F-8C adaptive flight control extensions

An adaptive concept which combines gain-scheduled control laws with explicit maximum likelihood estimation (MLE) identification to provide the scheduling values is described. The MLE algorithm was improved by incorporating attitude data, estimating gust statistics for setting filter gains, and improving parameter tracking during changing flight conditions. A lateral MLE algorithm was designed to improve true air speed and angle of attack estimates during lateral maneuvers. Relationships between the pitch axis sensors inherent in the MLE design were examined and used for sensor failure detection. Design details and simulation performance are presented for each of the three areas investigated

Kernel-based fault diagnosis of inertial sensors using analytical redundancy

Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution

Algorithms for sensor validation and multisensor fusion

Existing techniques for sensor validation and sensor fusion are often based on analytical sensor models. Such models can be arbitrarily complex and consequently Gaussian distributions are often assumed, generally with a detrimental effect on overall system performance. A holistic approach has therefore been adopted in order to develop two novel and complementary approaches to sensor validation and fusion based on empirical data. The first uses the Nadaraya-Watson kernel estimator to provide competitive sensor fusion. The new algorithm is shown to reliably detect and compensate for bias errors, spike errors, hardover faults, drift faults and erratic operation, affecting up to three of the five sensors in the array. The inherent smoothing action of the kernel estimator provides effective noise cancellation and the fused result is more accurate than the single 'best sensor'. A Genetic Algorithm has been used to optimise the Nadaraya-Watson fuser design. The second approach uses analytical redundancy to provide the on-line sensor status output μH∈[0,1], where μH=1 indicates the sensor output is valid and μH=0 when the sensor has failed. This fuzzy measure is derived from change detection parameters based on spectral analysis of the sensor output signal. The validation scheme can reliably detect a wide range of sensor fault conditions. An appropriate context dependent fusion operator can then be used to perform competitive, cooperative or complementary sensor fusion, with a status output from the fuser providing a useful qualitative indication of the status of the sensors used to derive the fused result. The operation of both schemes is illustrated using data obtained from an array of thick film metal oxide pH sensor electrodes. An ideal pH electrode will sense only the activity of hydrogen ions, however the selectivity of the metal oxide device is worse than the conventional glass electrode. The use of sensor fusion can therefore reduce measurement uncertainty by combining readings from multiple pH sensors having complementary responses. The array can be conveniently fabricated by screen printing sensors using different metal oxides onto a single substrate

Multidimensional prognostics for rotating machinery: A review

open access articleDetermining prognosis for rotating machinery could potentially reduce maintenance costs and improve safety and avail- ability. Complex rotating machines are usually equipped with multiple sensors, which enable the development of multidi- mensional prognostic models. By considering the possible synergy among different sensor signals, multivariate models may provide more accurate prognosis than those using single-source information. Consequently, numerous research papers focusing on the theoretical considerations and practical implementations of multivariate prognostic models have been published in the last decade. However, only a limited number of review papers have been written on the subject. This article focuses on multidimensional prognostic models that have been applied to predict the failures of rotating machinery with multiple sensors. The theory and basic functioning of these techniques, their relative merits and draw- backs and how these models have been used to predict the remnant life of a machine are discussed in detail. Furthermore, this article summarizes the rotating machines to which these models have been applied and discusses future research challenges. The authors also provide seven evaluation criteria that can be used to compare the reviewed techniques. By reviewing the models reported in the literature, this article provides a guide for researchers considering prognosis options for multi-sensor rotating equipment

The Role of Modern Control Theory in the Design of Controls for Aircraft Turbine Engines

Accomplishments in applying Modern Control Theory to the design of controls for advanced aircraft turbine engines were reviewed. The results of successful research programs are discussed. Ongoing programs as well as planned or recommended future thrusts are also discussed

Quantum inspired concepts in decision making

In this study, several new applications of quantum-inspired techniques are presented to advance the state of the art in decision making. In the first part, a quantum boost scheme for multiple-model filters based on extended Grover’s algorithm is presented for fault detection and parameter estimation. The quantum boost scheme accelerates the convergence of the posterior probabilities in the multiple-model filter. Stability proofs for the quantum boost scheme is presented and its performance is analyzed using benchmark examples. In the second part, the scope of application is extended to modeling human decision making. The open quantum system cognition model is applied to a case of drive-assist system where the car needs to mitigate inattentiveness of the human driver. The interaction between the car and the driver is modeled and analyzed with game theory. Moreover, the concept of equilibrium in traditional game theory is extended so that the player is not necessarily rational. Analytical solutions of the open quantum model are derived and various equilibrium points are analyzed. Note that most real-world decision making problems involve multiple people reaching a consensus after group discussion. Since the cognition of each individual has been shown to have features violating classical probability, a quantum walk model is developed to model the group decision making process and the idea propagation in social network. The traditional quantum walk solely built on unitary operators are symmetric. However, the interactions between agents in a social network are not symmetric and the influence may even by unidirectional. To overcome this constraint, the quantum walk is lifted, i.e., its state is the tensor product of of the original state and the mirrored state, so that the controlled-U gate can be applied to account for asymmetric interactions. The dynamics of idea propagation in the social network with line graph connection and agreement formation in group decision making with ring graph connection are investigated --Abstract, page v

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Multiple bottlenecks sorting criterion at initial sequence in solving permutation flow shop scheduling problem

This paper proposes a heuristic that introduces the application of bottleneck-based concept at the beginning of an initial sequence determination with the objective of makespan minimization. Earlier studies found that the scheduling activity become complicated when dealing with machine, m greater than 2, known as non-deterministic polynomial-time hardness (NP-hard). To date, the Nawaz-Enscore-Ham (NEH) algorithm is still recognized as the best heuristic in solving makespan problem in scheduling environment. Thus, this study treated the NEH heuristic as the highest ranking and most suitable heuristic for evaluation purpose since it is the best performing heuristic in makespan minimization. This study used the bottleneck-based approach to identify the critical processing machine which led to high completion time. In this study, an experiment involving machines (m =4) and n-job (n = 6, 10, 15, 20) was simulated in Microsoft Excel Simple Programming to solve the permutation flowshop scheduling problem. The overall computational results demonstrated that the bottleneck machine M4 performed the best in minimizing the makespan for all data set of problems

Adaptive Estimation and Heuristic Optimization of Nonlinear Spacecraft Attitude Dynamics

For spacecraft conducting on-orbit operations, changes to the structure of the spacecraft are not uncommon. These planned or unanticipated changes in inertia properties couple with the spacecraft\u27s attitude dynamics and typically require estimation. For systems with time-varying inertia parameters, multiple model adaptive estimation (MMAE) routines can be utilized for parameter and state estimates. MMAE algorithms involve constructing a bank of recursive estimators, each assuming a different hypothesis for the systems dynamics. This research has three distinct, but related, contributions to satellite attitude dynamics and estimation. In the first part of this research, MMAE routines employing parallel banks of unscented attitude filters are applied to analytical models of spacecraft with time-varying mass moments of inertia (MOI), with the objective of estimating the MOI and classifying the spacecraft\u27s behavior. New adaptive estimation techniques were either modified or developed that can detect discontinuities in MOI up to 98 of the time in the specific problem scenario.Second, heuristic optimization techniques and numerical methods are applied to Wahba\u27s single-frame attitude estimation problem,decreasing computation time by an average of nearly 67 . Finally, this research poses MOI estimation as an ODE parameter identification problem, achieving successful numerical estimates through shooting methods and exploiting the polhodes of rigid body motion with results, on average, to be within 1 to 5 of the true MOI values