This paper designs an active fault-tolerant control system for spacecraft attitude control in the presence of actuator faults, fault estimation errors, and control input constraints. The developed fault-tolerant control system is able to detect the actuator fault without false alarms caused by external disturbances, and also estimate the total fault effects accurately through an indirect fault identification approach, in which an auxiliary variable is utilized to build the relation between fault and system states. Once the fault identification is completed with certain degree of reconstruction accuracy, a fault-tolerant backstepping controller using the nonlinear virtual control input is reconfigured to accommodate the detected actuator faults effectively, in spite of actuator saturation limitations and fault estimation errors. Numerical simulation is carried out to demonstrate that the proposed active fault-tolerant control system is successful in fault detection, identification, and controller reconfiguration for handling actuator faults in attitude control systems.

Extreme learning machine (ELM) was initially proposed for single-hidden-layer feedforward neural networks (SLFNs). In the hidden layer (feature mapping), nodes are randomly generated independently of training data. Furthermore, a unified ELM was proposed, providing a single framework to simplify and unify different learning methods, such as SLFNs, least square support vector machines, proximal support vector machines, and so on. However, the solution of unified ELM is dense, and thus, usually plenty of storage space and testing time are required for large-scale applications. In this paper, a sparse ELM is proposed as an alternative solution for classification, reducing storage space and testing time. In addition, unified ELM obtains the solution by matrix inversion, whose computational complexity is between quadratic and cubic with respect to the training size. It still requires plenty of training time for large-scale problems, even though it is much faster than many other traditional methods. In this paper, an efficient training algorithm is specifically developed for sparse ELM. The quadratic programming problem involved in sparse ELM is divided into a series of smallest possible sub-problems, each of which are solved analytically. Compared with SVM, sparse ELM obtains better generalization performance with much faster training speed. Compared with unified ELM, sparse ELM achieves similar generalization performance for binary classification applications, and when dealing with large-scale binary classification problems, sparse ELM realizes even faster training speed than unified ELM.

The problem of flexible spacecraft attitude tracking control with guaranteeing the prescribed performance is investigated. First, the Lagrangian model of the flexible spacecraft is derived. Then, a model-free control law is proposed, in which information of spacecraft and flexible appendage is not required. The proposed control law is able to guarantee the prescribed transient and steady-state behavior for both attitude and angular velocity errors in the presence of bounded external disturbances. The stability of the resulting closed-loop system is guaranteed by the Lyapunov approach. Two special forms of the proposed control law are subsequently obtained. The effectiveness of both complete form and special forms of the proposed control law is verified by numerical simulations. Since the modal information of spacecraft is not involved in the control design, the proposed control law can also be used for rigid spacecraft.

This paper investigates the cyber-security problem for autonomous vehicles under sensor attacks. In particular, a model-based framework is proposed which can detect sensor attacks and identify their sources in order to achieve the secure localization of self-driving vehicles. To ensure robustness of the vehicle against cyber-attacks, sensor redundancy is introduced, that is to deploy multiple sensors, each of which provides real-time pose observations of the vehicle. A bank of attack detectors is developed to capture anomalies in each sensor measurement, which is a combination of an extended Kalman filter (EKF) and a cumulative sum (CUSUM) discriminator. EKFs are employed to estimate the vehicle position and orientation recursively, while each CUSUM discriminator is designed to analyze the residual generated by its combined EKF to detect the possible deviation of the sensor measurement from the expected pose derived according to the mathematical model of the vehicle. To monitor the inconsistency amongst multiple sensor measurements, an auxiliary detector is introduced which fuses observations from multiple sensors. Based on the results of all the detectors, a rule-based isolation scheme is developed to identify the source anomalous sensor. The effectiveness of our proposed framework has been demonstrated on real vehicle data.