N. Sigrimis

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.

Identifying design parameters for fuzzy control of staged ventilation control systems

Conventional staged ventilation systems are commonly used in agriculture to maintain interior environments near desired conditions for livestock housing and greenhouses. This paper identifies design parameters for fuzzy-based control of these staged ventilation systems. A simple non-steady state heat balance is used in conjunction with a broiler house simulation model, and coupled with a model for the control system, to simulate control system performance. Difficulties with implementation of conventional staged ventilation control, and the proposed fuzzy inference technique, arise because of the discontinuous nature of these highly non-linear systems. Comparisons between the new fuzzy stage controller and conventional staged control are made. Effects of varying the identified design parameters for the fuzzy stage controller, including different degrees of control precision and energy use, rule base complexity, and the rate of change of house temperature are made. Results indicate that existing staged ventilation control systems which utilize microprocessors could realize significantly enhanced control flexibility by a simple software modification to incorporate the fuzzy staged controller method.

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.

Energy saving in greenhouses using temperature integration: a simulation survey

Abstract A method is presented for controlling greenhouse air temperature achieving significant energy savings by use of temperature integration. The method is implemented in a commercial greenhouse control system using intelligent tools available in most modern control and management systems. Results compare the proposed technique with standard growers’ temperature control practice based on simulation of a model greenhouse. Tests against two types of weather show how the energy savings relate to weather spectral characteristics and crop tolerance bounds. The available tools allow the user to define time windows where the temperature setpoint can be either ‘strictly user specified’ or ‘model derived’ or ‘float within constraints for energy savings’. The method was developed for immediate application so the grower can define his policy in a simple way, without any requirements for a priori or predicted weather information. Results presented prove the viability of the method and its potential for energy savings.

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.

Heuristic optimization methods for motion planning of autonomous agricultural vehicles

In this paper, two heuristic optimization techniques are tested and compared in the application of motion planning for autonomous agricultural vehicles: Simulated Annealing and Genetic Algorithms. Several preliminary experimentations are performed for both algorithms, so that the best neighborhood definitions and algorithm parameters are found. Then, the two tuned algorithms are run extensively, but for no more than 2000 cost function evaluations, as run-time is the critical factor for this application. The comparison of the two algorithms showed that the Simulated Annealing algorithm achieves the better performance and outperforms the Genetic Algorithm. The final optimum found by the Simulated Annealing algorithm is considered to be satisfactory for the specific motion planning application.

A learning technique for a general purpose optimizer

The goal of the machine learning method implemented in this article is to broaden the region of operability of an adaptive control system by switching multiple controller models. The learning system determines a separate set of control parameter values, for optimal performance under given operating conditions, and stores them in memory. In this way, the controller is able to operate effectively over the whole environment. The basic scheme implements a single neuron, the perceptron, which approximates the process model and then directly computes the control signals. An example application is also described of an innovative sensing method, which has been developed to replace leaf sensors in plant propagation chambers, by emulating the sensor in software. Such chambers present critical situations for control because of the high humidity levels required, which makes direct sensing methods unsuitable. The proposed method enhanced the reliability of the control system and eliminated the need for costly electronic leaf sensors and the associated need for great care and frequent calibration. The method in principle combines ordinary measurements of ambient temperature, humidity and radiation, to calculate the controls of the humidification process in mist or fog propagation chambers. The performance surface was studied and a modification of the searching algorithm has improved the learning rate significantly. The method is applicable to any system whose performance can be defined and measured by simulation or experiment.

H∞-PI controller tuning for greenhouse temperature control

Abstract A technique for robust PI controller tuning, is applied for controlling the temperature in greenhouses, represented by first order plus dead time models and for which, their parameters vary with the ventilation as well as the velocity of the inside air. The effectiveness of the proposed technique is demonstrated by several simulation results, which show that, the PI controller designed in the context of H ∞ , can effectively face large changes in model parameters, and retains a satisfactory performance in cases of load disturbances as well as set point changes.

AN INTELLIGENT NONINTERACTING TECHNIQUE FOR CLIMATE CONTROL OF GREENHOUSES

Abstract A new approach to system linearization and decoupling is presented for climate control of greenhouses and more specifically for the operation of heating/cooling and moisturizing. High-level programming, which provides an easy way to building models, is a feature of most research but also field control systems. The method is applicable to any air-conditioning system and is expected to gain wide acceptance in modern SCADA systems with extended computational capabilities.

An Adaptive Optimizer for Process Control

Abstract An optimization based methodology for control of irrigation and nutrient supply is developed using common measurements of greenhouse climate. The method allows onsite on-line identification of plant water needs and as an added benefit provides information for the creation of crop transpiration models.