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Related Experiment Video

Updated: May 20, 2026

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

Nonlinear adaptive PID control for greenhouse environment based on RBF network.

Songwei Zeng¹, Haigen Hu, Lihong Xu

¹School of Information Engineering, Zhejiang Agriculture & Forestry University, Lin'an 311300, China. zsw@zafu.edu.cn

Sensors (Basel, Switzerland)

|July 11, 2012

Summary

This summary is machine-generated.

This study introduces an adaptive Neuro-PID control strategy for greenhouse climate control. It effectively tunes Proportional, Integral, Derivative (PID) gains online, demonstrating robust performance for complex environmental systems.

Keywords:

Genetic Algorithm (GA)Radial Basis Function (RBF)greenhouse environment control neuro-PID control nonlinear adaptive control

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Published on: November 24, 2021

Area of Science:

Agricultural Engineering
Control Systems
Artificial Intelligence

Background:

Greenhouse climate control is complex due to nonlinear dynamics and multiple interacting variables.
Conventional Proportional, Integral, Derivative (PID) controllers often require manual tuning, which is challenging for dynamic systems.
Adaptive control strategies are needed to maintain optimal environmental conditions for plant growth.

Purpose of the Study:

To develop and validate a hybrid control strategy for greenhouse climate control.
To implement an adaptive online tuning mechanism for PID controller gains using a Radial Basis Function (RBF) network.
To compare the performance of the proposed adaptive Neuro-PID controller against an offline Genetic Algorithm (GA) tuned controller.

Main Methods:

Formulation of a nonlinear mathematical model based on conservation laws of enthalpy and matter.
Integration of a Radial Basis Function (RBF) network for online, adaptive tuning of PID controller parameters.
Simulation-based validation including set-point tracking and disturbance rejection tests.

Main Results:

The proposed Neuro-PID control scheme demonstrated strong adaptability and robustness in simulations.
The adaptive online tuning method achieved satisfactory control performance for the nonlinear greenhouse system.
Real-time performance was validated, showing effectiveness compared to offline Genetic Algorithm tuning.

Conclusions:

The hybrid RBF-PID control strategy offers an effective solution for complex greenhouse climate control.
The adaptive online tuning capability provides significant advantages over traditional methods.
This approach offers a valuable reference for practical environmental control in greenhouse production.