Imperial College London
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ProfessorThomasParisini

Faculty of Engineering, Department of Electrical and Electronic Engineering

Chair in Industrial Control, Head of Group for CAP
 
 
 
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Contact

 

+44 (0)20 7594 6240t.parisini Website

 
 
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Location

 

1114Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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399 results found

Boem F, Xu Y, Fischione C, Parisini Tet al., 2015, A distributed pareto-optimal dynamic estimation method, 2015 European Control Conference (ECC 2015), Publisher: IEEE, Pages: 3673-3680

In this paper, a novel distributed model-based prediction method is proposed using sensor networks. Each sensor communicates with the neighboring nodes for state estimation based on a consensus protocol without centralized coordination. The proposed distributed estimator consists of a consensus-filtering scheme, which uses a weighted combination of sensors information, and a model-based predictor. Both the consensus-filtering weights and the model-based prediction parameter for all the state components are jointly optimized to minimize the variance and bias of the prediction error in a Pareto framework. It is assumed that the weights of the consensus-filtering phase are unequal for the different state components, unlike consensus-based approaches from literature. The state, the measurements, and the noise components are assumed to be individually correlated, but no probability distribution knowledge is assumed for the noise variables. The optimal weights are derived and it is established that the consensus-filtering weights and the model-based prediction parameters cannot be designed separately in an optimal way. The asymptotic convergence of the mean of the prediction error is demonstrated. Simulation results show the performance of the proposed method, obtaining better results than distributed Kalman filtering.

Conference paper

Chen B, Pin G, Ng WM, Hui SYR, Parisini Tet al., 2015, A parallel prefiltering approach for the identification of a biased sinusoidal signal: theory and experiments, International Journal of Adaptive Control and Signal Processing, Vol: 29, Pages: 1591-1608, ISSN: 1099-1115

The problem of estimating the amplitude, frequency, and phase of an unknown sinusoidal signal from a noisy-biased measurement is addressed in this paper by a family of parallel prefiltering schemes. The proposed methodology consists in using a pair of linear filters of specified order to generate a suitable number of auxiliary signals that are used to estimate—in an adaptive way—the frequency, the amplitude, and the phase of the sinusoid. Increasing the order of the prefilters improves the noise immunity of the estimator, at the cost of an increase of the computational complexity. Among the whole family of estimators realizable by varying the order of the filters, the simple parallel prefilters of orders 2 + 2 and 3 + 3 are discussed in detail, being the most attractive from the implementability point of view. The behavior of the two algorithms with respect to bounded external disturbances is characterized by input-to-state stability arguments. Finally, the effectiveness of the proposed technique is shown both by comparative numerical simulations and by a real experiment addressing the estimation of the frequency of the electrical mains from a noisy voltage measurement.

Journal article

Yin J, Lin D, Lee CK, Parisini T, Hui SYRet al., 2015, Front-End Monitoring of Multiple Loads in Wireless Power Transfer Systems Without Wireless Communication Systems, IEEE Transactions on Power Electronics, Vol: 31, Pages: 2510-2517, ISSN: 0885-8993

This paper describes a method for monitoring multiple loads from the front end of a wireless power transfer system without using any wireless communication systems. A mathematical approach based on scanning the frequency around the resonant frequency has been developed for deriving the load conditions. The proposal requires only information of the input voltage and current, thereby eliminating the requirements of using wireless communication systems for feedback control. The proposal has been practically confirmed in hardware prototype with good results.

Journal article

Fenu G, Parisini T, 2015, Nonparametric kernel smoothing for model-free fault symptom generation, Pages: 1619-1624

This paper describes some new developments to a recently proposed approach to the generation of fault symptoms in dynamic systems. The method is model-free, in the sense that no analytical model of the plant is needed. The kernel-smoother makes it possible to detect changes in the plant dynamics, possibly due to some malfunction. A simple sufficient condition for fault detectability is presented and an application example is given, showing the effectiveness of the proposed method.

Conference paper

Parisini T, Sacone S, 2015, Hybrid receding-horizon control: Formulation and stability analysis, Pages: 955-960

The stability analysis of an hybrid receding-horizon control scheme for non-linear discrete-time systems is addressed in the paper. The control scheme is composed of a continuous state-feedback controller and a discrete-event supervisor. Such a structure is further embedded into the structure of abstract hybrid systems. This allows to exploit the general stability theory for abstract hybrid systems to prove a novel stability result for the proposed hybrid control scheme.

