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This paper demonstrates the capability to obtain and dynamically track eigenvalues and eigenvectors using continuous optimization on constraint manifolds.
An an overview and discussion of those aspects of airframe structural dynamics that have a strong influence on rotor design optimization is provided. Primary emphasis is on vibration requirements. The vibration problem is described, the key vibratory forces are identified, the role of airframe response in rotor design is summarized, and the types of constraints which need to be imposed on rotor design due to airframe dynamics are discussed. Some considerations of ground and air resonance as they might affect rotor design are included.
A recursive algorithm is developed for the solution of the simulation dynamics problem for a chain of rigid bodies. Arbitrary joint constraints are permitted, that is, joints may allow translational and/or rotational degrees of freedom. The recursive procedure is shown to be identical to that encountered in a discrete-time optimal control problem. For each relevant quantity in the multibody dynamics problem, there exists an analog in the context of optimal control. The performance index that is minimized in the control problem is identified as Gibbs' function for the chain of bodies.
Sensitivity reduction in specific optimal control by use of dynamical controller
Parallel processing of real-time dynamic systems simulation on a multiprocessor system named OSCAR is presented. In the simulation of dynamic systems, generally, the same calculation are repeated every time step. However, we cannot apply to Do-all or the Do-across techniques for parallel processing of the simulation since there exist data dependencies from the end of an iteration to the beginning of the next iteration and furthermore data-input and data-output are required every sampling time period. Therefore, parallelism inside the calculation required for a single time step, or a large basic block which consists of arithmetic assignment statements, must be used. In the proposed method, near fine grain tasks, each of which consists of one or more floating point operations, are generated to extract the parallelism from the calculation and assigned to processors by using optimal static scheduling at compile time in order to reduce large run time overhead caused by the use of near fine grain tasks. The practicality of the scheme is demonstrated on OSCAR (Optimally SCheduled Advanced multiprocessoR) which has been developed to extract advantageous features of static scheduling algorithms to the maximum extent.
NASA's WIND mission has been operating in a large amplitude Lissajous orbit in the vicinity of the interior libration point of the Sun-Earth/Moon system since 2004. Regular stationkeeping maneuvers are required to maintain the orbit due to the instability around the collinear libration points. Historically these stationkeeping maneuvers have been performed by applying an incremental change in velocity, or (delta)v along the spacecraft-Sun vector as projected into the ecliptic plane. Previous studies have shown that the magnitude of libration point stationkeeping maneuvers can be minimized by applying the (delta)v in the direction of the local stable manifold found using dynamical systems theory. This paper presents the analysis of this new maneuver strategy which shows that the magnitude of stationkeeping maneuvers can be decreased by 5 to 25 percent, depending on the location in the orbit where the maneuver is performed. The implementation of the optimized maneuver method into operations is discussed and results are presented for the first two optimized stationkeeping maneuvers executed by WIND.
A multilevel feedback control scheme is proposed for optimization of large-scale systems composed of a number of (not necessarily weakly coupled) subsystems. Local controllers are used to optimize each subsystem, ignoring the interconnections. Then, a global controller may be applied to minimize the effect of interconnections and improve the performance of the overall system. At the cost of suboptimal performance, this optimization strategy ensures invariance of suboptimality and stability of the systems under structural perturbations whereby subsystems are disconnected and again connected during operation.
The problem of controller design for flexible spacecraft is addressed. Model-based compensators, which rely on the knowledge of the system parameters to tune the state estimator, are considered. The instability mechanisms resulting from high sensitivity to parameter uncertainties are investigated. Dissipative controllers, which use collocated actuators and sensors, are also considered, and the robustness properties of constant-gain dissipative controllers in the presence of unmodeled elastic-mode dynamics, sensor/actuator nonlinearities, and actuator dynamics are summarized. In order to improve the performance without sacrificing robustness, a class of dissipative dynamic compensators is proposed and is shown to retain robust stability in the presence of second-order actuator dynamics if acceleration feedback is employed. A class of dissipative dynamic controllers is proposed which consists of a low-authority, constant-gain controller and a high-authority dynamic compensator. A procedure for designing an optimal dissipative dynamic compensator is given which minimizes a quadratic performance criterion. Such compensators offer the promise of better performance while still retaining robust stability.
This work considers static and dynamic aeroelastic optimization of a cantilevered platewing in transonic flow. Low- and high-fidelity aeroelastic predictions are fed into a standard trust region model management scheme in order to efficiently solve this expensive optimization problem with multifidelity methods. Differences in fidelity are entirely driven by different aerodynamic solvers: linear panel methods for the low-fidelity method, and inviscid CFD-based solvers for the high-fidelity response. The optimization problem utilizes shape, sizing, and trim variables to satisfy stress, trim, and flutter constraints, and is solved across a range of subsonic and transonic Mach numbers. The multifidelity method is able to provide a speed-up for all cases considered here, despite sizable inaccuracies in the low-fidelity response for transonic flows.
