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Design of Soft Computing Based Optimal PI Controller for Greenhouse System

机译：基于软计算的温室系统最优PI控制器设计

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Greenhouse system (GHS) is the worldwide fastest growing phenomenon in agricultural sector. Greenhouse models are essential for improving control efficiencies. The Relative Gain Analysis (RGA) reveals that the GHS control is complex due to 1) high nonlinear interactions between the biological subsystem and the physical subsystem and 2) strong coupling between the process variables such as temperature and humidity. In this paper, a decoupled linear cooling model has been developed using a feedback-feed forward linearization technique. Further, based on the model developed Internal Model Control (IMC) based Proportional Integrator (PI) controller parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to achieve minimum Integral Square Error (ISE). The closed loop control is carried out using the above control schemes for set-point change and disturbance rejection. Finally, closed loop servo and servo-regulatory responses of GHS are compared quantitatively as well as qualitatively. The results implicate that IMC based PI controller using PSO provides better performance than the IMC based PI controller using GA. Also, it is observed that the disturbance introduced in one loop will not affect the other loop due to feedback-feed forward linearization and decoupling. Such a control scheme used for GHS would result in better yield in production of crops such as tomato, lettuce and broccoli.
机译：温室系统（GHS）是全球农业领域增长最快的现象。温室模型对于提高控制效率至关重要。相对增益分析（RGA）表明，由于1）生物子系统和物理子系统之间的高度非线性相互作用以及2）过程变量（如温度和湿度）之间的强耦合，GHS控制非常复杂。在本文中，已使用反馈前馈线性化技术开发了一种解耦线性冷却模型。此外，基于开发的模型，使用遗传算法（GA）和粒子群优化（PSO）对基于内部模型控制（IMC）的比例积分器（PI）控制器参数进行优化，以实现最小积分平方误差（ISE）。使用上述控制方案进行闭环控制，以实现设定点变化和干扰抑制。最后，定量和定性比较GHS的闭环伺服和伺服调节响应。结果表明，使用PSO的基于IMC的PI控制器比使用GA的基于IMC的PI控制器具有更好的性能。同样，可以观察到，由于反馈前馈线性化和去耦，一个环路中引入的干扰不会影响另一环路。这种用于GHS的控制方案将导致诸如番茄，生菜和西兰花的农作物的更好的产量。

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