### Abstract

For the simplest case with a single point measurement and a single heater under operational constraint on heater, a model predictive control [1] was already proposed and applied to evaluate in a real field test. The obtained results indicated the effectiveness of the control although searching for an optimal solution was essentially based on the brute-force attack. It is not feasible to extend the same approach to determine an optimal solution in the case of multipoint temperature measurements and multipoint heaters for calculations in a real time operation. For his problem, a genetic algorithm was applied as a solution to regulate greenhouse temperature for the above-mentioned problem. Model predictive control performance strongly depended on the precision of the mathematical model of greenhouse temperature dynamics, and thus, a recursive least square identification strategy was also introduced to update parameters in the predictor on line. The control results in a laboratory demonstration greenhouse indicated that the proposed method performed well and showed an average decrease of 20-35% in the temperature error. Furthermore, the temperature difference also decreased by an average of 36%.

Language | English |
---|---|

Title of host publication | 2017 25th Mediterranean Conference on Control and Automation, MED 2017 |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 54-59 |

Number of pages | 6 |

ISBN (Electronic) | 9781509045334 |

DOIs | |

State | Published - 2017 Jul 18 |

Event | 25th Mediterranean Conference on Control and Automation, MED 2017 - Valletta, Malta Duration: 2017 Jul 3 → 2017 Jul 6 |

### Other

Other | 25th Mediterranean Conference on Control and Automation, MED 2017 |
---|---|

Country | Malta |

City | Valletta |

Period | 17/7/3 → 17/7/6 |

### Fingerprint

### ASJC Scopus subject areas

- Control and Optimization
- Modelling and Simulation

### Cite this

*2017 25th Mediterranean Conference on Control and Automation, MED 2017*(pp. 54-59). [7984095] Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/MED.2017.7984095

**Multipoint-measurement multipoint-heating greenhouse temperature control with wooden pellet fuel using an adaptive model predictive control approach with a genetic algorithm.** / Ito, Kazuhisa; Hara, Yuma.

Research output: Research › Conference contribution

*2017 25th Mediterranean Conference on Control and Automation, MED 2017.*, 7984095, Institute of Electrical and Electronics Engineers Inc., pp. 54-59, 25th Mediterranean Conference on Control and Automation, MED 2017, Valletta, Malta, 17/7/3. DOI: 10.1109/MED.2017.7984095

}

TY - CHAP

T1 - Multipoint-measurement multipoint-heating greenhouse temperature control with wooden pellet fuel using an adaptive model predictive control approach with a genetic algorithm

AU - Ito,Kazuhisa

AU - Hara,Yuma

PY - 2017/7/18

Y1 - 2017/7/18

N2 - For the simplest case with a single point measurement and a single heater under operational constraint on heater, a model predictive control [1] was already proposed and applied to evaluate in a real field test. The obtained results indicated the effectiveness of the control although searching for an optimal solution was essentially based on the brute-force attack. It is not feasible to extend the same approach to determine an optimal solution in the case of multipoint temperature measurements and multipoint heaters for calculations in a real time operation. For his problem, a genetic algorithm was applied as a solution to regulate greenhouse temperature for the above-mentioned problem. Model predictive control performance strongly depended on the precision of the mathematical model of greenhouse temperature dynamics, and thus, a recursive least square identification strategy was also introduced to update parameters in the predictor on line. The control results in a laboratory demonstration greenhouse indicated that the proposed method performed well and showed an average decrease of 20-35% in the temperature error. Furthermore, the temperature difference also decreased by an average of 36%.

AB - For the simplest case with a single point measurement and a single heater under operational constraint on heater, a model predictive control [1] was already proposed and applied to evaluate in a real field test. The obtained results indicated the effectiveness of the control although searching for an optimal solution was essentially based on the brute-force attack. It is not feasible to extend the same approach to determine an optimal solution in the case of multipoint temperature measurements and multipoint heaters for calculations in a real time operation. For his problem, a genetic algorithm was applied as a solution to regulate greenhouse temperature for the above-mentioned problem. Model predictive control performance strongly depended on the precision of the mathematical model of greenhouse temperature dynamics, and thus, a recursive least square identification strategy was also introduced to update parameters in the predictor on line. The control results in a laboratory demonstration greenhouse indicated that the proposed method performed well and showed an average decrease of 20-35% in the temperature error. Furthermore, the temperature difference also decreased by an average of 36%.

UR - http://www.scopus.com/inward/record.url?scp=85028507428&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85028507428&partnerID=8YFLogxK

U2 - 10.1109/MED.2017.7984095

DO - 10.1109/MED.2017.7984095

M3 - Conference contribution

SP - 54

EP - 59

BT - 2017 25th Mediterranean Conference on Control and Automation, MED 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -