Abstract
Recently, there has been a rising interest in small satellites such as CubeSats in the aerospace community due to their small size and cost-effective operation. It is challenging to ensure precision performance for satellites with minimum cost and energy consumption. To support maneuverability, the CubeSat is equipped with a propellant tank, in which the fuel must be maintained in the appropriate temperature range. Simultaneously, the energy production should be maximized, such that the other components of the satellite are not overheated. To meet the technological requirements, we propose a multicriteria optimal control design using a nonlinear dynamical thermal model of the CubeSat system. First, a PID control scheme with an anti-windup compensation is employed to evaluate the minimum heat flux necessary to keep the propellant tank at a given reference temperature. Secondly, a linearization-based controller is designed for temperature control. Thirdly, the optimization of the solar cell area and constrained temperature control is solved as an integrated nonlinear model predictive control problem using the quasilinear parameter varying form of the state equations. Several simulation scenarios for different power limits and solar cell coverage cases are shown to illustrate the trade-offs in control design and to show the applicability of the approach.
Highlights
The increase of interest toward cube-shaped miniaturized satellites (CubeSats) has grown rapidly in the space community including space agencies, industry, and academic research due to the low cost of a CubeSat mission
The CubeSat electronics equipment is energized by a small battery, which recharges via solar panels mounted on the satellite surface
As the CubeSat mission capabilities are of great interest, many propulsion systems such as electric, chemical or cold gas-based propulsion systems, and solar sails have been introduced for CubeSats maneuvers [2,3]
Summary
The increase of interest toward cube-shaped miniaturized satellites (CubeSats) has grown rapidly in the space community including space agencies, industry, and academic research due to the low cost of a CubeSat mission. From the on-line MPC, we compute a sequence of input values only at time step k = 0 and we consider a larger prediction horizon (e.g., N = 40, 60 or 100), which covers two consecutive orbital periods. Observing the results of the first two control design setups, we can conclude that the smaller temperature fluctuation can be achieved, if the solar panel area is small enough, and the actuator has a higher power limit to be able to provide the necessary heat flux during the shady parts of the orbit. In the two cases, our major objective is to analyse the precision and complexity of the proposed optimization method for two different sampling periods In these computations, the baseline tank temperature was raised to 300 K, and we allowed ±3 K fluctuation (namely, TT = 297 K, and TT = 303 K). This constraint is removed from the optimization to reduce its computational complexity
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