This article proposes a data-driven distributed Kalman filter (DKF)-based sensor fault isolation and estimation scheme for large-scale interconnected dynamic systems, composed of heterogeneous subsystems coupled through a directed topological graph. A local diagnosis unit (LDU) is established for each subsystem, where the data-driven DKF-based residual generator is constructed using local and neighboring process data, effectively decoupling the totally unknown interaction component. Subsequently, fully distributed sensor fault isolation is realized at the subsystem and element levels in simultaneous-fault cases. Both local and neighboring sensor fault isolation can be realized in the LDU, allowing the global system sensor fault isolation with only several key LDUs. Then, the data-driven DKF-based estimator is built in each LDU to estimate sensor faults occurring in multiple subsystems. The distributed Kalman gain is computed in a fully distributed manner, with stability analysis performed locally without overall system knowledge. Finally, the effectiveness and performance of the proposed scheme are validated through case studies on the power network system.

A flight control system acts as a core subsystem in aircraft for the purpose of attitude control and trajectory tracking. Due to the inaccurate system modeling and the internal/external disturbances (e.g., wind disturbance, aerodynamic parameter change, and load perturbation), the performance of controller designed for the typical operating points may be not optimal for the full flight envelope. This article aims to address the control performance optimization problem of flight control system in the presence of an incompletely known aircraft model and real-time flight data. First, the longitudinal aircraft dynamic model is formulated, followed by the establishment of two typical disturbance models, including periodic and constant disturbances. Subsequently, a real-time optimization framework for enhancing flight control performance is introduced, with the aid of residual-driven realization of Youla parameterized controller. Moreover, the gradient-based performance optimization strategy is proposed to mitigate the performance degradation induced by disturbances, using the online monitored actuator and sensor data of flight control system. By developing tools from optimization theory, the convergence and optimality of gradient-based performance optimization method are analyzed comprehensively. Finally, the proposed methods are testified on the model of an aircraft.

This article investigates the performance optimization structure and algorithm of the electromagnetic levitation (EML) system. Firstly, a functionalized control structure is proposed by the incremental attachment of a controller gain system. Under this control structure, the predesigned controller is designed for nominal performance while the controller gain system handles performance optimization. This is of great help because the system trajectory of the highly dynamic and open-loop unstable EML system can be contained in the admissible region during the optimization of the controller gain system. Secondly, it is demonstrated that optimizing the incrementally attached controller gain system (under the proposed control structure) for performance optimization is equivalent to optimizing the predesigned controller. The advantage of the equivalence is that the predesigned control system needs not to be modified and the incremental attachment can be performed even when the EML system is running. Furthermore,Q-learning algorithm is presented for the real-time implementation of the proposed structure and the incrementally attached controller gain system. Finally, the effectiveness of the structure and algorithm is validated on the EML system.Note to Practitioners—The EML system is key to maglev transportation. It is open-loop unstable, highly dynamic, and its accurate model is generally unavailable. On the other hand, the levitation performance may not be optimal given the predesigned controller. Moreover, during the long-term operation, it is desired in practice that the controller of EML system can be upgraded in a friendly fashion. This article presents a control structure that optimizes the levitation performance in an incremental style, such that the predesigned controller does not need to be modified. The incremental attachment can be realized via the onboard CAN network or UDP channel.Q-learning algorithm optimizes the incrementally attached controller gain system without distabilizing the levitation stability of the EML system under the proposed control structure. In such a way, the open-loop and highly dynamic characteristics can be handled and the friendly implementation style can be achieved.

This paper presents a novel approach for passivity analysis and hierarchical fault detection of passive systems employing model-free reinforcement learning (RL). The proposed method can analyze the passivity of a system without knowing or identifying the system model and furthermore construct a fault detection logic grounded on energy indicators from the analysis result. Initially, the data-driven Bellman optimality equation is formulated, which is equivalent to the system’s passivity condition. Subsequently, the RL algorithm is delineated, and its time efficient advantage is elucidated in terms of both convergence and computational complexity. Simultaneously, the Bellman optimality equation in RL is clarified to be equivalent to the energy conservation constraint in the system. Based on this revelation, a hierarchical detection method based on the energy performance indicator is introduced. This approach can effectively detect faults within passive systems online and assess the severity of their consequences. The effectiveness of the proposed method is validated through simulation.

In this article, an issue of data-driven optimal strictly stealthy attack design for the stochastic linear invariant systems is investigated, with the aim of maximizing the system performance degradation under an energy bounded constraint and bypassing the parity-space-based attack detector. Importantly, the proposed attack policy refrains from the assumption that the system knowledge is known to attackers. A novel strictly stealthy attack sequence (SSAS), coordinating the sensor and actuator signals simultaneously, is proposed with a sufficient and necessary condition for the existence of such an attack presented. Specifically, the SSAS is parameterized as a vector in the null space of a specific matrix which is constructed by a parity matrix and the system Markov parameters. For the purpose of data-driven attack realization, modified subspace identification methods are utilized to achieve an unbiased estimation of the required parameters via the closed-loop data. On this basis, the attack design is formulated as a constrained optimization problem, an explicit solution to which is given to characterize the optimal strictly stealthy attack. Finally, the vulnerability of the cyber-physical systems is analysed from the perspective of the parameter selection for the parity space-based detector. A case study on a three-tank model verifies the efficiency of the proposed approach.

