Multi-Objective Control Optimization for Greenhouse Environment Using Evolutionary Algorithms

This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an Evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production.

[1]  H. Madsen,et al.  Identification of a Linear Continuous Time Stochastic Model of the Heat Dynamics of a Greenhouse , 1998 .

[2]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[3]  J. Balmat.  Fuzzy Logic to the Identification and the Command of the Multidimensional Systems (invited Paper) , 2003 .

[4]  Flávio Neves,et al.  PID control of MIMO process based on rank niching genetic algorithm , 2008, Applied Intelligence.

[5]  N. Bennis,et al.  Greenhouse climate modelling and robust control , 2008 .

[6]  J. Boaventura Cunha,et al.  Greenhouse air temperature predictive control using the particle swarm optimisation algorithm , 2005 .

[7]  Lihong Xu,et al.  A Compatible Control Algorithm for Greenhouse Environment Control Based on MOCC Strategy , 2011, Sensors.

[8]  Miroslav Krstic,et al.  PID Tuning Using Extremum Seeking , 2005 .

[9]  Hartmut Pohlheim,et al.  Optimal control of greenhouse climate using real-world weather data and evolutionary algorithms , 1999 .

[10]  Konstantinos G. Arvanitis,et al.  A nonlinear feedback technique for greenhouse environmental control , 2003 .

[11]  Jean-François Balmat,et al.  Optimized fuzzy control of a greenhouse , 2002, Fuzzy Sets Syst..

[12]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[13]  M. K. Ghosal,et al.  Modeling and experimental validation of a greenhouse with evaporative cooling by moving water film over external shade cloth , 2003 .

[14]  Leandro dos Santos Coelho,et al.  A MULTIOBJECTIVE GENETIC ALGORITHM APPLIED TO MULTIVARIABLE CONTROL OPTIMIZATION , 2008 .

[15]  Xavier Blasco Ferragud,et al.  Multiobjective Tuning of Robust GPC Controllers using Evolutionary Algorithms , 2008, IJCCI.

[16]  José Boaventura Cunha,et al.  GREENHOUSE CLIMATE MODELS: AN OVERVIEW , 2003 .

[17]  Wei-Der Chang,et al.  A multi-crossover genetic approach to multivariable PID controllers tuning , 2007, Expert Syst. Appl..

[18]  M. Krstic,et al.  PID tuning using extremum seeking: online, model-free performance optimization , 2006, IEEE Control Systems.

[19]  Eduardo F. Camacho,et al.  Constrained predictive control of a greenhouse , 2000 .

[20]  Konstantinos G. Arvanitis,et al.  Environmental control for plants on Earth and in space , 2001 .

[21]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[22]  Fathi Fourati,et al.  A greenhouse control with feed-forward and recurrent neural networks , 2007, Simul. Model. Pract. Theory.

[23]  Chengwei Ma,et al.  Modeling greenhouse air humidity by means of artificial neural network and principal component analysis , 2010 .

[24]  António E. Ruano,et al.  A greenhouse climate multivariable predictive controller. , 2000 .

[25]  Konstantinos G. Arvanitis,et al.  Multirate adaptive temperature control of greenhouses , 2000 .

[26]  Rodrigo Castañeda-Miranda,et al.  Fuzzy Greenhouse Climate Control System based on a Field Programmable Gate Array , 2006 .

[27]  J. Ríos-Moreno,et al.  Greenhouse energy consumption prediction using neural networks models , 2009 .

[28]  C. Schugurensky,et al.  Optimal greenhouse control of tomato-seedling crops , 2006 .