The difficulty of the greenhouse climate control is the great uncertainties of the weather and greenhouse climate. Although many control approaches have been proposed to solve this problem, the structures of the controllers are usually complex, and the reliability of the controllers is usually not good in practice. Therefore, to improve the reliability of the controllers, this paper proposes a parameter self-tuning PID control approach (STPID). In this PID control scheme, three environmental outputs including the inside temperature, humidity and CO 2 concentration are firstly transformed into four equivalent unit outputs, and the equivalent outputs are controlled by four PID controllers respectively. Thus, each PID controller and the corresponding equivalent output construct a single closed-loop system, and the original system can be decoupled. To ensure the desired control performance, the Levenberg-Marquart (LM) algorithm is introduced to tune the PID parameters. Based on the real weather data, a simulation was performed to illustrate the effectiveness of the proposed method, and a comparison study was done to observe the performance of the proposed method. The results indicate that control performance is good.
Summary
Greenhouse production must create a suitable growth environment for its crop to improve yield while minimizing the energy used to maintain the greenhouse environment, thereby reducing production cost. To this end, this paper develops a nearly optimal control approach based on adaptive dynamic programming. In this method, 3 neural networks are used to estimate the value function and control policy and to compensate for the unmodeled dynamics of the greenhouse climate. Taking into account the greenhouse system typically being an overactuated system, the total control efforts for heat, fog, and CO2 are considered as the virtual control inputs to be generated by the optimal controller. To obtain the real control inputs, a control allocation technique is introduced to distribute the virtual control inputs to the actuators. Finally, Lyapunov stability analysis is performed to derive the update law of the neural networks to ensure the asymptotic convergence of the closed‐loop system, and simulation is carried out to illustrate the effectiveness and control performance of the proposed approach.
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