A model-free control strategy for an experimental greenhouse with an application to fault accommodation

Writing down mathematical models of agricultural greenhouses and regulating them via advanced controllers are challenging tasks since strong perturbations, like meteorological variations, have to be taken into account. This is why we are developing here a new model-free control approach and the corresponding intelligent controllers, where the need of a good model disappears. This setting, which has been introduced quite recently and is easy to implement, is already successful in many engineering domains. Tests on a concrete greenhouse and comparisons with Boolean controllers are reported. They not only demonstrate an excellent climate control, where the reference may be modified in a straightforward way, but also an efficient fault accommodation with respect to the actuators

Temperature and Humidity Controlling System for Baby Incubator

Baby incubator is very important to keep the newborn’s body temperature especially for premature babies. The research aimed to design a baby incubator with controlled temperature and humidity. The incubator is designed to have a length of 60 cm, a width of 40 cm, and a height of 30 cm. System of baby incubator will automatically turn on or turn off the fan and or heating in accordance with the normal range of temperature and humidity in the incubator. The normal limits of temperature used is 33°C to 35°C. While the normal limits of air humidity in the incubator used is between 40% and 60%. Data acquisition system consists of temperature and humidity sensor, microcontroller ATmega8535, fan, heater, and LCD. LCD is used to display the results of measurements of temperature and humidity. Heater is used to regulate the temperature in the incubator. While fan is used to regulate the humidity in the incubator. Test results show that the heater will turn on if the temperature is below the limits of 33°C. While the fan will turn on if the humidity is above 60

Simplified Environment Control System For Prototype Vertical Farm

Farming is one of the fundamental inventions that promoted the flourishing or human species on planet earth. Since many centuries have past, this fundamental invention namely the conventional way of it has become reverse which is promoting extinction. Conventional farming has led to many unsustainable acts such as deforestation, heavy use of pesticides and insecticides, feces as fertilizers, open burning and creation of cities. These acts in turn backfired resulting in global warming, extreme climate change (drought, flood, hurricanes etc), exponential rise in human population resulting in consumption of resources over the replenishing rate, pollution of water sources, diseases spreading such as typhoid and cholera. The very main reason such occurrence is because of direct interaction of farming with nature. Farming is not a natural behavior of an ecosystem (Despommier, 2009). It is a creation of human beings to increase its survival rate. Hence in a simple mathematical equation, if we remove farming from nature and put it in a closed system which resembles the nature ecosystem, we can remove the potential unsustainable acts of farming. The concept of vertical farming is to remove the factor of farming from nature to undo the bad deeds before it is too late. This simplistic solution might seem like a dreamer’s solution and appear to be impractical due to its high costs. To prove that argument wrong, this research will show the latest breakthrough in the agriculture industry in growing plants in a building and too how far is a vertical farm concept farfetched? Thus, this research will be proposing to construct simplified miniature 3 storeys vertical farm system that uses hydroponic system and govern by an expert system using the methodology proposed by CLAE

Closed-loop agriculture systems meta-research using text mining

The growing global population and climate change threaten the availability of many critical resources, and have been directly impacting the food and agriculture sector. Therefore, new cultivation technologies must be rapidly developed and implemented to secure the world's future food needs. Closed-loop greenhouse agriculture systems provide an opportunity to decrease resource reliance and increase crop yield. Greenhouses provide versatility in what can be grown and the resources required to function. Greenhouses can become highly efficient and resilient through the application of a closed-loop systems approach that prioritizes repurposing, reusing, and recirculating resources. Here, we employ a text mining approach to research the available research (meta-research) and publications within the area of closed-loop systems in greenhouses. This meta-research provides a clearer definition of the term “closed-loop system” within the context of greenhouses, as the term was previously vaguely defined. Using this meta-research approach, we identify six major existing research topic areas in closed-loop agriculture systems, which include: models and controls; food waste; nutrient systems; growing media; heating; and energy. Furthermore, we identify four areas that require further urgent work, which include the establishment of better connection between academic research to industry applications; clearer criteria surrounding growing media selection; critical operational requirements of a closed-loop system; and the functionality and synergy between the many modules that comprise a closed-loop greenhouse systems

