Recent advances on filtering and control for nonlinear stochastic complex systems with incomplete information: A survey

This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2012 Hindawi PublishingSome recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61104125, 61028008, 61174136, 60974030, and 61074129, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Distributed adaptive fault-tolerant leader-following formation control of nonlinear uncertain second-order multi-agent systems

This paper presents a distributed integrated fault diagnosis and accommodation scheme for leader‐following formation control of a class of nonlinear uncertain second‐order multi‐agent systems. The fault model under consideration includes both process and actuator faults, which may evolve abruptly or incipiently. The time‐varying leader communicates with a small subset of follower agents, and each follower agent communicates to its directly connected neighbors through a bidirectional network with possibly asymmetric weights. A local fault diagnosis and accommodation component are designed for each agent in the distributed system, which consists of a fault detection and isolation module and a reconfigurable controller module comprised of a baseline controller and two adaptive fault‐tolerant controllers, activated after fault detection and after fault isolation, respectively. By using appropriately the designed Lyapunov functions, the closed‐loop stability and asymptotic convergence properties of the leader‐follower formation are rigorously established under different modes of the fault‐tolerant control system

Decentralized sliding mode control and estimation for large-scale systems

This thesis concerns the development of an approach of decentralised robust control and estimation for large scale systems (LSSs) using robust sliding mode control (SMC) and sliding mode observers (SMO) theory based on a linear matrix inequality (LMI) approach. A complete theory of decentralized first order sliding mode theory is developed. The main developments proposed in this thesis are: The novel development of an LMI approach to decentralized state feedback SMC. The proposed strategy has good ability in combination with other robust methods to fulfill specific performance and robustness requirements. The development of output based SMC for large scale systems (LSSs). Three types of novel decentralized output feedback SMC methods have been developed using LMI design tools. In contrast to more conventional approaches to SMC design the use of some complicated transformations have been obviated. A decentralized approach to SMO theory has been developed focused on the Walcott-Żak SMO combined with LMI tools. A derivation for bounds applicable to the estimation error for decentralized systems has been given that involves unknown subsystem interactions and modeling uncertainty. Strategies for both actuator and sensor fault estimation using decentralized SMO are discussed.The thesis also provides a case study of the SMC and SMO concepts applied to a non-linear annealing furnace system modelderived from a distributed parameter (partial differential equation) thermal system. The study commences with a lumped system decentralised representation of the furnace derived from the partial differential equations. The SMO and SMC methods derived in the thesis are applied to this lumped parameter furnace model. Results are given demonstrating the validity of the methods proposed and showing a good potential for a valuable practical implementation of fault tolerant control based on furnace temperature sensor faults

Hierarchical Optimization-Based Model Predictive Control for a Class of Discrete Fuzzy Large-Scale Systems Considering Time-Varying Delays and Disturbances

Altres ajuts: Acord transformatiu CRUE-CSICIn this manuscript, model predictive control for class of discrete fuzzy large-scale systems subjected to bounded time-varying delay and disturbances is studied. The considered method is Razumikhin for time-varying delay large-scale systems, in which it includes a Lyapunov function associated with the original non-augmented state space of system dynamics in comparison with the Krasovskii method. As a rule, the Razumikhin method has a perfect potential to avoid the inherent complexity of the Krasovskii method especially in the presence of large delays and disturbances. The considered large-scale system in this manuscript is decomposed into several subsystems, each of which is represented by a fuzzy Takagi-Sugeno (TS) model and the interconnection between any two subsystems is considered. Because the main section of the model predictive control is optimization, the hierarchical scheme is performed for the optimization problem. Furthermore, persistent disturbances are considered that robust positive invariance and input-to-state stability under such circumstances are studied. The linear matrix inequalities (LMIs) method is performed for our computations. So the closed-loop large-scale system is asymptotically stable. Ultimately, by two examples, the effectiveness of the proposed method is illustrated, and a comparison with other papers is made by remarks

Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

Data-Driven Architecture to Increase Resilience In Multi-Agent Coordinated Missions

The rise in the use of Multi-Agent Systems (MASs) in unpredictable and changing environments has created the need for intelligent algorithms to increase their autonomy, safety and performance in the event of disturbances and threats. MASs are attractive for their flexibility, which also makes them prone to threats that may result from hardware failures (actuators, sensors, onboard computer, power source) and operational abnormal conditions (weather, GPS denied location, cyber-attacks). This dissertation presents research on a bio-inspired approach for resilience augmentation in MASs in the presence of disturbances and threats such as communication link and stealthy zero-dynamics attacks. An adaptive bio-inspired architecture is developed for distributed consensus algorithms to increase fault-tolerance in a network of multiple high-order nonlinear systems under directed fixed topologies. In similarity with the natural organisms’ ability to recognize and remember specific pathogens to generate its immunity, the immunity-based architecture consists of a Distributed Model-Reference Adaptive Control (DMRAC) with an Artificial Immune System (AIS) adaptation law integrated within a consensus protocol. Feedback linearization is used to modify the high-order nonlinear model into four decoupled linear subsystems. A stability proof of the adaptation law is conducted using Lyapunov methods and Jordan decomposition. The DMRAC is proven to be stable in the presence of external time-varying bounded disturbances and the tracking error trajectories are shown to be bounded. The effectiveness of the proposed architecture is examined through numerical simulations. The proposed controller successfully ensures that consensus is achieved among all agents while the adaptive law v simultaneously rejects the disturbances in the agent and its neighbors. The architecture also includes a health management system to detect faulty agents within the global network. Further numerical simulations successfully test and show that the Global Health Monitoring (GHM) does effectively detect faults within the network

Distributed fault detection and isolation of large-scale nonlinear systems: an adaptive approximation approach

