Multi-objective control optimization for greenhouse environment using evolutionary algorithms.

Area of Science:

• Agricultural Engineering
• Control Systems Engineering
• Computational Intelligence

Background:

• Greenhouse climate control is complex due to nonlinear thermodynamic laws and interacting variables.
• Optimizing Proportional Integral and Derivative (PID) controller parameters is crucial for effective system performance.
• Existing tuning methods may struggle with conflicting performance criteria and system nonlinearities.

Purpose of the Study:

• To investigate an Evolutionary Algorithm (EA) for tuning PID controller parameters in greenhouse climate control.
• To achieve optimal static-dynamic performance and smooth control processes.
• To address challenges posed by nonlinearities and conflicting performance criteria.

Main Methods:

• Formulation of a nonlinear thermodynamic model for greenhouse climate variables.
• Application of an EA for multi-objective optimization of PID controller gains.
• Minimization of integrated time square error (ITSE) and control increment in simulation experiments.

Main Results:

• The EA-based tuning achieved significant improvements in step responses, including reduced overshoot, faster settling times, and minimized rise time and steady-state error.
• The proposed method demonstrated effectiveness in handling systems with strong variable interactions and nonlinearities.
• Successful application to conflicting performance criteria, indicating robustness.

Conclusions:

• The developed EA-based tuning method is a highly effective and promising approach for complex greenhouse climate control systems.
• Multi-objective optimization algorithms offer a robust solution for tuning controllers in challenging agricultural environments.
• This technique enhances control performance, ensuring stable and efficient greenhouse operations.