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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Direct Fault Tolerant RLV Altitude Control: A Singular Perturbation Approach

In this paper, we present a direct fault tolerant control (DFTC) technique, where by "direct" we mean that no explicit fault identification is used. The technique will be presented for the attitude controller (autopilot) for a reusable launch vehicle (RLV), although in principle it can be applied to many other applications. Any partial or complete failure of control actuators and effectors will be inferred from saturation of one or more commanded control signals generated by the controller. The saturation causes a reduction in the effective gain, or bandwidth of the feedback loop, which can be modeled as an increase in singular perturbation in the loop. In order to maintain stability, the bandwidth of the nominal (reduced-order) system will be reduced proportionally according to the singular perturbation theory. The presented DFTC technique automatically handles momentary saturations and integrator windup caused by excessive disturbances, guidance command or dispersions under normal vehicle conditions. For multi-input, multi-output (MIMO) systems with redundant control effectors, such as the RLV attitude control system, an algorithm is presented for determining the direction of bandwidth cutback using the method of minimum-time optimal control with constrained control in order to maintain the best performance that is possible with the reduced control authority. Other bandwidth cutback logic, such as one that preserves the commanded direction of the bandwidth or favors a preferred direction when the commanded direction cannot be achieved, is also discussed. In this extended abstract, a simplistic example is proved to demonstrate the idea. In the final paper, test results on the high fidelity 6-DOF X-33 model with severe dispersions will be presented.

Zhu, J. J.↗
DOE OSTI2021

Asynchronous quadratic control for constrained hidden markov jump linear systems with incomplete MTPM and MOCPM

Abstract This paper investigates the quadratic optimal control problem for constrained Markov jump linear systems with incomplete mode transition probability matrix (MTPM). Considering original system mode is not accessible, observed mode is utilized for asynchronous controller design where mode observation conditional probability matrix (MOCPM), which characterizes the emission between original modes and observed modes is assumed to be partially known. An LMI optimization problem is formulated for such constrained hidden Markov jump linear systems with incomplete MTPM and MOCPM. Based on this, a feasible state-feedback controller can be designed with the application of free-connection weighting matrix method. The desired controller, dependent on observed mode, is an asynchronous one which can minimize the upper bound of quadratic cost and satisfy restrictions on system states and control variables. Furthermore, clustering observation where observed modes recast into several clusters, is explored for simplifying the computational complexity. Numerical examples are provided to illustrate the validity.

Zhu, Jin↗
DOE OSTI2018

Coordinated Optimal Control of Constrained DERs

In this paper, a coordinated optimal control is designed to simultaneously satisfy multiple power system objectives at the distribution network level. With high penetration of renewables, traditional generation and control may not be enough to compensate for intermittent changes in net load. Dynamic coordination of battery storage devices and demand response at aggregated distributed energy resource (DER) sites becomes necessary to maintain power system operation, however, each dynamic power component operates at different time scales. Frequency stabilization can be achieved by designing optimal controls for traditional generation, storage devices and demand response. The proposed optimal controls naturally render frequency separation of these load balancing mechanisms. Given a 24-hour net load forecast, optimal tracking control inputs are designed subject to constraints of selected system elements to ensure optimal performance of the power system. Simulation results are presented and discussed.

Harvey, Roland↗
NASA NTRS2004

Adaptive, Distributed Control of Constrained Multi-Agent Systems

Product Distribution (PO) theory was recently developed as a broad framework for analyzing and optimizing distributed systems. Here we demonstrate its use for adaptive distributed control of Multi-Agent Systems (MASS), i.e., for distributed stochastic optimization using MAS s. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (Probability dist&&on on the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. One common way to find that equilibrium is to have each agent run a Reinforcement Learning (E) algorithm. PD theory reveals this to be a particular type of search algorithm for minimizing the Lagrangian. Typically that algorithm i s quite inefficient. A more principled alternative is to use a variant of Newton's method to minimize the Lagrangian. Here we compare this alternative to RL-based search in three sets of computer experiments. These are the N Queen s problem and bin-packing problem from the optimization literature, and the Bar problem from the distributed RL literature. Our results confirm that the PD-theory-based approach outperforms the RL-based scheme in all three domains.

Bieniawski, Stefan↗
DOE OSTI2021

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings

We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems. DPC poses an approximate solution to multiparametric programming problems emerging from explicit nonlinear model predictive control (MPC). Contrary to approximate MPC, DPC does not require supervision by an expert controller. Instead, a system dynamics model is learned from a small dataset of recorded observations of the perturbed system's dynamics and the control law is optimized offline by interaction with the learned system model. The DPC method is based on two sequential steps, i) system identification using a constrained neural state-space model, and ii) optimization of an explicit control law parametrized by another neural network in closed-loop simulation with the identified neural state-space model. The combination of a differentiable closed-loop system and penalty methods for constraint handling of system outputs and inputs allows us to optimize the control law's parameters directly by backpropagating economic MPC loss through the learned system model. By incorporating domain knowledge and leveraging established techniques from optimal control, our method leverages deep neural networks as nonlinear function approximators for system identification and control while avoiding concomitant costs of intractably large datasets, and computationally expensive over-parametrized models. The scalability, data efficiency, and constrained optimal control capability of the proposed DPC method are demonstrated in simulation using a multi-zone building emulator.