2007/2008The present thesis work introduces some recent and novel results about the problem of fault diagnosis for distributed nonlinear and large scale systems. The problem of automated fault diagnosis and accommodation is motivated by the need to develop more autonomous and intelligent systems that operate reliably in the presence of system faults. In dynamical systems, faults are characterized by critical and unpredictable changes in the system dynamics, thus requiring the design of suitable fault diagnosis schemes. A fault diagnosis scheme that drew considerable attention and provided remarkable results is the so called model based scheme, which is based upon a mathematical model of the healthy behavior of the system that is being monitored. At each time instant, the model is used to compute an estimate of what should be the current behavior of the system, assuming it is not affected by a fault. If the behavior of the system is characterized by the time evolution of its state vector x(t), and the inputs to the system are denoted as u(t), then the most general nonlinear and uncertain discrete time model can be represented by x(t + 1) = f (x(t), u(t)) + η(t) , where the nonlinear function f represents the nominal model of the healthy system, and η(t) is an uncertainty term. A proven way to compute an estimate of the state x(t) is by using a diagnostic observer, so that in healthy conditions the residual between the true and the estimated value is, in practice, close to zero. Should the residual cross at a certain point a suitable threshold ̄ǫ(t), the observed difference between the model estimate and the actual measurements will be explained by the presence of a fault. The model-based scheme outlined so far has showed many interesting properties and advantages over signal-based ones, but anyway poses practical implementation problems when one tries to apply it to actual distributed, large-scale systems. In fact an implicit assumption about the model-based scheme is that the task of measuring all the state and input vectors components, and the task of computing the estimate of x(t) can be done in real-time by some single and powerful computer. But for large enough systems, this assumptions cannot be fulfilled by available measurement, communication and computation hardware. This problem constitutes the motivation of the present work. It will be solved by developing decomposition strategies in order to break down the original centralized diagnosis problem into many distributed diagnosis subproblems, that are tackled by agents called Local Fault Diagnosers that have a limited view about the system, but that are allowed to communicate between neighboring agents. In order to take advantage of the distributed nature of the proposed schemes, the agents are allowed to cooperate on the diagnosis of parts of the system shared by more than one diagnoser, by using consensus techniques. Chapter 2 introduces the problem of model-based fault diagnosis by presenting recent results about the centralized diagnosis of uncertain nonlinear discrete time systems. The development of a distributed fault diagnosis architecture is covered in the key Chapter 3, while Chapters 4 and 5 show how this distributed architecture is implemented for discrete and continuous time nonlinear and uncertain large–scale systems. In every chapter an illustrative example is provided, as well as analytical results that characterize the performances attainable by the proposed architecture. ---------------------------------------------------Questo lavoro di tesi presenta alcuni risultati recenti ed innovativi sulla diagnostica di guasto per sistemi nonlineari distribuiti e su larga scala. Il problema della diagnostica automatica di guasto è motivata dal bisogno di sviluppare sistemi maggiormenti autonomi e robusti, che possano operare in modo affidabile anche in presenza di guasti. Nei sistemi dinamici, i guasti sono caratterizati da variazioni critiche ed imprevedibili della dinamica, e richiedono perciò la progettazione di schemi di diagnostica adeguati. Uno schema che ha riscosso notevole successo è il cosidetto schema basato su modello, che si fonda su un modello matematico del comportamento sano del sistema sotto osservazione. Ad ogni istante, il modello è usato per calcolare una stima di quello che dovrebbe essere il comportamento attuale, supponendo l’assenza di guasti. Se il comportamento del sistema è caratterizzato attraverso l’evoluzione temporale del vettore di stato x(t), ed il vettore degli ingressi è indicato con u(t), allora il modello più generale per un sistema non lineare ed incerto a tempo discreto è x(t + 1) = f (x(t), u(t)) + η(t) , dove la funzione nonlineare f rappresenta la dinamica del sistema sano, mentre η(t) è l’incertezza di modello. Un modo comprovato per calcolare una stima dello stato x(t) fa uso di un osservatore diagnostico, cosicché in condizioni normali il residuo tra il valore vero e quello stimato è, in pratica, quasi nullo. Se dovesse ad un certo punto superare un’opportuna soglia, la differenza osservata tra la stima del modello ed il valore vero misurato sarebbe spiegabile con la presenza di un guasto. Lo schema basato su modello riassunto finora ha mostrato molte proprietà interessanti e vantaggi rispetto quelli basati su segnali, ma pone in ogni caso problemi di tipo pratico quando lo si voglia applicare a sistemi reali distribuiti e su larga scala. Infatti un’ipotesi sottointesa dello schema basato su modello è che il compito di misurare tutte le componenti di x(t) e di u(t), e quello di calcolare la stima di x(t) possa essere portato a termine in tempo reale da un singolo nodo di calcolo. Nel caso di sistemi sufficientemente vasti, però, questa ipotesi non può essere rispettata da alcuna delle risorse di calcolo disponibili in pratica. Questo problema è alla base del presente lavoro di tesi. Verrà risolto sviluppando delle strategie di decomposizione in modo da suddividere il problema di diagnostica centralizzato in molteplici sotto-problemi distribuiti, dati in carico ad agenti detti Diagnostici Locali, che hanno una visione limitata del sistema, ma che possono comunicare con agenti vicini. In modo da sfruttare la natura distribuita dello schema proposto, gli agenti potranno cooperare sulla diagnostica di parti del sistema che siano comuni a più diagnostici, attraverso tecniche di consenso. Il Capitolo 2 introduce il problema della diagnostica basata su modello attraverso dei risultati recenti sulla diagnostica centralizzata di sistemi a tempo discreto con dinamica non lineare ed incerta. Lo sviluppo dell’architettura di diagnostica distribuita è trattato nel fondamentale Capitolo 3, mentre i Capitoli 4 e 5 mostrano come questa architettura distribuita è implementata a tempo discreto e a tempo continuo. In ogni capitolo è presente un esempio didattico, oltre a risultati analitici che caratterizzano le prestazioni ottenibili dall’architettura proposta.XX Ciclo197

