In this study, a thermal model of a space telescope is established in Simulink. An intelligent autonomous thermal control strategy based on actor-critic reinforcement learning (RL) for proportional...

The thermal design of spacecraft becomes increasingly complicated as various advanced technologies are continuously introduced to the spacecraft. Determining and optimizing the uncertainties of a spacecraft thermal control system through global sensitivity analysis has long been an essential task for thermal engineers. It is a difficult task that relies heavily on engineering experience and is a time-intensive, trial-and-error endeavor that may not even lead to global optimization. Hence, an intelligent optimization strategy based on statistical machine learning for spacecraft thermal design, called IOSML, is proposed. An intelligent batch processing system (IBPS) based on MATLAB, Python, and NX/TMG real-time data interaction is designed. The IBPS uses a surrogate model to reduce the computational cost of global sensitivity analysis while using a detailed thermal mathematical model to maintain accuracy. We combine a Bayesian inference framework with a neural network surrogate spacecraft-thermophysical model that is 100× faster than numerical solvers. This paper first reports on a density-based global sensitivity analysis that evaluates the effect of design parameters on the temperature difference between the complementary metal–oxide–semiconductor and cold screen of the Lehman Alpha Solar Space Telescope detector. From 42 design parameters, the most sensitive four are selected for optimization, and the temperature difference and the boundary temperature are used as the objective function. Adopting IOSML, under no supervision, four design parameters are optimized through the IBPS, and the effectiveness of the algorithm is verified by comparison with traditional methods. Additionally, IOSML is versatile and can be used in various complex engineering applications to provide guidance for the better selection of appropriate parameters and optimization.

The thermal design parameters of space telescopes are mainly optimized through traversal and iterative attempts. These optimization techniques are time consuming, rely heavily on the experience of the engineer, bear a large computational workload, and have difficulty in achieving optimal outcomes. In this paper, we propose a design method (called SMPO) based on an improved back-propagation neural network (called GAALBP) that builds a surrogate model and uses a genetic algorithm to optimize the model parameters. The surrogate model of a space telescope that measures the atmospheric density is established using GAALBP and then compared with surrogate models established using a traditional BP neural network and radial-basis-function neural network. The results show that the regression rate of the surrogate model based on the GAALBP reaches 99.99%, a mean square error of less than 2 × 10⁻⁶, and a maximum absolute error of less than 4 × 10⁻³. The thermal design parameters of the surrogate model are optimized using a genetic algorithm, and the optimization results are verified in a finite element simulation. Compared with the design results of the manually determined thermal design parameters, the maximum temperature of the CMOS is reduced by 5.33 °C, the minimum temperature is increased by 0.39 °C, and the temperature fluctuation is reduced by a factor of 4. Additionally, SMPO displays versatility and can be used in various complex engineering applications to provide guidance for the better selection of appropriate parameters and optimization.

With the development of deep space exploration technology, thermal control systems for space telescopes are becoming increasingly complex, leading to the key parameters of conventional thermal control systems are difficult to adjust online automatically. To achieve these adjustments, this paper provided detailed verification of the application of deep reinforcement learning to space telescope thermal control from three perspectives: thermophysical modeling, intelligent sensing-based radiator, and online self-tuning of thermal control parameters. This paper presents a high-speed and high-precision thermophysical modeling strategy in matlab/simulink with better computational efficiency than conventional approaches. And an intelligent sensing-based radiator is proposed that can realize autonomous regulation of the radiating cold plate by sensing the external space environment and the thermal load inside the spacecraft. A strategy for online self-tuning of the thermal control parameters based on deep reinforcement learning is also proposed. Theoretical and experimental results show that deep reinforcement learning thermal control (DRLPID) can achieve temperature control accuracy of 0.05 °C. The steady-state errors in the simulations were reduced by 22.7%, 37.4%, and 47.4% when compared with the reinforcement learning proportional–integral–derivative (PID), the neural network PID, and the fuzzy PID, respectively. The experimental steady-state errors were reduced by 20.4%, 32.5%, and 42.7%, respectively.

In practical applications, it is difficult for the control parameters of the proportional integral derivative (PID) thermal controller to be self-tuning online. As the control object or environment...