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This paper presents the development of novel transition flight controllers for a class of urban air mobility aircrafts configured with a fixed-wing and six distributed electric rotor assemblies. Only the two tilt-rotors are utilized for thrust vectoring during transition flight from hovering to steady-level flight, while the four lift-rotors are modulated with aerodynamic lift induced by fixed-wing to maintain stable altitude-hold. Three tractable tilt-rotor articulation profiles are proposed by taking into account of various aircraft and hardware constraints. Given a predefined nominal tilting profile, a family of linear models is obtained by linearizing the nonlinear aircraft model at multiple tilt-rotor angular positions along the tilting profile. Using tilt-rotor angular position as a scheduling parameter, a discrete-time linear parameter-varying model can be constructed, which is then used to develop a novel transition flight control architecture that integrates the adaptive model predictive control law with feedforward effect of the dynamic reference compensation. The simulation results demonstrate the effectiveness of proposed transition flight controllers and its robustness subject to external disturbance.

This paper proposes a sequential design scheme for switching ℌ∞ LPV (Linear Parameter-Varying) control, aiming to reduce the computational complexity of the associated optimization problem. Different from the traditional approach that simultaneously designs switching LPV controllers and solves a high-dimensional optimization problem, the proposed sequential design approach renders a bundle of low-dimensional optimization problems to be solved iteratively. Individual ℌ∞ LPV controller for each subregion is synthesized by independent PLMIs (Parametric Linear Matrix Inequalities) to guarantee ℌ∞ performance, and controller variables are interpolated on the overlapped subregions such that the ℌ∞ performance is also guaranteed on the overlapped subregion. Numerical examples are used to demonstrate the effectiveness of this method to reduce the computational load in each design iteration and improved ℌ∞ performance over the conventional simultaneous design method with well-tuned interpolation coefficient.

The sensitivity of additive manufacturing (AM) to the variability of feedstock quality, machine calibration, and accuracy drives the need for frequent characterization of fabricated objects for a robust material process. The constant testing is fiscally and logistically intensive, often requiring coupons that are manufactured and tested in independent facilities. As a step toward integrating testing and characterization into the AM process while reducing cost, we propose the automated testing and characterization of AM (ATCAM). ATCAM is configured for fused deposition modeling (FDM) and introduces the concept of dynamic coupons to generate large quantities of basic AM samples. An in situ actuator is printed on the build surface to deploy coupons through impact, which is sensed by a load cell system utilizing machine learning (ML) to correlate AM data. We test ATCAM’s ability to distinguish the quality of three PLA feedstock at differing price points by generating and comparing 3000 dynamic coupons in 10 repetitions of 100 coupon cycles per material. ATCAM correlated the quality of each feedstock and visualized fatigue of in situ actuators over each testing cycle. Three ML algorithms were then compared, with Gradient Boost regression demonstrating a 71% correlation of dynamic coupons to their parent feedstock and provided confidence for the quality of AM data ATCAM generates.

In this article, motor failure control of a six-rotor electric vertical take-off and landing (eVTOL) urban air mobility aircraft is investigated using adaptive model predictive control (MPC) based on the linear parameter-varying (LPV) model developed using the nonlinear rigid-body aircraft model. For capturing the aircraft dynamics under motor failure conditions, a family of linearized models are obtained by trimming the nonlinear aircraft model at multiple equilibrium conditions and the LPV model is obtained by linking the linear models using the failed rotor speed, where the system transition from healthy to failure is modeled by a scheduling parameter calculated based on failed rotor speed caused by available motor peak power after failure. The proposed adaptive MPC is developed to optimize the system output performance, including the rigid-body aircraft velocity and altitude, by using quadratic programming optimization with reference compensation subject to a set of time-varying constraints representing the current available propeller acceleration calculated based on the motor power. Simulation study is conducted based on the developed LPV control design and original nonlinear rigid-body model, and the simulation results demonstrate that the designed adaptive MPC controller is able to recover and maintain the aircraft at desired stable condition after motor failure.

Hard constrained control problems are popular in practical applications due to physical and power limitations. For instance, the displacement of a linear actuator is finite. A lot of studies have been conducted in this area to deal with certain hard constrained control problems and some are computationally expensive. This paper introduces a novel LPV (linear parameter-varying) virtual control scheme to deal with a class of hard constrained control problems with an application to flutter suppression of a smart airfoil, leading to a state-feedback LPV gain scheduling controller with the guaranteed $${{\cal H}_\infty}$$ performance. The basic idea of LPV virtual control is to add virtual components (such as variable stiffness springs and dampers) near to the hard constraints to prevent actuators from reaching their limits. The LPV virtual controller will be designed based on the model with virtual components and in the implementation stage, these virtual dynamics becomes part of the gain-scheduling controller. The concept is validated by a smart airfoil example. In the smart airfoil example, the virtual varying springs and dampers are placed at both ends of groove to constrain the mass movement. Comparisons studies with conventional LPV hard constrained control, nonlinear control, and regular LPV control without considering hard constraints are conducted to assess the performance of the proposed method and showed advantage over the existing methods. For instance, the control mass $${{\cal L}_2}$$ norm is reduced by 77.5% over the nonlinear control and 35% over the conventional LPV control.

In this paper, we present an innovative design of smooth-switching LPV (Linear Parameter-Varying) dynamic output-feedback (DOF) controllers. For a given partition of scheduling parameter region, a family of LPV controllers are designed simultaneously with guaranteed system performance on each subregion and switching smoothness between adjacent subregions. The proposed control design, called smooth-switching mixed Input Covariance Constraint (ICC) and $${{\cal H}_\infty}$$ LPV control design, minimizes a combined cost of system output $${{\cal H}_2}$$ performance and smooth-switching index subject to $${{\cal H}_2}$$ constraints on control inputs and $${{\cal H}_\infty}$$ performance constraint. These stability and performance criteria are then formulated into a set of Parametric Linear Matrix Inequalities (PLMIs). In addition, a tunable coefficient is introduced in cost function to provide an optimal trade-off between system $${{\cal H}_2}$$ performance and switching smoothness, and therefore, the corresponding optimal LPV controllers can be derived iteratively by convex optimization. For illustration, an active magnetic bearing (AMB) example is used to show the effectiveness of the proposed simultaneous design approach by demonstrating significantly improved switching smoothness and an optimal trade-off between achievable output performance and switching smoothness.

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.

Ultralight materials present an opportunity to dramatically increase the efficiency of load-bearing aerostructures. To date, however, these ultralight materials have generally been confined to the laboratory bench-top, due to dimensional constraints of the manufacturing processes. We show a programmable material system applied as a large-scale, ultralight, and conformable aeroelastic structure. The use of a modular, lattice-based, ultralight material results in stiffness typical of an elastomer (2.6 MPa) at a mass density typical of an aerogel (5.6 [Formula: see text]). This, combined with a building block based manufacturing and configuration strategy, enables the rapid realization of new adaptive structures and mechanisms. The heterogeneous design with programmable anisotropy allows for enhanced elastic and global shape deformation in response to external loading, making it useful for tuned fluid-structure interaction. We demonstrate an example application experiment using two building block types for the primary structure of a 4.27m wingspan aircraft, where we spatially program elastic shape morphing to increase aerodynamic efficiency and improve roll control authority, demonstrated with full-scale wind tunnel testing.

The problem of optimal sensor placement (OSP) plays an essential role in developing the active vibration control system. In this paper, the OSP for a flexible wing is addressed using the greedy algorithm. The vibration behavior of a flexible wing structure is modeled into an LPV (Linear Parameter Varying) model, and the closed-loop system performance with an $\mathcal{H}_{2}$ LPV controller is considered as the objective function. Then the OSP problem is formulated into a hybrid optimization problem with integer variables and matrix-valued variables. The greedy algorithm is applied to solve for the optimal policy of sensor placement and the $\mathcal{H}_{2}$ LPV controller. Simulation results demonstrate the effectiveness of this approach and illustrate the trade-off between the number of selected sensors and the best achievable closed-loop system performance.

This paper demonstrates a method for developing in-situ actuation retrofitted onto an additive manufacturing platform. By printing and programming a cantilever beam automated to deploy and actuate without human intervention, one may extend the functionality of an additive manufacturing platform without installing additional hardware. The design leverages a common Fused Filament Fabrication (FFF) platform to physically validate the concept through experiment. Kinetics are repeatedly measured and referenced to develop over two dozen generations of in-situ actuator designs, each a function of programming the specific manipulation of the FFF platform’s gantry-code. The resulting lever fabricated onto the build plate can be embedded into the machine’s design envelope to deploy other fabricated objects off of the build plate. As a proof of concept to demonstrate the actuator’s utility, a Prusa MK3S3D printer is used to fabricate an in-situ actuator which is automated to deploy a simultaneously printed spherical 3D printed structure.

In this article, two novel linear parameter–varying modeling and control techniques are proposed for active flutter suppression of a smart airfoil model. The smart airfoil model is instrumented wit...

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.

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.

This paper develops a novel disturbance torque estimator for an orbiting spacecraft by using the adaptive least-squares parameter estimation technique. The disturbance estimation is first formulated as an adaptive least-squares minimization problem using a set of polynomial functions and then integrated with the feedback momentum estimator. The covariance update law with a variable forgetting factor is used, and it is shown that the convergent rate for estimation errors can be made at the same level as the forgetting factor. The proposed approach is particularly suited for orbiting small or microsatellite applications, where the momentum management capacity is often limited. The onboard estimated disturbance torque input can then be used as a part of control resource for spacecraft momentum management. The simulation results demonstrate the efficacy of the proposed concept.

