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406. The Future of Our Conferences: An Unmissable Opportunity for Outreach? [President’s Message]

Author

Parisini, Thomas

Abstract

Welcome to my third column of 2022. It follows the great “President’s Message” invited column by Giovanni Cherubini (IBM Research Zurich, Switzerland) that focused on the impact on big data storage systems of advanced control techniques for magnetic tape drives (see the August 2022 issue of our magazine [1]). Here, I would like to build a virtuous future connection between the Outreach Program of the IEEE Control Systems Society (CSS) and the “new normal” of our conferences I alluded to in my June 2021 “President’s Message” column [2].

Published
2022
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407. A distributed estimation method for sensor networks based on Pareto optimization

Author

Boem, Francesca, Xu, Yuzhe, Fischione, Carlo, Parisini, Thomas, Boem, Francesca, Xu, Yuzhe, Fischione, Carlo, and Parisini, Thomas

Abstract

A novel distributed estimation method for sensor networks is proposed. The goal is to track a time-varying signal that is jointly measured by a network of sensor nodes despite the presence of noise: each node computes its local estimate as a weighted sum of its own and its neighbors' measurements and estimates and updates its weights to minimize both the variance and the mean of the estimation error by means of a suitable Pareto optimization problem. The estimator does not rely on a central coordination: both parameter optimization and estimation are distributed across the nodes. The performance of the distributed estimator is investigated in terms of estimation bias and estimation error. Moreover, an upper bound of the bias is provided. The effectiveness of the proposed estimator is illustrated via computer simulations and the performances are compared with other distributed schemes previously proposed in the literature. The results show that the estimation quality is comparable to that of one of the best existing distributed estimation algorithms, guaranteeing lower computational cost and time., QC 20130304

Published
2012
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408. Neural approximations for infinite-horizon optimal control of nonlinear stochastic systems

Author

Parisini, Thomas and Zoppoli, Ricardo

Subjects
Discrete-time systems -- Models, Feedback control systems -- Models, Mathematical optimization -- Usage, Neural networks -- Models, Nonlinear networks -- Models, Stochastic systems -- Models, Business, Computers, Electronics, Electronics and electrical industries
Abstract

A feedback control law is proposed that drives the controlled vector [v.sub.t] of a discrete-time dynamic system (in general, nonlinear) to track a reference [Mathematical Expression Omitted] over an infinite time horizon, while minimizing a given cost function (in general, nonquadratic). The behavior of [Mathematical Expression Omitted] over time is completely unpredictable. Random noises act on the dynamic system and the state observation channel, which may be nonlinear, too. The random noises and the initial state are, in general, non-Gaussian; it is assumed that all such random vectors are mutually independent, and that their probability density functions are known. As is well known, so general a non-LQG (linear quadratic Gaussian) optimal control problem is very difficult to solve. The proposed solution is based on three main approximating assumptions: 1) the optimal control problem is stated in a receding-horizon framework where [Mathematical Expression Omitted] is assumed to remain constant within a shifting-time window; 2) the control law is assigned a given structure (the one of a multilayer feedforward neural network) in which a finite number of parameters have to be determined in order to minimize the cost function (this makes it possible to approximate the original functional optimization problem by a nonlinear programming one); and 3) the control law is given a 'limited memory,' which prevents the amount of data to be stored from increasing over time. The errors resulting from the second and third assumptions are discussed. Due to the very general assumptions under which the approximate optimal control law is derived, we are not able to report stability results. However, simulation results show that the proposed method may constitute an effective tool for solving, to a sufficient degree of accuracy, a wide class of control problems traditionally regarded as difficult ones (an example of freeway traffic optimal control is given that may be of practical importance). Index Terms - Limited-memory regulators, neural control, receding-horizon regulators, stochastic optimal control.

Published
1998

425. Distributed Fault Diagnosis using Sensor Networks and Consensus-based Filters

Author

Franco, Elisa, Olfati-Saber, Reza, Parisini, Thomas, Polycarpou, Marios M., Franco, Elisa, Olfati-Saber, Reza, Parisini, Thomas, and Polycarpou, Marios M.

Abstract

This paper considers the problem of designing distributed fault diagnosis algorithms for dynamic systems using sensor networks. A network of distributed estimation agents is designed where a bank of local Kalman filters is embedded into each sensor. The diagnosis decision is performed by a distributed hypothesis testing method that relies on a belief consensus algorithm. Under certain assumptions, both the distributed estimation and the diagnosis algorithms are derived from their centralized counterparts thanks to dynamic average-consensus techniques. Simulation results are provided to demonstrate the effectiveness of the proposed architecture and algorithm.

Published
2006

435. Distributed Clustering-based Sensor Fault Diagnosis for HVAC Systems**This work has been partially supported by the research project “Stability and Control of Power Networks with Energy Storage (STABLE-NET),” funded by the RCUK Energy Programme (contract no: EP/L014343/1) and by the Italian MIUR Project SEAL -Smart&safe Energy-aware Assisted Living (SCN-00398)

Author

Boem, Francesca, Reci, Redona, Cenedese, Angelo, and Parisini, Thomas

Abstract

The paper presents a distributed Sensor Fault Diagnosis architecture for Industrial Wireless Sensor Networks monitoring HVAC systems, by exploiting a recently proposed distributed clustering method. The approach allows the detection and isolation of multiple sensor faults and considers the possible presence of modeling uncertainties and disturbances. Detectability and isolability conditions are provided. Simulation results show the effectiveness of the proposed method for an HVAC system.

Published
2017
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