Local-error control and its effects on the optimization of orbital integration
Local error control for optimization of numerical integration orbit calculation techniques
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Local error control for optimization of numerical integration orbit calculation techniques
An empirically obtained reaction control jet relay control law with deadband is used as the basis for determining an equivalent weighted time-fuel optimal switching curve according to a least-squares criterion. The derived transformation from the empirical to the optimal law is found to be reversible and to yield a unique transformed control law. The proposed method provides a basis for determining the behavior of an easily implemented relay control law using well-known optimal control results, as well as determining the equivalent relay law corresponding to an analytically determined optimal control law. A numerical example illustrates the transformation technique and simulation results are presented to compare the two control laws.
When a flexible structure is to be controlled actively, optimum performance is obtained by integrated, or simultaneous, design of the structure and the controller, as opposed to the common practice of designing the structure independently of control consideration and then designing a controller for a fixed structure. The primary design objective from the structural point of view usually is to minimize weight, while the control design objectives depend on the application. An important requirement for a practical control system is robustness with respect to uncertain plant parameters. This paper discusses simultaneous control/structure design when the overall design objective combines the weight of the structure and the robustness of the closed-loop control system. For numerical optimization, robustness is represented by the sensitivity of the closed-loop eigenvalues with respect to uncertain parameters. An example illustrates the optimal design of a flexible structure along with a robust compensator.
Optimal control theory is employed to determine the performance of abort to orbit (ATO) and return to launch site (RTLS) maneuvers for a single-stage to orbit vehicle. The vehicle configuration examined is a seven engine, winged-body vehicle, that lifts-off vertically and lands horizontally. The abort maneuvers occur as the vehicle ascends to orbit and are initiated when the vehicle suffers an engine failure. The optimal control problems are numerically solved in discretized form via a nonlinear programming (NLP) algorithm. A description highlighting the attributes of this NLP method is provided. ATO maneuver results show that the vehicle is capable of ascending to orbit with a single engine failure at lift-off. Two engine out ATO maneuvers are not possible from the launch pad, but are possible after launch when the thrust to weight ratio becomes sufficiently large. Results show that single engine out RTLS maneuvers can be made for up to 180 seconds after lift-off and that there are scenarios for which RTLS maneuvers should be performed instead of ATP maneuvers.
An Optimal Integral Controller that readily accommodates Sensor Failure - without resorting to (Kalman) filter or observer generation - has been designed. The system is based on Navy-sponsored research for the control of high performance aircraft. In conjunction with a NASA developed Numerical Optimization Code, the Integral Feedback Controller will provide optimal system response even in the case of incomplete state feedback. Hence, the need for costly replication of plant sensors is avoided since failure accommodation is effected by system software reconfiguration. The control design has been applied to a particularly ill-behaved, third-order system. Dominant-root design in the classical sense produced an almost 100 percent overshoot for the third-order system response. An application of the newly-developed Optimal Integral Controller - assuming all state information available - produces a response with no overshoot. A further application of the controller design - assuming a one-third sensor failure scenario - produced a slight overshoot response that still preserved the steady state time-point of the full-state feedback response. The control design should have wide application in space systems.
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A numerical procedure is proposed for solving optimal control problems governed by linear retarded functional differential equations. The procedure is based on the idea of 'averaging approximations', due to Banks and Burns (1975). For illustration, numerical results generated on an IBM 370/158 computer, which demonstrate the rapid convergence of the method are presented.
An approach to calculating optimal, gliding flight paths of the type associated with the space shuttle's transition from entry to cruising flight is presented. Kinetic energy and total energy (per unit weight) replace velocity and time in the dynamic equations, reducing the dimension and complexity of the problem. The capability for treating integral and terminal penalties (as well as Mach number effects) is retained in the numerical optimization; hence, stability and control boundaries can be observed as trajectories to the desired final energy, flight path angle, and range are determined. Numerical results show that the 'jump' to the 'front-side of the L/D curve' need not be made until the end of the transition and that the dynamic model provides a conservative range estimate. Alternatives for real-time trajectory control are discussed.