This paper studies the placement of sensors to achieve optimal vibration suppression for a flexible BWB (Blended-Wing-Body) airplane wing. For a given flight speed range, vibration behaviors of the wing structure are evaluated by guaranteed ${\mathcal{H}}_{2}$ performance of the closed-loop system with the ${\mathcal{H}}_{2}$ LPV controller. Candidate sensor locations are identified on each wing, and the optimal sensor placements are found by globally searching through all possible combination over those candidate locations. With the LPV modeling of a flexible wing and ${\mathcal{H}}_{2}$ controller synthesis conditions, simulation results provide the optimal sensor locations for a given number of sensors. In addition the trade-off between optimal system performance and number of sensors can also be attained.

This paper proposes a sequential approach for designing switching LPV (Linear Parameter-Varying) $\mathscr{H}_{\infty}$ state-feedback controllers with given division of scheduling parameter subregions. Different from the traditional simultaneous design of switching LPV control that usually results in a relatively high-dimensional optimization problem, the proposed method designs the switching LPV controllers sequentially, leading to a bundle of low-dimensional optimization problems to be solved iteratively. Interpolated controller variables are utilized on the overlapped subregions and independent PLMIs (Parametric Linear Matrix Inequalities) are formulated on individual subregion to synthesize switching LPV controllers. At the same time, the formulated PLMIs are able to guarantee that the $\mathscr{H}_{\infty}$ performances on overlapped subregions are no worse than adjacent subregions. With the proposed method, the $\mathscr{H}_{\infty}$ performance over the entire scheduling parameter region can be guaranteed by sequentially designing individual controller and interpolating controller variables on overlapped subregions. Application example shows the effectiveness of the proposed method and demonstrates improved performance over the conventional simultaneous design approach.

Aircraft weight is one of the most critical factors in the design and operation of modern vehicles. The ability to integrate ultra-light materials into the primary load bearing structures has the potential to reduce aircraft weight significantly. Ultralight materials tend to be lattice-based meta-materials that are difficult and computationally expensive to model. One of the advantages of meta-materials is to be able to tune or "program" their bulk material properties through the placement of heterogeneous components in the material. A large amount of time devoted to the simulation in the development time for the tuning of the material can be a barrier to the adoption of large scale lattice materials. In this paper, we present a workflow and analysis tool-set to provide first-order estimates for rapid development of engineered lattice materials for aerospace applications. We present results for estimating the displacement and maximum structural stresses.

This paper presents the development of sun-safe controllers, which are designed to keep the spacecraft power positive and thermally balanced in the event an anomaly is detected. Employed by NASA’s Lunar Atmosphere and Dust Environment Explorer, the controllers use the measured sun vector and the spacecraft body rates for feedback control. To improve the accuracy of sun vector estimation, the least-square minimization approach is applied to process the sensor data. A rotation with respect to the sun vector, which is proven to be effective in mitigating the momentum buildup due to the lunar gravity gradient, hence significantly extending the sun-safe duration, is commanded. To validate the controllers, the Lunar Atmosphere and Dust Environment Explorer spacecraft model engaging the sun-safe mode is first simulated and then compared with the actual Lunar Atmosphere and Dust Environment Explorer orbital flight data. The results demonstrate the applicability of the proposed sun-safe controllers.

In this paper, optimum wing bending and torsion deformations are explored for a mission adaptive, highly flexible morphing aircraft. The complete highly flexible aircraft is modeled using a strain-based geometrically nonlinear beam formulation, coupled with unsteady aerodynamics and 6-dof rigid-body motions. Since there are no conventional discrete control surfaces for trimming the flexible aircraft, the design space for searching the optimum wing geometries is enlarged. To achieve high performance flight, the wing geometry is best tailored according to the specific flight mission needs. In this study, the steady level flight and the coordinated turn flight are considered, and the optimum wing deformations with the minimum drag at these flight conditions are searched by utilizing a modal-based optimization procedure, subject to the trim and other constraints. The numerical study verifies the feasibility of the modal-based optimization approach, and shows the resulting optimum wing configuration and its sensitivity under different flight profiles.

