Set-membership filtering for time-varying systems with mixed time-delays under Round-Robin and Weighted Try-Once-Discard protocols

This paper is concerned with the set-membership filtering problem for a class of time-varying systems with mixed time-delays and communication protocols. Two kinds of well-known protocols (Round-Robin protocol and Weighted Try-Once-Discard protocol) are considered, with which the data transmission between the sensor nodes and the filter is implemented via a shared communication network that allows only one sensor node to send its measurement data at each transmission instant in order to prevent the data from collisions. The transmission order of sensor nodes is orchestrated by the underlying protocol of the network. The aim of the problem addressed is to design a set-membership filter capable of confining the state estimate of the system to certain ellipsoidal region subject to the bounded non-Gaussian noises. Sufficient condition is first derived for the existence of the desired filter at each time step in terms of a recursive algorithm. Then, two optimization problems are solved by optimizing the constraint ellipsoid of the estimation error subject to the underlying protocol. Simulation results demonstrate the effectiveness of the proposed filter design scheme

Set-Membership Filtering Subject to Impulsive Measurement Outliers: A Recursive Algorithm

National Natural Science Foundation of China; China Postdoctoral Science Foundatio

Distributed Set-Based Observers Using Diffusion Strategy

Distributed estimation is more robust against single points of failure and requires less communication overhead compared to the centralized version. Among distributed estimation techniques, set-based estimation has gained much attention as it provides estimation guarantees for safety-critical applications and copes with unknown but bounded uncertainties. We propose two distributed set-based observers using interval-based and set-membership approaches for a linear discrete-time dynamical system with bounded modeling and measurement uncertainties. Both algorithms utilize a new over-approximating zonotopes intersection step named the set-based diffusion step. We use the term diffusion since our intersection of zonotopes formula resembles the traditional diffusion step in the stochastic Kalman filter. Our new zonotopes intersection takes linear time. Our set-based diffusion step decreases the estimation errors and the size of estimated sets and can be seen as a lightweight approach to achieve partial consensus between the distributed estimated sets. Every node shares its measurement with its neighbor in the measurement update step. The neighbors intersect their estimated sets constituting our proposed set-based diffusion step. We represent sets as zonotopes since they compactly represent high-dimensional sets, and they are closed under linear mapping and Minkowski addition. The applicability of our algorithms is demonstrated by a localization example. All used data and code to recreate our findings are publicly availabl

Distributed estimation techniques forcyber-physical systems

Nowadays, with the increasing use of wireless networks, embedded devices and agents with processing and sensing capabilities, the development of distributed estimation techniques has become vital to monitor important variables of the system that are not directly available. Numerous distributed estimation techniques have been proposed in the literature according to the model of the system, noises and disturbances. One of the main objectives of this thesis is to search all those works that deal with distributed estimation techniques applied to cyber-physical systems, system of systems and heterogeneous systems, through using systematic review methodology. Even though systematic reviews are not the common way to survey a topic in the control community, they provide a rigorous, robust and objective formula that should not be ignored. The presented systematic review incorporates and adapts the guidelines recommended in other disciplines to the field of automation and control and presents a brief description of the different phases that constitute a systematic review. Undertaking the systematic review many gaps were discovered: it deserves to be remarked that some estimators are not applied to cyber-physical systems, such as sliding mode observers or set-membership observers. Subsequently, one of these particular techniques was chosen, set-membership estimator, to develop new applications for cyber-physical systems. This introduces the other objectives of the thesis, i.e. to present two novel formulations of distributed set-membership estimators. Both estimators use a multi-hop decomposition, so the dynamics of the system is rewritten to present a cascaded implementation of the distributed set-membership observer, decoupling the influence of the non-observable modes to the observable ones. So each agent must find a different set for each sub-space, instead of a unique set for all the states. Two different approaches have been used to address the same problem, that is, to design a guaranteed distributed estimation method for linear full-coupled systems affected by bounded disturbances, to be implemented in a set of distributed agents that need to communicate and collaborate to achieve this goal

Energy-to-Peak State Estimation With Intermittent Measurement Outliers: The Single-Output Case

National Natural Science Foundation of China (Grant Number: 61703245, 61873148, 61933007 and 61873058); China Postdoctoral Science Foundation (Grant Number: 2018T110702); Postdoctoral Special Innovation Foundation of Shandong province of China (Grant Number: 201701015); Natural Science Foundation of Heilongjiang Province of China (Grant Number: ZD2019F001); European Unions Horizon 2020 Research and Innovation Programme (Grant Number: 820776 (INTEGRADDE)); Royal Society of the U.K.; Alexander von Humboldt Foundation of Germany

