水培养系统的有效性在很大程度上取决于为鱼类和植物提供的栖息地。作为aquaponics不可或缺的组成部分,水培栽培大大受益于温室的受控环境。在这种环境下,温度等因素,二氧化碳水平,湿度,湿度和光线可以仔细调整,以最大限度地提高植物的生长和发育。这种精确的监管确保了理想的生长环境,促进植物的繁荣,并为水生生态系统的整体成功做出贡献。本研究提出了一种水培温室系统的控制方法。它旨在保持温室气候参数(温度,CO2浓度,和湿度)处于理想水平。所提出的控制策略是一个两层机制,其中第一层提出了一个优化框架,使用粒子群优化(PSO)算法给出控制器的设定点,并且第二层演示了约束离散模型预测控制(CDMPC)策略以维持从优化层接收的期望轨迹。为了验证使用PSO获得的结果,这项研究结合了遗传算法(GA),并在比较中评估了它们的性能。鉴于两种算法的计算效率相似且计算时间短,采用粒子群优化(PSO)确定的最优值作为设定值。两个性能标准,相对平均偏差(RAD)和平均相对偏差(MRD),推导了所提出的CDMPC控制器在外部干扰下的跟踪性能。还提供了所提出的CDMPC与PI控制器的比较。根据比较结果,我们提出的CDMPC性能优于具有较低RAD值的PI控制器(温度,1.1315;CO2浓度,0.9225;湿度,2.547)和MRD值(温度,0.315;CO2浓度,0.25;湿度:1.013)。该控制器通过其强大的控制性能被验证是有效的,以稳健性为重点,高效的设定点跟踪,和足够的干扰抑制。这种新颖的方法可能被证明是开发环境控制策略的有用技术,可用于潜在提高水生温室系统的生产率。最大限度地提高盈利能力,减少劳动力需求。通过保持最佳条件,它可以增强生态系统的健康,提高产量,精简操作,为更高的系统性能和可持续性铺平道路。
The effectiveness of an aquaponic system significantly relies on the habitat provided for both the fish and plants. As an integral component of aquaponics, hydroponic cultivation benefits greatly from the controlled environment of a greenhouse. Within this environment, factors such as temperature, carbon dioxide levels, humidity, and light can be carefully adjusted to maximize plant growth and development. This precise regulation ensures an ideal growing environment, fostering the flourishing of plants and contributing to the overall success of the aquaponic ecosystem. This study presented a control approach for an aquaponic greenhouse system. It aims to keep the greenhouse climate parameters (temperature, CO2 concentration, and humidity) at their ideal levels. The proposed control strategy is a two-layered mechanism in which the first layer presents an optimization framework using particle swarm optimization (PSO) algorithm to give the setpoints for the controller, and the second layer demonstrates a constrained discrete model predictive control (CDMPC) strategy to maintain the desired trajectories received from the optimization layer. To validate the results obtained using PSO, this study incorporates genetic algorithms (GA) and assesses their performance in comparison. Given similar computational efficiency and low computational time for both algorithms, the optimal values identified by particle swarm optimization (PSO) are adopted as the setpoints. Two performance criteria, relative average deviation (RAD) and mean relative deviation (MRD), are derived to evaluate the tracking performance of the proposed CDMPC controller under external disturbances. A comparison of the proposed CDMPC with the PI controller is also offered. According to the comparison results, our proposed CDMPC performs better than the PI controller with lower RAD values (temperature, 1.1315; CO2 concentration, 0.9225; humidity, 2.547) and MRD values (temperature, 0.315; CO2 concentration, 0.25; humidity: 1.013). The controller is validated to be efficient by its strong control performance, highlighted by robustness, efficient setpoint tracking, and adequate disturbance rejection. This novel approach might prove to be a useful technique for developing environmental control strategies that can be used for potentially boosting production rates of aquaponic greenhouse systems, maximizing profitability, and reducing labor needs. By maintaining optimal conditions, it can enhance ecosystem health, improve yields, and streamline operations, paving the way for greater system performance and sustainability.