Summary This paper presents the modeling and control of an aircraft wing structure constructed by lattice-based cellular materials/components. A novel model reduction process is proposed that utilizes the extended discrete-time transfer matrix method (E-DT-TMM). Through recursive application of the E-DT-TMM, an effective reduced-order model can be obtained in which a decentralized discrete-time linear quadratic regulator (LQR) controller can be designed. To demonstrate the efficiency of the proposed concept, a prototype wing structure is studied. The analysis and simulation results show that the performance of the proposed E-DT-TMM based decentralized LQR controller is comparable with that of the full-state continuous LQR controller. Copyright © 2016 John Wiley & Sons, Ltd.

This paper presents an approach to simultaneously design smooth switching state-feedback LPV controllers. Using hysteresis switching strategy, a family of mixed $ICC/\mathscr{H}_{\infty}$ LPV controllers are designed to stabilize the open-loop system over each subregion and switching region. This control problem is formulated into Parametric Linear Matrix Inequalities (PLMIs) and can be numerically solved by convex optimization algorithms. A tunable cost function consisting of switching smoothness index and output system performance index is proposed to solve for the optimal LPV controller. With given robustness level, switching smoothness is improved by tuning the cost function to trade-off switching smoothness and weighted output performance. To demonstrate the feasibility of the proposed approach and balance output performances and switching smoothness, a vibratory model of flexible Blended-Wing-Body (BWB) airplane is applied. Simulation results show that switching smoothness can be improved and balanced with output performance by proposed switching controller design approach.

In this paper, a novel technique of linear parameter varying (LPV) modeling and control of a smart airfoil for active flutter suppression is proposed, where the smart airfoil has a groove along its chord and contains a moving mass that is used to control the airfoil pitching and plunging motions. The new LPV modeling technique is proposed that uses mass position as a scheduling parameter to describe the physical constraint of the moving mass, in addition the hard constraint at the boundaries is realized by proper selection of the parameter varying function. Therefore, the position of the moving mass and the free stream airspeed are considered the scheduling parameters in the study. A state-feedback based LPV gain-scheduling controller with guaranteed H infinity performance is presented by utilizing the dynamics of the moving mass as scheduling parameter at a given airspeed. The numerical simulations demonstrate the effectiveness of the proposed LPV control architecture by significantly improving the performance while reducing the control effort.

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 s-shifted H2- and H Infinity-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.

We propose a flexible, reconfigurable robot which achieves rectilinear locomotion by coupling structural deformation and directional friction promoting a locomotion strategy ideal for traversing narrow channels. The robot uses two linear actuators to generate structural waves, which propagate through the robot lifting and changing the direction of motion of the contact points (feet) between the robot and ground. The reconfigurations of the robot allow different structural waveforms to alter the robot's gait. The paper describes the modular lattice structure used to build the robot; the finite element modeling approach used to understand the structural deformation induced by the linear actuators as well as the experimental validation of the prototype robot.

This paper presents a study of fuzzy optimal variance control problem for dynamical systems subject to actuator amplitude and rate constraints. Using Takagi-Sugeno fuzzy modeling and dynamic Parallel Distributed Compensation technique, the stability and the constraints can be cast as a multi-objective optimization problem in the form of Linear Matrix Inequalities. By utilizing the formulations and solutions for the input and output variance constraint problems, we develop a fuzzy full-state feedback controller. The stability and performance of the proposed controller are demonstrated through its application to the airfoil flutter suppression.

This paper reviews the development of the Mission Adaptive Digital Composite Aerostructures Technologies (MADCAT) v0 demonstrator aircraft, utilizing a novel aerostructure concept that combines advanced composite materials manufacturing and fabrication technologies with a discrete construction approach to achieve high stiffness-to-density ratio ultra-light aerostructures that provide versatility and adaptability. This revolutionary aerostructure concept has the potential to change how future air vehicles are designed, built, and flown, with dramatic reductions in weight and manufacturing complexity the number of types of structural components needed to build air vehicles while enabling new mission objectives. We utilize the innovative digital composite materials and discrete construction technologies to demonstrate the feasibility of the proposed aerostructure concept, by building and testing a scaled prototype UAV, MADCAT v0. This paper presents an overview of the design and development of the MADCAT v0 flight demonstrator.

This paper presents a new method to design Robust Switching State-Feedback Gain-Scheduling (RSSFGS) controllers for Linear Parameter Varying (LPV) systems with uncertain scheduling parameters. The domain of scheduling parameters are divided into several overlapped subregions to undergo hysteresis switching among a family of simultaneously designed LPV controllers over the corresponding subregion with the guaranteed ℋ ∞ performance. The synthesis conditions are given in terms of Parameterized Linear Matrix Inequalities that guarantee both stability and performance at each subregion and associated switching surfaces. The switching stability is ensured by descent parameter-dependent Lyapunov function on switching surfaces. By solving the optimization problem, RSSFGS controller can be obtained for each subregion. A numerical example is given to illustrate the effectiveness of the proposed approach over the non-switching controllers.