Probabilistic data-driven methods for forecasting, identification and control

This dissertation presents contributions mainly in three different fields: system identification, probabilistic forecasting and stochastic control. Thanks to the concept of dissimilarity and by defining an appropriate dissimilarity function, it is shown that a family of predictors can be obtained. First, a predictor to compute nominal forecastings of a time-series or a dynamical system is presented. The effectiveness of the predictor is shown by means of a numerical example, where daily predictions of a stock index are computed. The obtained results turn out to be better than those obtained with popular machine learning techniques like Neural Networks. Similarly, the aforementioned dissimilarity function can be used to compute conditioned probability distributions. By means of the obtained distributions, interval predictions can be made by using the concept of quantiles. However, in order to do that, it is necessary to integrate the distribution for all the possible values of the output. As this numerical integration process is computationally expensive, an alternate method bypassing the computation of the probability distribution is also proposed. Not only is computationally cheaper but it also allows to compute prediction regions, which are the multivariate version of the interval predictions. Both methods present better results than other baseline approaches in a set of examples, including a stock forecasting example and the prediction of the Lorenz attractor. Furthermore, new methods to obtain models of nonlinear systems by means of input-output data are proposed. Two different model approaches are presented: a local data approach and a kernel-based approach. A kalman filter can be added to improve the quality of the predictions. It is shown that the forecasting performance of the proposed models is better than other machine learning methods in several examples, such as the forecasting of the sunspot number and the R¨ossler attractor. Also, as these models are suitable for Model Predictive Control (MPC), new MPC formulations are proposed. Thanks to the distinctive features of the proposed models, the nonlinear MPC problem can be posed as a simple quadratic programming problem. Finally, by means of a simulation example and a real experiment, it is shown that the controller performs adequately. On the other hand, in the field of stochastic control, several methods to bound the constraint violation rate of any controller under the presence of bounded or unbounded disturbances are presented. These can be used, for example, to tune some hyperparameters of the controller. Some simulation examples are proposed in order to show the functioning of the algorithms. One of these examples considers the management of a data center. Here, an energy-efficient MPC-inspired policy is developed in order to reduce the electricity consumption while keeping the quality of service at acceptable levels

Fault-tolerant Stochastic Distributed Systems

The present doctoral thesis discusses the design of fault-tolerant distributed systems, placing emphasis in addressing the case where the actions of the nodes or their interactions are stochastic. The main objective is to detect and identify faults to improve the resilience of distributed systems to crash-type faults, as well as detecting the presence of malicious nodes in pursuit of exploiting the network. The proposed analysis considers malicious agents and computational solutions to detect faults. Crash-type faults, where the affected component ceases to perform its task, are tackled in this thesis by introducing stochastic decisions in deterministic distributed algorithms. Prime importance is placed on providing guarantees and rates of convergence for the steady-state solution. The scenarios of a social network (state-dependent example) and consensus (time- dependent example) are addressed, proving convergence. The proposed algorithms are capable of dealing with packet drops, delays, medium access competition, and, in particular, nodes failing and/or losing network connectivity. The concept of Set-Valued Observers (SVOs) is used as a tool to detect faults in a worst-case scenario, i.e., when a malicious agent can select the most unfavorable sequence of communi- cations and inject a signal of arbitrary magnitude. For other types of faults, it is introduced the concept of Stochastic Set-Valued Observers (SSVOs) which produce a confidence set where the state is known to belong with at least a pre-specified probability. It is shown how, for an algorithm of consensus, it is possible to exploit the structure of the problem to reduce the computational complexity of the solution. The main result allows discarding interactions in the model that do not contribute to the produced estimates. The main drawback of using classical SVOs for fault detection is their computational burden. By resorting to a left-coprime factorization for Linear Parameter-Varying (LPV) systems, it is shown how to reduce the computational complexity. By appropriately selecting the factorization, it is possible to consider detectable systems (i.e., unobservable systems where the unobservable component is stable). Such a result plays a key role in the domain of Cyber-Physical Systems (CPSs). These techniques are complemented with Event- and Self-triggered sampling strategies that enable fewer sensor updates. Moreover, the same triggering mechanisms can be used to make decisions of when to run the SVO routine or resort to over-approximations that temporarily compromise accuracy to gain in performance but maintaining the convergence characteristics of the set-valued estimates. A less stringent requirement for network resources that is vital to guarantee the applicability of SVO-based fault detection in the domain of Networked Control Systems (NCSs)

Distributed observers for LTI systems :an approach based on subspace decomposition