This article aims to investigate the data-driven attack detection and identification problem for cyber-physical systems under sparse actuator attacks, by developing tools from subspace identification and compressive sensing theories. First, two sparse actuator attack models (additive and multiplicative) are formulated and the definitions of I/O sequence and data models are presented. Then, the attack detector is designed by identifying the stable kernel representation of cyber-physical systems, followed by the security analysis of data-driven attack detection. Moreover, two sparse recovery-based attack identification policies are proposed, with respect to sparse additive and multiplicative actuator attack models. These attack identification policies are realized by the convex optimization methods. Furthermore, the identifiability conditions of the presented identification algorithms are analyzed to evaluate the vulnerability of cyber-physical systems. Finally, the proposed methods are verified by the simulations on a flight vehicle system.

Aeroengine performance optimization rem- ains significant for both efficiency and safety during specific operating conditions. Previous works usually solve this optimization problem under a single-objective optimization framework, while multiple objectives need to be optimized simultaneously. Besides, the underlying optimization process requires a variety of function evaluations, and the evaluation cost for an aeroengine is expensive. In reality, the aeroengine model has multiple information sources with different costs and accuracy. The different costs and accuracy of the multiple information sources should be traded off to guide the search for the optimal in a cost-efficient way. Therefore, we propose a multi-information-source framework for enabling efficient multiobjective Bayesian optimization. We construct the surrogate model with a multifidelity Gaussian process and choose the location–source pair with a modified acquisition function. Finally, we apply the proposed method to improve the performance indexes of the aeroengine, which confirms the efficiency of the proposed algorithm.

High-performance and reliable control of systems that are highly dynamic and open-loop unstable is challenging but of considerable practical interest. Thus, this article investigates the performance optimization and fault tolerance of highly dynamic systems. First, an incremental control structure is proposed, where a controller gain system is attached to the predesigned controller, and by reconfiguring the controller gain system, the performance can be equivalently optimized as configuring the predesigned one. The incremental attachment of the controller gain system does not modify the existing control system, and it can be easily attached via various communication channels. Second, a structure integrating fault-tolerance strategy and hardware redundancy is proposed. Under this structure, command fusion and fault-tolerance strategies are developed where the control commands from different control units are optimally fused, and each control unit can be reconfigured w.r.t. the performance of the other ones. Furthermore, Q -learning algorithms are developed to realize the proposed structures and strategies in real-time model-freely. As such, varying operational conditions of the highly dynamic system can be tackled. Finally, the proposed structures and algorithms are validated case by case to show their effectiveness.

This article proposes a novel data-driven gap metric fault detection and isolation (FDI) approach for small multiplicative fault. First, the scheme of model-based fault classification and gradation is developed by means of the gap metric. Subsequently, the data-driven gap metric is utilized to detect a small fault via the mechanism model. Furthermore, fault detectability criterion is derived with the help of the developed fault detectability indicator. The relationship between fault detectability indicator and fault detection index is then investigated to analyze fault detection performance. To enhance fault isolability, a solution of appropriate fault cluster center model and radius is provided under the condition of fault isolation. Third, a gap metric fault-tolerant control strategy is exploited to guarantee system stability when a large fault is diagnosed by the developed FDI approach. The speed regulation of dc-motor and dc–dc converter are used for simulation and experiment verifications. Moreover, the comparison results and Monte Carlo simulation demonstrate the superiority and reliability of the proposed method.

A “smart” automatic feedback control system is supposed to be aware of its operational performance throughout the service time. Driven by this desire, this article addresses the problem of monitoring performance variations caused by multiplicative factors, such as components’ faults, repairing, or replacement. Differing from the conventional performance indices (e.g., the quadratic value function), it detects the performance variation information from symmetric positive-definite (SPD) kernel matrices. As a carrier of performance variation information, the SPD kernel matrix is identified online. Furthermore, the performance variation is given a fresh insight in the sense that it drives the sliding of the SPD kernel matrix on a Riemannian manifold. Thus, performance variation monitoring is achieved by quantizing the geodesic between SPD kernel matrices directly on the Riemannian manifold. At last, the performance variation is visualized on the Riemannian manifold and the proposed schemes are verified via a simulation study.

The commercial operation of the maglev train puts critical demands on reliability and operation performance. Since maglev trains’ levitation system is open-loop unstable, its closed-loop stability under various operation conditions is a primary consideration. This paper thus addresses real-time stability performance monitoring and evaluation of maglev trains’ levitation system. First, a novel real-time performance indicator for stability performance monitoring is proposed. It utilizes only the online data of the levitation system, without knowing its accurate model. Then, performance evaluation is carried out by grading the monitored stability performance into different levels for further handling. Moreover, the proposed methods offer a way of controller evaluation for the levitation system before the train is put into commercial operation. It is noteworthy that, the proposed methods do not require the model of the levitation system to be known and run in real-time. The effectiveness and efficiency of the proposed methods are validated on a levitation system.

In this article, a data-driven design scheme of undetectable false data-injection attacks against cyber-physical systems is proposed first, with the aid of the subspace identification technique. Then, the impacts of undetectable false data-injection attacks are evaluated by solving a constrained optimization problem, with the constraints of undetectability and energy limitation considered. Moreover, the detection of designed data-driven false data-injection attacks is investigated via the coding theory. Finally, the simulations on the model of a flight vehicle are illustrated to verify the effectiveness of the proposed methods.

This paper addresses fault-tolerant control (FTC) issues for linear systems with model uncertainty and multiplicative faults. The left and right coprime factorization techniques are first adopted for system modeling. Then, the fault detection (FD) approaches are investigated in the coprime factorization context. Based on the information provided by the FD systems, the corresponding FTC architectures and design schemes are presented. Moreover, the gap metric techniques are applied to fault detectability analysis, including the fault detectability indicators to quantify the detection performance in the presence of model uncertainty. The effectiveness of the developed methods for industrial application is illustrated by a case study on a dc motor.