Christopher I. Marrison

Design of Robust Control Systems for a Hypersonic Aircraft

Robuste ightcontrolsystemsaresynthesizedforthelongitudinalmotionofahypersonicaircraft.Aircraftmotion is modeled by nonlinear longitudinal dynamic equations containing 28 uncertain parameters. Each controller is designed using a genetic algorithm to search a design coefe cient space; Monte Carlo evaluation at each search point estimates stability and performance robustness. Robustness of a compensator is indicated by the probability that stability and performance of the closed-loop system will fall within allowable bounds, given likely parameter variations. A stochastic cost function containing engineering design criteria (in this case, a stability metric plus 38 step-response metrics )is minimized, producing feasible control system coefe cient sets for specie ed control system structures. This approach trades the likelihood of satisfying design goals against each other, and it identie es the plant parameter uncertainties that are most likely to compromise robustness goals. The approach makes efe cient useofcomputationaltoolsandbroadlyacceptedengineeringknowledgetoproducepracticalcontrolsystemdesigns.

Probabilistic evaluation of control system robustness

Practical control systems must operate satisfactorily with uncertain variations in plant parameters (i.e., control systems must be robust), but there are limits to the degree of robustness that may be considered desirable. Tolerance to parameter variations that never occur is not useful, and it could lead to closed-loop systems whose normal performance has been compromised unnecessarily. A probabilistic definition of robustness based on expected parameter variations is consistent with accepted design principles, and it is readily evaluated by simulation. Stochastic Robustness Analysis predicts the effects of likely parameter variations on closed-loop stability and performance through evaluation of commonly accepted criteria. Competing control designs are judged by the likelihood that system response and design metrics will fall within desired bounds. Together with numerical search, probabilistic evaluation is a powerful approach not only for comparing alternative controllers but for designing control systems that satisfy robustness and performance requirements.

Synthesis of robust control systems for a hypersonic aircraft

Stochastic robustness analysis is a flexible probabilistic framework for defining the robustness of control systems. Here, robust linear-quadratic-regulators are synthesized to control the nonlinear longitudinal dynamics of a hypersonic aircraft with uncertainties in 28 parameters. The compensators are designed using a genetic algorithm to search a design parameter space and Monte Carlo evaluation to define the robustness cost surface. The method is shown to produce control structures that satisfy nominal stability and performance goals, while minimizing robustness cost functions.<<ETX>>

Economic Scales for First-Generation Biomass-Gasifier/Gas Turbine Combined Cycles Fueled From Energy Plantations

This paper assesses the scales at which commercial, first-generation biomass integrated-gasifier/gas turbine combined cycle (BIG/GTCC) technology are likely to be most economic when fueled by plantation-derived biomass. First-generation BIG/GTCC systems are likely to be commercially offered by vendors beginning around 2000 and will be based on either pressurized or atmospheric-pressure gasification. Both plant configurations are considered here, with estimates of capital and operating costs drawn from published and other sources. Prospective costs of a farm-grown energy crop (switchgrass) delivered to a power plant are developed with the aid of a geographic information system (GIS) for agricultural regions in the North Central and Southeast US in the year 2000 and 2020. A simplified approach is applied to estimate the cost of delivering chipped eucalyptus from an existing plantation in Northeast Brazil.The “optimum” capacity (MWopt), defined as that which yields the minimum calculated cost of electricity (COEm), varies by geographic region due to differences in delivered biomass costs. With pressurized BIG/GTCC plants, MWopt is in the range of 230–320 MWs for the sites considered, assuming most of the land around the power plant is farmed for energy crop production. For atmospheric-pressure BIG/GTCC plants, MWopt ranges from 110 to 142 MWe. When a lower fraction of the land around a plant is used for energy farming, values for MWopt are smaller than these. In all cases, the cost of electricity is relatively insensitive to plant capacity over a wide range around MWopt.Copyright