Deep Learning-Based, Passive Fault Tolerant Control Facilitated by a Taxonomy of Cyber-Attack Effects

In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control (FTC) is unique in the research literature. The proposed controller is applied to both linear and nonlinear systems. Additionally, the application and testing are accomplished with both actuators and sensors being affected by attacks and /or faults

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

Robust model-based fault estimation and fault-tolerant control : towards an integration

To maintain robustly acceptable system performance, fault estimation (FE) is adopted to reconstruct fault signals and a fault-tolerant control (FTC) controller is employed to compensate for the fault effects. The inevitably existing system and estimation uncertainties result in the so-called bi-directional robustness interactions defined in this work between the FE and FTC functions, which gives rise to an important and challenging yet open integrated FE/FTC design problem concerned in this thesis. An example of fault-tolerant wind turbine pitch control is provided as a practical motivation for integrated FE/FTC design.To achieve the integrated FE/FTC design for linear systems, two strategies are proposed. A H∞ optimization based approach is first proposed for linear systems with differentiable matched faults, using augmented state unknown input observer FE and adaptive sliding mode FTC. The integrated design is converted into an observer-based robust control problem solved via a single-step linear matrix inequality formulation.With the purpose of an integrated design with more freedom and also applicable for a range of general fault scenarios, a decoupling approach is further proposed. This approach can estimate and compensate unmatched non-differentiable faults and perturbations by combined adaptive sliding mode augmented state unknown input observer and backstepping FTC controller. The observer structure renders a recovery of the Separation Principle and allows great freedom for the FE/FTC designs.Integrated FE/FTC design strategies are also developed for Takagi-Sugeno fuzzy modelling nonlinear systems, Lipschitz nonlinear systems, and large-scale interconnected systems, based on extensions of the H∞ optimization approach for linear systems.Tutorial examples are used to illustrate the design strategies for each approach. Physical systems, a 3-DOF (degree-of-freedom) helicopter and a 3-machine power system, are used to provide further evaluation of the proposed integrated FE/FTC strategies. Future research on this subject is also outlined

Active fault-tolerant control of nonlinear systems with wind turbine application

The thesis concerns the theoretical development of Active Fault-Tolerant Control (AFTC) methods for nonlinear system via T-S multiple-modelling approach. The thesis adopted the estimation and compensation approach to AFTC within a tracking control framework. In this framework, the thesis considers several approaches to robust T-S fuzzy control and T-S fuzzy estimation: T-S fuzzy proportional multiple integral observer (PMIO); T-S fuzzy proportional-proportional integral observer (PPIO); T-S fuzzy virtual sensor (VS) based AFTC; T-S fuzzy Dynamic Output Feedback Control TSDOFC; T-S observer-based feedback control; Sliding Mode Control (SMC). The theoretical concepts have been applied to an offshore wind turbine (OWT) application study. The key developments that present in this thesis are:• The development of three active Fault Tolerant Tracking Control (FTTC) strategies for nonlinear systems described via T-S fuzzy inference modelling. The proposals combine the use of Linear Reference Model Fuzzy Control (LRMFC) with either the estimation and compensation concept or the control reconfiguration concept.• The development of T-S fuzzy observer-based state estimate fuzzy control strategy for nonlinear systems. The developed strategy has the capability to tolerate simultaneous actuator and sensor faults within tracking and regulating control framework. Additionally, a proposal to recover the Separation Principle has also been developed via the use of TSDOFC within the FTTC framework.• The proposals of two FTTC strategies based on the estimation and compensation concept for sustainable OWTs control. The proposals have introduced a significant attribute to the literature of sustainable OWTs control via (1) Obviating the need for Fault Detection and Diagnosis (FDD) unit, (2) Providing useful information to evaluate fault severity via the fault estimation signals.• The development of FTTC architecture for OWTs that combines the use of TSDOFC and a form of cascaded observers (cascaded analytical redundancy). This architecture is proposed in order to ensure the robustness of both the TSDOFC and the EWS estimator against the generator and rotor speed sensor faults.• A sliding mode baseline controller has been proposed within three FTTC strategies for sustainable OWTs control. The proposals utilise the inherent robustness of the SMC to tolerate some matched faults without the need for analytical redundancy. Following this, the combination of SMC and estimation and compensation framework proposed to ensure the close-loop system robustness to various faults.• Within the framework of the developed T-S fuzzy based FTTC strategies, a new perspective to reduce the T-S fuzzy control design conservatism problem has been proposed via the use of different control techniques that demand less design constraints. Moreover, within the SMC based FTTC, an investigation is given to demonstrate the SMC robustness against a wider than usual set of faults is enhanced via designing the sliding surface with minimum dimension of the feedback signals

