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In this study, a novel framework for the flight control of a morphing unmanned aerial vehicle (UAV) based on linear parameter-varying (LPV) methods is proposed. A high-fidelity nonlinear model and LPV model of an asymmetric variable-span morphing UAV were obtained using the NASA generic transport model. The left and right wing span variation ratios were decomposed into symmetric and asymmetric morphing parameters, which were then used as the scheduling parameter and the control input, respectively. LPV-based control augmentation systems were designed to track the normal acceleration, angle of sideslip, and roll rate commands. The span morphing strategy was investigated considering the effects of morphing on various factors to aid the intended maneuver. Autopilots were designed using LPV methods to track commands for airspeed, altitude, angle of sideslip, and roll angle. A nonlinear guidance law was coupled with the autopilots for three-dimensional trajectory tracking. A numerical simulation was performed to demonstrate the effectiveness of the proposed scheme.

Setting the appropriate controllers for aircraft stability and control augmentation systems are complicated and time consuming tasks. As in the Linear Quadratic Regulator method gains are found by selecting the appropriate weights or as in the Proportional Integrator Derivative control by tuning gains. A trial and error process is usually employed for the determination of weighting matrices, which is normally a time consuming procedure. Flight Control Law were optimized and designed by combining the Deferential Evolution algorithm, the Linear Quadratic Regulator method, and the Proportional Integral controller. The optimal controllers were used to reach satisfactory aircraft's dynamic and safe flight operations with respect to the augmentation systems' handling qualities, and design requirements for different flight conditions. Furthermore the design and the clearance of the controllers over the flight envelope were automated using a Graphical User Interface, which offers to the designer, the flexibility to change the design requirements. In the aim of reducing time, and costs of the Flight Control Law design, one fitness function has been used for both optimizations, and using design requirements as constraints. Consequently the Flight Control Law design process complexity was reduced by using the meta-heuristic algorithm.

In this paper the Cessna Citation X clearance criteria were evaluated for a new Flight Controller. The Flight Control Law were optimized and designed for the Cessna Citation X flight envelope by combining the Deferential Evolution algorithm, the Linear Quadratic Regulator method, and the Proportional Integral controller during a previous research presented in part 1. The optimal controllers were used to reach satisfactory aircraft's dynamic and safe flight operations with respect to the augmentation systems ' handling qualities, and design requirements. Furthermore the number of controllers used to control the aircraft in its flight envelope was optimized using the Linear Fractional Representations features. To validate the controller over the whole aircraft flight envelope, the linear stability, eigenvalue, and handling qualities criteria in addition of the nonlinear analysis criteria were investigated during this research to assess the business aircraft for flight control clearance and certification. The optimized gains provide a very good stability margins as the eigenvalue analysis shows that the aircraft has a high stability, and a very good flying qualities of the linear aircraft models are ensured in its entire flight envelope, its robustness is demonstrated with respect to uncertainties due to its mass and center of gravity variations.

This chapter deals with automatic flight control systems. It describes the effect of flight conditions on the aircraft modes, presents some background in handling qualities and control design criteria, and describes the purpose and design requirements of a large number of commonly used control systems. Stability augmentation systems are conventionally designed separately for the longitudinal dynamics and the lateral‐directional dynamics, and this is made possible by the decoupling of the aircraft dynamics in most flight conditions. In the case of high‐performance military aircraft, where the pilot may have to maneuver the aircraft to its performance limits and perform tasks such as precision tracking of targets, specialized control augmentation systems are needed. The chapter shows how algebraic expressions for the rigid‐body modes can be derived so that their dependence on the stability derivatives and on the flight conditions can be examined, and conditions for stability can be deduced.

This chapter introduces the control laws that require the designer to ‘solve’ for the control vector. At one extreme the control ‘law’ could be trivial: simple cables and pulleys connected to the control stick and rudder pedal at one end, to the control surfaces at the other end. In many cases some form of automatic flight control is used. This can range from systems that modify the airplane's dynamic response, to control‐augmentation systems that translate pilot inputs into specific responses, such as making a given longitudinal stick input command a fixed pitch rate, independent of the airplane's state. The amount of pilot compensation required to perform a task is a measure of an airplane's flying qualities. Historically flight control system design has been based on feedback control, typically using transfer functions of control effectors to system outputs to find feedback gains that give desired responses.

