Luigi Chisci

Systems with persistent disturbances: predictive control with restricted constraints

Predictive regulation of linear discrete-time systems subject to unknown but bounded disturbances and to state/control constraints is addressed. An algorithm based on constraint restrictions is presented and its stability properties are analysed.

Brief paper: Recursive state bounding by parallelotopes

In this paper, the problem of recursively estimating the state uncertainty set of a discrete-time linear dynamical system is addressed. A novel approach based on minimum-volume bounding parallelotopes is introduced and an algorithm of polynomial complexity is derived. Simulation results and performance comparisons with ellipsoidal recursive state-bounding algorithms are also given.

Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines

This paper addresses the prediction of epileptic seizures from the online analysis of EEG data. This problem is of paramount importance for the realization of monitoring/control units to be implanted on drug-resistant epileptic patients. The proposed solution relies in a novel way on autoregressive modeling of the EEG time series and combines a least-squares parameter estimator for EEG feature extraction along with a support vector machine (SVM) for binary classification between preictal/ictal and interictal states. This choice is characterized by low computational requirements compatible with a real-time implementation of the overall system. Moreover, experimental results on the Freiburg dataset exhibited correct prediction of all seizures (100 % sensitivity) and, due to a novel regularization of the SVM classifier based on the Kalman filter, also a low false alarm rate.

Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability

This paper addresses distributed state estimation over a sensor network wherein each node-equipped with processing, communication and sensing capabilities-repeatedly fuses local information with information from the neighbors. Estimation is cast in a Bayesian framework and an information-theoretic approach to data fusion is adopted by formulating a consensus problem on the Kullback-Leibler average of the local probability density functions (PDFs) to be fused. Exploiting such a consensus on local posterior PDFs, a novel distributed state estimator is derived. It is shown that, for a linear system, the proposed estimator guarantees stability, i.e. mean-square boundedness of the state estimation error in all network nodes, under the minimal requirements of network connectivity and system observability, and for any number of consensus steps. Finally, simulation experiments demonstrate the validity of the proposed approach.

Block recursive parallelotopic bounding in set membership identification

In this paper, a procedure for the recursive approximation of the feasible parameter set of a linear model with a set membership uncertainty description is provided. Approximating regions of parallelotopic shape are considered. The new contribution of this paper consists in devising a general procedure allowing for block processing of q > 1 measurements at each recursion step. Based on this, several approximation strategies for polytopes are presented. Simulation experiments are performed, showing the effectiveness of the algorithm as compared to the original algorithm processing one measurement at each step.In this work, we formulate a new approach to simultaneous constrained model predictive control and identification (MPCI). The proposed approach relies on the development of a persistent excitation (PE) criterion for processes described by DARX models. That PE criterion is used as an additional constraint in the standard on-line optimization of MPC. The resulting on-line optimization problem of MPCI is handled by successively solving a series of semi-definite programming problems. Advantages of MPCI in comparison to other closed-loop identification methods are (a) Constraints on process inputs and outputs are handled explicitly, (b) Deterioration of output regulation is kept to a minimum, while closed-loop identification is performed. The applicability of the method is illustrated by a number of simulation studies. Theoretical and computational issues for further investigation are suggested.

Consensus CPHD Filter for Distributed Multitarget Tracking

The paper addresses distributed multitarget tracking over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. The contribution has been to develop a novel consensus Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filter that provides a fully distributed, scalable and computationally efficient solution to the problem. The effectiveness of the proposed approach is demonstrated via simulation experiments on realistic scenarios.

Consensus-Based Linear and Nonlinear Filtering

This note addresses Distributed State Estimation (DSE) over sensor networks. Two existing consensus approaches for DSE, i.e., consensus on information (CI) and consensus on measurements (CM), are combined to provide a novel class of hybrid consensus filters (named Hybrid CMCI) which enjoy the complementary benefits of CM and CI. Novel theoretical results, limitedly to linear systems, on the guaranteed stability of the Hybrid CMCI filters under collective observability and network connectivity are proved. Finally, the effectiveness of the proposed class of consensus filters is evaluated on a target tracking case study with both linear and nonlinear sensors.

Brief paper: Data-driven communication for state estimation with sensor networks

This paper deals with the problem of estimating the state of a discrete-time linear stochastic dynamical system on the basis of data collected from multiple sensors subject to a limitation on the communication rate from the sensors. More specifically, the attention is devoted to a centralized sensor network consisting of: (1) multiple remote nodes which collect measurements of the given system, compute state estimates at the full measurement rate and transmit data (either raw measurements or estimates) at a reduced communication rate; (2) a fusion node that, based on received data, provides an estimate of the system state at the full rate. Local data-driven transmission strategies are considered and issues related to the stability and performance of such strategies are investigated. Simulation results confirm the effectiveness of the proposed strategies.

On the stabilizing property of SIORHC

It is shown that the stabilizing property of SIORHC (stabilizing I/O receding horizon control) holds for general stabilizable discrete-time linear plants irrespective of the condition that the plant has no poles at the origin.

Dual-Receding Horizon Control of Constrained Discrete Time Systems

We present a dual-receding horizon controller with deadzone for the control of constrained linear discrete-time systems. This controller relaxes the feasibility requirements of the classical receding horizon controller with terminal equality constraint, while preserves a guaranteed stabilising property for both stable and unstable plants.