Simplified Environment Control System For Prototype Vertical Farm

Farming is one of the fundamental inventions that promoted the flourishing or human species on planet earth. Since many centuries have past, this fundamental invention namely the conventional way of it has become reverse which is promoting extinction. Conventional farming has led to many unsustainable acts such as deforestation, heavy use of pesticides and insecticides, feces as fertilizers, open burning and creation of cities. These acts in turn backfired resulting in global warming, extreme climate change (drought, flood, hurricanes etc), exponential rise in human population resulting in consumption of resources over the replenishing rate, pollution of water sources, diseases spreading such as typhoid and cholera. The very main reason such occurrence is because of direct interaction of farming with nature. Farming is not a natural behavior of an ecosystem (Despommier, 2009). It is a creation of human beings to increase its survival rate. Hence in a simple mathematical equation, if we remove farming from nature and put it in a closed system which resembles the nature ecosystem, we can remove the potential unsustainable acts of farming. The concept of vertical farming is to remove the factor of farming from nature to undo the bad deeds before it is too late. This simplistic solution might seem like a dreamer’s solution and appear to be impractical due to its high costs. To prove that argument wrong, this research will show the latest breakthrough in the agriculture industry in growing plants in a building and too how far is a vertical farm concept farfetched? Thus, this research will be proposing to construct simplified miniature 3 storeys vertical farm system that uses hydroponic system and govern by an expert system using the methodology proposed by CLAE

Theoretical Assessment of Sustainability Principles for Renewable Smart Air-Conditioning

Upon an improvement in the quality of life, air-conditioning has generally been applied. Nevertheless, environmental and health issues related with the use of air-conditioning occurs more often. Therefore, this paper aims to theoretically assess the principles of sustainability to achieve sustainability for renewable smart air-conditioning. Not only with consideration to the geometry (i.e. system mechanisms and components), fuzzy logic control and proportional-integral-derivative that such studies drawn particular attention to, but with concerns to a matter which has been previously ignored. That is with consideration to the potential which the renewable-based options, advanced smart control technique and profitability measures of air-conditioning reinforces the three pillars of sustainability, and their sustainable indicators as context-specific transformations have, to not only eradicate indoor health effects, lower the levels of energy consumption and rate of carbon emissions, but to uncover the significance of and particular contribution renewables and smart control opportunities makes to the sustainability of the system. In meeting this aim and demonstrating the sustainability of the theoretical framework, this paper reveals renewable and smart control system as the fundamental key components of the air-conditioning as it promotes to reduce levels of energy consumption and lower carbon emissions, vis-à-vis establish a comfortable and healthy indoor environment as an exercise in the sustainable theoretical framework whose status as renewable smart air-conditioning not only tackle poor indoor air quality but also combat global warming and climate change

Hierarchical model predictive control of a venlo-type greenhouse

Greenhouse cultivation can increase crop yield and alleviate the food shortage caused by population growth and reduction of arable land. However, the greenhouse production process consumes lots of energy and water. The energy consumed mainly comes from the combustion of fossil fuels, which will produce lots of greenhouse gases. In addition, the operating efficiency of some greenhouses is low, resulting in energy and water waste and increasing production costs. Therefore, the greenhouse system needs to be optimized to improve the operating efficiency. In this thesis, different methods of greenhouse operation efficiency optimization to improve energy efficiency and water efficiency are studied. In Chapter 3, three strategies for greenhouse operation optimization are studied. Strategy 1 focuses on the optimization of the greenhouse heating system to save energy. The optimization of the heating system can effectively reduce energy consumption. However, people often pay more attention to reducing energy costs than reducing energy consumption in the production process to obtain more profits. Strategy 2 is to reduce the energy cost. It should be noted that Strategy 2 only considers the cost of heating and cooling, while the cost of ventilation and carbon dioxide (CO2) is not considered. Strategy 3 reduces the cost of greenhouse heating, cooling, ventilation and CO2 consumption. In addition, greenhouse environmental factors must be kept within the required ranges. In Chapter 3, a dynamic greenhouse climate model is proposed. In the modeling process, the influence of crop growth and the interaction between different variables are considered to improve model accuracy. The proposed optimization problems are solved by ‘fmincon’ function with sequential quadratic programming (SQP) algorithm in MATLAB. Compared with Strategy 1, Strategy 2 has higher energy consumption but lower energy cost. Because Strategy 2 can shift some loads from high electricity price period to low electricity price period. Moreover, among the three strategies proposed, Strategy 3 has the lowest cost. It should be pointed out that the strategies studied in Chapter 3 only consider the impact of the greenhouse climate, but ignore the irrigation, which is also important for greenhouse production. In Chapter 4, four optimization methods are proposed. These optimization methods consider climate control and irrigation control. Therefore, strategies proposed in this chapter can not only improve energy efficiency, but also increase water efficiency. Method 1 reduces the energy consumption. Method 2 reduces the water consumption. Method 3 reduces the CO2 consumption. Method 4 reduces the total cost of greenhouse heating, cooling, ventilation, irrigation and CO2 supply. In addition, greenhouse environmental factors and crop water demand need to be met. The dynamic model of greenhouse environmental factors presented in Chapter 3 is used for greenhouse climate control. A modified crop evapotranspiration model is proposed to predict crop water demand. Moreover, a sensitivity analysis method is introduced. The influence of prices and system constraints on optimization results is studied. The cost of Method 4 can be reduced compared with other methods. In addition, changes of prices and system constraints have a great impact on optimization results. In Chapters 3 and 4, open loop optimization strategies for a greenhouse system operation are studied. However, these strategies have low control accuracy under system disturbances. Therefore, it is necessary to adopt some control methods to improve the control accuracy. In Chapter 5, a hierarchical model predictive control method is presented. The upper layer generates the optimal reference trajectories by solving greenhouse operation optimization problems. The lower layer designs controllers to follow obtained reference trajectories. Two model predictive controllers (MPC) are designed. Two performance indicators, namely relative average deviation (RAD) and maximum relative deviation (MRD), are used to compare designed controllers. The simulation results show that the proposed MPC can deal with greenhouse system disturbances and the problem of model plant mismatch better than the open loop control method. In Chapter 6, the findings of this thesis are summarized. Moreover, some topics for future research are proposed.Thesis (PhD (Electrical Engineering))--University of Pretoria, 2021.Electrical, Electronic and Computer EngineeringPhD (Electrical Engineering)Unrestricte

