1,720,996 research outputs found
Robust Switching Control: Stability Analysis and Application to Active Disturbance Attenuation
No full textThis note deals with the problem of controlling an uncertain discrete-time linear system by means of a hybrid controller in the form of a linear system whose parameters switch among a finite number of possible configurations, called modes. We suppose that each single controller is designed in order to individually ensure robust stability for a dual-Youla uncertainty model. Then, we show that robust stability of the switching controller can be directly related to robust stability of each single controller mode, in that it is possible to implement the switching controller so that robust stability is guaranteed for any possible switching sequence. This allows one to freely select the switching signal so as to enhance performance, for instance by selecting in real time the control mode displaying the best potential performance with respect to the current operating conditions. An application of the ideas to the problem of active disturbance attenuation is presented and simulation results are shown to validate the proposed solution
Distributed averaging of exponential-class densities with discrete-time event-triggered consensus
No full textThe paper addresses discrete-time event-driven consensus on exponential-class probability densities (including Gaussian, binomial, Poisson, Rayleigh, Wishart, Inverse Wishart, and many other distributions of interest) completely specified by a finite-dimensional vector of so-called natural parameters. First, it is proved how such exponential classes are closed under Kullback-Leibler fusion (average), and how the latter is equivalent to a weighted arithmetic average over the natural parameters. Then, a novel event-driven transmission strategy is proposed in order to trade off the data-communication rate and, hence, energy consumption, versus consensus speed and accuracy. A theoretical analysis of the convergence properties of the proposed algorithm is provided by exploiting the Fisher metric as a local approximation of the Kullback-Leibler divergence. Some numerical examples are presented in order to demonstrate the effectiveness of the proposed event-driven consensus. It is expected that the latter can be successfully exploited for energy-and/or bandwidth-efficient networked state estimation
Towards direct data-driven model-free design of optimal controllers
No full textThe most critical step in modern direct data-driven control design approaches, such as virtual reference feedback tuning and non-iterative correlation-based tuning, is the choice of an adequate closed-loop reference model. Indeed, the chosen reference model should reflect the desired closed-loop performance but also be reproducible by the underlying unknown process when in closed loop with the synthesized controller. In this paper, we propose a novel approach to compute, directly from data, an “optimal” reference model along with the corresponding controller. The performance index used to define the optimality of the reference model measures the tracking error and the actuator efforts (as it is typical in performance-driven controllers such as linear-quadratic Gaussian control and model predictive control), along with a term penalizing the expected mismatch between the reference model and the actual closed-loop system. The performance index depends on the variables used to parametrize the reference model and the controller, which are optimized through a suitable combination of particle swarm optimization and virtual reference feedback tuning
Stability of consensus-based distributed estimation under denial of service
Full text linkThis article aims to study the stability properties of consensus-based distributed state estimation (DSE) in the presence of denial-of-service (DoS) attacks. Specifically, we adopt a model that describes DoS in terms of its average frequency and duration. We focus on a family of DSE algorithms enjoying stability properties in the ideal case of no attacks, and prove that such properties are preserved under DoS provided that appropriate conditions
are satisfied, with specific emphasis on the relation between transmission rate on one hand, and bounds on DoS frequency and duration on the other. Numerical simulation tests are shown concerning a target tracking case study
Switching-based adaptive disturbance attenuation with guaranteed robust stability
No full textThe paper deals with the problem of adaptive
attenuation of disturbances with uncertain and possibly timevarying
characteristics when the plant model is also affected
by a non-negligible uncertainty. The proposed solution is based
on the Adaptive Switching Control paradigm and relies on a
family of pre-synthesized controllers, such that, for any possible
operating condition, at least one controller is able to ensure
desired attenuation capabilities. Then, a supervisory unit selects
the controller providing the best potential performance on
the basis of appropriately defined test functionals. We show
that, when each of the candidate controllers robustly stabilizes
the uncertain plant, it is possible to implement the switching
controller so that robust stability is guaranteed for any possible
switching sequence. Further, we study the properties of the proposed
test functionals by analyzing the effects of the plant/model
