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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Active flutter suppression using optical output feedback digital controllers

A method for synthesizing digital active flutter suppression controllers using the concept of optimal output feedback is presented. A convergent algorithm is employed to determine constrained control law parameters that minimize an infinite time discrete quadratic performance index. Low order compensator dynamics are included in the control law and the compensator parameters are computed along with the output feedback gain as part of the optimization process. An input noise adjustment procedure is used to improve the stability margins of the digital active flutter controller. Sample rate variation, prefilter pole variation, control structure variation and gain scheduling are discussed. A digital control law which accommodates computation delay can stabilize the wing with reasonable rms performance and adequate stability margins.

Source record↗
NASA NTRS1985

An Integrated Control and Minimum Mass Structural Optimization Algorithm for Large Space Structures

A new approach is discussed for solving dual structural control optimization problems for high-order flexible space structures, where reduced-order structural models are employed and minimum mass designs are sought. For a given initial structural design, a quadratic control cost is minimized subject to a constant-mass constraint. The sensitivity of the optimal control cost with respect to the structural design variables is then determined and used to obtain successive structural redesigns, using a constrained gradient optimization algorithm. This process is repeated until the constrained control cost sensitivity becomes negligible. The minimum mass design is obtained by solving a sequence of neighboring optimal constant mass designs, where the sequence of optimal performance indices has a minimum at the optimal minimum mass design. A numerical example is presented which demonstrates that this new approach effectively addresses the problem of dual optimization for potentially very high-order structures.

Messac, A.↗
NASA NTRS1984

Dual structural-control optimization of large space structures

A new approach is proposed for solving dual structural-control optimization problems for high-order flexible space structures where reduced-order structural models are employed. For a given initial structural dessign, a quadratic control cost is minimized subject to a constant-mass constraint. The sensitivity of the optimal control cost with respect to the stuctural design variables is then determined and used to obtain successive structural redesigns using a contrained gradient optimization algorithm. This process is repeated until the constrained control cost sensitivity becomes negligible. A numerical example is presented which demonstrates that this new approach effectively addresses the problem of dual optimization for potentially very high-order structures.

Messac, A.↗
NASA NTRS2024

Shape Servoing of Deformable Objects Using Model Estimation and Barrier Lyapunov Function

An adaptive shape servoing control method is presented in this article to manipulate a deformable object into a desired shape in 3-D. A finite-point-based representation of the deformable object is used and the deformation Jacobian matrix is approximated using Fourier series basis functions. The unknown parameters of the deformation Jacobian are learned by using the velocity applied to a control point on the object and corresponding change of positions of the points describing the entire object. An integral concurrent learning (ICL)-based parameter update law is designed along with a constrained controller to satisfy the state constraints on the motion of the control point using Barrier Lyapunov function analysis. ICL-based parameter update law uses data history of velocity and corresponding positions of the points along with their current values. An efficient algorithm to update the history stack using singular value maximization is proposed based on the structure of the regressor matrix. Simulations using a physical simulator and experiments using a robot platform are performed to validate the performance of the proposed controller on two different deformable objects.

Vrithik Raj Guthikonda↗
NASA NTRS1982

A dynamic measure of controllability and observability for the placement of actuators and sensors on large space structures

The degree of controllability of a large space structure is found by a four step procedure: (1) finding the minimum control energy for driving the system from a given initial state to the origin in the prescribed time; (2) finding the region of initial state which can be driven to the origin with constrained control energy and time using optimal control strategy; (3) scaling the axes so that a unit displacement in every direction is equally important to control; and (4) finding the linear measurement of the weighted "volume" of the ellipsoid in the equicontrol space. For observability, the error covariance must be reduced toward zero using measurements optimally, and the criterion must be standardized by the magnitude of tolerable errors. The results obtained using these methods are applied to the vibration modes of a free-free beam.

Vandervelde, W. E.↗
NASA NTRS1983

Active flutter control using discrete optimal constrained dynamic compensators

A method for synthesizing digital active flutter suppression controllers using the concept of optimal output feedback is presented. A recently developd convergent algorithm is employed to determine constrained control law parameters that minimize an infinite-time discrete quadratic performance index. Low-order compensator dynamics are included in the control law and the compensator parameters are computed along with the output feedback gain as part of the optimization process. An input noise adjustment procedure is used to improve the stability margins of the digital active flutter controller. Results from investigations into sample rate variation, prefilter pole variation, and effects of varying flight condtions are discussed. The study indicates that a digital control law which accommodates computation delay can stabilize the wing with reasonable rms performance and adequate stability margins.