Relative Threshold-Based Event-Triggered Control for Nonlinear Constrained Systems With Application to Aircraft Wing Rock Motion

This paper concentrates upon the event-driven controller design problem for a class of nonlinear single input single output (SISO) parametric systems with full state constraints. A varying threshold for the triggering mechanism is exploited, which makes the communication more flexible. Moreover, from the viewpoint of energy conservation and consumption reduction, the system capability becomes better owing to the contribution of the proposed event triggered mechanism. In the meantime, the developed control strategy can avoid the Zeno behavior since the lower bound of the sample time is provided. The considered plant is in a lower-triangular form, in which the match condition is not satisfied. To ensure that all the states to retain in a predefined region, a barrier Lyapunov function (BLF) based adaptive control law is developed. Due to the existence of the parametric uncertainties, an adaptive algorithm is presented as an estimated tool. All the signals appearing in the closed-loop systems are then proven to be uniformly ultimately bounded (UUB). Meanwhile, the output of the system can track a given signal as far as possible. In the end, the effectiveness of the proposed approach is validated by an aircraft wing rock motion system

Data analytics for stochastic control and prognostics in cyber-physical systems

In this dissertation, several novel cyber fault diagnosis and prognosis and defense methodologies for cyber-physical systems have been proposed. First, a novel routing scheme for wireless mesh network is proposed. An effective capacity estimation for P2P and E2E path is designed to guarantee the vital transmission safety. This scheme can ensure a high quality of service (QoS) under imperfect network condition, even cyber attacks. Then, the imperfection, uncertainties, and dynamics in the cyberspace are considered both in system model and controller design. A PDF identifier is proposed to capture the time-varying delays and its distribution. With the modification of traditional stochastic optimal control using PDF of delays, the assumption of full knowledge of network imperfection in priori is relaxed. This proposed controller is considered a novel resilience control strategy for cyber fault diagnosis and prognosis. After that, we turn to the development of a general framework for cyber fault diagnosis and prognosis schemes for CPSs wherein the cyberspace performance affect the physical system and vice versa. A novel cyber fault diagnosis scheme is proposed. It is capable of detecting cyber fault by monitoring the probability of delays. Also, the isolation of cyber and physical system fault is achieved with cooperating with the traditional observer based physical system fault detection. Next, a novel cyber fault prognosis scheme, which can detect and estimate cyber fault and its negative effects on system performance ahead of time, is proposed. Moreover, soft and hard cyber faults are isolated depending on whether potential threats on system stability is predicted. Finally, one-class SVM is employed to classify healthy and erroneous delays. Then, another cyber fault prognosis based on OCSVM is proposed --Abstract, page iv

Cloud-Based Event-Triggered Predictive Control for Heterogeneous NMASs Under Both DoS Attacks and Transmission Delays

A novel compensation control method for heterogeneous multiagent systems under Denial-of-Service (DoS) attacks and transmission delays is investigated in this article. This control method has all the advantages of the cloud-based computation strategy, the adaptive event-triggered strategy, and the predictive control scheme. The adaptive event-triggering mechanism can adjust the event numbers adaptively, the predictive control can reduce or eliminate the negative effects brought out by both DoS attacks and transmission delays actively, while the cloud-based computation strategy can eliminate the negative effects completely as the same as there are no DoS attacks and transmission delays. Through the interval decomposition skill and the augmented system modeling method, the compensated geschlossenes system model is established. Moreover, the joint design for the feedback gain matrices and the event-triggered parameters is implemented. In the simulation part, five VTOL aircraft are used to demonstrate the theoretical results