Design Method Of Greenhouse Production Hierarchical Control System

The greenhouse production automation is the development direction of modern intelligent agriculture.The modelling and controlling of greenhouse climate system is the basis of industrialization and automation.Aiming at the characteristic of dual time scale during the process of greenhouse production,considering that the greenhouse system model has the characteristics of nonlinearity,strong coupling,uncertainty and time variance,adaptive system model and hierarchical control system was studied.This work can provide scientific theoretical basis and technical support in improving the economic benefit in the actual greenhouse automatic cultivation production.Main research contents include the following five parts:[1]The greenhouse crop-climate system adaptive physical model was studied.Taking system model having some parameters with time variance and uncertainty into consideration,Unscented Kalman Filter was applied to construct the adaptive system model.Both the parameters and states in the system model can be estimated online.The simulation results show that the adaptive model can effectively predictive the future state in the system with time-varying parameters.[2]The greenhouse hierarchical control system structure was investigated.To solve the problem of system dual time scale,singular perturbation theory was used to decompose and reduce the system model.The decomposition can decouple the states of crop and climate,obtaining model with large time scale in the crop layer and model with small time scale in the climate layer.Exploiting the decomposition results of system model,hierarchical control system structure was proposed.The structure contained optimal controller in the crop layer and the tracking controller in the climate layer,combining the functions of acquiring and tracking the climate set values.[3]The closed-loop optimal controller in the crop layer was proposed.Using the Unscented Kalman Filter to predict the crop state and realize the feedback control,under the constraint of both the condition of indoor climate and actuator state,the closed-loop optimal controller was designed based on receding horizon optimization and the optimal climate set values were obtained,in order to optimize the economic performance.The modified conjugate gradient method was applied to solve the formed constrained optimization problem.The simulation results show that the designed optimal controller can acquire the valid climate set values within various constraints.[4]The robust predictive controller based on accurate feedback linearization was proposed.Nonlinear feedback principle was used to linearize the system accurately and decouple the climate states.On the basis of the obtained linearized model,the min-max robust predictive controller was proposed,which can optimize the performance in the worst noise.The modified particle swarm optimization algorithm was applied to solve the problem of the formed nonlinear programming with constraints and the optimal inputs were acquired.The simulation results show that,with the presence of disturbance,compared the common model predictive controller with feedback linerization,the proposed controller can offer more valid control inputs and better tracking performance.[5]Greenhouse climate remote measurement and control system was developed,in order to offer a platform for control algorithm test.The system contained three parts as VB user application layer,Web server layer and bottom sensor and the input-output control circuit board layer.The test results show that the system can obtain the greenhouse climate data and actuator states and control the actuator states remotely and that the system can run stably.