Broussard, J. R.↗
DOE OSTI2018

A Suite of Mechanistic Epidemiological Decision Support Tools

Objective: We will demonstrate tools that allow mechanistic contraints on disease progression and epidemic spread to play off against interventions, mitigation, and control measures. The fundamental mechanisms of disease progression and epidemic spread provide important constraints on interpreting changing epidemic cases counts with time and geography in the context of on-going interventions, mitigations, and controls. Models such as these that account for the effects of human actions can also allow evaluation of the importance of categories of epidemic and disease controls. Introduction: We present the EpiEarly, EpiGrid, and EpiCast tools for mechanistically-based biological decision support. The range of tools covers coarse-, medium-, and fine-grained models. The coarse-grained, aggregated time-series only data tool (EpiEarly) provides a statistic quantifying epidemic growth potential and associated uncertainties. The medium grained, geographically-resolved model (EpiGrid) is based on differential equation type simulations of disease and epidemic progression in the presence of various human interventions geared toward understanding the role of infection control, early vs. late diagnosis, vaccination, etc. in outbreak control. A fine-grained hybrid-agent epidemic model (EpiCast) with diurnal agent travel and contagion allows the analysis of the importance of contact-networks, travel, and detailed intervention strategies for the control of outbreaks and epidemics. Methods: We use three types of methods for simulation and analysis. They are: (1) Bayesian and regression methods allowing estimation of the basic reproductive number from case-count data; (2) ordinary-differential equation integration with modifications to account for discreteness of disease spread when case counts are small (we include space- and time-dependent effects); and (3) methods that hybridize agent-based travel, mixing, and disease progression with nested-mass action contagion (i.e. not fully agent-based). From the perspective of decision support, the crucial feature of mechanistic infectious epidemiological models is a way to capture the human interventions that determine epidemic outcome. Categorizing types of mitigation into those that change the force of infection, and those that branch disease progression allows a common framework that can be extended from medium-grained models through fine-grained. Our canonical example is our EpiGrid tool which allows for the modulation of the force of infection (i.e. contagion) with time (and potentially space), the vaccination of a susceptible population in a geographically-targeted manner, movement controls, and branching our disease progression model to account for early- vs. late-intervention during host disease progression.ResultsWe will present analysis of diseases that exemplify the various aspects of analysis in support of outbreak and epidemic control. Human and animal diseases relevant to this demonstration include rinderpest, avian influenza, and measles. We will begin with EpiEarly’s estimate of epidemic potential using aggregated time-dependent case-count data. The key observation for EpiEarly is that under a wide range of situations a disease's reproductive number should be generalized to a distribution of possibilities to account for inherent randomness and other factors (including the variability of a disease contact network). We will then continue with a demonstration of EpiGrid’s capabilities for understanding and modelling the role of interventions including contagion control (the force of infection), treatment (changing disease progression and infectiousness depending on treatment), vaccination, culling, and movement controls. We will briefly touch on the capabilities of EpiCast for more detailed analysis of specific intervention strategies. Conclusions: We will demonstrate examples where modeling either contributed or plausibly would contribute to informing epidemic and outbreak control constrained by the possibilities of the underlying epidemic and disease dynamics.

Geospatial↗
NASA NTRS1981

Minimum fuel horizontal flightpaths in the terminal area

The problem of minimum fuel airplane trajectories from arbitrary initial states to be fixed final state is considered. There are four state variables (two position coordinates, heading, and constrained velocity) and two constrained controls (thrust and bank angle). The fuel optimality of circular and straight line flightpaths is examined. Representative extremals (trajectories satisfying the necessary conditions of the minimum principle) of various types are computed and used to evaluate trajectories generated by an on line algorithm. Attention is paid to the existence of Darboux points (beyond which an extremal ceases to be globally optimal). One fuel flow rate model includes a term quadratic in thrust; hence, the optimal thrust is continuous and nonsingular. The other fuel flow rate model is linear in thrust, and consequently the optimal thrust is discontinuous and singular.

Kreindler, E.↗
NASA NTRS2012

Investigation of Optimal Control Allocation for Gust Load Alleviation in Flight Control

Advances in sensors and avionics computation power suggest real-time structural load measurements could be used in flight control systems for improved safety and performance. A conventional transport flight control system determines the moments necessary to meet the pilot's command, while rejecting disturbances and maintaining stability of the aircraft. Control allocation is the problem of converting these desired moments into control effector commands. In this paper, a framework is proposed to incorporate real-time structural load feedback and structural load constraints in the control allocator. Constrained optimal control allocation can be used to achieve desired moments without exceeding specified limits on monitored load points. Minimization of structural loads by the control allocator is used to alleviate gust loads. The framework to incorporate structural loads in the flight control system and an optimal control allocation algorithm will be described and then demonstrated on a nonlinear simulation of a generic transport aircraft with flight dynamics and static structural loads.

Frost, Susan A.↗
NASA NTRS1989

Digital robust control law synthesis using constrained optimization

Development of digital robust control laws for active control of high performance flexible aircraft and large space structures is a research area of significant practical importance. The flexible system is typically modeled by a large order state space system of equations in order to accurately represent the dynamics. The active control law must satisy multiple conflicting design requirements and maintain certain stability margins, yet should be simple enough to be implementable on an onboard digital computer. Described here is an application of a generic digital control law synthesis procedure for such a system, using optimal control theory and constrained optimization technique. A linear quadratic Gaussian type cost function is minimized by updating the free parameters of the digital control law, while trying to satisfy a set of constraints on the design loads, responses and stability margins. Analytical expressions for the gradients of the cost function and the constraints with respect to the control law design variables are used to facilitate rapid numerical convergence. These gradients can be used for sensitivity study and may be integrated into a simultaneous structure and control optimization scheme.

