Remote Sensing Satellites use IR (Infra-Red) detectors for imaging in 1 um to 15 um bands. Most of these detectors are operated at cryogenic temperatures to extract the low-level signal from the background noise. Stirling cycle based miniature active linear cryocoolers are now being widely used for cooling these IR detectors to cryogenic temperatures. The Cryocooler Control Electronics (CCE) used for driving the cryocoolers, often uses a proportional-Integral-Derivative (PID) controller for controlling the cold tip temperature. Besides, the CCE for most space-borne missions uses a Feed-Forward (FF) control to reduce the vibration export coming from the movement of compressor pistons. The stringent cold-tip temperature stability and vibration control requirements pose a challenge to traditional cryocooler controllers, motivating to investigate and design more appropriate control structure and well-tuned well-performing controllers. This paper presents a novel scheme of simultaneous temperature and vibration control for a single-piston linear cryocooler based on an advanced Model Predictive Control (MPC) algorithm using multivariable (SIMO/MIMO) model of a miniature linear cryocooler. The MPC is one of the most popular advanced control algorithm which uses mathematical model of the underlying system or plant to predict the future response i.e. predicts future errors (in controlled variables) and uses constrained optimization to compute as decision variables a set of control actions (or manipulated variables) to minimize certain error-norm of controlled variables for specified horizon in future optimally respecting specified operational constraints. Because of its inherent capabilities to predict future errors using the multivariable model, and systematic handling of operational constraints, the proposed approach can provide better performance compared to existing cryocooler control approaches. Data-driven First Order Plus Dead Time (FOPDT) model of cryocooler has been developed and a low-cost speaker based actuator has been used for controlling the cryocooler vibration export. The paper presents the comparisons of simulated and measured control performance obtained using PI (SIMO case), PI + FF Control (MIMO case) and proposed MPC schemes (SIMO and MIMO cases) for the temperature and vibration control of a single-piston Stirling cryocooler. Significant improvement in the controller performance parameters such as Integral Absolute Error (ISE) and Integral Square Error (ISE) has been achieved with the MPC scheme. Besides, MPC is found to demonstrate significantly better performance in terms of satisfying the specific operational constraints, e.g. vibrations limits.

遥感卫星使用IR（红外线）检测器在1 um至15 um波段内成像。这些检测器中的大多数都在低温下运行，以从背景噪声中提取低电平信号。基于斯特林循环的微型有源线性制冷机现已广泛用于将这些红外探测器冷却至低温。用于驱动低温制冷器的低温制冷器控制电子设备（CCE）通常使用比例积分微分（PID）控制器来控制冷端温度。此外，大多数太空任务的CCE使用前馈（FF）控制来减少压缩机活塞运动引起的振动输出。严格的冷端温度稳定性和振动控制要求对传统的低温冷却器控制器构成了挑战，鼓励研究和设计更合适的控制结构和性能良好的控制器。本文基于一种先进的模型预测控制（MPC）算法，利用微型线性制冷机的多变量（SIMO / MIMO）模型，提出了一种单活塞线性制冷机的温度和振动同时控制的新方案。MPC是最流行的高级控制算法之一，它使用基础系统或工厂的数学模型来预测未来响应，即预测（在控制变量中）未来错误，并使用约束优化来计算一组控制动作作为决策变量（或受控变量），以在将来最佳地遵守指定的操作约束的情况下，最大程度地减少指定范围的受控变量的某些误差范数。由于其固有的使用多变量模型预测未来错误的能力以及对操作约束的系统处理，与现有的低温冷却器控制方法相比，该方法可提供更好的性能。已经开发了数据驱动的低温冷却器一阶加死区时间（FOPDT）模型，并且已使用基于低成本扬声器的执行器来控制低温冷却器的振动输出。本文介绍了使用PI（SIMO情况），PI + FF Control（MIMO情况）和建议的MPC方案（SIMO和MIMO情况）对单活塞斯特林低温冷却器进行温度和振动控制所获得的模拟和实测控制性能的比较。 。使用MPC方案已经实现了控制器性能参数的显着改善，例如积分绝对误差（ISE）和积分平方误差（ISE）。此外，发现MPC在满足特定的操作约束（例如振动限制）方面表现出明显更好的性能。