Multi-objective re-tuning of nonlinear model for degrading greenhouse

Abstract

Greenhouse cultivation has gained a lot of growers’ interest because of higher yield achieved by maintaining desired environment and provision for additional protective measures. Greenhouses degrade with time, which is caused by mechanical deterioration of construction elements and equipment, as well as by severe climate conditions. These factors are responsible for the gradual change of greenhouse parameter. Therefore, parameters are commonly re-tuned periodically to ensure desired model accuracy for realistic control action generation. The limitation of conventional parameter estimation is that while minimizing the deviation from sensors’ measurements, it ignores the fact that parameters change slowly, and hence, it can overfit to noisy measurements. This paper presents a re-tuning strategy , which utilizes the prior knowledge contained in the previously tuned parameters. For this, the strategy incorporates the deviation from the prior tuned parameters as an objective along with deviation from the sensor measurements, which may be beneficial in overcoming the overfitting of parameters. Multi-objective algorithms were considered for re-tuning , as objectives are of conflicting nature. Multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm (NSGA)-II were used to obtain the Pareto fronts for multiple runs and different population sizes. The Pareto fronts obtained by NSGA-II were found favourable compared to MOPSO regarding the approximation quality of the Pareto front. The selection of the desired parameter set was made based on the balance amongst objectives.