F. Rodríguez

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.

Navigation Techniques for Mobile Robots in Greenhouses

This article discusses navigation methods for mobile agricultural robots in greenhouses. Today, many dangers of repetitive and hazardous agricultural tasks for humans could be avoided by using robots. The problem of autonomous navigation in greenhouses has been solved using both deliberative and pseudo-reactive techniques. The first one utilizes a map-based algorithm to create a safe, obstacle-free path to circulate throughout the greenhouse. The second technique applies a sensor-feedback algorithm to move through the greenhouse corridors. Furthermore, along the trajectory a sensorial map is built. Both approaches have been implemented and tested in a real environment, with promising results.

Multiobjective hierarchical control architecture for greenhouse crop growth

The problem of determining the trajectories to control greenhouse crop growth has traditionally been solved by using constrained optimization or applying artificial intelligence techniques. The economic profit has been used as the main criterion in most research on optimization to obtain adequate climatic control setpoints for the crop growth. This paper addresses the problem of greenhouse crop growth through a hierarchical control architecture governed by a high-level multiobjective optimization approach, where the solution to this problem is to find reference trajectories for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being currently fostered by international rules. Illustrative results selected from those obtained in an industrial greenhouse during the last eight years are shown and described.

Adaptive Control for a Mobile Robot Under Slip Conditions Using an LMI-Based Approach

This paper presents the synthesis of a control law which guarantees asymptotic stability for a wheeled mobile robot under slip conditions subject to both constraints and system dynamics. This control law is obtained using Linear Matrix Inequalities. The improvements provided by the pro- posed adaptive control are compared with other control laws, dealing with slip effect, through simulation.

Online robust tube-based MPC for time-varying systems: a practical approach

This article focuses on the design of a robust model predictive control law for constrained discrete-time time-varying systems with additive uncertainties. The proposed solution to the control problem is a tube-based MPC ensuring robustness and constraints fulfilment. Reachable sets are calculated online taking into account the system dynamics by means of an adaptive local control law and additive uncertainties. The proposed method represents a trade-off between small conservativeness and efficient real-time execution. This approach is applied to solve the trajectory tracking problem of a mobile robot. Simulation results provide a comparison between the tube-based MPC scheme and established motion control algorithms, showing the efficient execution and satisfactory behaviour of the proposed controller.

An interactive tool for mobile robot motion planning

This paper presents an interactive tool aimed at facilitating the understanding of several well-known algorithms and techniques involved in solving mobile robot motion problems. These range from those modelling the mechanics of mobility to those used in navigation. The tool focuses on describing these problems in a simple manner in order to be useful for education purposes among different disciplines. By highlighting interactivity, the tool provides a novel means to study robot motion planning ideas in a manner that enhances full understanding. Furthermore, the paper discuses how the tool can be used in an introductory course of mobile robotics.

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.

Robust Tube-based Predictive Control for Mobile Robots in Off-Road Conditions

This paper focuses on the design of a tube-based Model Predictive Control law for the control of constrained mobile robots in off-road conditions with longitudinal slip while ensuring robustness and stability. A time-varying trajectory tracking error model is used, where uncertainties are assumed to be bounded and additive. The robust tube-based MPC is compared with other motion control techniques through simulation and physical experiments. These tests show the satisfactory behavior of the presented control strategy.

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.

Takagi-Sugeno control of nocturnal temperature in greenhouses using air heating.

A solution to the problem of controlling the minimum temperature in greenhouses using controllers developed from nonlinear models of the system is discussed and applied on a real greenhouse. More precisely, the controllers designed are Takagi-Sugeno type controllers, and the proposed design method is an iterative method based on solving a set of Linear Matrix Inequalities, which ensures stability and performance in closed-loop. The tests in a real greenhouse show that it is possible to design controllers for control of nocturnal temperature that give good performance, and guarantee stability in a wide range of working conditions.