Versión / Versió:Postprint del autor This paper addresses the state estimation in linear time-varying systems with several sensors with different availability, randomly sampled in time, and whose measurements have a time-varying delay. The approach is based on a modification of the Kalman filter with the negative-time measurement update strategy, avoiding running back the full standard Kalman filter, the use of full augmented order models or the use of reorganization techniques, leading to a lower implementation cost algorithm. The update equations are run every time a new measurement is available, independently of the time when it was taken. The approach is useful for networked control systems, systems with long delays and scarce measurements, and for out-of-sequence measurements.

This paper proposes a model-free real-time optimization and control strategy for CO2 transcritical refrigeration plants that assures covering the cooling demand and continuous tracking of conditions for maximum efficiency. Our approach obtains the feedback with only three measurements, and controls the opening degree of a back-pressure valve and the speed of the compressor. The strategy minimizes the power consumption of the compressor instead of maximizing the coefficient of performance, which avoids several sensors, and we demonstrate mathematically that both approaches are equivalent. We implemented the strategy with an algorithm that includes two independent auto tuned controllers, one devoted to regulate the high-pressure and another to regulate the outlet temperature of the secondary fluid of the evaporator. It also incorporates a real time perturb and observe procedure to locate on-line the optimum high-pressure that minimizes the compressor power consumption. The paper presents the experimental evaluation of the control strategy, verifying the stable operation of the algorithm and the energy optimization of the plant. KEYWORDS

In this paper, the problem of estimating signals from a dynamic system at regular periods from scarce, delayed and possibly time disordered measurements acquired through a network is addressed. A model based predictor that takes into account the delayed and irregularly gathered measurements from different devices is used. Robustness of the predictor to the time-delays and scarce data availability as well as disturbance and noise attenuation is dealt with via H ∞ performance optimization. The result is a time variant estimator gain that depends on the measurement characteristics, but belonging to an offline precalculated finite set, and hence, the online needed computer resources are low. An alternative to reduce the number of gains to be stored has been proposed, based on defining the gain as a function of the sampling parameters. The idea allows reaching a compromise between online computer cost and performance.

PostprintThis work addresses the design of a state observer for systems whose outputs are measured through a communication network. The measurements from each sensor node are assumed to arrive randomly, scarcely and with a time-varying delay. The proposed model of the plant and the network measurement scenarios cover the cases of multiple sensors, out-of-sequence measurements, buffered measurements on a single packet and multi-rate sensor measurements. A jump observer is proposed, that selects a different gain depending on the number of periods elapsed between successfully received measurements and on the available data. A finite set of gains is precalculated off-line with a tractable optimization problem, where the complexity of the observer implementation is a design parameter. The computational cost of the observer implementation is much lower than in the Kalman filter, whilst the performance is similar. Several examples illustrate the observer design for different measurements scenarios and observer complexity and show the achievable performance.

In this paper, the problem of observing the state of a class of discrete nonlinear system is addressed. The design of the observer is dealt with using H∞ performance techniques, taking into account disturbance and noise attenuation. The result is an LMI optimization problem that can be solved by standard optimization techniques. A design strategy is proposed based on the available disturbances information.