Cuando consideramos plantas de gran escala, como pueden ser fábricas, canales de irrigación de agua o campos solares, la estimación de estado se convierte en un problema más difícil de resolver que en pequeños sistemas. Cabe señalar que la información de estos sistemas con frecuencia es recopilada por muchos agentes individuales que están ubicados en zonas geográficamente remotas, lo que complica el diseño de los estimadores. Además, estos agentes deben comunicarse entre sí para lograr objetivos comunes de todo el sistema, lo que desencadena en problemas derivados de la red de comunicación tales como retrasos, pérdida de paquetes, ancho de banda limitado, etc. El objetivo de esta Tesis es el de proporcionar nuevas soluciones para el problema de la estimación distribuida del estado de una planta Lineal Invariante en el Tiempo (LTI) por parte de una red de agentes. Para lograr este objetivo, se presentan varias novedosas estructuras de observador. Dichas estructuras tienen un principio común: el uso de una descomposición del espacio de estados en los subespacios observables y no observables de cada agente. Primero, se presenta una estructura de observador basada en el principio de la descomposición del espacio de estados mencionado anteriormente. Dicha estructura utiliza las propias medidas del agente para reconstruir la parte observable del estado e incorpora consenso para reconstruir la parte del estado no observable por el agente. Como principales características destacan que es una estructura que puede diseñarse de forma distribuida y tiene la capacidad de fijar de forma arbitraria la velocidad de convergencia del estimador. Por otro lado, cuando se trabaja con modelos perturbados, la tesis presenta un método de diseño distribuido basado en LQ para la estructura de observador introducida anteriormente. Bajo el diseño propuesto, se establecen condiciones de estabilidad y optimidad. Además, se muestra en simulación la respuesta del algoritmo para los escenarios no perturbados y perturbados. Finalmente, el método de diseño presentado permite al usuario, mediante el uso de un parámetro escalar, modificar el diseño del observador de acuerdo con su experiencia con la planta. Finalmente, se presenta una segunda estructura de observador basada en el mismo principio de descomposición en subespacios, pero esta vez, el planteamiento es algo diferente. Cada uno de los agentes involucrados en la red debe realizar un monitoreo en tiempo real del estado de la planta a partir de sus medidas locales del estado y las medidas tomadas por el resto de la red. Esta comunicación inter-agente se lleva a cabo dentro de una red multi-salto. Por lo tanto, la información transmitida sufre un retraso en función de la posición del agente que actúa como fuente de información y el agente receptor de dicha información. Así, para resolver el problema, se presenta una novedosa estructura de observador basada en la fusión de datos. Por último, se abordan dos problemas principales: el diseño del observador para estabilizar el error de estimación cuando no existen perturbaciones y un diseño óptimo de observador para minimizar las incertidumbres en la estimación cuando entran en juego perturbaciones en la planta y ruidos en las medidas. Todas las aportaciones de esta tesis son de carácter teórico. Sin embargo, las soluciones adoptadas podrían aplicarse a una amplia variedad de sistemas distribuidos como pueden ser el control de redes de distribución de agua, la formación de vehículos autónomos, transporte y logística, sistemas eléctricos de potencia o edificios inteligentes, por mencionar algunas aplicaciones

Locally Minimum-Variance Filtering of 2-D Systems over Sensor Networks with Measurement Degradations: A Distributed Recursive Algorithm

10.13039/501100012166-National Key Research and Development Program of China (Grant Number: 2018AAA0100202); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61673110, 61873148, 61933007, 61903082 and 61973080); 10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 2018M640443); Jiangsu Planned Projects for Postdoctoral Research Funds of China (Grant Number: 2019K192); 10.13039/100005156-Alexander von Humboldt Foundation of German

Cooperative Vehicle Perception and Localization Using Infrastructure-based Sensor Nodes

Reliable and accurate Perception and Localization (PL) are necessary for safe intelligent transportation systems. The current vehicle-based PL techniques in autonomous vehicles are vulnerable to occlusion and cluttering, especially in busy urban driving causing safety concerns. In order to avoid such safety issues, researchers study infrastructure-based PL techniques to augment vehicle sensory systems. Infrastructure-based PL methods rely on sensor nodes that each could include camera(s), Lidar(s), radar(s), and computation and communication units for processing and transmitting the data. Vehicle to Infrastructure (V2I) communication is used to access the sensor node processed data to be fused with the onboard sensor data. In infrastructure-based PL, signal-based techniques- in which sensors like Lidar are used- can provide accurate positioning information while vision-based techniques can be used for classification. Therefore, in order to take advantage of both approaches, cameras are cooperatively used with Lidar in the infrastructure sensor node (ISN) in this thesis. ISNs have a wider field of view (FOV) and are less likely to suffer from occlusion. Besides, they can provide more accurate measurements since they are fixed at a known location. As such, the fusion of both onboard and ISN data has the potential to improve the overall PL accuracy and reliability. This thesis presents a framework for cooperative PL in autonomous vehicles (AVs) by fusing ISN data with onboard sensor data. The ISN includes cameras and Lidar sensors, and the proposed camera Lidar fusion method combines the sensor node information with vehicle motion models and kinematic constraints to improve the performance of PL. One of the main goals of this thesis is to develop a wind induced motion compensation module to address the problem of time-varying extrinsic parameters of the ISNs. The proposed module compensates for the effect of the motion of ISN posts due to wind or other external disturbances. To address this issue, an unknown input observer is developed that uses the motion model of the light post as well as the sensor data. The outputs of the ISN, the positions of all objects in the FOV, are then broadcast so that autonomous vehicles can access the information via V2I connectivity to fuse with their onboard sensory data through the proposed cooperative PL framework. In the developed framework, a KCF is implemented as a distributed fusion method to fuse ISN data with onboard data. The introduced cooperative PL incorporates the range-dependent accuracy of the ISN measurements into fusion to improve the overall PL accuracy and reliability in different scenarios. The results show that using ISN data in addition to onboard sensor data improves the performance and reliability of PL in different scenarios, specifically in occlusion cases