. A mathematical model of nonlinear quarter car is developed and simulated for control and optimization in Matlab/ Simulink® environment. Class C road is selected as input road condition with the vehicle traveling at 80 kmph. Active control of the suspension system is achieved using FLC and PID control actions. Instead of guessing and or trial and error method, genetic algorithm (GA)-based optimization algorithm is implemented to tune PID parameters and FLC membership functions' range and scaling factors. The optimization function is modeled as a multi-objective problem comprising of frequency weighted RMS seat acceleration, Vibration dose value (VDV), RMS suspension space, and RMS tyre deflection. ISO 2631-1 standard is adopted to assess the ride and health criterion. Results: The nonlinear quarter model along with the controller is modeled and simulated and optimized in a Matlab/Simulink environment. It is observed that GA-optimized FLC gives better control as compared to PID and passive suspension system. Further simulations are validated on suspension system with seat and human model. Parameters under observation are frequency-weighted RMS head acceleration, VDV at the head, crest factor, and amplitude ratios at the head and upper torso (AR_h and AR_ut). Simulation results are presented in time and frequency domain. Conclusion: Simulation results show that GA-based FLC and PID controller gives better ride comfort and health criterion by reducing RMS head acceleration, VDV at the head, CF, and AR_h and AR_ut over passive suspension system.

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“…The recent developments in the field of fractional calculus have provided an opportunity to design an alternative controller with improved perspectives. The fractional calculus theory enables the inclusion of fractional powers on integral (λ) and derivative (μ) terms against the traditional integer terms, and they ensure the modelling of natural processes as more realistic (Chen, Yuan, Tian, & Ji, ; Kesarkar & Selvaganesan, ). The inclusion of additional terms λ and μ offers two extra degrees of freedom, which in turn provide higher flexibility during the design of controller.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective robust fuzzy fractional order proportional–integral–derivative controller design for nonlinear hydraulic turbine governing system using evolutionary computation techniques

Piraisoodi

Siluvairaj

Kappuva³

2018

Expert Systems

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The present paper proposes a novel multi‐objective robust fuzzy fractional order proportional–integral–derivative (PID) controller design for nonlinear hydraulic turbine governing system (HTGS) by using evolutionary computation techniques. The fuzzy fractional order PID (FOPID) controller takes closed loop error and its fractional derivative as inputs and performs fuzzy logic operations. Then, it produces the output through the fractional order integrator. The predominant advantages of the proposed controller are its capability to handle complex nonlinear processes like HTGS in heuristic manner, due to fuzzy incorporation and extending an additional flexibility in tuning the order of fractional derivative/integral terms to enhance the closed loop performance. The present work formulates the optimal tuning problem of fuzzy FOPID controller for HTGS as a multi‐objective one instead of a traditional single‐objective one towards satisfying the conflicting criteria such as less settling time and minimum damped oscillations simultaneously to ensure the improved dynamic performance of HTGS. The multi‐objective evolutionary computation techniques such as non‐dominated sorting genetic algorithm‐II (NSGA‐II) and modified NSGA‐II have been utilized to find the optimal input/output scaling factors of the proposed controller along with the order of fractional derivative/integral terms for HTGS system under no load and load turbulence conditions. The performance of the proposed fuzzy FOPID controller is compared with PID and FOPID controllers. The simulations have been conducted to test the tracking capability and robust performance of HTGS during dynamic set point changes for a wide range of operating conditions and model parameter variations, respectively. The proposed robust fuzzy FOPID controller has ensured better fitness value and better time domain specifications than the PID and FOPID controllers, during optimization towards satisfying the conflicting objectives such as less settling time and minimum damped oscillations simultaneously, due to its special inheritance of fuzzy and FOPID properties.

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Tuning of optimal fractional-orderPIDcontroller using an artificial bee colony algorithm

Cited by 68 publications

References 16 publications

Flower Pollination Optimization Based PID Controller Tuning Scheme With Robust Stabilization of Gain Scheduled CSTR

Flower Pollination Optimization Based PID Controller Tuning Scheme With Robust Stabilization of Gain Scheduled CSTR

GA-based multi-objective optimization of active nonlinear quarter car suspension system—PID and fuzzy logic control

Multi‐objective robust fuzzy fractional order proportional–integral–derivative controller design for nonlinear hydraulic turbine governing system using evolutionary computation techniques

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