The emerging urban air mobility (UAM) sector in aerospace is driving development of unconventional multi-modal vehicle configurations and autonomous flight. The combination of multi-modal vehicle dynamics, complex environment, requirements to deal with flight contingencies in an efficient and safe manner, as well as necessity for precise trajectory following and performance, are the driving influence behind adaptive optimization for system performance. We are interested in trajectory optimization algorithm that would system parameter estimation and identifying the optimal switching time between modes of hybrid dynamical systems. This presentation discusses a parameterized optimal control trajectory optimization algorithm that is an extended and generalized version of Differential Dynamic Programming (DDP), titled Parameterized Differential Dynamic Programming (PDDP). DDP is an efficient trajectory optimization algorithm relying on second order approximations of a system’s dynamics and cost function and has recently been applied to optimize systems with time invariant parameters. Experiments are presented applying PDDP to solve model predictive control (MPC) and moving horizon estimation (MHE) tasks simultaneously. In particular, PDDP is used to determine the optimal transition point between flight regimes of a complex urban air mobility (UAM) class vehicle exhibiting multiple phases of flight and to identify and compensate for actuation faults.
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This paper provides a survey of software-based power management techniques in High Performance Computing (HPC) systems. Seven existing power management and monitoring tools and frameworks are discussed. These are: Variorum, dynamic energy-performance optimizer (DEPO), Powersched, Bull Dynamic Power Optimizer (BDPO), Energy Aware Runtime (EAR), Global Extensible Open Power Manager (GEOPM), and PoLiMEr. Each of these tools is evaluated based on hardware abstraction, optimization methods, usability, and experimental validation. This survey highlights the diversity of approaches in managing energy efficiency, from vendor-neutral APIs to algorithm-driven power capping, and dynamic frequency adjustments. Given that energy requirements for large computational systems is increasing quickly, the importance of integrating these tools into existing HPC environments and the need for further research in this rapidly evolving field is also discussed.
NASA has scheduled the retirement of the space shuttle orbiter fleet at the end of 2010. The Constellation program was created to develop the next generation of human spaceflight vehicles and launch vehicles, known as Orion and Ares respectively. The Orion vehicle is a return to the capsule configuration that was used in the Mercury, Gemini, and Apollo programs. This configuration allows for the inclusion of an abort system that safely removes the capsule from the booster in the event of a failure on launch. The Flight Test Office at NASA's Dryden Flight Research Center has been tasked with the flight testing of the abort system to ensure proper functionality and safety. The abort system will be tested in various scenarios to approximate the conditions encountered during an actual Orion launch. Every abort will have a closed-loop controller with an open-loop backup that will direct the vehicle during the abort. In order to provide the best fit for the desired total angle of attack profile with the open-loop pitch table, the table is tuned using simulated abort trajectories. A pitch table optimization program was created to tune the trajectories in an automated fashion. The program development was divided into three phases. Phase 1 used only the simulated nominal run to tune the open-loop pitch table. Phase 2 used the simulated nominal and three simulated off nominal runs to tune the open-loop pitch table. Phase 3 used the simulated nominal and sixteen simulated off nominal runs to tune the open-loop pitch table. The optimization program allowed for a quicker and more accurate fit to the desired profile as well as allowing for expanded resolution of the pitch table.
In this work, we consider the optimization of non-invariant systems with both safety and control constraints. We present a new approach based on Bayesian optimization for the dynamic, safe and controlled optimization of such systems. Although there are other possible use cases, we focus on the application to the electron cyclotron resonance ion source VENUS. From experimental data, we have observed that VENUS behaves to first order as a non-invariant dynamic system with moving areas of instability. Our novel approach aims at providing a tool that can maintain system optimization in a safe way. This is accomplished by making sure the objective function, the beam current in the case of VENUS, does not fall under an operational minimum, while simultaneously requiring the optimization to avoid areas where VENUS is unstable. We compare the result of our approach on synthetic data modeled to mimic the behavior of VENUS with two methods from the literature, a standard Bayesian optimizer and a safe Bayesian optimizer, both adapted to deal with dynamic systems. A cross Student T-test is conducted to show the significance of the improvement given by the new method we introduce here, regarding the two preexisting methods we compared to. The results of the tests conducted on synthetic data show that the proposed method succeeds at maintaining the system optimized and obeys the predefined constraints better than the literature methods explored.
Consideration of stochastic optimal control problems in which the measurement equation contains a control variable. Conditions under which it is possible to separately optimize the dynamic and measurement controls are studied, with particular emphasis on showing (by counterexample) that certain results already available for the linear-Gaussian-quadratic case do not extend to more general problems. Conditions under which the extension is possible are discussed.
The paper deals with the problem of dynamic structural optimization where constraints relating to flutter of a wing (or other dynamic aeroelastic performance) are imposed along with conditions of a more conventional nature such as those relating to stress under load, deflection, minimum dimensions of structural elements, etc. The discussion is limited to a flutter problem for a linear system with a finite number of degrees of freedom and a single constraint involving aeroelastic stability, and the structure motion is assumed to be a simple harmonic time function. Three search schemes are applied to the minimum-weight redesign of a particular wing: the first scheme relies on the method of feasible directions, while the other two are derived from necessary conditions for a local optimum so that they can be referred to as optimality-criteria schemes. The results suggest that a heuristic redesign algorithm involving an optimality criterion may be best suited for treating multiple constraints with large numbers of design variables.
Neighboring optimal feedback control of multi-input nonlinear dynamic systems
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