An Investigation into Soft Error Detection Efficiency at Operating System Level

Electronic equipment operating in harsh environments such as space is subjected to a range of threats. The most important of these is radiation that gives rise to permanent and transient errors on microelectronic components. The occurrence rate of transient errors is significantly more than permanent errors. The transient errors, or soft errors, emerge in two formats: control flow errors (CFEs) and data errors. Valuable research results have already appeared in literature at hardware and software levels for their alleviation. However, there is the basic assumption behind these works that the operating system is reliable and the focus is on other system levels. In this paper, we investigate the effects of soft errors on the operating system components and compare their vulnerability with that of application level components. Results show that soft errors in operating system components affect both operating system and application level components. Therefore, by providing endurance to operating system level components against soft errors, both operating system and application level components gain tolerance

IEEE Access Special Section Editorial: Data-Driven Monitoring, Fault Diagnosis and Control of Cyber-Physical Systems

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Uncertainty and disturbance estimator-based control of a flapping-wing aerial vehicle withwith unknown backlash-like hysteresis

Robust and accurate control of a flapping-wing aerial vehicle (FWAV) system is a challenging problem due to the existence of backlash-like hysteresis nonlinearity. This paper proposes uncertainty and disturbance estimator (UDE)-based control with output feedback for FWAV systems. The approach enables the acquisition of the approximate plant model with only a partial knowledge of system parameters. For the design of the controller, only the bandwidth information of the unknown plant model is needed, which is available through the UDE filter. The stability analysis of the closed-loop system with the UDE-based controller is presented. It is shown that the proposed control scheme can ensure the boundedness of the control signals. A number of numerical simulations are carried out to demonstrate the satisfactory trajectory tracking performance of the proposed method

Disturbance observer-based neural network control of cooperative multiple manipulators with input saturation

In this paper, the complex problems of internal forces and position control are studied simultaneously and a disturbance observer-based radial basis function neural network (RBFNN) control scheme is proposed to: 1) estimate the unknown parameters accurately; 2) approximate the disturbance experienced by the system due to input saturation; and 3) simultaneously improve the robustness of the system. More specifically, the proposed scheme utilizes disturbance observers, neural network (NN) collaborative control with an adaptive law, and full state feedback. Utilizing Lyapunov stability principles, it is shown that semiglobally uniformly bounded stability is guaranteed for all controlled signals of the closed-loop system. The effectiveness of the proposed controller as predicted by the theoretical analysis is verified by comparative experimental studies

Recognition of Gestural Object Reference with Auditory Feedback

Bax I, Bekel H, Heidemann G. Recognition of Gestural Object Reference with Auditory Feedback. In: Kaynak O, ed. Artificial neural networks and neural information processing. Proceedings. Lecture notes in computer science. Vol 2712. Berlin: Springer; 2003: 425-432.We present a cognitively motivated vision architecture for the evaluation of pointing gestures. The system views a scene of several structured objects and a pointing human hand. A neural classifier gives an estimation of the pointing direction, then the object correspondence is established using a sub-symbolic representation of both the scene and the pointing direction. The system achieves high robustness because the result (the indicated location) does not primarily depend on the accuracy of the pointing direction classification. Instead, the scene is analysed for low level saliency features to restrict the set of all possible pointing locations to a subset of highly likely locations. This transformation of the "continuous" to a "discrete" pointing problem simultaneously facilitates an auditory feedback whenever the object reference changes, which leads to a significantly improved human-machine interaction

On the tuning of the lagrangian parameters in a uniform structure in adaptative robot control

In this paper the application of "uniform structures" formerly introduced for the Hamiltonian description of robtos is investigated in the case of a SCARA within the frames of the Lagrangian model. This model does not suffer from measurability problems of the Hamiltonian one. Via simulation it was found that this simple method can improve the control of an imperfectly modeled system under unmodeled environmental interaction. It uses the Lie parameters of the Orthogonal Group and the "Sliding Simplex Algorithm" for the on-line tuning the parameters in the uniform structures. This method is very similar to the learning process of the artificial neural Networks. To evade the problem of local optima tuning starts from the estimtaed vicinity of the Global Optimum at the begining. It is shown that a fast enough tuning can "stick" in this optimum and it propagates together with it in time as the local dynamics of the system is changes in time.N/