Control of CSTR using firefly and hybrid firefly-biogeography based optimization (BBFFO) algorithm

Khanduja, Neha; Bhushan, Bharat; Mishra, Sandipan

doi:10.1080/02522667.2020.1809098

Cited by 9 publications

(10 citation statements)

References 9 publications

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“…The maximum and minimum values of the controller parameters was chosen from (Khanduja et al, 2020) , (Ayas & Sahin, 2021). The Figure 8 shows the block diagram of the proposed controller for temperature control in CSTR.…”

Section: Design Of Proposed Controllersmentioning

confidence: 99%

“…The hybrid algorithm based FOPID was analysed in terms of transient, frequency response, and disturbance rejection and error indices to show it's improved performance. (Khanduja et al, 2020)proposed a Fire Fly (FF) algorithm hybridized with Biogeography based Optimization (BBO) to tune the parameters of PID controller applied to the temperature and concentration control of the CSTR. The hybrid algorithm starts with the FF algorithm iterating to find the global optimum and then the BBO executes in order to improve the convergence characteristics of the algorithm.…”

mentioning

confidence: 99%

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A novel FOPID2FF2 controller optimized by a new Opposition based learning (OBL)-Black Widow Optimization (BWO) algorithm: application to CSTR

v¹,

Prabhakaran²,

Thirumarimurugan³

2022

Preprint

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In this study a novel controller FOPID2FF2 which is a FOPID controller with two fractional order filter in the two fractional order derivatives was proposed to improve the performance of the Continuous Stirred Tank Reactor (CSTR) system. The proposed controller has nine independent tunable parameters which are optimized by a new metaheuristic algorithm opposition based learning (OBL)-Black widow optimization (BWO). The OBL-BWO chooses its initial population using the opposition based learning (OBL). The OBL helps in improving the exploration capability of the algorithm, avoids the stagnation in local optima, and improves the quality of the initial population for the BWO. The proposed OBL-BWO was tested on standard benchmark functions using the statistical performance and the non-parametric analysis such as Wilcoxon signed rank test, convergence performance was carried out and compared to other state-of-art algorithms. To verify the superiority of the FOPID2FF2 controller optimized using OBL-BWO; FOPID without any filter, FOPID with a integer order filter (FOPIDF), FOPID with one fractional order filter (FOPIDFF) optimized using the same was proposed. The performance of the controller was analysed using the time domain response, frequency domain response and robustnesscriterion. From the statistical analysis the new OBL-BWO was better compared to the state of art algorithms and was used to optimize the FOPID2FF2 controller. From the performance analysis of the mentioned controllers it was identified that the OBL-BWO optimized FOPID2FF2 controller outperforms the other controllers.

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Section: Design Of Proposed Controllersmentioning

confidence: 99%

mentioning

confidence: 99%

A novel FOPID2FF2 controller optimized by a new Opposition based learning (OBL)-Black Widow Optimization (BWO) algorithm: application to CSTR

v¹,

Prabhakaran²,

Thirumarimurugan³

2022

Preprint

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“…3. Estimate the fitness of the coot follower and leader using the objective function and identify the global best score and best position using (13)(14)(15)(16) until the end criterion is not satisfied 4. Generate the random values, accordingly update the position of coots based on random values using ( 17), ( 19) and ( 21) and calculate coot's fitness.…”

Section: Reactor Temperature Closed Loop Controlmentioning

confidence: 99%

“…15 An approach based on the hybridization of firefly algorithm (FFO) and biogeography-based optimization (BBO) algorithm has been implemented to tune the PID parameters for the CSTR process wherein the efficacy of the hybridization has provided good enhancement in the performance. 16 A comparison between swarm-based algorithm (PSO) and human-based algorithm (teaching-learning-based optimization [TLBO]) has been presented to maintain the concentration and temperature of the reactor in which the performance is enhanced by minimizing the MSE while applying TLBO-PID. 17 The effectiveness of an optimization algorithm named ABC-MIT, which is designed based on model reference adaptive controller (MRAC), has been discussed.…”

Section: Introductionmentioning

confidence: 99%

Coot bird optimization algorithm for the temperature control of continuous stirred tank reactor process

Begum

2022

Asia-Pacific J Chem Eng

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“…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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Control of CSTR using firefly and hybrid firefly-biogeography based optimization (BBFFO) algorithm

Cited by 9 publications

References 9 publications

A novel FOPID2FF2 controller optimized by a new Opposition based learning (OBL)-Black Widow Optimization (BWO) algorithm: application to CSTR

A novel FOPID2FF2 controller optimized by a new Opposition based learning (OBL)-Black Widow Optimization (BWO) algorithm: application to CSTR

Coot bird optimization algorithm for the temperature control of continuous stirred tank reactor process

Improved fractional augmented control strategies for continuously stirred tank reactors

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