Andrzej Pawlowski

Simulation of Greenhouse Climate Monitoring and Control with Wireless Sensor Network and Event-Based Control

Monitoring and control of the greenhouse environment play a decisive role in greenhouse production processes. Assurance of optimal climate conditions has a direct influence on crop growth performance, but it usually increases the required equipment cost. Traditionally, greenhouse installations have required a great effort to connect and distribute all the sensors and data acquisition systems. These installations need many data and power wires to be distributed along the greenhouses, making the system complex and expensive. For this reason, and others such as unavailability of distributed actuators, only individual sensors are usually located in a fixed point that is selected as representative of the overall greenhouse dynamics. On the other hand, the actuation system in greenhouses is usually composed by mechanical devices controlled by relays, being desirable to reduce the number of commutations of the control signals from security and economical point of views. Therefore, and in order to face these drawbacks, this paper describes how the greenhouse climate control can be represented as an event-based system in combination with wireless sensor networks, where low-frequency dynamics variables have to be controlled and control actions are mainly calculated against events produced by external disturbances. The proposed control system allows saving costs related with wear minimization and prolonging the actuator life, but keeping promising performance results. Analysis and conclusions are given by means of simulation results.

Generalized Predictive Control With Actuator Deadband for Event-Based Approaches

This work presents an event-based control structure using the generalized predictive control (GPC) algorithm with actuator deadband. The main objective of this work is to limit the number of controlled system updates. In this approach, the controlled process is sampled with a constant sampling time and is updated in an asynchronous way that depends on the obtained control signal value. To achieve the desired control properties, a hybrid system framework is used to model the virtual actuator deadband. The deadband is modeled as system input constraints in the GPCs optimization procedure, exploring mixed integer quadratic programming (MIQP) techniques. The presented control structure considers adjustable actuator deadband as an additional tuning parameter and allows a tradeoff between control performance and the number of events/updates. This fact is very important for the lifetime of the actuators and minimizes their wear, especially for mechanical and electromechanical systems. The minimization of actuator usage can be also understood as an economical saving. Other benefits can be found in distributed control systems, where various control system agents are connected through computer networks, and where a reduction in communication is always welcome.

Effective utilization of flue gases in raceway reactor with event-based pH control for microalgae culture

This work addresses effective utilization of flue gases through the proper pH control in raceway reactors. The pH control problem has been addressed with an event-based control approach using a Generalized Predictive Controller (GPC) with actuator deadband. Applying this control strategy it is possible to reduce the control effort, and at the same time saving control resources. In the pH process case, the event-based controller with actuator deadband can be tuned to supply only necessary amount of CO2 to keep the pH close to its optimal value. On the other hand, the evaluated control algorithm significantly improves the pH control accuracy, what has a direct influence on biomass production. In order to test the performance of the event-based GPC controller, several experiments have been performed on a real raceway reactor. Additionally, several control performance indexes have been used to compare the analyzed technique with commonly used on/off controller.

Event-based control and wireless sensor network for greenhouse diurnal temperature control: A simulated case study

Traditionally, greenhouse installations have required a great effort to connect and distribute all the sensors and data acquisition systems. These installations need many data and power wires to be distributed along the green-houses making the system complex and expensive. For this reason, and others such as unavailability of distributed actuators, only individual sensors are usually located in a fixed point of the greenhouse selected as representative of greenhouse dynamics. On the other hand, the actuation system in greenhouses are usually composed by mechanical devices controlled using relays, being desirable to reduce the number of commutations of the control signal from security and economical point of views. The greenhouse climate control can be represented as an event-based system, where low-frequency dynamics variables have to be controlled and this control usually acts against events governed by external disturbances. Therefore, this work presents preliminary ideas and results of wireless sensor network and event-based control applied to the greenhouse climate control problem.