Conference paper

Parisini T, 2015, Untitled, IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, Vol: 23, Pages: 413-414, ISSN: 1063-6536

Journal article

Keliris C, Polycarpou MM, Parisini T, 2015, A robust nonlinear observer-based approach for distributed fault detection of input-output interconnected systems, AUTOMATICA, Vol: 53, Pages: 408-415, ISSN: 0005-1098

Journal article

Keliris C, Polycarpou MM, Parisini T, 2015, Distributed fault diagnosis for process and sensor faults in a class of interconnected input-output nonlinear discrete-time systems, International Journal of Control, Vol: 88, Pages: 1472-1489, ISSN: 1366-5820

This paper presents a distributed fault diagnosis scheme able to deal with process and sensor faults in an integrated way for aclass of interconnected input–output nonlinear uncertain discrete-time systems. A robust distributed fault detection schemeis designed, where each interconnected subsystem is monitored by its respective fault detection agent, and according to thedecisions of these agents, further information regarding the type of the fault can be deduced. As it is shown, a process faultoccurring in one subsystem can only be detected by its corresponding detection agent whereas a sensor fault in a subsystemcan be detected by either its corresponding detection agent or the detection agent of another subsystem that is affected by thesubsystem where the sensor fault occurred. This discriminating factor is exploited for the derivation of a high-level isolationscheme. Moreover, process and sensor fault detectability conditions characterising quantitatively the class of detectable faultsare derived. Finally, a simulation example is used to illustrate the effectiveness of the proposed distributed fault detectionscheme.

Journal article

Pin G, Wang Y, Chen B, Parisini Tet al., 2015, Semi-Global Direct Estimation of Multiple Frequencies with an Adaptive Observer having Minimal Parameterization, 54th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 3693-3698, ISSN: 0743-1546

Conference paper

Chen B, Pin G, Parisini T, 2015, Frequency Estimation of Periodic Signals: an Adaptive Observer approach, American Control Conference, Publisher: IEEE, Pages: 2505-2510, ISSN: 0743-1619

Conference paper

Pin G, Chen B, Parisini T, 2015, Deadbeat Kernel-based Frequency Estimation of a Biased Sinusoidal Signal, European Control Conference (ECC), Publisher: IEEE, Pages: 479-484

Conference paper

Pin G, Chen B, Parisini T, 2015, The Modulation Integral Observer for Linear Continuous-Time Systems, European Control Conference (ECC), Publisher: IEEE, Pages: 2932-2939

Conference paper

Zhou Y, Parisini T, Polycarpou MM, 2015, Detection of Drift Sensor Faults in a Class of Nonlinear Uncertain Systems, 54th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 3169-3174, ISSN: 0743-1546

Conference paper

Ascencio P, Astolfi A, Parisini T, 2015, Backstepping PDE Design, Volterra and Fredholm Operators: a Convex Optimization Approach, 54th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 7048-7053, ISSN: 0743-1546

Conference paper

Khalili M, Zhang X, Cao Y, Polycarpou MM, Parisini Tet al., 2015, Distributed Adaptive Fault-Tolerant Control of Nonlinear Uncertain Second-order Multi-agent Systems, 54th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 4480-4485, ISSN: 0743-1546

Conference paper

Assalone A, Pin G, Parisini T, 2015, Kernel-based Continuous-Time Identification of Hammerstein Models: Application to the case of Ankle Joint Stiffness Dynamics, European Control Conference (ECC), Publisher: IEEE, Pages: 2015-2020

Conference paper

Chen B, Pin G, Ng WM, Lee CK, Hui SYR, Parisini Tet al., 2014, An adaptive observer-based switched methodology for the identification of a perturbed sinusoidal signal: theory and experiments, IEEE Transactions on Signal Processing, Vol: 62, Pages: 6355-6365, ISSN: 1053-587X

This paper deals with a novel adaptive observer-based technique for estimating the amplitude, frequency, and phase of a single sinusoidal signal from a measurement affected by structured and unstructured disturbances. The structured disturbances are modeled as a time-polynomial so as to represent bias and drift phenomena typically present in applications, whereas the unstructured disturbances are modelled as bounded noise signals. The proposed estimation technique exploits a specific adaptive observer scheme equipped with a switching criterion allowing to properly address in a stable way poor excitation scenarios. The estimator's stability properties are analyzed by input-to-state stability arguments. The practical characteristics of the proposed estimation approach are evaluated and compared with other existing tools by extensive simulation trials. Real experimental results are provided as well.

