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Results for “adaptive dynamic programming”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Overvoltage Prevention and Curtailment Reduction Using Adaptive Droop-Based Supplementary Control in Smart Inverters

Recent developments in the renewable energy sector have seen an unprecedented growth in residential photovoltaic (PV) installations. However, high PV penetration levels often lead to overvoltage problems in low-voltage (LV) distribution feeders. Smart inverter control such as active power curtailment (APC)-based overvoltage control can be implemented to overcome these challenges. The APC technique utilizes a constant droop-based approach which curtails power rigidly, which can lead to significant energy curtailment in the LV distribution feeders. In this paper, different variations of the APC technique with linear, quadratic, and exponential droops have been analyzed from the point-of-view of energy curtailment for a LV distribution network in North America. Further, a combinatorial approach using various droop-based APC methods in conjunction with adaptive dynamic programming (ADP) as a supplementary control scheme has also been proposed. The proposed approach minimizes energy curtailment in the LV distribution network by adjusting the droop gains. Simulation results depict that ADP in conjunction with exponential droop reduces the energy curtailment to approximately 50% compared to using the standard linear droop.

42 ENGINEERING↗
DOE OSTI2025

Adaptive PID Gain Scheduling Control for Hydropower Turbine Using Neural CDE and Stochastic Distribution Shaping

This paper introduces a gain-scheduling PID controller design strategy for hydroturbine frequency control mode. This scheme first uses real data to learn the nonlinear dynamics of the hydroturbine using neural controlled differential equations and then perturbs the obtained nonlinear system at different equilibrium points, based on which a static output feedback adaptive dynamic programming algorithm is then used to optimize the PID gains for each equilibrium point. Moreover, a continuous-time version of stochastic distribution control is proposed to further fine-tune the optimized PID gains. Finally, the controller is obtained by implementing linear interpolation between the optimized PID control gains. The simulation results show that the proposed gain-scheduling PID controller can control a larger range of operation points compared with the given fixed PID controller and the baseline method. Compared with the given fixed PID controller, the proposed gain-scheduling PID controller can regulate hydroturbine frequency against disturbances induced by power-load variation with over 50% less overshoot for some operation points.

13 HYDRO ENERGY↗
DOE OSTI2020

Value Gradient Learning Approach in Power and Frequency Regulation of Grid-Connected Synchronverters

In this paper, a neural network adaptive critic design (ACD) method based on value gradient learning (VGL) is used to optimally control a grid-connected synchronverter. The main drawback of the traditional synchronverters is their infeasibility to face non-inductive grids. To be able to implement a synchronverter technique in any impedance angle, a neural network-based adaptive controller is used. The advantage of adaptive dynamic programing is its ability to adjust itself when it faces changes and uncertainties in the power system. The proposed VGL consists of two subnetworks: the critic network and the action network. The action network is trained during the operation, and the critic network can be pretrained offline or can be simultaneously trained with the action network. To compare the effectiveness of the traditional synchronverter to the VGL-based synchronverter, the simulation results are provided.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1990

ISIS and META projects

ISIS and META are two distributed systems projects at Cornell University. The ISIS project, has developed a new methodology, virtual synchrony, for writing robust distributed software. This approach is directly supported by the ISIS Toolkit, a programming system that is distributed to over 300 academic and industrial sites. Several interesting applications that exploit the strengths of ISIS, including an NFS-compatible replicated file system, are being developed. The META project, is about distributed control in a soft real time environment incorporating feedback. This domain encompasses examples as diverse as monitoring inventory and consumption on a factory floor and performing load-balancing on a distributed computing system. One of the first uses of META is for distributed application management: the tasks of configuring a distributed program, dynamically adapting to failures, and monitoring its performance. Recent progress and current plans are presented. This approach to distributed computing, a philosophy that is believed to significantly distinguish the work from that of others in the field, is explained.

Birman, Kenneth↗
NASA NTRS1990

ISIS and META projects

The ISIS project has developed a new methodology, virtual synchony, for writing robust distributed software. High performance multicast, large scale applications, and wide area networks are the focus of interest. Several interesting applications that exploit the strengths of ISIS, including an NFS-compatible replicated file system, are being developed. The META project is distributed control in a soft real-time environment incorporating feedback. This domain encompasses examples as diverse as monitoring inventory and consumption on a factory floor, and performing load-balancing on a distributed computing system. One of the first uses of META is for distributed application management: the tasks of configuring a distributed program, dynamically adapting to failures, and monitoring its performance. Recent progress and current plans are reported.

Birman, Kenneth↗
NASA NTRS1971

Dynamic programming training period for an MSE adaptive equalizer.

A steepest descent variable step-size algorithm has been designed using dynamic programming for a mean-square-error adaptive equalizer. Additive noise and a constraint have been included. It is found that the new algorithm converges faster than the common fixed step-size algorithm.

Richman, S. H.↗
NASA NTRS2013

Sustaining a Mature Risk Management Process: Ensuring the International Space Station for a Vibrant Future

The International Space Station (ISS) risk management methodology is an example of a mature and sustainable process. Risk management is a systematic approach used to proactively identify, analyze, plan, track, control, communicate, and document risks to help management make risk-informed decisions that increase the likelihood of achieving program objectives. The ISS has been operating in space for over 14 years and permanently crewed for over 12 years. It is the longest surviving habitable vehicle in low Earth orbit history. Without a mature and proven risk management plan, it would be increasingly difficult to achieve mission success throughout the life of the ISS Program. A successful risk management process must be able to adapt to a dynamic program. As ISS program-level decision processes have evolved, so too has the ISS risk management process continued to innovate, improve, and adapt. Constant adaptation of risk management tools and an ever-improving process is essential to the continued success of the ISS Program. Above all, sustained support from program management is vital to risk management continued effectiveness. Risk management is valued and stressed as an important process by the ISS Program.