Application of time-series methods to disturbance estimation in predictive control problems

Disturbance effect is a very important issue to be analyzed and considered in any control problem. Models representing the disturbance effect on the process variable are usually obtained for this purpose. These models are useful to design, for instance, classical feedforward compensators. However, when predictive control strategies are being used, no only the model capturing the disturbance effect is necessary, but that future estimations of the disturbances would be also extremely useful to get advance in the prediction capabilities. Measurable disturbances can be represented as time-series data and processed in order to estimate future variations. Therefore, this paper is focused on studying how well-known time-series methods can be used to obtain disturbance forecasts in order to be taken into account in predictive control algorithms. The methods used here have two main objectives: to detect the nature and properties of any variable represented as time-series data, and second, to forecast future behavior based on acquired data series. An example using the solar radiation measured from an industrial greenhouse is presented to show how these future estimations can be obtained.

The influence of event-based sampling techniques on data transmission and control performance

Event-based systems are becoming increasingly commonplace, particularly for distributed real-time sensing and control. Furthermore, remote monitoring and control through data-communication networks are very popular for process supervision and control. The usage of networks provide many well-known benefits, but it also presents some limitations in the amount of transmitted data. This fact is especially visible in Wireless Sensor Network (WSN), where the bandwidth of communication channel is limited and typically all nodes are battery powered. Event-based sampling techniques appear as a some possible solution to face this problem allowing considerably saving network resources and reducing the power consumption. On the other hand, the control system performance is highly affected due to the event-based sampling techniques, being necessary to analyze and study a compromise between control quality and reduction in the control signal commutations. This paper presents all these ideas applied to the greenhouse climate control problem.

Predictive Control with Disturbance Forecasting for Greenhouse Diurnal Temperature Control

Abstract This work is devoted to analyze the Generalized Predictive Control (GPC) strategy considering measurable disturbances. This well known control architecture is extended using Double Exponential Smoothing (DES) technique to perform future disturbance estimation. The disturbance models are obtained, validated, and embedded within a GPC controller to compensate for future disturbances. The proposed system is compared with a typical GPC without feedforward action, a GPC with feedforward considering constant disturbances in the future, and a GPC with feedforward taking the original real data in the future. The proposed control scheme was tested by simulation of a greenhouse inside temperature control. The obtained results show that the GPC with disturbance forecasting provide improved behavior that standard techniques.

A Wireless Sensor Network for greenhouse climate monitoring

In the last years, the presence of WSN (Wireless Sensor Network) is spreading worldwide. The reason for this success is that they facilitate the creation of data acquisition networks minimizing the necessary wire infrastructure. However, the WSN distributed modules typically have high dependencies on their batteries consumption. In the other hand, event-based systems are spreading fastly day by day. This kind of system allows to perform asynchronous actions based on a schedule or rules. The WSN distributed modules allow to perform a process before transmitting the data. If a event-based algorithm is used to define when it is necessary to transmit the acquired data, transmissions could be minimized. In this way, modules energy consumption can be reduced, extending the battery life. Therefore, mixing both technologies improves the WSN autonomy. In this work, these ideas are performed and analyzed in the climate monitoring framework.

Symmetric Send-On-Delta PI Control of a Greenhouse System

Abstract This paper deals with a symmetric send-on-delta PI control strategy for controlling the internal temperature of a greenhouse. It is shown that, by properly designing the control system, the disturbances represented by the soil temperature, the solar radiation, the wind velocity, and the outside temperature can be effectively compensated with a limited number of events. The role of the design parameters is outlined and simulation results demonstrate the efficacy of the methodology by comparing it with previously proposed techniques.

Lagrange interpolation for signal reconstruction in event-based GPC

This work presents the application of Lagrange interpolation method for a signal reconstruction in event-based Generalized Predictive Control (GPC). The event-based control system is governed by level crossing sampling techniques, which monitors the controlled variable. The Lagrange interpolation method is used to reconstruct signal values between two consecutive events. The interpolated signal values are used to unify the obtained values to a base signal, which is resampled with fixed frequency. The developed event-based GPC with Lagrange interpolation is verified through a simulation study, considering several process models commonly used in industrial applications. The obtained results show a proper operation of the event-based controllers, due to good interpolation accuracy.