mismatch on their inference capabilities. Simulation results are
shown to validate the proposed control solution
Resilience Analysis of a Localization Solution for Railway and Tramway Vehicles under Failures in GPS Measurement Data Acquisition
No full textIn this paper, a localization solution for railway and tramway vehicles, relying on low-cost on-board sensor technology and on a Kalman-based data fusion scheme, is tested against failures in GPS measurement acquisition. Such failures can be caused by either non-favorable operating conditions (e.g, the presence of tunnels), or intentional/accidental service disruptions. Our analysis, carried out through simulation of a three-dimensional multi-body vehicle, highlights failure conditions under which the localization solution is shown to be resilient with respect to an acceptable performance level, as well as situations that the original framework is not able to cope with. In the latter case, a variant solution, still based on low-cost on-board technology, is shown to be able to recover satisfactory performance
Event-triggered consensus on exponential families
No full textThe paper deals with discrete-time event-triggered
consensus on exponential families of probability distributions
(including Gaussian, binomial, Poisson and many other distributions
of interest) completely characterized by a finite-dimensional
vector of so called natural parameters. It is first shown how
such exponential families are closed under Kullback-Leibler
fusion (average), and that the latter is equivalent to a weighted
arithmetic average over the natural parameters. Then, a novel
event-triggered transmission strategy is proposed so as to tradeoff
data communication rate versus consensus speed and accuracy.
Some numerical examples are worked out to demonstrate the
effectiveness of the proposed method. It is expected that eventtriggered
consensus can be successfully exploited for bandwidthefficient
networked state estimation
Optimal trade-off between sample size, precision of supervision, and selection probabilities for the unbalanced fixed effects panel data model
Full text linkThis paper is focused on the unbalanced fixed effects panel data model. This is a linear regression model able to represent unobserved heterogeneity in the data, by allowing each two distinct observational units to have possibly different numbers of associated observations. We specifically address the case in which the model includes the additional possibility of controlling the conditional variance of the output given the input and the selection probabilities of the different units per unit time. This is achieved by varying the cost associated with the supervision of each training example. Assuming an upper bound on the expected total supervision cost and fixing the expected number of observed units for each instant, we analyze and optimize the trade-off between sample size, precision of supervision (the reciprocal of the conditional variance of the output) and selection probabilities. This is obtained by formulating and solving a suitable optimization problem. The formulation of such a problem is based on a large-sample upper bound on the generalization error associated with the estimates of the parameters of the unbalanced fixed effects panel data model, conditioned on the training input dataset. We prove that, under appropriate assumptions, in some cases “many but bad” examples provide a smaller large-sample upper bound on the conditional generalization error than “few but good” ones, whereas in other cases the opposite occurs. We conclude discussing possible applications of the presented results, and extensions of the proposed optimization framework to other panel data models
Energy-efficient distributed state estimation via event-triggered consensus on exponential families
No full textThe paper addresses distributed state estimation
over a peer-to-peer sensor network with an eye to communication/
energy efficiency. In particular, consensus on exponential
families of probability distributions is first introduced and
shown to be equivalent to iteratively performing convex linear
combinations on the natural parameters of such distributions.
Then, an event-triggered consensus strategy is presented and
exploited to derive a novel energy-efficient consensus Kalman
filter algorithm for distributed state estimation. Simulation
results are provided to demonstrate the effectiveness of the
proposed algorithm
Event-triggered distributed Bayes filter
No full textThe aim of this paper is to devise a strategy that is able to reduce communication bandwidth and, consequently, energy consumption in the context of distributed state estimation over a peer-to-peer sensor network. Specifically, a distributed Bayes filter with event-triggered communication is developed by enforcing each node to transmit its local information to the neighbors only when the Kullback-Leibler divergence between the current local posterior and the one predictable from the last transmission exceeds a preset threshold. The stability of the proposed event-triggered distributed Bayes filter is proved in the linear-Gaussian (Kalman filter) case. The performance of the proposed algorithm is also evaluated through simulation experiments concerning a target tracking application
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