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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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Optimal control and stability for stochastic systems, viewing linear diffusion models based upon Gaussian-Markov process as finite dimensional linear system driven by white noise
Optimization of class of multistable hybrid systems having both continuous and discrete state variables
Optimality and strong stability of control system
A difficult aspect of system optimization is the choice of a single cost function relating system parameters to the quality of the design; there are generally conflicting criteria by which the system can be evaluated. This paper discusses the problem of optimization with respect to multiple criteria, i.e., with a vector-valued cost function, without prespecifying constraints or the weighting of the criteria. The paper discusses theoretical results concerning the set of non-inferior solutions so generated and a computational algorithm for obtaining a characteristic set of non-inferior solutions.
Optimal momentum exchange desaturation in attitude control systems for orbiting spacecraft, concerning gravity gradient and disturbance torques
Optimum redundancy of multistage parallel systems using variational technique to maximize profit
The fundamental concepts of the optimality criteria method of structural optimization are presented. The effect of the separability properties of the objective and constraint functions on the optimality criteria expressions is emphasized. The single constraint case is treated first, followed by the multiple constraint case with a more complex evaluation of the Lagrange multipliers. Examples illustrate the efficiency of the method.
The presentation was delivered at the 2022 Smart Manufacturing Experience event in Pittsburgh, PA.
Optimal control theory with emphasis on inverse problem of finding conditions under which given manual system is optimum
Optimal space flight conditions for vehicles using multiple propulsion systems, noting performance of solar electric vehicle in Jupiter probe
Power system resilience has become a critical topic in recent years because of the increasing trend of extreme events and the growing integration of intermittent renewable energy sources. To enhance grid resilience against high-impact, low-frequency events, two questions should be answered: how to quantify the resilience of a given grid and how to incorporate the quantification into power system planning, operation, and restoration. Here this paper develops a new set of quantitative metrics with clear physical interpretation to comprehensively evaluate power system resilience. Using microgrids as an example, an event-based corrective scheduling (ECS) model and an online model predictive control (OMPC) model are developed to integrate the proposed quantitative resilience metrics into power system optimization models for resilience enhancement. The ECS model employs extreme event data to investigate the optimal restoration solution and to help microgrid operators prepare to respond to similar events. The OMPC model provides online decision-making support for operators to handle ongoing outages in the most resilient fashion. The effectiveness and superiority of the proposed quantitative resilience metrics and the resilience enhancement models are demonstrated through simulations and comparative studies on an IEEE test feeder and a real distribution feeder in Southern California.
Optimal desaturation of angular momentum exchange controllers in spacecraft attitude control systems, using natural environmental torques
Optimal control and trajectory optimization in aperiodic discrete time functional systems
Optimal linear controller synthesis with minimized trajectory sensitivity achieved by using quadratic cost function
Optimal time regulator for sampled-data fuel control system with input saturation
The currently common sequential design process for engineering systems is likely to lead to suboptimal designs. Recently developed decomposition methods offer an alternative for coming closer to optimum by breaking the large task of system optimization into smaller, concurrently executed and, yet, coupled tasks, identified with engineering disciplines or subsystems. The hierarchic and non-hierarchic decompositions are discussed and illustrated by examples. An organization of a design process centered on the non-hierarchic decomposition is proposed.
The currently common sequential design process for engineering systems is likely to lead to suboptimal designs. Recently developed decomposition methods offer an alternative for coming closer to optimum by breaking the large task of system optimization into smaller, concurrently executed and, yet, coupled tasks, identified with engineering disciplines or subsystems. The hierarchic and non-hierarchic decompositions are discussed and illustrated by examples. An organization of a design process centered on the non-hierarchic decomposition is proposed.
In this study a method is presented to integrate the design space of structural/control system optimization problems in the case of state feedback control. Conventional structural sizing variables and elements of the feedback gain matrix are both treated as strictly independent design variables in the optimization by extending design variable linking concepts to the control gains. Examples which involve a variety of behavior constraints, including dynamic transient response and control force limits, are effectively solved by using the method presented.