Networked distributed fusion estimation under uncertain outputs with random transmission delays, packet losses and multi-packet processing

This paper investigates the distributed fusion estimation problem for networked systems whose mul- tisensor measured outputs involve uncertainties modelled by random parameter matrices. Each sensor transmits its measured outputs to a local processor over different communication channels and random failures –one-step delays and packet dropouts–are assumed to occur during the transmission. White sequences of Bernoulli random variables with different probabilities are introduced to describe the ob- servations that are used to update the estimators at each sampling time. Due to the transmission failures, each local processor may receive either one or two data packets, or even nothing and, when the current measurement does not arrive on time, its predictor is used in the design of the estimators to compensate the lack of updated information. By using an innovation approach, local least-squares linear estimators (filter and fixed-point smoother) are obtained at the individual local processors, without requiring the signal evolution model. From these local estimators, distributed fusion filtering and smoothing estimators weighted by matrices are obtained in a unified way, by applying the least-squares criterion. A simula- tion study is presented to examine the performance of the estimators and the influence that both sensor uncertainties and transmission failures have on the estimation accuracy.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)

Centralized Fusion Approach to the Estimation Problem with Multi-Packet Processing under Uncertainty in Outputs and Transmissions

This paper is concerned with the least-squares linear centralized estimation problem in multi-sensor network systems from measured outputs with uncertainties modeled by random parameter matrices. These measurements are transmitted to a central processor over different communication channels, and owing to the unreliability of the network, random one-step delays and packet dropouts are assumed to occur during the transmissions. In order to avoid network congestion, at each sampling time, each sensor’s data packet is transmitted just once, but due to the uncertainty of the transmissions, the processing center may receive either one packet, two packets, or nothing. Different white sequences of Bernoulli random variables are introduced to describe the observations used to update the estimators at each sampling time. To address the centralized estimation problem, augmented observation vectors are defined by accumulating the raw measurements from the different sensors, and when the current measurement of a sensor does not arrive on time, the corresponding component of the augmented measured output predictor is used as compensation in the estimator design. Through an innovation approach, centralized fusion estimators, including predictors, filters, and smoothers are obtained by recursive algorithms without requiring the signal evolution model. A numerical example is presented to show how uncertain systems with state-dependent multiplicative noise can be covered by the proposed model and how the estimation accuracy is influenced by both sensor uncertainties and transmission failures.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)

Robust Controller for Delays and Packet Dropout Avoidance in Solar-Power Wireless Network

Solar Wireless Networked Control Systems (SWNCS) are a style of distributed control systems where sensors, actuators, and controllers are interconnected via a wireless communication network. This system setup has the benefit of low cost, flexibility, low weight, no wiring and simplicity of system diagnoses and maintenance. However, it also unavoidably calls some wireless network time delays and packet dropout into the design procedure. Solar lighting system offers a clean environment, therefore able to continue for a long period. SWNCS also offers multi Service infrastructure solution for both developed and undeveloped countries. The system provides wireless controller lighting, wireless communications network (WI-FI/WIMAX), CCTV surveillance, and wireless sensor for weather measurement which are all powered by solar energy

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

In this paper, some recent advances on the estimation, filtering and fusion for networked systems are reviewed. Firstly, the network-induced phenomena under consideration are briefly recalled including missing/fading measurements, signal quantization, sensor saturations, communication delays, and randomly occurring incomplete information. Secondly, the developments of the estimation, filtering and fusion for networked systems from four aspects (linear networked systems, nonlinear networked systems, complex networks and sensor networks) are reviewed comprehensively. Subsequently, some recent results on the estimation, filtering and fusion for systems with the network-induced phenomena are reviewed in great detail. In particular, some latest results on the multi-objective filtering problems for time-varying nonlinear networked systems are summarized. Finally, conclusions are given and several possible research directions concerning the estimation, filtering, and fusion for networked systems are highlighted