Drgona, Jan↗
NASA NTRS1979

A procedure for optimal control of flexible surface vibrations

The vibration controller design for a thin flexible surface with a given boundary is formulated as an unconstrained optimization problem. It is shown that this formulation is equivalent to a system of uncoupled optimization problems each of which is a linear regulator design problem for a second-order modal system. The regulator solutions give the gains on the modal amplitude and velocity associated with each modal system. For the constrained control case corresponding to a finite number of actuators, an approximation to the optimal distributed control is given. For the case of a limited number of physical sensors, a dual approach yields a system of second-order observers or filters. Simulations indicate that the constrained controller has a satisfactory performance as long as the number of actuators is more than half the number of controlled modes.

Caglayan, A. K.↗
DOE OSTI2017

Topographically driven differences in energy and water constrain climatic control on forest carbon sequestration

Mountains are vital to ecosystems and human society given their influence on global carbon and water cycles. Yet the extent to which topography regulates montane forest carbon uptake and storage remains poorly understood. To address this knowledge gap, we compared forest aboveground carbon loading to topographic metrics describing energy balance and water availability across three headwater catchments of the Boulder Creek Watershed, Colorado, USA. The catchments range from 1800 to 3500 m above mean sea level with 46–102 cm/yr mean annual precipitation and -1.2° to 12.3°C mean annual temperature. In all three catchments, we found mean forest carbon loading consistently increased from ridges (27 ± 19 Mg C ha) to valley bottoms (60 ± 28 Mg C ha). Low topographic positions held up to 185 ± 76 Mg C ha, more than twice the peak value of upper positions. Toe slopes fostered disproportionately high net carbon uptake relative to other topographic positions. Carbon storage was on average 20–40 Mg C ha greater on north to northeast aspects than on south to southwest aspects, a pattern most pronounced in the highest elevation, coldest and wettest catchment. Both the peak and mean aboveground carbon storage of the three catchments, crossing an 11°C range in temperature and doubling of local precipitation, defied the expectation of an optimal elevation-gradient climatic zone for net primary production. These results have important implications for models of forest sensitivity to climate change, as well as to predicted estimates of continental carbon reservoirs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

State-Constrained Optimal Control Problems of Impulsive Differential Equations

The present paper studies an optimal control problem governed by measure driven differential systems and in presence of state constraints. The first result shows that using the graph completion of the measure, the optimal solutions can be obtained by solving a reparametrized control problem of absolutely continuous trajectories but with time-dependent state-constraints. The second result shows that it is possible to characterize the epigraph of the reparametrized value function by a Hamilton-Jacobi equation without assuming any controllability assumption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Improved Sensitivity Relations in State Constrained Optimal Control

Sensitivity relations in optimal control provide an interpretation of the costate trajectory and the Hamiltonian, evaluated along an optimal trajectory, in terms of gradients of the value function. While sensitivity relations are a straightforward consequence of standard transversality conditions for state constraint free optimal control problems formulated in terms of control-dependent differential equations with smooth data, their verification for problems with either pathwise state constraints, nonsmooth data, or for problems where the dynamic constraint takes the form of a differential inclusion, requires careful analysis. In this paper we establish validity of both ‘full’ and ‘partial’ sensitivity relations for an adjoint state of the maximum principle, for optimal control problems with pathwise state constraints, where the underlying control system is described by a differential inclusion. The partial sensitivity relation interprets the costate in terms of partial Clarke subgradients of the value function with respect to the state variable, while the full sensitivity relation interprets the couple, comprising the costate and Hamiltonian, as the Clarke subgradient of the value function with respect to both time and state variables. These relations are distinct because, for nonsmooth data, the partial Clarke subdifferential does not coincide with the projection of the (full) Clarke subdifferential on the relevant coordinate space. We show for the first time (even for problems without state constraints) that a costate trajectory can be chosen to satisfy the partial and full sensitivity relations simultaneously. The partial sensitivity relation in this paper is new for state constraint problems, while the full sensitivity relation improves on earlier results in the literature (for optimal control problems formulated in terms of Lipschitz continuous multifunctions), because a less restrictive inward pointing hypothesis is invoked in the proof, and because it is validated for a stronger set of necessary conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Robust trajectory-constrained frequency control for microgrids considering model linearization error

Grid supportive modes integrated within inverter-based resources can improve the frequency response of renewable-rich microgrids. The synthesis of grid supportive modes to guarantee frequency trajectory constraints under a predefined disturbance set is challenging but essential. To tackle this challenge, a numerical optimal control (NOC)-based control synthesis methodology is proposed. Without loss of generality, a wind-diesel fed microgrid is studied, where we aim to design grid supportive functions in the wind turbine. In the control design, linearized models are used, and the linearization-induced errors are quantitatively analyzed by reachability and interval arithmetics and represented in the form of interval uncertainties. Then, the NOC problem can be formulated into a robust mixed-integer linear program. The control structure is strategically configured into two levels to realize online deployment. The proposed control is verified on the modified 33-node microgrid with a full-order three-phase nonlinear model in Simulink. In conclusion, the simulation results show the effectiveness of the proposed control paradigm and the necessity of considering linearization-induced uncertainty.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Robust constrained tension control for high-precision roll-to-roll processes