An approach to calculating optimal, gliding flight paths of the type associated with the space shuttle's transition from entry to cruising flight is presented. Kinetic energy and total energy (per unit weight) replace velocity and time in the dynamic equations, reducing the dimension and complexity of the problem. The capability for treating integral and terminal penalties (as well as Mach number effects) is retained in the numerical optimization; hence, stability and control boundaries can be observed as trajectories to the desired final energy, flight path angle, and range are determined. Numerical results show that the jump to the front-side of the L/D curve need not be made until the end of the transition and that the dynamic model provides a conservative range estimate. Alternatives for real time trajectory control are discussed.
Robust control of a quantum system is essential to utilize the current noisy quantum hardware to its full potential, such as quantum algorithms. To achieve such a goal, a systematic search for an optimal control for any given experiment is essential. The design of optimal control pulses requires accurate numerical models and, therefore, accurate characterization of the system parameters. We present an online Bayesian approach for quantum characterization of qutrit systems, which automatically and systematically identifies optimal experiments that provide maximum information on the system parameters, thereby greatly reducing the number of experiments that need to be performed on the quantum testbed. Unlike most characterization protocols that provide point-estimates of the parameters, the proposed approach is able to estimate their probability distribution. The applicability of the Bayesian experimental design technique was demonstrated on test problems, where each experiment was defined by a parameterized control pulse. In addition to this, we also present an approach for iterative pulse extension, which is robust under uncertainties in transition frequencies and coherence times, and shot noise, despite being initialized with wide uninformative priors. Furthermore, we provide a mathematical proof of the theoretical identifiability of the model parameters and present conditions on the quantum state under which the parameters are identifiable. The proof and conditions for identifiability are presented for both closed and open quantum systems using the Schrödinger equation and the Lindblad master equation, respectively.
The problems of active and passive control of sound and vibration has been investigated by many researchers for a number of years. However, few of the articles are concerned with the sound and vibration with flow-structure interaction. Experimental and numerical studies on the coupling between panel vibration and acoustic radiation due to flow excitation have been done by Maestrello and his associates at NASA/Langley Research Center. Since the coupled system of nonlinear partial differential equations is formidable, an analytical solution to the full problem seems impossible. For this reason, we have to simplify the problem to that of the nonlinear panel vibration induced by a uniform flow or a boundary-layer flow with a given wall pressure distribution. Based on this simplified model, we have been able to consider the control and stabilization of the nonlinear panel vibration, which have not been treated satisfactorily by other authors. Although the sound radiation has not been included, the vibration suppression will clearly reduce the sound radiation power from the panel. The major research findings are presented in three sections. In section two we describe results on the boundary control of nonlinear panel vibration, with or without flow excitation. Sections three and four are concerned with some analytical and numerical results in the optimal control of the linear and nonlinear panel vibrations, respectively, excited by the flow pressure fluctuations. Finally, in section five, we draw some conclusions from research findings.
Three neural network processing approaches in a direct numerical optimization model reduction scheme are proposed and investigated. Large structural systems, such as large space structures, offer new challenges to both structural dynamicists and control engineers. One such challenge is that of dimensionality. Indeed these distributed parameter systems can be modeled either by infinite dimensional mathematical models (typically partial differential equations) or by high dimensional discrete models (typically finite element models) often exhibiting thousands of vibrational modes usually closely spaced and with little, if any, damping. Clearly, some form of model reduction is in order, especially for the control engineer who can actively control but a few of the modes using system identification based on a limited number of sensors. Inasmuch as the amount of 'control spillover' (in which the control inputs excite the neglected dynamics) and/or 'observation spillover' (where neglected dynamics affect system identification) is to a large extent determined by the choice of particular reduced model (RM), the way in which this model reduction is carried out is often critical.
