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At altitudes below about 600 km, satellite drag is one of the most important and variable forces acting on a satellite. Neutral mass density predictions in the upper atmosphere are therefore critical for (a) designing satellites; (b) performing adjustments to stay in an intended orbit; and (c) collision avoidance maneuver planning. Density predictions have a great deal of uncertainty, including model biases and model misrepresentation of the atmospheric response to energy input. These may stem from inaccurate approximations of terms in the Navier‐Stokes equations, unmodeled physics, incorrect boundary conditions, or incorrect parameterizations. Two commonly parameterized source terms are the thermal conduction and eddy diffusion. Both are critical components in the transfer of the heat in the thermosphere. Determining how well the major constituents (N₂, O₂, and O) are as heat conductors will have effects on the temperature and mass density changes from a heat source. This work shows the effectiveness of using the retrospective cost model refinement (RCMR) technique at removing model bias caused by different sources within the Global Ionosphere Thermosphere Model. Numerical experiments, Challenging Minisatellite Payload and Gravity Recovery and Climate Experiment data during real events are used to show that RCMR can compensate for model bias caused by both inaccurate parameterizations and drivers. RCMR is used to show that eliminating model bias before a storm allows for more accurate predictions throughout the storm.

 

In some applications of control, the objective is to optimize the constant asymptotic response of the system by moving the state of the system from one forced equilibrium to another. Since suppression of the transient response is not the main objective, the feedback control law can operate quasistatically, that is, extremely slowly relative to the open-loop dynamics. Although integral control can be used to achieve the desired setpoint, three issues must be addressed, namely, nonlinearity, uncertainty, and multistability, where multistability refers to the fact that multiple locally stable equilibria may exist for the same constant input. In fact, multistability is the mechanism underlying hysteresis. The present paper applies an adaptive digital PID controller to achieve quasi-static control of systems that are nonlinear, uncertain, and multistable. The approach is demonstrated on multistable systems involving unmodeled cubic and backlash nonlinearities.