Constrained predictive control of a greenhouse

Piñón, Sandra; Camacho, Eduardo F.; Kuchen, B.; Peña, M.

doi:10.1016/j.compag.2005.08.007

Cited by 74 publications

(12 citation statements)

References 3 publications

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“…In [88], the cost function aimed to maintain the divergence of temperature and humidity in respect to a range of suitable values in order to minimize energy and water consumption. Other approaches proposed to track the indoor temperature in respect to a reference trajectory operating on the control variables related to heating and ventilation by different methodologies such as genetic algorithms (GA) [92], particle swarm optimization algorithm (PSO) [93,94], sequential quadratic programming [95], or a combination of MPC and a feedback linearization technique [96]. The authors in [93] presented a particle swarm optimization framework based robust MPC control scheme for greenhouse temperature systems.…”

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confidence: 99%

Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption

et al. 2020

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Modern agriculture represents an economic sector that can mainly benefit from technology innovation according to the principles suggested by Industry 4.0 for smart farming systems. Greenhouse industry is significantly becoming more and more technological and automatized to improve the quality and efficiency of crop production. Smart greenhouses are equipped with forefront IoT- and ICT-based monitoring and control systems. New remote sensors, devices, networking communication, and control strategies can make available real-time information about crop health, soil, temperature, humidity, and other indoor parameters. Energy efficiency plays a key role in this context, as a fundamental path towards sustainability of the production. This paper is a review of the precision and sustainable agriculture approaches focusing on the current advance technological solution to monitor, track, and control greenhouse systems to enhance production in a more sustainable way. Thus, we compared and analyzed traditional versus model predictive control methods with the aim to enhance indoor microclimate condition management under an energy-saving approach. We also reviewed applications of sustainable approaches to reach nearly zero energy consumption, while achieving nearly zero water and pesticide use.

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confidence: 99%

Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption

et al. 2020

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“…In order to overcome the computational complexity inherent to developing a control strategy based on non-linear models, Reference [20] proposed a model predictive control strategy based on feedback linearization, i.e., approximating the model with linear models valid over the current operation point, leading to an easier implementation of the MPC algorithm and a significant reduction of the computational burden involved in solving the non-linear optimization problem. The method was evaluated in a simulation environment using a discrete time model, extracted from several datasets obtained in the field.…”

Section: Related Workmentioning

confidence: 99%

Improving the Ambient Temperature Control Performance in Smart Homes and Buildings

Fontes

Antão

Mota

et al. 2021

Sensors

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Currently, it is becoming increasingly common to find numerous electronic devices installed in office and residential spaces as part of building automation solutions. These devices provide a rich set of data related to the inside and outside environment, such as indoor and outdoor temperature, humidity, and solar radiation. However, commercial of-the-shelf climatic control systems continue to rely on simple controllers like proportional-integral-derivative or even on-off, which do not take into account such variables. This work evaluates the potential performance gains of adopting more advanced controllers, in this case based on pole-placement, enhanced with additional variables, namely solar radiation and external temperature, obtained with dedicated low-cost sensors. This approach is evaluated both in simulated and real-world environments. The obtained results show that pole-placement controllers clearly outperform on-off controllers and that the use of the additional variables in pole-placement controllers allows relevant performance gains in key parameters such as error signal MSE (17%) and control signal variance (40%), when compared with simple PP controllers. The observed energy consumption savings obtained by using the additional variables are marginal (≈1%, but the reduction of the error signal MSE and control signal variance have a significant impact on energy consumption peaks and on equipment lifetime, thus largely compensating the increase in the system complexity.

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“…More specifically, control strategies include robust controls proposed by Linker [26] and Bennis [27], nonlinear controls used by Pasgianos [28] and Wu Xiuhua [29], adaptive controls used by Cunha [30], Guzman [31], Ahmed [32], etc., and model prediction control, etc. used by Camacho [33], Ramirez [34], Berenguer [35], etc. However, the greenhouse is a complicated system that has nonlinear multi-inputs and multi-outputs [36].…”

Section: Related Workmentioning

confidence: 99%

Complex Event Processing Methods for Greenhouse Control

Jia

2021

Agriculture

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In the greenhouse environment, the application of complex event processing (CEP) technology can effectively tackle the problem of recognition of the complex patterns that appeared in greenhouse conditions. In the existing research, few scholars have proposed a scheme to integrate complicated scenes within the greenhouse environment with high efficiency, convenience, and low coupling. Therefore, in order to solve the problem of hard recognition and fusion of complex patterns in the greenhouse environment, based on the characteristics of the greenhouse, this paper proposes a complex event processing method for greenhouse control. Our method has high applicability and high expansibility, including 13 types of event processing agents and 21 types of typical events involved in greenhouse automatic control. This method has the advantages of low information coupling and multi-domain integration, which can be directly used by agricultural experts and related workers and is of great significance to promote the extensive application of CEP technology in the greenhouse field. Our experiment successfully realized a greenhouse intelligent control system based on CEP technology is successfully realized in our experiment. The experimental statistics shows that the structure of the control system was accessible and effective.

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Constrained predictive control of a greenhouse

Cited by 74 publications

References 3 publications

Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption

Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption

Improving the Ambient Temperature Control Performance in Smart Homes and Buildings

Complex Event Processing Methods for Greenhouse Control

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