Multi-objective optimization and decision making for greenhouse climate control system considering user preference and data clustering

Mahdavian, M.; Sudeng, Sufian; Wattanapongsakorn, Naruemon

doi:10.1007/s10586-017-0772-0

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…For FI-PID controllers, there are four parameters that need to be tuned, including the rate of reaction K, the coefficient of stabilizing effect η, the integral coefficient k i ′ , and the differential coefficient k d ′ [14,15]. Appropriate optimization objectives are important for parameter tuning, and it is a multiobjective optimization problem in general [7][8][9][10][11]. With an optimization model and a corresponding algorithm, an optimization process was introduced to solve this problem in the study.…”

Section: Parameters Tuning Methodmentioning

confidence: 99%

“…Xue et al [6] used particle swarm optimization (PSO), Z-N method, and advanced fireworks (AFW) algorithm to tune a PID controller and found that AFW algorithm was more effective than the others. Mahdavian et al [7] used nondominated sorting genetic algorithm-II (NSGA-II) to optimize the control parameters of a PID controller and found that NSGA-II had more advantages than Z-N method. Behroozsarand and Shafiei [8] also designed NSGA-II to tune a PID controller and obtained better control parameters than the T-L method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…e optimization of parameters is very important for the performance of the PID controller or the FI-PID controller [32,36]. e problem is often multiobjective, and NSGA-II can solve almost any multiobjective optimization problem [7]. Multiple optimal solution sets of control parameters are 2…”

Section: Proposed Approachesmentioning

confidence: 99%

“…Fuzzy controller Process database [7,8,21,51,54]. NSGA-II is a multiobjective optimization method derived from GA with an elite preservation strategy and a fast-nondominated sorting method [7,55,56]. NSGA-II can find optimal solutions based on initial feasible solutions.…”

Section: Immune Controllermentioning

confidence: 99%

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NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System

Chu

Dong

et al. 2020

Mathematical Problems in Engineering

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Automatic train operation (ATO) system is one of the important components in advanced train operation control systems. Ideal controllers are expected for the automatic driving function of ATO systems. Aiming at the intelligence requirements of the systems, an NSGA-II-based parameter tuning method for the fuzzy immune PID (FI-PID) controller and a grey model GM(1,1)-based fuzzy grey immune PID (FGI-PID) controller were proposed. Taking a maglev train’s model as the control object and a velocity-time curve as the input, the feasibility of the parameter tuning method for the FI-PID controller and the applicability of the FI-PID controller and the FGI-PID controller for the ATO system were tested. The results showed that the optimized parameters were ideal, the two controllers all showed good performance on the indicators of traceability and comfort level, and the FGI-PID controller performed better than the FI-PID controller. The results exhibited the effectiveness of the proposed methods.

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Section: Parameters Tuning Methodmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Proposed Approachesmentioning

confidence: 99%

Section: Immune Controllermentioning

confidence: 99%

See 2 more Smart Citations

NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System

Chu

Dong

et al. 2020

Mathematical Problems in Engineering

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“…Mahdavian et al (2016, 2017) used both NSGA-II and post Pareto-optimal pruning algorithm, in an optimisation followed by a decision-making approach. The greenhouse climate control (temperature and lighting) were tuned using the NSGA-II algorithm.…”

Section: Review Of Nabi Metaheuristics For Greenhouse Controlmentioning

confidence: 99%

Review of nature and biologically inspired metaheuristics for greenhouse environment control

Oliveira

Pires

Boaventura‐Cunha

et al. 2020

Transactions of the Institute of Measurement and Control

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A significant number of search and optimisation techniques whose principles seek inspiration from nature and biology phenomena have been proposed in the last decades. These methods have been successfully applied to solve a wide range of engineering problems. This is also the case of greenhouse environment control, which has been incorporating this type of techniques into its design. This paper addresses evolutionary and bio-inspired methods in the context of greenhouse environment control. Algorithm principles for reference techniques are reviewed, namely: simulated annealing, genetic algorithm, differential evolution and particle swarm optimisation. The last three techniques are considered using single and multiple objective formulations. A review of these algorithms within greenhouse environment control applications is presented, considering single and multiple objective problems, as well as their current trends.

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Controller of Fatigue Testing Machine for Aerospace Thermal Connections based on Improved NSGA-III Algorithm

et al. 2021

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Multi-objective optimization and decision making for greenhouse climate control system considering user preference and data clustering

Cited by 14 publications

References 13 publications

NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System

NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System

Review of nature and biologically inspired metaheuristics for greenhouse environment control

Controller of Fatigue Testing Machine for Aerospace Thermal Connections based on Improved NSGA-III Algorithm

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