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

Passive fault-tolerant control for vehicle active suspension system based on H2/H∞ approach

In this paper, a robust passive fault-tolerant control (RPFTC) strategy based on H2/H∞ approach and an integral sliding mode passive fault tolerant control (ISMPFTC) strategy based on H2/H∞ approach for vehicle active suspension are presented with considering model uncertainties, loss of actuator effectiveness and time-domain hard constraints of the suspension system. H∞ performance index less than γ and H2 performance index is minimized as the design objective, avoid choosing weighting coefficient. The half-car model is taken as an example, the robust passive fault-tolerant controller and the integral sliding mode passive fault tolerant control law is designed respectively. Three different fault modes are selected. And then compare and analyze the control effect of vertical acceleration of the vehicle body and pitch angular acceleration of passive suspension control, robust passive fault tolerant control and integral sliding mode passive fault tolerant control to verify the feasibility and effectiveness of passive fault tolerant control algorithm of active suspension. The studies we have performed indicated that the passive fault tolerant control strategy of the active suspension can improve the ride comfort of the suspension system

Observer-based fault detection of technical systems over networks

The introduction of networks into technical systems for facilitating remote data transmission, low complexity in wiring and easy diagnosis and maintenance, raises new challenges in fault detection (FD), such as how to handle network-induced time-varying transmission delays, packet dropouts, quantization errors and bit errors. These factors lead to increasing interest in developing new structures and design schemes for FD of technical systems over networks. In this thesis all network-induced effects are analyzed and modeled systematically at first. By observing the stochastic inheritance of networks, an FD framework of Markov jumping linear systems is presented as a basis for the later developments. Then two observer-based schemes for the purpose of FD over networks with guaranteed false alarm rate (FAR) are proposed: a remote FD system and an FD system of networked control systems (NCSs). The remote FD scheme is for detecting faults in technical systems at a remote site, where system measurements are transmitted via networks. In this scheme, the coding mechanism of communication channels is investigated from the view point of control engineering and new methods are developed for optimal residual generation and evaluation by considering network-induced data loss and corruption. A novel design scheme of FD system is also developed for NCSs, where the technical system is networked, i.e. controllers, actuators and sensors are connected with communication channels. In this scheme, network-induced transmission delays, packet dropouts, quantization errors are taken into account for the design of the optimal FD system. The linear matrix inequalities (LMIs) and convex optimization techniques are applied for assisting the design procedures. The developed schemes are tested with numerical examples and implemented in a three-tank system benchmark, and their superiority to existing solutions is demonstrated. Existing restrictions are overcome and new observer-based FD schemes over networks are introduced having the following characteristics: (1) the residual generators in both schemes are optimal in the sense of achieving the best trade-off between sensitivity to system faults and robustness against system disturbances and network-induced effects; (2) the proposed schemes can provide reliability information of rising fault alarms by analyzing the mean and variance of residual signals. Such information is very useful for practical applications in industries; (3) the design of residual generators and computation of thresholds can be efficiently solved by means of existing LMI-solvers

Discrete Time Systems

Discrete-Time Systems comprehend an important and broad research field. The consolidation of digital-based computational means in the present, pushes a technological tool into the field with a tremendous impact in areas like Control, Signal Processing, Communications, System Modelling and related Applications. This book attempts to give a scope in the wide area of Discrete-Time Systems. Their contents are grouped conveniently in sections according to significant areas, namely Filtering, Fixed and Adaptive Control Systems, Stability Problems and Miscellaneous Applications. We think that the contribution of the book enlarges the field of the Discrete-Time Systems with signification in the present state-of-the-art. Despite the vertiginous advance in the field, we also believe that the topics described here allow us also to look through some main tendencies in the next years in the research area