Journal article

Pin G, Chen B, Parisini T, Bodson Met al., 2014, Robust Sinusoid Identification With Structured and Unstructured Measurement Uncertainties, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 59, Pages: 1588-1593, ISSN: 0018-9286

Journal article

Zhang Q, Zhang X, Polycarpou MM, Parisini Tet al., 2014, Distributed sensor fault detection and isolation for multimachine power systems, INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Vol: 24, Pages: 1403-1430, ISSN: 1049-8923

Journal article

Parisini T, 2014, Untitled, IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, Vol: 22, Pages: 825-826, ISSN: 1063-6536

Journal article

Pin G, Parisini T, 2014, On the Robustness of Nominal Nonlinear Minimum-Time Control and Extension to Non-Robustly Controllable Target Sets, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 59, Pages: 863-875, ISSN: 0018-9286

Journal article

Pin G, Filippo M, Pellegrino FA, Parisini Tet al., 2014, Approximate off-line receding horizon control of constrained nonlinear discrete-time systems, Pages: 2420-2425

The present paper concerns the design of approximate off-line model predictive control laws for nonlinear discrete-time systems subject to hard constraints on state and input variables. The possibility to obtain an approximate receding horizon control law by performing off-line optimization, leads to a dramatic reduction of the real-time computational complexity with respect to on-line algorithms, and allows the application of the developed control technique to plants with fast dynamics, that require small sampling periods. The main feature of the proposed approximation scheme consists in the possibility to cope with possibly discontinuous state-feedback control laws, while guaranteeing the fulfillment of hard constraints on state and input variables despite the perturbations due to the use of an approximate controller. Finally, the resulting closed-loop system is shown to be input-to-state-stable with respect to the approximation-induced perturbations.

Conference paper

Chen B, Pin G, Parisini T, 2014, An adaptive observer-based estimator for multi-sinusoidal signals, Pages: 3450-3455, ISSN: 0743-1619

This paper deals with a novel robust estimation methodology yielding the amplitudes, frequencies and phases of the components of a biased multi-sinusoidal signal in presence of a bounded disturbance on the measurement. The proposed method is based on a suitable adaptive observer in which the parameters' adaptation law is equipped with an excitation-based switching logic. The stability analysis shows the existence of a set of tuning parameter guaranteeing that the estimator's dynamics is input-to-state stable with respect to bounded measurement disturbances. The effectiveness of the algorithm is illustrated by some simulation examples also reporting a few comparison results. © 2014 American Automatic Control Council.

Conference paper

Parisini T, Tempo R, 2014, 52nd IEEE conference on decision and control, Pages: 93-105, ISSN: 1066-033X

The 52nd IEEE Conference on Decision and Control (CDC) was held in Florence, Italy from December 10 13, 2013. The CDC was and engineering conference dedicated to the advancement of the theory and practice of systems and control. It brought together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, systems and control, and related areas. The event was hosted by the IEEE Control Systems Society (CSS) in cooperation with the Society for Industrial and Applied Mathematics (SIAM) and the Institute for Operations Research and the Management Sciences (INFORMS). The 52nd IEEE CDC had 1604 registrants, including 546 student registrations.

Conference paper

Keliris C, Polycarpou MM, Parisini T, 2014, A Distributed Fault Diagnosis Approach Utilizing Adaptive Approximation for a Class of Interconnected Continuous-Time Nonlinear Systems, 53rd IEEE Annual Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 6536-6541, ISSN: 0743-1546

Conference paper

Chen B, Pin G, Parisini T, 2014, Robust Parametric Estimation of Biased Sinusoidal Signals: a Parallel Pre-filtering Approach, 53rd IEEE Annual Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 1804-1809, ISSN: 0743-1546

Conference paper

Pin G, Karimi-Ghartemani M, Chen B, Parisini Tet al., 2014, Sinusoidal Signal Estimation from a Noisy-Biased Measurement by an Enhanced PLL with Generalized Error Filtering, 53rd IEEE Annual Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 4071-4076, ISSN: 0743-1546

Conference paper

Riverso S, Boem F, Ferrari-Trecate G, Parisini Tet al., 2014, Fault Diagnosis and Control-reconfiguration in Large-scale Systems: a Plug-and-Play Approach, 53rd IEEE Annual Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 4977-4982, ISSN: 0743-1546

Conference paper

Keliris C, Polycarpou MM, Parisini T, 2013, A Distributed Fault Detection Filtering Approach for a Class of Interconnected Continuous-Time Nonlinear Systems, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 58, Pages: 2032-2047, ISSN: 0018-9286

Journal article

Parisini T, Tempo R, 2013, 52nd IEEE conference on decision and control in Florence, Italy, Pages: 85-89, ISSN: 1066-033X

The 52nd IEEE Conference on Decision and Control was held in Florence, Italy , during December 10-13, 2013. All the members of the operating committee are contributing significantly, with diligence and expertise. The CDC 2013 will feature three plenary lectures and the Bode lecture. The Bode lecture will be delivered by Prof. B. Ross Barmish and the three plenary speakers are Prof. Raffaello D'Andrea, Prof. Lucy Pao, and Prof. Jeff S. Shamma. Rossella Spangaro of The Office, Trieste, is providing great support for planning and on-site operations. Florence's historic center attracts millions of tourists each year, and the city was declared a World Heritage Site by UNESCO in 1982. Florence is extremely rich in palaces and buildings, from various eras.

Conference paper

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