Benchmark for PID control of Refrigeration Systems based on Vapour Compression ⁎ ⁎This work was supported by MCeI (Grant DPI2015-70973-R).

Bejarano, Guillermo; Alfaya, José A.; Rodríguez, David; Morilla, Fernando; Ortega, Manuel G.

doi:10.1016/j.ifacol.2018.06.144

Cited by 35 publications

(42 citation statements)

References 14 publications

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“…Since Controller 2 shows a better performance compared to controller 1 according to the relative quantitative and qualitative comparisons made in Bejarano et al (2017a), comparison figures are only shown for comparison of RMPC with Controller 2, while the quantitative comparisons with both controllers are shown in Table 2. Table 2, the proposed controller significantly outperforms both discrete decentralized PID and multi-variable PID as it improves the overall index, J, by 80% compared to former and by 70% compared to latter.…”

Section: Resultsmentioning

confidence: 99%

“…Here the PID2018 Benchmark Challenge (Bejarano et al (2017a)) is briefly described from control design perspective to introduce the later-used parameters and variables. The original refrigeration model plus the control system is shown in Fig.1.…”

Section: Pid2018 Benchmark Challengementioning

confidence: 99%

“…The details of implementing the RIOTS based MPC for the benchmark problem are discussed in section 3. The results of the implementation are showed in section 4 and comparisons are made with the results shown in Bejarano et al (2017a). Finally section 5 concludes the article by summarizing the achievements and pointing out the potential future works.…”

Section: Introductionmentioning

confidence: 98%

“…Due to these benefits, a RIOTS based model predictive control enhanced by a conditional integral control is proposed to control the refrigeration system introduced in PID2018 benchmark problem (Bejarano et al (2017a)) and the results are compared with the results shown in the main document. The rest of this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

PID2018 Benchmark Challenge: Model Predictive Control With Conditional Integral Control Using A General Purpose Optimal Control Problem Solver – RIOTS.

Dehghan

Zhao

et al. 2018

IFAC-PapersOnLine

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This paper presents a multi-variable Model Predictive Control (MPC) based controller for the one-staged refrigeration cycle model described in the PID2018 Benchmark Challenge. This model represents a two-input, two-output system with strong nonlinearities and high coupling between its variables. A general purpose optimal control problem (OCP) solver Matlab toolbox called RIOTS is used as the OCP solver for the proposed MPC scheme which allows for straightforward implementation of the method and for solving a wide range of constrained linear and nonlinear optimal control problems. A conditional integral (CI) compensator is embedded in the controller to compensate for the small steady state errors. This method shows significant improvements in performance compared to both discrete decentralized control (C1) and multi-variable PID controller (C2) originally given in PID2018 Benchmark Challenge as a baseline. Our solution is introduced in detail in this paper and our final results using the overall relative index, J, are 0.2 over C1 and 0.3 over C2, respectively. In other words, we achieved 80% improvement over C1 and 70% improvement over C2. We expect to achieve further improvements when some optimized searching efforts are used for MPC and CI parameter tuning.

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Section: Resultsmentioning

confidence: 99%

Section: Pid2018 Benchmark Challengementioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

PID2018 Benchmark Challenge: Model Predictive Control With Conditional Integral Control Using A General Purpose Optimal Control Problem Solver – RIOTS.

Dehghan

Zhao

et al. 2018

IFAC-PapersOnLine

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show abstract

“…Chłodzenie z wykorzystaniem kompresji pary jest obecnie wiodącą technologią w chłodnictwie, zarówno w klimatyzacji, ochładzaniu bądź zamrażaniu [51]. Utrzymywanie niskiej, zadanej temperatury jest kluczowe w wielu aspektach, takich jak komfort ludzi, przechowywanie i transport żywności oraz w procesach przemysłowych.…”

Section: Obiekt Regulacjiunclassified

Comparison of Various Controller Parameters Optimization Strategies for Industrial Processes Using Different Control Performance Assessment Indexes

Bogusz¹,

Rajkowski²,

Domański³

2019

PAR

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Adaptive PID Control Strategy with Online Model Parameter Identification for Dynamic Systems: Design and Simulation

Miranda,

Cajo,

Recalde

et al. 2024

Lecture Notes in Networks and Systems

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Benchmark for PID control of Refrigeration Systems based on Vapour Compression ⁎ ⁎This work was supported by MCeI (Grant DPI2015-70973-R).

Cited by 35 publications

References 14 publications

PID2018 Benchmark Challenge: Model Predictive Control With Conditional Integral Control Using A General Purpose Optimal Control Problem Solver – RIOTS.

PID2018 Benchmark Challenge: Model Predictive Control With Conditional Integral Control Using A General Purpose Optimal Control Problem Solver – RIOTS.

Comparison of Various Controller Parameters Optimization Strategies for Industrial Processes Using Different Control Performance Assessment Indexes

Adaptive PID Control Strategy with Online Model Parameter Identification for Dynamic Systems: Design and Simulation

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