Reliable Vehicle State and Parameter Estimation

Diverse vehicle active safety systems including vehicle electronic stability control (ESC) system, anti-lock braking system (ABS), and traction control system (TCS) are significantly relying on information about the vehicle's states and parameters, as well as the vehicle's surroundings. However, many important states or parameters, such as sideslip angle, tire-road friction coefficient, road gradient and vehicle mass are hard to directly measure, and hence advanced estimation algorithms are needed. Furthermore, enhancements of sensor technologies and the emergence of new concepts such as {\it Internet of Things} and their automotive version, {\it Internet of Vehicles}, facilitate reliable and resilient estimation of vehicle states and road conditions. Consequently, developing a resilient estimation structure to operate with the available sensor data in commercial vehicles and be flexible enough to incorporate new information in future cars is the main objective of this thesis. This thesis presents a reliable corner-based vehicle velocity estimation and a road condition classification algorithm. For vehicle velocity estimation, a combination of vehicle kinematics and the LuGre tire model is introduced in the design of a corner-based velocity observer. Moreover, the observability condition for both cases of time-invariant and parameter varying is studied. The effect of suspension compliance on enhancing the accuracy of the vehicle corner velocity estimation is also investigated and the results are verified via several experimental tests. The performance and the robustness of the proposed corner-based vehicle velocity estimation to model and road condition uncertainties is analyzed. The stability of the observer is discussed, and analytical expressions for the boundedness of the estimation error in the presence of system uncertainties for the case of fixed observer gains are derived. Furthermore, the stability of the observer under arbitrary and stochastic observer gain switching is studied and the performances of the observer for these two switching scenarios are compared. At the end, the sensitivity of the proposed observer to tire parameter variations is analyzed. These analyses are referred to as offline reliability methods. In addition to the off-line reliability analysis, an online reliability measure of the proposed velocity estimation is introduced, using vehicle kinematic relations. Moreover, methods to distinguish measurement faults from estimation faults are presented. Several experimental results are provided to verify the approach. An algorithm for identifying (classifying) road friction is proposed in this thesis. The analytical foundation of this algorithm, which is based on vehicle response to lateral excitation, is introduced and its performance is discussed and compared to previous approaches. The sensitivity of this algorithm to vehicle/tire parameter variations is also studied. At the end, various experimental results consisting of several maneuvers on different road conditions are presented to verify the performance of the algorithm

Systems reliability issues for future aircraft

The reliability of adaptive controls for future aircraft are discussed. The research, formulation, and experimentation for improved aircraft performance are considered

BTLD+:A BAYESIAN APPROACH TO TRACKING LEARNING DETECTION BY PARTS

The contribution proposed in this thesis focuses on this particular instance of the visual tracking problem, referred as Adaptive Ap- iv \ufffcpearance Tracking. We proposed different approaches based on the Tracking Learning Detection (TLD) decomposition proposed in [55]. TLD decomposes visual tracking into three components, namely the tracker, the learner and detector. The tracker and the detector are two competitive processes for target localization based on comple- mentary sources of informations. The former searches for local fea- tures between consecutive frames in order to localize the target; the latter exploits an on-line appearance model to detect confident hy- pothesis over the entire image. The learner selects the final solution among the provided hypothesis. It updates the target appearance model, if necessary, reinitialize the tracker and bootstraps the detec- tor\u2019s appearance model. In particular, we investigated different ap- proaches to enforce the TLD stability. First, we replaced the tracker component with a novel one based on mcmc particle filtering; after- wards, we proposed a robust appearance modeling component able to characterize deformable objects in static images; after all, we inte- grated a modeling component able to integrate local visual features learning into the whole approach, lying to a couple layered represen- tation of the target appearance

Graph-theoretic channel modeling and topology control protocols for wireless sensor networks

This report addresses two different research problems: (i) It presents a wireless channel model that reduces the complexity associated with high order Markov chains; and (ii) presents energy efficient topology control protocols which provide reliability while maintaining the topology in an energy efficient manner. For the above problems, real wireless sensor network traces were collected and extensive simulations were performed for evaluating the proposed protocols. Accurate simulation and analysis of wireless networks are inherently dependent on accurate models which are able to provide real-time channel characterization. High-order Markov chains are typically used to model errors and losses over wireless channels. However, complexity (i.e., the number of states) of a high-order Markov model increases exponentially with the memory-length of the underlying channel. In this report, a novel graph-theoretic methodology that uses Hamiltonian circuits to reduce the complexity of a high-order Markov model to a desired state budget is presented. The implication of unused states in complexity reduction of higher order Markov model is also explained. The trace-driven performance evaluations for real wireless local area network (WLAN) and wireless sensor network (WSN) channels demonstrate that the proposed Hamiltonian Model, while providing orders of magnitude reduction in complexity, renders an accuracy that is comparable to the Markov model and better than the existing reduced state models. Furthermore, a methodology to preserve energy is presented to increase the network lifetime by reducing the node degree forming an active backbone while considering network connectivity. However, in energy stringent wireless sensor networks, it is of utmost importance to construct the reduced topology with the minimal control overhead. Moreover, most wireless links in practice are lossy links with connectivity probability which desires that a routing protocol provides routing flexibility and reliability at a minimum energy consumption cost. For this purpose, distributed and semi-distributed novel graph-theoretic topology construction protocols are presented that exploit cliques and polygons in a WSN to achieve energy efficiency and reliability. The proposed protocols also facilitate load rotation under topology maintenance, thereby extending the network lifetime. In addition to the above, the report also evaluates why the backbone construction using connected dominating set (CDS) in certain cases remains unable to provide connected sensing coverage in the area covered. For this purpose, a novel protocol that reduces the topology while considering sensing area coverage is presented