Raftery, Michael↗
NASA NTRS1974

Dynamic transformation method

Eigenvalue problem associated with modal-synthesis vibration analysis of complex structures requires simplifying assumptions for solution. Computer program, Dynamic-transformation Adapted to Modal-synthesis Using Stiffness-coupling, improves computational economy for vibration analysis of complex structures while still considering substructure modes.

Admire, J. R.↗
NASA NTRS1990

Optimal dynamic remapping of data parallel computations

A large class of data parallel computations is characterized by a sequence of phases, with phase changes occurring unpredictably. Dynamic remapping of the workload to processors may be required to maintain good performance. The problem considered, for which the utility of remapping and the future behavior of the workload are uncertain, arises when phases exhibit stable execution requirements during a given phase, but requirements change radically between phases. For these situations, a workload assignment generated for one phase may hinder performance during the next phase. This problem is treated formally for a probabilistic model of computation with at most two phases. The authors address the fundamental problem of balancing the expected remapping performance gain against the delay cost, and they derive the optimal remapping decision policy. The promise of the approach is shown by application to multiprocessor implementations of an adaptive gridding fluid dynamics program and to a battlefield simulation program.

Nicol, David M.↗
NASA NTRS1972

Adaption of a corrector module to the IMP dynamics program

The corrector module of the RAEIOS program and the IMP dynamics computer program were combined to achieve a date-fitting capability with the more general spacecraft dynamics models of the IMP program. The IMP dynamics program presents models of spacecraft dynamics for satellites with long, flexible booms. The properties of the corrector are discussed and a description is presented of the performance criteria and search logic for parameter estimation. A description is also given of the modifications made to add the corrector to the IMP program. This includes subroutine descriptions, common definitions, definition of input, and a description of output.

Source record↗
NASA NTRS2001

Parallel Programming Strategies for Irregular Adaptive Applications

Achieving scalable performance for dynamic irregular applications is eminently challenging. Traditional message-passing approaches have been making steady progress towards this goal; however, they suffer from complex implementation requirements. The use of a global address space greatly simplifies the programming task, but can degrade the performance for such computations. In this work, we examine two typical irregular adaptive applications, Dynamic Remeshing and N-Body, under competing programming methodologies and across various parallel architectures. The Dynamic Remeshing application simulates flow over an airfoil, and refines localized regions of the underlying unstructured mesh. The N-Body experiment models two neighboring Plummer galaxies that are about to undergo a merger. Both problems demonstrate dramatic changes in processor workloads and interprocessor communication with time; thus, dynamic load balancing is a required component.

Biswas, Rupak↗
DOE OSTI2020

Systems, methods and computer program products for self-tuning sensor data processing

Systems and methods are disclosed that include tools that utilize Dynamic Detector Tuning (DDT) software that identifies near-optimal parameter settings for each sensor using a neuro-dynamic programming (reinforcement learning) paradigm. DDT adapts parameter values to the current state of the environment by leveraging cooperation within a neighborhood of sensors. The key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The DDT algorithm adapts in near real-time to changing conditions in an attempt to automatically self-tune a signal detector to identify (detect) only signals from events of interest. The disclosed systems and methods reduce the number of missed legitimate detections and the number of false detections, resulting in improved event detection.

Draelos, Timothy J.↗
DOE OSTI2022

Adaptive management of large-scale ecosystem restoration: increasing certainty of habitat outcomes in the Columbia River Estuary, U.S.A.

Ecological restoration programs in dynamic coastal environments can benefit from adaptive management, including an iterative process for identifying and addressing critical uncertainties. We highlight key developments under the three pillars that have increased the rate of restoration by the Columbia Estuary Ecosystem Restoration Program (CEERP) over 20 years: science, coordination, and management. We show how such programs can be institutionalized to ensure that estuary ecosystems are better understood, conserved, and restored. The principal conservation effort under CEERP is to reconnect historical floodplain wetlands to the mainstem. The program also supports other restoration actions that demonstrate a high potential to benefit ecosystem function and endangered salmon populations, however, there is greater uncertainty regarding these less-utilized techniques. Through adaptive management, we address both technical uncertainty regarding benefits to the environmental resource and programmatic uncertainty pertaining to decision-making. Here, we examine three periods of CEERP growth to establish how complementary research and restoration actions have improved program outcomes over time. We highlight the tools and processes that were developed and integrated into the program to refine program strategy, improve project design, and maximize ecological benefits. CEERP supported 77 restoration projects and reconnected over 7,000 acres of floodplain habitat to the lower Columbia River between 2004 and 2021. Building on these successes, we outline current plans to better engage landowners and local communities, solicit new project types, and maintain enough flexibility within the program to adapt to new priorities.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Adaptive Optimization for System Performance and Combined Bernstein Polynomial, Optimal Reciprocal Collision Avoidance, Differential Dynamic Programming for Trajectory Replanning and Collision Avoidance for UAM Vehicles

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.

optimization↗