This chapter contains sections titled: Introduction Principles of Flight Control Flight Control Surfaces Primary Flight Control Secondary Flight Control Commercial Aircraft Flight Control Linkage Systems High Lift Control Systems Trim and Feel Flight Control Actuation Civil System Implementations Fly‐By‐Wire Control Laws A380 Flight Control Actuation Boeing 777 Implementation Interrelationship of Flight Control, Guidance and Flight Management References

In many cases of machine learning, research suggests that the development of training data might have a higher relevance than the choice and modelling of classifiers themselves. Thus, data augmentation methods have been developed to improve classifiers by artificially created training data. In NLP, there is the challenge of establishing universal rules for text transformations which provide new linguistic patterns. In this paper, we present and evaluate a text generation method suitable to increase the performance of classifiers for long and short texts. We achieved promising improvements when evaluating short as well as long text tasks with the enhancement by our text generation method. Especially with regard to small data analytics, additive accuracy gains of up to 15.53% and 3.56% are achieved within a constructed low data regime, compared to the no augmentation baseline and another data augmentation technique. As the current track of these constructed regimes is not universally applicable, we also show major improvements in several real world low data tasks (up to +4.84 F1-score). Since we are evaluating the method from many perspectives (in total 11 datasets), we also observe situations where the method might not be suitable. We discuss implications and patterns for the successful application of our approach on different types of datasets.

In this paper, the principle and benefits of stability and control augmentation system (SCAS) are introduced. Four schemes of the longitudinal SCAS for civil aircraft in normal mode are analyzed and compared. One of them, i.e., C * response type, is selected. Its architecture is determined. Then, based on a typical civil aircraft, the flight control law of the longitudinal SCAS is designed. A simulation model of the longitudinal SCAS is established through MATLAB software. Finally, the simulation and verification of the longitudinal SCAS model is carried out. The simulation results show that the designed longitudinal SCAS is feasible and can be applied to engineering practice. Compared with either normal load or pitch rate command response type, with proportional and integral feedback for mixed control signals of pitch rate and normal load, C * not only improves the short period characteristics but also effectively separates phugoid and short period frequencies, thus achieving high control accuracy.

In this paper, we first introduce the necessity, history, and classification of stability and control augmentation systems. The structure of the lateral-directional stability and control augmentation control system in the normal mode is studied, and a comparison of advantages and disadvantages of several design schemes including yawing rate feedback, rolling rate feedback, and sideslip angle feedback is given. Lateral-directional stability and control augmentation control law for a certain type of civil aircraft simulator is designed. Finally, the stability and control augmentation system is simulated and the simulation results are analyzed, which shows that the handling qualities of the aircraft with stability and control augmentation system are better than those without it.

Much contemporary rhetoric regards the prospects and pitfalls of using artificial intelligence techniques to automate an increasing range of tasks, especially those once considered the purview of people alone. These accounts are often wildly optimistic, understating outstanding challenges while turning a blind eye to the human labor that undergirds and sustains ostensibly “automated” services. This long-standing focus on purely automated methods unnecessarily cedes a promising design space: one in which computational assistance augments and enriches, rather than replaces, people’s intellectual work. This tension between human agency and machine automation poses vital challenges for design and engineering. In this work, we consider the design of systems that enable rich, adaptive interaction between people and algorithms. We seek to balance the often-complementary strengths and weaknesses of each, while promoting human control and skillful action. We share case studies of interactive systems we have developed in three arenas—data wrangling, exploratory analysis, and natural language translation—that integrate proactive computational support into interactive systems. To improve outcomes and support learning by both people and machines, we describe the use of shared representations of tasks augmented with predictive models of human capabilities and actions. We conclude with a discussion of future prospects and scientific frontiers for intelligence augmentation research.