State Estimation with Unconventional and Networked Measurements

This dissertation consists of two main parts. One is about state estimation with two types of unconventional measurements and the other is about two types of network-induced state estimation problems. The two types of unconventional measurements considered are noise-free measurements and set measurements. State estimation with them has numerous real supports. For state estimation with noisy and noise-free measurements, two sequential forms of the batch linear minimum mean-squared error (LMMSE) estimator are obtained to reduce the computational complexity. Inspired by the estimation with quantized measurements developed by Curry [28], under a Gaussian assumption, the minimum mean-squared error (MMSE) state estimator with point measurements and set measurements of any shape is proposed by discretizing continuous set measurements. State estimation under constraints, which are special cases of the more general framework, has some interesting properties. It is found that under certain conditions, although constraints are indispensable in the evolution of the state, update by treating them as measurements is redundant in filtering. The two types of network-induced estimation problems considered are optimal state estimation in the presence of multiple packet dropouts and optimal distributed estimation fusion with transformed data. An alternative form of LMMSE estimation in the presence of multiple packet dropouts, which can overcome the shortcomings of two existing ones, is proposed first. Then under a Gaussian assumption, the MMSE estimation is also obtained based on a hard decision by comparing the measurements at two consecutive time instants. It is pointed out that if this comparison is legitimate, our simple MMSE solution largely nullifies existing work on this problem. By taking linear transformation of the raw measurements received by each sensor, two optimal distributed fusion algorithms are proposed. In terms of optimality, communication and computational requirements, three nice properties make them attractive

State Estimation with Unconventional and Networked Measurements

This dissertation consists of two main parts. One is about state estimation with two types of unconventional measurements and the other is about two types of network-induced state estimation problems. The two types of unconventional measurements considered are noise-free measurements and set measurements. State estimation with them has numerous real supports. For state estimation with noisy and noise-free measurements, two sequential forms of the batch linear minimum mean-squared error (LMMSE) estimator are obtained to reduce the computational complexity. Inspired by the estimation with quantized measurements developed by Curry [28], under a Gaussian assumption, the minimum mean-squared error (MMSE) state estimator with point measurements and set measurements of any shape is proposed by discretizing continuous set measurements. State estimation under constraints, which are special cases of the more general framework, has some interesting properties. It is found that under certain conditions, although constraints are indispensable in the evolution of the state, update by treating them as measurements is redundant in filtering. The two types of network-induced estimation problems considered are optimal state estimation in the presence of multiple packet dropouts and optimal distributed estimation fusion with transformed data. An alternative form of LMMSE estimation in the presence of multiple packet dropouts, which can overcome the shortcomings of two existing ones, is proposed first. Then under a Gaussian assumption, the MMSE estimation is also obtained based on a hard decision by comparing the measurements at two consecutive time instants. It is pointed out that if this comparison is legitimate, our simple MMSE solution largely nullifies existing work on this problem. By taking linear transformation of the raw measurements received by each sensor, two optimal distributed fusion algorithms are proposed. In terms of optimality, communication and computational requirements, three nice properties make them attractive

Networked Control System Design and Parameter Estimation

Networked control systems (NCSs) are a kind of distributed control systems in which the data between control components are exchanged via communication networks. Because of the attractive advantages of NCSs such as reduced system wiring, low weight, and ease of system diagnosis and maintenance, the research on NCSs has received much attention in recent years. The first part (Chapter 2 - Chapter 4) of the thesis is devoted to designing new controllers for NCSs by incorporating the network-induced delays. The thesis also conducts research on filtering of multirate systems and identification of Hammerstein systems in the second part (Chapter 5 - Chapter 6). Network-induced delays exist in both sensor-to-controller (S-C) and controller-to-actuator (C-A) links. A novel two-mode-dependent control scheme is proposed, in which the to-be-designed controller depends on both S-C and C-A delays. The resulting closed-loop system is a special jump linear system. Then, the conditions for stochastic stability are obtained in terms of a set of linear matrix inequalities (LMIs) with nonconvex constraints, which can be efficiently solved by a sequential LMI optimization algorithm. Further, the control synthesis problem for the NCSs is considered. The definitions of H₂ and H∞ norms for the special system are first proposed. Also, the plant uncertainties are considered in the design. Finally, the robust mixed H₂/H∞ control problem is solved under the framework of LMIs. To compensate for both S-C and C-A delays modeled by Markov chains, the generalized predictive control method is modified to choose certain predicted future control signal as the current control effort on the actuator node, whenever the control signal is delayed. Further, stability criteria in terms of LMIs are provided to check the system stability. The proposed method is also tested on an experimental hydraulic position control system. Multirate systems exist in many practical applications where different sampling rates co-exist in the same system. The l₂-l∞ filtering problem for multirate systems is considered in the thesis. By using the lifting technique, the system is first transformed to a linear time-invariant one, and then the filter design is formulated as an optimization problem which can be solved by using LMI techniques. Hammerstein model consists of a static nonlinear block followed in series by a linear dynamic system, which can find many applications in different areas. New switching sequences to handle the two-segment nonlinearities are proposed in this thesis. This leads to less parameters to be estimated and thus reduces the computational cost. Further, a stochastic gradient algorithm based on the idea of replacing the unmeasurable terms with their estimates is developed to identify the Hammerstein model with two-segment nonlinearities. Finally, several open problems are listed as the future research directions