COOPERATIVE AND CONSENSUS-BASED CONTROL FOR A TEAM OF MULTI-AGENT SYSTEMS

Cooperative control has attracted a noticeable interest in control systems community due to its numerous applications in areas such as formation flying of unmanned aerial vehicles, cooperative attitude control of spacecraft, rendezvous of mobile robots, unmanned underwater vehicles, traffic control, data network congestion control and routing. Generally, in any cooperative control of multi-agent systems one can find a set of locally sensed information, a communication network with limited bandwidth, a decision making algorithm, and a distributed computational capability. The ultimate goal of cooperative systems is to achieve consensus or synchronization throughout the team members while meeting all communication and computational constraints. The consensus problem involves convergence of outputs or states of all agents to a common value and it is more challenging when the agents are subjected to disturbances, measurement noise, model uncertainties or they are faulty. This dissertation deals with the above mentioned challenges and has developed methods to design distributed cooperative control and fault recovery strategies in multi-agent systems. Towards this end, we first proposed a transformation for Linear Time Invariant (LTI) multi-agent systems that facilitates a systematic control design procedure and make it possible to use powerful Lyapunov stability analysis tool to guarantee its consensus achievement. Moreover, Lyapunov stability analysis techniques for switched systems are investigated and a novel method is introduced which is well suited for designing consensus algorithms for switching topology multi-agent systems. This method also makes it possible to deal with disturbances with limited root mean square (RMS) intensities. In order to decrease controller design complexity, a iii method is presented which uses algebraic connectivity of the communication network to decouple augmented dynamics of the team into lower dimensional parts, which allows one to design the consensus algorithm based on the solution to an algebraic Riccati equation with the same order as that of agent. Although our proposed decoupling method is a powerful approach to reduce the complexity of the controller design, it is possible to apply classical pole placement methods to the transformed dynamics of the team to develop and obtain controller gains. The effects of actuator faults in consensus achievement of multi-agent systems is investigated. We proposed a framework to quantitatively study actuator loss-of-effectiveness effects in multi-agent systems. A fault index is defined based on information on fault severities of agents and communication network topology, and sufficient conditions for consensus achievement of the team are derived. It is shown that the stability of the cooperative controller is linked to the fault index. An optimization problem is formulated to minimize the team fault index that leads to improvements in the performance of the team. A numerical optimization algorithm is used to obtain the solutions to the optimal problem and based on the solutions a fault recovery strategy is proposed for both actuator saturation and loss-of-effectiveness fault types. Finally, to make our proposed methodology more suitable for real life scenarios, the consensus achievement of a multi-agent team in presence of measurement noise and model uncertainties is investigated. Towards this end, first a team of LTI agents with measurement noise is considered and an observer based consensus algorithm is proposed and shown that the team can achieve H∞ output consensus in presence of both bounded RMS disturbance input and measurement noise. In the next step a multi-agent team with both linear and Lipschitz nonlinearity uncertainties is studied and a cooperative control algorithm is developed. An observer based approach is also developed to tackle consensus achievement problem in presence of both measurement noise and model uncertainties

Integrated fault-tolerant control approach for linear time-delay systems using a dynamic event-triggered mechanism

In this study, a novel integrated fault estimation (FE) and fault-tolerant control (FTC) design approach is developed for a system with time-varying delays and additive fault based on a dynamic event-triggered communication mechanism. The traditional static event-triggered mechanism is modified by adding an internal dynamic variable to increase the inter-event interval and decrease the amount of data transmission. Then, a dynamical observer is designed to estimate both the system state and the unknown fault signal simultaneously. A fault estimation-based FTC approach is then given to remove the effects generated by unknown actuator faults, which guarantees that the faulty closed-loop systems are asymptotical stable with a disturbance attenuation level γ. By theory analysis, the Zeno phenomenon is excluded in this study. Finally, a real aircraft engine example is provided to illustrate the feasibility of the proposed integrated FE and FTC method