G.D. Pasgianos

A nonlinear feedback technique for greenhouse environmental control

Abstract Climate control for protected crops brings the added dimension of a biological system into a physical system control situation. The plants in a greenhouse impose their own needs, significantly affect their ambient conditions in a nonlinear way, and add long-time constants to the system response. Moreover, the thermally dynamic nature of a greenhouse suggests that disturbance attenuation (load control of external temperature, humidity, and sunlight) is far more important than is the case for controlling other types of buildings. This paper presents a feedback–feedforward approach to system linearization and decoupling for climate control of greenhouses and more specifically for the operation of ventilation/cooling and moisturizing. The proposed method consists of three parts: (a) a model-based feedback–feedforward compensation of external disturbances (loads) on the basis of input–output linearization and decoupling; (b) the transformation of user-defined desired settings for temperature and humidity into feasible controller setpoints, taking into account the constraints imposed by the capacities of the actuators and the psychrometric laws; and (c) additional PI outer loops to compensate for model uncertainties and deviations from expected disturbances (weather). Moreover, some tuning tests lump together several physical system parameters to be easily identified, and the method guarantees accuracy in setpoint tracking while simplifying stability issues. The proposed method is applicable to any air-conditioning system and is expected to gain wide acceptance in modern climate control systems.

Synergism of high and low level systems for the efficient management of greenhouses

The advantages of using artificial intelligence (AI) decision support tools in synergism with low level process controllers or schedulers are investigated in this paper. The development of a modern control and management system for greenhouses used recent advances in software design, and development tools, to provide an open system for rapid program development. To effectively integrate expert system applications in a control and management system, an environment was built that supports all required interfaces between AI applications and the greenhouse management system (GMS). This environment incorporates a native fuzzy knowledge based system (KBS) and a number of procedural control functions, in the GMS, that can effectively interact. The programmable logic controller (PLC) houses all well-known control function blocks, in library form, callable to implement various control loop designs. Functions that have not been foreseen in the PLC control library can be instantly implemented using the open KBS system. The innovative addition of integral initial conditions on a proportional-integral-derivative (PID) controller, for repetitive load switching applications, is an example, demonstrated in this paper. The usefulness of other control blocks such as a self-adjusting Smith predictor is also tested for a real application of a mixing process with long dead time. Synergism of fuzzy decisions and fuzzy controllers, at the supervisory level, with low level process regulators provide adaptive systems, which can optimize both long-term objectives and the short time dynamic responses.

Hydroponics water management using adaptive scheduling with an on-line optimiser

An optimisation-based methodology for irrigation control and nutrient supply is developed using common measurements of greenhouse climate. The process measurement has a long time delay, and a feedforward (FF) control loop based on a model-based estimate of water losses is used. A long feedback loop, by which the FF model is adapted using output error feedback, is the mechanism used to minimise the control error. To read the output error, a drain measuring device, or soil moisture meter, is necessary. The optimisation method used is a general tool developed for real-time application and is capable of optimising linear and non-linear systems. The minimisation algorithm used is based on a variant of the Powell direction set method in multiple dimensions. It compares favourably in speed of convergence and accuracy when compared with linear regressors for linear systems. It is therefore used as a generalised tool embedded in a modern greenhouse management system. The method allows on-site on-line identification of plant water needs. As an added benefit, the method provides information for the creation of crop transpiration models.

Pseudo-derivative feedback-based identification of unstable processes with application to bioreactors

The use of a pseudo-derivative feedback structure for closed-loop parameter identification of unstable first-order plus dead-time processes is investigated in this paper. Two alternative identification methods are presented. The proposed identification methods require small computational effort and they are particularly useful for on-line applications. The new methods and the simpler structure of the pseudo-derivative feedback controller used ensure considerably smoother response (i.e., less overshoot), than other well-known identification methods based on standard PI/PID controllers, and better accuracy along with simplicity in implementation. The synergism of intelligent tools, which can be based on functional and linguistic rules for advanced decisions at the supervisory level, with low-level process PD-0F controllers and identifiers, can provide adaptive systems. These can optimize for both the long-term objectives and the short-term process dynamics.

A Non-Linear Feedback Technique for Greenhouse Environmental Control

Abstract A new approach to system linearization and decoupling is presented for the operation of heating/cooling and moisturizing of greenhouses. High level programming. such as the virtual variables which provide an easy way to building models. is a feature of most research but also field control systems. The method is applicable to any airconditioning system and is expected to be gain wide acceptance in modern SCADA systems with extended computational capabilities.

On-Line Controller Tuning for Unstable Processes with Application to a Biological Reactor

Abstract New methods for tuning PID controllers for unstable dead-time processes are reported. The proposed methods ensure smooth closed-loop response to set-point changes, fast regulatory control and robustness against parametric uncertainty while retaining the classical PID control structure. Their application to an unstable transfer function model describing a biological reactor is also presented. The simulation results prove the effectiveness of the proposed PID tuning methods.

New Ways on Supervisory Control: A Virtual Greenhouse: To Train, To Control and to Manage

Abstract Due to demands for higher levels of automation, the use of hierarchical and distributed control systems that integrate estimation, prediction, adaptation, learning and fault tolerance, is becoming a reality. A scada system, including its own special PLC with a class of generic control functions and signal processing math library, was built to control and manage complex process operations properly. It also includes an embedded fuzzy inference system for high level decision making, coordination and management of hierarchical systems. Added some special built-in functions for event driven control loop activation, it has been tested for applications in greenhouses. With recent additions for IP connectivity, operations modelling, a callable optimiser and remote camera support it has become a virtual laboratory system for training but also for remote management. This paper explores the potentialities of a new approach to the general virtual laboratory concept.

Pseudo-Derivative Feedback Based Identification of Unstable Processes with Application to a Bioreactor

Abstract The use of a pseudo-derivative feedback structure for parameter identification of unstable first order plus dead-time processes is investigated. Two alternative identification methods are presented. The proposed identification methods require small computational effort and they are particularly useful for on-line applications. They ensure considerably smoother response (i.e. less overshoot), than known identification methods based on standard PI/PID controllers, while providing better accuracy and more simplicity in implementation, due to the simpler structure of the pseudo-derivative feedback controller used.

On-line PID controller tuning for open-loop unstable processes

Abstract New methods for tuning PID controllers for unstable first-order plus dead-time processes are reported. The proposed methods ensure smooth closed-loop response to set-point changes, fast regulatory control and robustness against parametric uncertainty while retaining the classical PID control structure. Their application to an unstable transfer function model describing a biological reactor is also presented. The simulation results prove the effectiveness of the proposed PID tuning methods.