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a b s t r a c tA new event-based proportional-integral controller, based on a specific send-on-delta sampling strategy, is analyzed in this paper. In particular, necessary and sufficient conditions on the controller parameters for the existence of equilibrium points without limit cycles are given for a first-order-plus-dead-time process. These conditions can be usefully exploited for the tuning of the controller, thus making the overall design easier. Practical issues related to the controller implementation are also addressed. Simulation and experimental results are provided as illustrative examples.

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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.

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This paper presents a distributed event-based control strategy for a networked dynamical system consisting of N linear time-invariant interconnected subsystems. Each subsystem broadcasts its state over the network according to certain triggering rules which depend on local information only. The system can converge asymptotically to the equilibrium point under the proposed control design, and the existence of a lower bound for the broadcasting period is guaranteed. A novel model-based approach is derived to reduce the communication between the agents. Simulation results show the effectiveness of the proposed approaches and illustrate the theoretical results. * M. Guinaldo, J. Sánchez and S. Dormido are with the

This paper is an experimental study of the utilization of different event-based strategies for the automatic control of a simple but very representative industrial process: the level control of a tank. In an event-based control approach it is the triggering of a specific event, and not the time, that instructs the sensor to send the current state of the process to the controller, and the controller to compute a new control action and send it to the actuator. In the document, five control strategies based on different event-based sampling techniques are described, compared, and contrasted with a classical time-based control approach and a hybrid one. The common denominator in the time, the hybrid, and the event-based control approaches is the controller: a proportional-integral algorithm with adaptations depending on the selected control approach. To compare and contrast each one of the hybrid and the pure event-based control algorithms with the time-based counterpart, the two tasks that a control strategy must achieve (set-point following and disturbance rejection) are independently analyzed. The experimental study provides new proof concerning the ability of event-based control strategies to minimize the data exchange among the control agents (sensors, controllers, actuators) when an error-free control of the process is not a hard requirement.