Receding horizon based greenhouse air temperature control using grey wolf optimization algorithm

Abstract

This work describes the receding horizon control of an inside air temperature of greenhouse using the grey wolf optimization algorithms based on constraints on manipulative variables. Its performance being compared with that of Genetic algorithm and Particle swarm optimization. Classical control methods are difficult to implement for greenhouse air temperature control problem because of high nonlinear and complex nature of system. Meta-heuristic based algorithms are implemented because of their easy to understand, appropriate for different types of problems, stochastic nature helps in evading local extrema and derivative dependency. The results of GWO clearly shown better power saving & smoother control when compared with already implemented GA & PSO meta-heuristic algorithms for greenhouse air temperature control.