Engineering-oriented dynamic optimal control of a greenhouse environment using an improved genetic algorithm with engineering constraint rules

Abstract

To effectively and practically solve the dynamic economic optimal control problem in greenhouse environments, an improved genetic algorithm with engineering constraint rules (R-GA) is proposed. Based on a dynamic greenhouse-crop model and control vector parameterization (CVP) method to discretize the control variables, the economic optimal control problem is transformed into a nonlinear programming (NLP) problem with finite-dimension parameters, and then R-GA is used to effectively solve the NLP problem. Three components were investigated, such as a smooth penalty function to deal with state variable path constraints, engineering constraint rules to improve the optimization performance and the algorithm feasibility, and the number of collocation points (Nc) to meet the actual control laws. The simulation results demonstrate that the proposed approach greatly improves the effectiveness and feasibility to solve the dynamic optimal control problem in greenhouse environments.