Temperature stability is a critical factor affecting the performance of the most subsystems in the lithography system, due to the high precision and sensitivity of system components to temperature variations. The temperature control system of the lithography machine is characterized by its large inertial constant, time delay characteristics, as well as susceptibility to multiple disturbances. The temperature control system of the lithography machine chiefly requires response speed, high accuracy, and stable and constant temperature control. The contribution of this study is not only avoiding complex precision modeling processes based on real-time parameter estimation and neural network self-tuning but also improving the performance of temperature control in real time under external disturbances. A novel adaptive algorithm with a cascade structure based on generalized predictive control (GPC) and backpropagation (BP) neural network proportional-integral (PI) control is successfully proposed for high accuracy temperature control of lithography machine with a large inertial constant, time delay, and multiple disturbances. In this study, firstly, the liquid circulating temperature control system is developed based on heat exchanger and heater. Secondly, an adaptive controller composed of GPC and BP neural network PI control is successfully proposed. A BP neural network is employed to enable the parameters of the PI controller to adjust in real time, and the mathematical model parameters of the control system are identified in real time by the least square method. Also, the performance of the proposed controller is evaluated comparing with conventional PI controller and GPC controller in terms of robustness and quantitative study of error analysis. Finally, the temperature stability and robustness of the temperature control system controlled with the proposed adaptive GPC-PI algorithm has been investigated by the simulation results carried out in different working scenarios. The simulation results show that the steady-state error from the proposed algorithm is less than 0.01°C under the action of disturbance input. It can effectively counteract the influence of environmental interference and time-varying system parameters. The results of the simulation experiment indicate that the proposed adaptive GPC and PI control algorithm exhibits significant advantages in terms of control accuracy, anti-interference ability, and robustness compared to the conventional control method.

中文翻译：

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基于广义预测控制和BP神经网络PI控制的光刻机温度控制算法

由于系统组件对温度变化的高精度和敏感性，温度稳定性是影响光刻系统中大多数子系统性能的关键因素。光刻机的温度控制系统具有惯性常数大、时滞特性以及易受多重扰动的特点。光刻机的温度控制系统主要要求响应速度快、精度高、温度控制稳定恒定。这项研究的贡献不仅在于避免了基于实时参数估计和神经网络自调节的复杂精密建模过程，而且还提高了外部干扰下实时温度控制的性能。成功提出了一种基于广义预测控制（GPC）和反向传播（BP）神经网络比例积分（PI）控制的级联结构自适应算法，用于大惯性常数、时滞、以及多重干扰。本研究首先开发了基于换热器和加热器的液体循环温度控制系统。其次，成功提出了由GPC和BP神经网络PI控制组成的自适应控制器。采用BP神经网络对PI控制器的参数进行实时调整，并通过最小二乘法实时辨识控制系统的数学模型参数。此外，在鲁棒性和误差分析的定量研究方面，与传统的 PI 控制器和 GPC 控制器相比，对所提出的控制器的性能进行了评估。最后，通过不同工作场景下的仿真结果，研究了采用所提出的自适应GPC-PI算法控制的温度控制系统的温度稳定性和鲁棒性。仿真结果表明，在扰动输入作用下，该算法的稳态误差小于0.01℃。它可以有效地抵消环境干扰和时变系统参数的影响。仿真实验结果表明，与传统控制方法相比，所提出的自适应GPC和PI控制算法在控制精度、抗干扰能力和鲁棒性方面表现出显着的优势。