Optimum Design of Fractional Order Pid Controller Using Chaotic Firefly Algorithms for a Control CSTR System

Ravari, Mahdieh Alamdar; Yaghoobi, Mahdi

doi:10.1002/asjc.1836

Cited by 30 publications

(11 citation statements)

References 17 publications

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“…However, in the process of optimizing PID control parameters, the swarm intelligence optimization algorithms have some problems, such as complex parameter setting, limited global optimization capability, weak adaptability, and low precision. The FA is a novel swarm intelligence algorithm, which has been widely used in scientific computing and engineering applications due to its simple algorithm idea, few parameters to be adjusted, and easy implementation of the program 16 , 17 . Specifically, FA shows better performance in many scientific problems, but it still has some limitations, such as slow convergence and the tendency to trap local optimality in complex problems.…”

Section: Introductionmentioning

confidence: 99%

Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

Huang

Tian

Liu

et al. 2022

Sci Rep

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Proportional Integral Derivative (PID) control is the main control method in the process of agricultural water and fertilizer regulation, and its parameter setting directly affects the control effect of water and fertilizer regulation. However, the traditional PID parameters are adjusted manually such as using the critical proportionality method, which is time-consuming and difficult to achieve optimal control effects. To solve the optimal combination of PID control parameters and improve the control effect of water and fertilizer regulation, a partial attraction adaptive firefly algorithm (PAAFA) is proposed in this paper. Specifically, a partial attraction strategy is designed to speed up the convergence of the PAAFA and reduce the oscillation problem at the late stage of the algorithm. In addition, an adaptive inertia weight operator is proposed to balance the global search capability and local search capability of PAAFA and avoid the algorithm from trapping in the local optimum. Subsequently, to test the performance of PAAFA, the algorithm is subjected to a series of simulation experiments and bench tests with the latest methods, i.e., genetic algorithm (GA), Adaptive genetic algorithm (AGA), and firefly algorithm (FA) applied to PID parameter optimization problems. The simulation results demonstrate that the regulation times of the response curve of PAAFA-based PID control are reduced by 22.75%,10.10%and 20.61%, respectively, compared with GA, AGA, and FA. The bench test results show that the PAAFA-based PID control has the smallest relative error, and best control accuracy compared to GA, AGA, and FA, with an average relative error reduction of 3.99, 2.42, and 3.50 percentage points respectively.

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Section: Introductionmentioning

confidence: 99%

Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

Huang

Tian

Liu

et al. 2022

Sci Rep

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“…Similarly, Yu et al [18] analyzed the convergence result for

P

‐type,

P{I}&amp;#x0005E;{\alpha }

‐type, and

D

‐type iterative learning control schemes. Chen et al [19] proved some interesting results on Ulam type stability and ILC of impulsive differential equations with Riemann‐Liouville derivative, and Alamdar Ravari and Yaghoobi [20] improved the firefly chaotic algorithm to optimize the design of fractional‐order PID controller. Recent research in ILC for the fractional‐order system includes ILC of multi‐agent system with Lebesgue‐

p

norm by Lan et al [21] and linear time‐varying predictive problem by Liang et al [22].…”

Section: Introductionmentioning

confidence: 99%

Convergence on iterative learning control of Hilfer fractional impulsive evolution equations

Raghavan

Sukavanam

2022

Asian Journal of Control

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In this paper, impulsive fractional differential equations with Hilfer fractional derivatives of order 0<μ<1$$ 0<\mu <1 $$ and type 0≤ν≤1$$ 0\le \nu \le 1 $$ is considered. Convergence analysis of P$$ P $$‐type and PIμ$$ P{I}^{\mu } $$‐type open‐loop iterative learning scheme is studied in the sense of λ$$ \lambda $$‐norm. Examples are provided to explain the theory developed.

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“…It must be noted that integer order controllers were used in previous works [2–22]. Ravari and Yaghoobi [23] have obtained the optimal parameters of fractional‐order PID controllers using Chaotic firefly algorithm for a CSTR system. Khanduja et al [24] have obtained the optimal settings of PID controller using firefly and biogeography‐based optimization algorithm for a CSTR.…”

Section: Introductionmentioning

confidence: 99%

Improved fractional augmented control strategies for continuously stirred tank reactors

Mukherjee

Raja

Kundu

et al. 2022

Asian Journal of Control

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Controlling concentration and temperature of continuously stirred tank reactor (CSTR) is an extremely challenging task in chemical process industries. This is because conventional unity feedback schemes do not guarantee stable operating conditions for the CSTR process. Therefore, fractional calculus is augmented with multi‐loop control to achieve enhanced stability and closed‐loop performance than unity feedback structure in this work. Accordingly, three different fractional‐order‐based novel multi‐loop control structures are proposed based on time domain analysis. In two of the proposed strategies, fractional‐order PID controller (FOPID) and internal model control (IMC)‐based FOPID controller with fractional filter are used in the inner‐loop. Moreover, fractional‐order‐based Lyapunov stability rule of model reference adaptive control (MRAC) is used in outer‐loops for both of the above mentioned methods. Finally, an advanced multi‐loop predictor with FOPD controller in the inner‐loop and FOIMC controller in the outer‐loop is also proposed. In this work, fractional‐order, filter parameters, and FOPID settings are obtained by minimizing multi‐objective functions using modified particle swarm optimization (PSO) algorithm. Closed‐loop responses and control efforts of the proposed control strategies are compared with that of FO‐Lyapunov‐based MRAC scheme. Quantitative performance analysis is also carried out on all proposed methods based on error metrics and total variation of the control signal.

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Optimum Design of Fractional Order Pid Controller Using Chaotic Firefly Algorithms for a Control CSTR System

Cited by 30 publications

References 17 publications

Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

Convergence on iterative learning control of Hilfer fractional impulsive evolution equations

Improved fractional augmented control strategies for continuously stirred tank reactors

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