In this article, we present a reduced order modeling approach suitable for active control of fluid dynamical systems based on proper orthogonal decomposition (POD). The rationale behind the reduced order modeling is that numerical simulation of Navier-Stokes equations is still too costly for the purpose of optimization and control of unsteady flows. We examine the possibility of obtaining reduced order models that reduce computational complexity associated with the Navier-Stokes equations while capturing the essential dynamics by using the POD. The POD allows extraction of certain optimal set of basis functions, perhaps few, from a computational or experimental data-base through an eigenvalue analysis. The solution is then obtained as a linear combination of these optimal set of basis functions by means of Galerkin projection. This makes it attractive for optimal control and estimation of systems governed by partial differential equations. We here use it in active control of fluid flows governed by the Navier-Stokes equations. We show that the resulting reduced order model can be very efficient for the computations of optimization and control problems in unsteady flows. Finally, implementational issues and numerical experiments are presented for simulations and optimal control of fluid flow through channels.
The technical progress of researches Alternatives for Jet Engine Control is reported. A numerical study employing feedback tensors for optimal control of nonlinear systems was completed. It is believed that these studies are the first of their kind. State regulation, with a decrease in control power is demonstrated. A detailed treatment follows.
An optimal active noise controller is formulated and analyzed for three different active noise control problems. The first problem formulated is the active control of enclosed or partially enclosed harmonic sound fields where the noise source strengths and enclosure boundary description are known. The enclosure boundary is described by either pressure, velocity, or impedance boundary conditions. The second problem formulated is the active control of the free field power radiated from a distributed noise source with a known time harmonic surface velocity. The third problem formulated is the active control of enclosed or partially enclosed harmonic sound field where the noise source strengths of enclosure boundary description may not be known. All three formulations are derived using an indirect boundary element technique. Formulation and verification of an indirect boundary element method is presented. The active noise controller formulations for enclosures are capable of analyzing systems with generalized enclosure shapes, point noise sources, and/or locally reacting impedance boundary conditions. For each formulation, representative results of optimal active noise controller case studies are presented, and some general conclusions are drawn.
A methodology for design of helicopter control systems is proposed that can account for various types of concurrent specifications: stability, decoupling between longitudinal and lateral motions, handling qualities, and physical limitations of the swashplate motions. This is achieved by synergistic use of analytical techniques (Q-parameterization of all stabilizing controllers, transfer function interpolation) and advanced numerical optimization techniques. The methodology is used to design a controller for the UH-60 helicopter in hover. Good results are achieved for decoupling and handling quality specifications.
We address problem of on-line scheduling of the aircraft descent and approach trajectory. We formulate a general multiphase optimal control problem for optimization of the descent trajectory and review available methods of its solution. We develop a fast algorithm for solution of this problem using two key components: (i) fast inference of the dynamical and control variables of the descending trajectory from the low dimensional flight profile data and (ii) efficient local search for the resulting reduced dimensionality non-linear optimization problem. We compare the performance of the proposed algorithm with numerical solution obtained using optimal control toolbox General Pseudospectral Optimal Control Software. We present results of the solution of the scheduling problem for aircraft descent using novel fast algorithm and discuss its future applications.
Here, we investigate optimized quantum state preparation for quantum metrology applications in noisy environments. Using the QFI-OPT package, we simulate a low-depth variational quantum circuit (VQC) composed of a sequence of global rotations and entangling operations applied to a chain of qubits that are subject to dephasing noise. The parameters controlling the VQC are numerically optimized to maximize the quantum Fisher information, which characterizes the ultimate metrological sensitivity of a quantum state with respect to a global rotation. We find that, regardless of the details of the entangling operation implemented in the VQC, the optimal quantum states can be broadly classified into a trio of qualitative regimes, i.e., catlike, squeezed like, and product states, associated with different dephasing rates. Our findings are relevant for designing optimal state-preparation strategies for next-generation quantum sensors exploiting entanglement, such as time and frequency standards and magnetometers, aimed at achieving state-of-the-art performance in the presence of noise and decoherence.