A genetic algorithm was used to optimize performance of a fuzzy inference system acting as a controller for a magnetically actuated CubeSat. A solely magnetically controlled satellite is a nonlinear, underactuated system for which the uncontrollable axis varies as a function of orbit position and attitude; variation is approximately periodic with orbit position. Therefore, controllability is not guaranteed, making solely magnetic control a less than ideal option for spacecraft requiring a high degree of pointing accuracy or spacecraft subject to relatively large disturbances. However, for small spacecraft, such as CubeSats, with modest pointing and disturbance rejection requirements, solely magnetic actuation is a good option. The genetic-algorithm-tuned fuzzy controller solution was compared to a similar linear quadratic regulator solution that was tuned to minimize the cost function used by the genetic algorithm. Both were optimized with respect to a single set of initial conditions. The genetic-algorithm-tuned fuzzy controller was found to be a lower-cost solution than the linear quadratic regulator for the optimized set of initial conditions. Additionally, a Monte Carlo analysis showed the genetic-algorithm-tuned fuzzy controller tended to settle faster than the linear quadratic regulator over a variety of initial conditions.

The problem of magnetic attitude control of a CubeSat is analyzed. Three controller types are examined: a Constant-Gain Simple PD controller, a Linear Constant-Gain Optimal PD controller (i.e. an LQR), and a Fuzzy Gain-Scheduled PD controller. Each subsequent controller type utilizes a more-complex design algorithm. The Simple PD controller is tuned by hand iteration, the LQR is tuned using rule-of-thumb algorithms, and the Fuzzy Gain-Scheduled PD controller is designed using a Genetic Algorithm operating on two Fuzzy Inference Systems. Though the basic structures of these three controllers are identical, the differing design processes lead to different controller performance. The use of a Genetic-Fuzzy System is of particular interest, because this demonstrates the use of an intelligent algorithm to automate the controller design process. The techniques presented herein are directly applicable to any magnetically actuated satellite that can be modeled as a rigid body, although the mass distribution, geometry, and orbit of the satellite will determine controller-specific constants.

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Sierra Lobo, Inc. is developing a technology that can provide both cooling and electric power generation using heat. When coupled with a radioisotope heat source, the technology is ideally suited to the needs of a long-lived Venus lander. The heat source powers a Thermoacoustic Stirling Heat Engine (TASHE), which is directly coupled to a Pulse Tube Refrigerator (PTR) in a duplex configuration. A unique feature of the Venus Duplex System is the use of the supercritical carbon dioxide Venus atmosphere as the working fluid. A linear alternator, also directly coupled, generates electricity. The initial SBIR Phase I detailed thermoacoustic modeling results indicate that a TASHE working at 23 percent efficiency and a PTR operating at 23.7 percent efficiency can effectively produce 20 W of electrical power and 154 W of cooling at a temperature of 350°C using the heat from 15 General Purpose Heat Sources (GPHS). The Venus Duplex System thermoacoustic model was used to design a Similitude Duplex System that has the same geometry but uses supercritical nitrogen as the working fluid. This results in a Similitude Duplex System that operates at reduced pressure and temperature enabling the manufacture and testing of Similitude Duplex System hardware for validating the thermoacoustic models without the need for exotic high temperature materials. Physical models of the resulting Similitude Duplex System hardware configuration have been developed. Nomenclaturep = pressure ρ = density p crit = critic pressure T = temperature T crit = critical temperature L = length δ ν = viscous penetration depth δ κ = thermal penetration depth λ = wavelength Pr = Prandtl number γ = ratio of specific heat capacities c p = isobaric specific heat capacity c s = solid specific heat capacity k = thermal conductivity θ = temperature difference r = fin radial position h = heat transfer coefficient t = fin thickness Q = heat transfer T w = wall temperature T ∞ = freestream temperature 2 r 1 ,r 2 = inner/outer radii C 1 ,C 2 = constant coefficients of differential equation β = eigen value K 0 ,K 1 = modified Bessel functions of the first order I 0 ,I 1 = modified Bessel functions of the second order Nu d = Nusselt number d = cylinder diameter Re = Reynolds number C,m = empirical heat transfer coefficient constants η f = fin efficiency A b= surface area of heat exchanger at fin base g = inverse effective fin length P fin = fin perimeter A fin = fin area R fin = fin thermal resistance R 0 = convective wall thermal resistance R wall = conductive wall thermal resistance