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 |
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ASJC Scopus subject areas
- Control and Optimization
- Modelling and Simulation
Cite this
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.
2017 25th Mediterranean Conference on Control and Automation, MED 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 54-59 7984095.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
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 -