Mukhopadhyay, Vivekananda↗
NASA NTRS1992

An framework for robust flight control design using constrained optimization

An analytical framework is described for the design of feedback control systems to meet specified performance criteria in the presence of structured and unstructured uncertainty. Attention is focused upon the linear time invariant, single-input, single-output problem for the purposes of exposition. The framework provides for control of the degree of the stabilizing compensator or controller.

Palazoglu, A.↗
DOE OSTI2021

Controlled Islanding Strategy Considering Uncertainty of Renewable Energy Sources Based on Chance-constrained Model

Controlled islanding plays an essential role in preventing the blackout of power systems. Although there are several studies on this topic in the past, not enough attention is paid to the uncertainty brought by renewable energy sources (RESs) that may cause unpredictable unbalanced power and the observability of power systems after islanding that is essential for back-up black-start measures. Therefore, a novel controlled islanding model based on mixed-integer second-order cone and chance-constrained programming (MISOCCP) is proposed to address these issues. First, the uncertainty of RESs is characterized by their possibility distribution models with chance constraints, and the requirements, e. g., system observ-ability, for rapid back-up black-start measures are also considered. Then, a law of large numbers (LLN) based method is employed for converting the chance constraints into deterministic ones and reformulating the non-convex model into convex one. Finally, case studies on the revised IEEE 39-bus and 118-bus power systems as well as the comparisons among different models are given to demonstrate the effectiveness of the proposed model. The results show that the proposed model can result in less unbalanced power and better observability after islanding compared with other models.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Data-Driven Chance-Constrained Design of Voltage Droop Control for Distribution Networks: Preprint

This paper addresses the design of local control methods for voltage control in distribution networks with high level of distributed energy resources (DERs). The designed control methods adapt the active and reactive power output of distributed energy resources proportional to the deviation of the local measured voltage magnitudes from a reference voltage, which is referred to as droop control. Thus, the design focuses on determining the droop characteristics which satisfy network-wide voltage magnitude constraints. The uncertainty and variability of DERs renders the design of optimal droop controls very challenging. Hence, this paper proposes chance constraints to limit the risk from intermittent DERs, by designing droop control coefficients that guarantee the satisfaction of network operational constraints with a specific probability. In addition, the proposed approach relies entirely on historical data rather than assuming knowledge of the probability distributions that characterize the uncertainty of DERs. The efficacy of the proposed method is demonstrated on a 37-bus distribution feeder.

chance-constrained optimization↗
NASA NTRS1992

Constrained modes in control theory - Transmission zeros of uniform beams

Mathematical arguments are presented demonstrating that the well-established control system concept of the transmission zero is very closely related to the structural concept of the constrained mode. It is shown that the transmission zeros of a flexible structure form a set of constrained natural frequencies for it, with the constraints depending explicitly on the locations and the types of sensors and actuators used for control. Based on this formulation, an algorithm is derived and used to produce dimensionless plots of the zero of a uniform beam with a compatible sensor/actuator pair.

Williams, T.↗
NASA NTRS2005

Robust Constrained Optimization Approach to Control Design for International Space Station Centrifuge Rotor Auto Balancing Control System

This thesis discusses application of a robust constrained optimization approach to control design to develop an Auto Balancing Controller (ABC) for a centrifuge rotor to be implemented on the International Space Station. The design goal is to minimize a performance objective of the system, while guaranteeing stability and proper performance for a range of uncertain plants. The Performance objective is to minimize the translational response of the centrifuge rotor due to a fixed worst-case rotor imbalance. The robustness constraints are posed with respect to parametric uncertainty in the plant. The proposed approach to control design allows for both of these objectives to be handled within the framework of constrained optimization. The resulting controller achieves acceptable performance and robustness characteristics.

Postma, Barry Dirk↗
DOE OSTI2024

Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees

We present differentiable predictive control (DPC), a method for offline learning of constrained neural control policies for nonlinear dynamical systems with performance guarantees. We show that the sensitivities of the parametric optimal control problem can be used to obtain direct policy gradients. Specifically, we employ automatic differentiation (AD) to efficiently compute the sensitivities of the model predictive control (MPC) objective function and constraints penalties. To guarantee safety upon deployment, we derive probabilistic guarantees on closed-loop stability and constraint satisfaction based on indicator functions and Hoeffding’s inequality. We empirically demonstrate that the proposed method can learn neural control policies for various parametric optimal control tasks. In particular, we show that the proposed DPC method can stabilize systems with unstable dynamics, track time-varying references, and satisfy nonlinear state and input constraints. Our DPC method has practical time savings compared to alternative approaches for fast and memory-efficient controller design. Specifically, DPC does not depend on a supervisory controller as opposed to approximate MPC based on imitation learning. We demonstrate that, without losing performance, DPC is scalable with greatly reduced demands on memory and computation compared to implicit and explicit MPC while being more sample efficient than model-free reinforcement learning (RL) algorithms.

97 MATHEMATICS AND COMPUTING↗