Quadratic estimation for stochastic systems in the presence of random parameter matrices, time-correlated additive noise and deception attacks

This research was suported by the ``Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación'' of Spain and the European Regional Development Fund [grant number PID2021-124486NB-I00].Networked systems usually face different random uncertainties that make the performance of the least-squares (LS) linear filter decline significantly. For this reason, great attention has been paid to the search for other kinds of suboptimal estimators. Among them, the LS quadratic estimation approach has attracted considerable interest in the scientific community for its balance between computational complexity and estimation accuracy. When it comes to stochastic systems subject to different random uncertainties and deception attacks, the quadratic estimator design has not been deeply studied. In this paper, using covariance information, the LS quadratic filtering and fixed-point smoothing problems are addressed under the assumption that the measurements are perturbed by a time-correlated additive noise, as well as affected by random parameter matrices and exposed to random deception attacks. The use of random parameter matrices covers a wide range of common uncertainties and random failures, thus better reflecting the engineering reality. The signal and observation vectors are augmented by stacking the original vectors with their second-order Kronecker powers; then, the linear estimator of the original signal based on the augmented observations provides the required quadratic estimator. A simulation example illustrates the superiority of the proposed quadratic estimators over the conventional linear ones and the effect of the deception attacks on the estimation performance.Ministerio de Ciencia e Innovación MICINNEuropean Regional Development Fund PID2021-124486NB-I00 ERDFAgencia Estatal de Investigación AE

Design and implementation of event-based multi-rate controllers for networked control systems