A Novel Adaptive PID Controller Design for a PEM Fuel Cell Using Stochastic Gradient Descent with Momentum Enhanced by Whale Optimizer

This paper presents an adaptive PID using stochastic gradient descent with momentum (SGDM) for a proton exchange membrane fuel cell (PEMFC) power system. PEMFC is a nonlinear system that encounters external disturbances such as inlet gas pressures and temperature variations, for which an adaptive control law should be designed. The SGDM algorithm is employed to minimize the cost function and adapt the PID parameters according to the perturbation changes. The whale optimization algorithm (WOA) was chosen to enhance the adaptive rates in the offline mode. The proposed controller is compared with PID stochastic gradient descent (PIDSGD) and PID Ziegler Nichols tuning (PID-ZN). The control strategies’ robustnesses are tested under a variety of temperatures and loads. Unlike the PIDSGD and PID-ZN controllers, the PIDSGDM controller can attain the required control performance, such as fast convergence and high robustness. Simulation results using Matlab/Simulink have been studied and illustrate the effectiveness of the proposed controller.The authors wish to express their gratitude to the Basque Government through the project EKOHEGAZ (ELKARTEK KK-2021/00092), to the Diputación Foral de Álava (DFA) through the project CONAVANTER, and to the UPV/EHU through the project GIU20/063 for supporting this work

Studies on SI engine simulation and air/fuel ratio control systems design

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.More stringent Euro 6 and LEV III emission standards will immediately begin execution on 2014 and 2015 respectively. Accurate air/fuel ratio control can effectively reduce vehicle emission. The simulation of engine dynamic system is a very powerful method for developing and analysing engine and engine controller. Currently, most engine air/fuel ratio control used look-up table combined with proportional and integral (PI) control and this is not robust to system uncertainty and time varying effects. This thesis first develops a simulation package for a port injection spark-ignition engine and this package include engine dynamics, vehicle dynamics as well as driving cycle selection module. The simulations results are very close to the data obtained from laboratory experiments. New controllers have been proposed to control air/fuel ratio in spark ignition engines to maximize the fuel economy while minimizing exhaust emissions. The PID control and fuzzy control methods have been combined into a fuzzy PID control and the effectiveness of this new controller has been demonstrated by simulation tests. A new neural network based predictive control is then designed for further performance improvements. It is based on the combination of inverse control and predictive control methods. The network is trained offline in which the control output is modified to compensate control errors. The simulation evaluations have shown that the new neural controller can greatly improve control air/fuel ratio performance. The test also revealed that the improved AFR control performance can effectively restrict engine harmful emissions into atmosphere, these reduce emissions are important to satisfy more stringent emission standards

Design and Implementation of Deep Learning Based Model Predictive Controller to Automatically Adjust Nutrient of Solution for Hydroponic Crop

Smart farming is the future of agriculture sector and brings a new era in agriculture; it enables farmers to increase the production and quality of crops with minimal use of resources. In current scenario land availability decreases enormously, hence soilless hydroponic cultivation is considered as the fastest growing sector of agriculture. However, in hydroponic system it is a very challenging task to manage nutrient for crop. To solve these issues this study was conducted which could control robustly EC and pH of hydroponic solution with the help of deep learning model long short-term memory (LSTM). A model predictive controller (MPC) using LSTM was designed and simulated to control EC and pH in hydroponic farm. The predicted outcome of LSTM was operating time of pH buffer solution pump (Ton_pH) and nutrient solution pump (Ton_EC).  The proposed MPC adjust these operating times to control EC and pH with an RMSE of 0.24 and 0.27s, respectively. Furthermore, proposed system improves the predicting accuracy of Ton_pH and Ton_EC of 77% and 61%, respectively, as compared to fuzzy logic controller. This study provides a smart and efficient way to predict and estimate the optimum value for robustly manage the nutrient as per crop requirements