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Abstract In this paper, active vibration suppression of a Blended-Wing-Body flexible airplane wing is studied by utilizing a smooth-switching linear parameter-varying (LPV) dynamic output-feedback control. For the reduced-order LPV models, developed for each divided flight envelop subregion, a family of mixed Input Covariance Constraint and H ∞ LPV controllers are designed to robustly suppress the wing bending displacement using hard-constrained control surfaces, while achieving smooth-switching between adjacent controllers. The proposed LPV controllers are developed by minimizing a combination of weighted H 2 output performance and smoothness index, subject to a set of Parametric Linear Matrix Inequalities derived from stability and performance conditions. In addition, the weighting coefficient in the cost function is tuned to balance between H 2 performance and switching smoothness by iteratively solving convex optimization problems. Simulation results demonstrate that simultaneous smooth-switching and improved performance can be achieved by the proposed LPV control. [ABSTRACT FROM AUTHOR]

One of the earliest approaches in gain-scheduling control is the gridding based approach, in which a set of local linear time-invariant models are obtained at various gridded points corresponding to the varying parameters within the flight envelop. In order to ensure smooth and effective Linear Parameter-Varying control, aligning all the flexible modes within each local model and maintaining small number of representative local models over the gridded parameter space are crucial. In addition, since the flexible structural models tend to have large dimensions, a tractable model reduction process is necessary. In this paper, the notion of σ -shifted H 2 - and H ∞ -norm are introduced and used as a metric to measure the model mismatch. A new modal alignment algorithm is developed which utilizes the defined metric for aligning all the local models over the entire gridded parameter space. Furthermore, an Adaptive Grid Step Size Determination algorithm is developed to minimize the number of local models required to represent the gridded parameter space. For model reduction, we propose to utilize the concept of Composite Modal Cost Analysis, through which the collective contribution of each flexible mode is computed and ranked. Therefore, a reduced-order model is constructed by retaining only those modes with significant contribution. The NASA Generic Transport Model operating at various flight speeds is studied for verification purpose, and the analysis and simulation results demonstrate the effectiveness of the proposed modeling approach. [ABSTRACT FROM AUTHOR]

Abstract This paper addresses the Input Covariance Constraint (ICC) control problem with guaranteed H ∞ performance for continuous-time Linear Parameter-Varying (LPV) systems. The upper bound of the output covariance is minimized subject to the constraints on input covariance and H ∞ output performance. This problem is an extension of the mixed H 2 / H ∞ LPV control problem, in that the resulting gain-scheduling controllers guarantee not only closed-loop system robustness in terms of H ∞ norm bound but also output covariance performance over the entire scheduling parameter space. It can be shown that this problem can be efficiently solved by utilizing the convex optimization of Parameterized Linear Matrix Inequalities (PLMIs). The main contributions of this paper are to characterize the mixed ICC / H ∞ LPV control problem using PLMIs and to develop the optimal state-feedback gain-scheduling controllers, while satisfying both input covariance and H ∞ constraints. The effectiveness of the proposed control scheme is demonstrated through vibration suppression of a blended-wing-body airplane model. [ABSTRACT FROM AUTHOR]

In highly flexible aircraft, the large structural slenderness associated to their high-aspect-ratio wings, while bringing challenges to the design, analysis, and control of such aircraft, can be pro-actively exploited for improving their flight performance, resulting in mission-adaptive morphing configurations. This paper studies the optimum wing bending and torsion deformation of highly flexible aircraft, with distributed control loads along the wing span to achieve the optimum wing geometry. With the goal of improving flight performance across the entire flight regime, a modal based wing shaping optimization is carried out, subject to the requirement of trim and control cost limitation. While a single objective of the minimum drag can be used to find the optimum wing geometry, this paper further considers a trade-off between flight efficiency and structural integrity. In this trade-off study, a multi-objective optimization is formulated and performed, targeting for both minimizing the drag to improve flight efficiency and reducing the gust-induced wing bending moment to enhance the structural integrity. Finally, this paper explores the minimum control cost for different targets of combined flight efficiency and structural integrity. This paper provides not only an efficient way to search for the desired wing planform geometry at a given flight condition but also insights of the required control effort that is necessary to maintain the wing geometry. [ABSTRACT FROM AUTHOR]

Traditionally, vehicle route planning problem focuses on route optimization based on traffic data and surrounding environment. This paper proposes a novel extended vehicle route planning problem, called vehicle macroscopic motion planning (VMMP) problem, to optimize vehicle route and speed simultaneously using both traffic data and vehicle characteristics to improve fuel economy for a given expected trip time. The required traffic data and neighbouring vehicle dynamic parameters can be collected through the vehicle connectivity (e.g. vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-cloud, etc.) developed rapidly in recent years. A genetic algorithm based co-optimization method, along with an adaptive real-time optimization strategy, is proposed to solve the proposed VMMP problem. It is able to provide the fuel economic route and reference speed for drivers or automated vehicles to improve the vehicle fuel economy. A co-simulation model, combining a traffic model based on SUMO (Simulation of Urban MObility) with a Simulink powertrain model, is developed to validate the proposed VMMP method. Four simulation studies, based on a real traffic network, are conducted for validating the proposed VMMP: (1) ideal traffic environment without traffic light and jam for studying the fuel economy improvement, (2) traffic environment with traffic light for validating the proposed traffic light penalty model, (3) traffic environment with traffic light and jam for validating the proposed adaptive real-time optimization strategy, and (4) investigating the effect of different powertrain platforms to fuel economy using two different vehicle platforms. Simulation results show that the proposed VMMP method is able to improve vehicle fuel economy significantly. For instance, comparing with the fastest route, the fuel economy using the proposed VMMP method is improved by up to 15%. [ABSTRACT FROM AUTHOR]

This paper aims at developing a control-centric analytical formulation for aeromechanics and flight dynamics of urban air mobility (UAM) vehicles. Such vehicles feature a hybrid configuration with tiltrotors and fixed wings, enabling vertical takeoff/landing capability while attaining level flight range and endurance. A comprehensive nonlinear rigid-body dynamic model is developed by incorporating multiple tiltrotor dynamics and their gyroscopic and inertial coupling effects. A quasi-steady aerodynamic formulation is used to derive the aerodynamic loads on all lifting surfaces. In addition to the conventional control surfaces of the fixed-wing aircraft, the tiltrotor angular positions and rotational speeds are also considered additional control inputs in the derivation of nonlinear flight dynamic equations. These nonlinear equations are then linearized with respect to a set of trimmed conditions to generate the corresponding linear time-invariant state-space models, which are best suited for flight control design. In particular, to capture the critical dynamical behaviors during the transition from vertical to forward flight, the linear models are attained at various angular positions of the tiltrotors. These linear models are then utilized to develop a linear parameter-varying (LPV) model in which the tiltrotor angular position is considered the scheduling parameter. Subsequently, the adaptive model predictive control (MPC) methodology is used to design the flight controllers to achieve a stable and smooth transition flight. The numerical simulations demonstrate the efficacy of the proposed modeling and control approach. [ABSTRACT FROM AUTHOR]

Abstract: An event-based sampled discrete-time linear system representing a port-fuel-injection process based on wall-wetting dynamics is obtained and formulated as a linear parameter varying (LPV) system. The system parameters used in the engine fuel system model are engine speed, temperature, and load. These system parameters can be measured in real-time through physical or virtual sensors. A gain-scheduling controller for the obtained LPV system is then designed based on the numerically efficient convex optimization or linear matrix inequality (LMI) technique. Simulation results show the effectiveness of the proposed scheme. [Copyright &y& Elsevier]