Tesis por compendio[ES] Con esta tesis se pretende dar solución a algunos de los problemas más habituales que aparecen en los Sistemas de control basados en red (NCS) como son los retardos variables en el tiempo, las pérdidas y el desorden de paquetes, y la restricción de ancho de banda y de recursos computacionales y energéticos de los dispositivos que forman parte del sistema de control. Para ello se ha planteado la integración de técnicas de control multifrecuencial, de control basado en paquetes, de control basado en predictor y de control basado en eventos. Los diseños de control realizados se han simulado utilizando Matlab-Simulink y Truetime, se ha analizado su estabilidad mediante LMIs y QFT, y se han validado experimentalmente en un péndulo invertido, un robot cartesiano 3D y en robots móviles de bajo coste. El artículo 1 aborda el control basado en eventos, el cual minimiza el ancho de banda consumido en el NCS mediante un control basado en eventos periódicos y presenta un método para obtener sus parámetros óptimos para el sistema específico en que se utilice. Los artículos 2, 4 y 6 añaden el control basado en paquetes, así como el control multifrecuencia, que aborda problemas de falta de datos por bajo uso del sensor y los retardos, pérdidas y desórdenes de paquetes en la red. También afrontan, mediante tecnicas de predicción basadas en un filtro de Kalman multifrecuencia variable en el tiempo, los problemas de ruido y perturbaciones, así como la observación de los estados completos del sistema. El artículo 7 hace frente a un modelo no lineal que utiliza las anteriores soluciones junto con un filtro de Kalman extendido para presentar otro tipo de estructura para un vehículo autónomo que, gracias a la información futura obtenida mediante estas técnicas, puede realizar de forma remota tareas de alto nivel como es la toma de decisiones y la monitorización de variables. Los artículos 3 y 5, presentan una forma de obtener y analizar la respuesta en frecuencia de sistemas SISO multifrecuencia y estudian su comportamiento ante ciertas incertidumbres o problemas en la red haciendo uso de procedimientos QFT.[CA] Amb aquesta tesi es pretén donar solució a alguns dels problemes més habituals que apareixen als Sistemes de Control Basats en xarxa (NCS) com son els retards d'accés i transferència variables en el temps, les pèrdues y desordenament de paquets, i la restricció d'ampli de banda així com de recursos computacionals i energètics dels dispositius que foment part del sistema de control. Per tal de resoldre'ls s'ha plantejat la integració de tècniques de control multifreqüencial, de control basat en paquets, de control basat en predictor i de control basat en events. Els dissenys de control realitzats s'han simulat fent ús de Matlab-Simulink i de TrueTime, s'ha analitzat la seua estabilitat mitjançant LMIs i QFT, i s'han validat experimentalment en un pèndul invertit, un robot cartesià 3D i en robots mòbils de baix cost. L'article 1 aborda el control basat en events, el qual minimitza l'ampli de banda consumit a l'NCS mitjançant un control basat en events periòdics i presenta un mètode per a obtindré els seus paràmetres òptims per al sistema específic en el qual s'utilitza. Els articles 2, 4 i 6 afegeixen el control basat en paquets, així com el control multifreqüència, que aborda problemes de falta de dades per el baix us del sensor i els retards, pèrdues i desordre de paquets en la xarxa. També afronten, mitjançant tècniques de predicció basades en un filtre de Kalman multifreqüència variable en el temps. Els problemes de soroll i pertorbacions, així com la observació dels estats complets del sistema. L'article 7 fa referència a un model no lineal que utilitza les anteriors solucions junt a un filtre de Kalman estès per a presentar altre tipus d'estructura per a un vehicle autònom que, gracies a la informació futura obtinguda mitjançant aquestes tècniques, pot realitzar de manera remota tasques d'alt nivell com son la presa de decisions i la monitorització de variables. Els articles 3 y 5 presenten la manera d'obtindre i analitzar la resposta en frequencia de sistemes SISO multifreqüència i estudien el seu comportament front a certes incerteses o problemes en la xarxa fent us de procediments QFT.[EN] This thesis attempts to solve some of the most frequent issues that appear in Networked Control Systems (NCS), such as time-varying delays, packet losses and packet disorders and the bandwidth limitation. Other frequent problems are scarce computational and energy resources of the local system devices. Thus, it is proposed to integrate multirate control, packet-based control, predictor-based control and event-based control techniques. The control designs have been simulated using Matlab-Simulink and Truetime, the stability has been analysed by LMIs and QFT, and the experimental validation has been done on an inverted pendulum, a 3D cartesian robot and in low-cost mobile robots. Paper 1 addresses event-based control, which minimizes the bandwidth consumed in NCS through a periodic event-triggered control and presents a method to obtain the optimal parameters for the specific system used. Papers 2, 4 and 6 include packet-based control and multirate control, addressing problems such as network delays, packet dropouts and packet disorders, and the scarce data due to low sensor usage in order to save battery in sensing tasks and transmissions of the sensed data. Also addressed, is how despite the existence of measurement noise and disturbances, time-varying dual-rate Kalman filter based prediction techniques observe the complete state of the system. Paper 7 tackles a non-linear model that uses all the previous solutions together with an extended Kalman filter to present another type of structure for an autonomous vehicle that, due to future information obtained through these techniques, can remotely carry out high level tasks, such as decision making and monitoring of variables. Papers 3 and 5, present a method for obtaining and analyzing the SISO dual-rate frequency response and using QFT procedures to study its behavior when faced with specific uncertainties or network problems.This work was supported by the Spanish Ministerio de Economía y Competitividad under Grant referenced TEC2012-31506.Alcaina Acosta, JJ. (2020). Design and implementation of event-based multi-rate controllers for networked control systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/159884TESISCompendi