Tension control is critical for maintaining good product quality in most roll-to-roll (R2R) production systems. Previous work has primarily focused on improving the disturbance rejection performance of tension controllers. Here, a robust linear parameter-varying model predictive control (LPV-MPC) scheme is designed to enhance the tension tracking performance of a pilot R2R system for deposition of materials used in flexible thin film applications. The performance of a tension controller may degrade due to disturbances associated with model uncertainties and the slowly-changing dynamics in R2R systems. We introduce a method that separately treats these two sources of disturbance. The controller utilizes an incremental model to eliminate the errors caused by the mismatch between the nominal model and the actual system. A tube-based MPC formulation combined with scheduled parameters adequately updates models and corrects for the time-varying dynamics. Constraints on the rated motor torque are incorporated in the MPC to maintain the controller reliability and avoid machine failures. We illustrate the operation of our control algorithm through simulation of an actual R2R system. The controller outperforms the benchmarks in terms of fast transient response and offset-free tension tracking. Furthermore, it also demonstrates immunity from variations due to parametric uncertainties.

42 ENGINEERING↗
DOE OSTI2023

Communication-Constrained Robust Control and Learning of Grid-Connected

The electric grid of things (EGoT) promises great potential for innovative grid services by tapping into vast load flexibility. However, the unique characteristics of EGoT, being a part of the cyber-physical electric power system, present both opportunities and challenges, especially concerning supply-demand balancing, stability, and communication constraints. Traditionally, centralized control was employed to ensure balance and stability in power systems. However, with the massive influx of EGoT devices, new strategies are needed to efficiently coordinate and control these distributed devices for optimal grid operations. While some studies have explored efficiency and economic models, there remains a gap in ensuring reliability under everyday operations and resilience during extreme conditions. Addressing this gap, this project develops the technology for an Energy Service Interface (ESI) that includes novel pricing, control, learning, and distributed optimization algorithms, which will enable utilities to recruit EGoT assets for crucial grid services such as load flexibility, voltage regulation, and situation-awareness. The key novelty of the proposed technology is the careful distribution of learning and control functions across utility and EGoT asset owners such that provably efficient and resilient grid operations are attained while respecting communication and information-exchange constraints. Specifically, the project team develops machine-learning enhanced load modeling methods to allow EGoT asset owners to learn their load capability and flexibility, and develops pricing-based and decentralized learning-based control so that asset owners can coordinate to meet system-wide demand-supply balance and reliability goals. For extreme situations involving high-impact, low-probability catastrophic events (termed the “black-sky” events), the team also develops (1) a “Feeder-Operating Center-on-a-Laptop” (FOCAL) software that can assist utility personnel in leveraging EGoT assets to accelerate the service recovery of damaged feeders, and (2) distributed optimization algorithms that can coordinate the operation points of EGoT devices under severe communication constraints. The proposed technology has been extensively tested and evaluated through simulations and on a testbed. In summary, as we transition into a more interconnected and digital power grid era, our project’s findings and developments offer a pivotal step toward guaranteeing both efficiency and resilience in the face of both everyday operations and rare “black-sky” events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1975

Constrained optimal controller for linear systems with state and control dependent disturbance

The problem is posed with the additional constraints that the dynamic controller uses only noise-corrupted outputs, and that its dimension is significantly lower than that of a Kalman filter. The unknown disturbance is viewed as an adversary which tries to maximize a performance criterion: a criterion that the controller gains attempt to minimize. The optimal controller gains are determined by solving a nonlinear matrix two-point boundary value problem.

Basuthakur, S.↗
NASA NTRS1985

Sensor/Actuator Selection for the Constrained Variance Control Problem

The problem of designing a linear controller for systems subject to inequality variance constraints is considered. A quadratic penalty function approach is used to yield a linear controller. Both the weights in the quadratic penalty function and the locations of sensors and actuators are selected by successive approximations to obtain an optimal design which satisfies the input/output variance constraints. The method is applied to NASA's 64 meter Hoop-Column Space Antenna for satellite communications. In addition the solution for the control law, the main feature of these results is the systematic determination of actuator design requirements which allow the given input/output performance constraints to be satisfied.

Delorenzo, M. L.↗
NASA NTRS1983

Control of the constrained planar simple inverted pendulum

Control of a constrained planar inverted pendulum by eigenstructure assignment is considered. Linear feedback is used to stabilize and decouple the system in such a way that specified subspaces of the state space are invariant for the closed-loop system. The effectiveness of the feedback law is tested by digital computer simulation. Pre-compensation by an inverse plant is used to improve performance.

Bavarian, B.↗