A. Pawlowski

Application of SSOD-PI and PI-SSOD event-based controllers to greenhouse climatic control.

In this work, an application of the Symmetric Send-On-Delta (SSOD) event-based controllers to the inside air temperature control of the greenhouse production process is presented. The control technique analysis is split into two stages. The first stage is devoted to determine the proper controller parameters and to check the influence of the Send-On-Delta (SOD) threshold value through simulation study. At the second stage, experimental tests on the real greenhouse facilities are performed. The obtained results show that the analyzed control techniques handle the control task with desired accuracy and performance. In particular, the proposed control system saves costs related with energy consumption and wear minimization, by achieving a satisfactory performance at the same time.

Architecture to develop semi-virtual industrial laboratories for the interactive learning of process automation

This paper presents a hardware and software architecture for the development of educational laboratories, through the connection of real Programmable Logic Controllers (PLCs) to industrial virtual plants. In addition, to illustrate the use of the proposed architecture, two laboratories based on a bottling plant and the automatic irrigation process of a greenhouse using Modicon M340 PLCs of Schneider have been developed. Moreover, real feedback results from students of the Bachelor degree in agricultural engineering degrees are provided and analyzed.

Event-based control for a greenhouse irrigation system

This work presents a simulation study of an event-based predictive control system for a greenhouse irrigation process. The control system objective is to maintain the desired humidity level, keeping the water usage as low as possible. The event-based control scheme uses a crop transpiration model and a water content virtual sensor to trigger the irrigation system events. In such a scheme, the event-based controller determines the volume of water required to compensate for the irrigation system disturbances. Simulation experiments were performed to analyze the behavior of the designed system and to study the water supply dynamics to the substrate and subsequent drainage and evaporation. The resulting control system is able to adapt the actuation rate to the state of the plant providing the efficient way of water consumption. The obtained results show that application of proposed event-based approach for the greenhouse irrigation system allows us to improve the control performance and to reduce the water usage being an important issue in intensive agriculture. The improved control performance is obtained due to event-based approach and the inclusion of information about the plant dynamic response for water supply and transpiration effect.

Event-based GPC for multivariable processes

In this work, a practical event-based framework for multivariable processes is introduced. The developed control algorithm is based on a Generalized Predictive Control (GPC) algorithm suitable for MIMO systems. The most important advantage of the proposed control scheme is the adaptability of control actions to process disturbances and process interactions. Moreover, event-based approach allows prioritizing selected control variables, paying more attention to important variables (often related to security or costs) while neglecting non-critical ones. This is possible through adjusting the sensor deadband value, making feasible a tradeoff between control effort and control performance. Using this approach, it is possible to keep less relevant variables within the established limits, focusing the control system attention on those critical for process operation. The analyzed event-based control technique for multivariable systems is tested through a simulation study and considering an industrial stirred tank reactor process. The obtained results show several benefits regarding event-based control application, keeping all properties of event-based approach as well as model predictive control ones.

Event-based GPC for depth of hypnosis in anesthesia for efficient use of propofol

This work presents a simulation study of an event-based predictive control system for depth of hypnosis in anesthesia using bispectral index as a controlled variable. The developed control structure uses a Wiener model structure to exploit the linear model predictive approach. Due to this architecture it is possible to use a well-established model predictive controller for linear system taking advantage of constraints handling mechanism and keeping the computational effort in reasonable limits. In such a scheme, the predictive controller is implemented within adjustable virtual deadband on actuator to limit changes in control signal and preserve the control system resources. The presence of the virtual deadband permits to establish the tradeoff between control performance and the use of the control resources (propofol administration). This feature could reduce the risk of drug overdosis during the anesthesia reducing negative effects on patients health with postoperative delirium. The analyzed control system is evaluated for different values of the actuator deadband in order to test its influence on the controlled variable. Additionally, a comparison with a standard time-based predictive controller is performed.

Event-Based Control Systems for Microalgae Culture in Industrial Reactors

In this chapter, event-based control approaches for microalgae culture in industrial reactors are evaluated. Those control systems are applied to regulate the microalgae culture growth conditions such as pH and dissolved oxygen concentration. The analyzed event-based control systems deal with sensor and actuator deadbands approaches in order to provide the desired properties of the controller. Additionally, a selective event-based scheme is evaluated for simultaneous control of pH and dissolved oxygen. In such configurations, the event-based approach provides the possibility to adapt the control system actions to the dynamic state of the controlled bioprocess. In such a way, the event-based control algorithm allows to establish a tradeoff between control performance and number of process update actions. This fact can be directly related with reduction of CO(_2) injection times, what is also reflected in CO(_2) losses. The application of selective event-based scheme allows the improved biomass productivity, since the controlled variables are kept within the limits for an optimal photosynthesis rate. Moreover, such a control scheme allows effective CO(_2) utilization and aeration system energy minimization. The analyzed control system configurations are evaluated for both tubular and raceway photobioreactors to proove its viability for different reactor configurations as well as control system objectives. Additionally, control performance indexes have been used to show the efficiency of the event-based control approaches. The obtained results demonstrated that the analyzed control algorithms improve the microorganism growth condition and in consequence the overall production rate is increased.

Multivariable GPC for processes with multiple time delays: Implementation issues

In this work, implementation issues related to multivariable Generalized Predictive Control (GPC) for processes with multiple time delays are analyzed. Due to specific properties of those processes, the resulting control system has to account for the existing delays between control variables changes and their effect on controlled outputs. In the case of Model Predictive Control (MPC) techniques, such as GPC, the dead-time issue can be captured in the process model and thus considered in the predictive mechanism of the controller. However, this working principle results in augmented matrix dimensions that considers input-output time delays as additional zero entries. This fact increases the computational load and has to be accounted for in the control system design, specially in systems where computing resources are scarce (eg. embedded systems). The reduction of the matrix dimension, and hence the computation required, depends on how the delay terms are structured. The presented analysis considers two different GPC implementations for multivariable dead-time processes, which are used to compensate for internal matrix dimensions for the multiple time delays. Each implementation mode is evaluated for two industrial multivariable processes, providing several performance indexes. Performed simulations show that the required computational load can be reduced when adequate implementation mode is selected.