Disturbance, which is generally unknown to the controller, is unavoidable in real-world systems and it may affect the expected system state and output. Existing control methods, like robust model predictive control, can produce robust solutions to maintain the system stability. However, these robust methods trade the solution optimality for stability. In this article, a method called generative adversarial control networks (GACNs) is proposed to train a controller via demonstrations of the optimal controller. By formulating the optimal control problem in the presence of disturbance, the controller trained by GACNs obtains neuro-optimal solutions without knowing the future disturbance and determines the objective function explicitly. A joint loss, composed of the adversarial loss and the least square loss, is designed to be used in the training of the generator. Experimental results on simulated systems with disturbance show that GACNs outperform other compared control methods.

中文翻译：

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使用生成对抗控制网络的干扰感知神经优化系统控制

控制器通常不知道的干扰在实际系统中是不可避免的，它可能会影响预期的系统状态和输出。现有的控制方法，如鲁棒模型预测控制，可以产生鲁棒解决方案来保持系统稳定性。然而，这些稳健的方法以解决方案的最优性换取稳定性。在本文中，提出了一种称为生成对抗控制网络 (GACN) 的方法，通过最佳控制器的演示来训练控制器。通过在存在干扰的情况下制定最优控制问题，由 GACN 训练的控制器在不知道未来干扰的情况下获得神经最优解，并明确确定目标函数。联合损失，由对抗损失和最小二乘损失组成，旨在用于生成器的训练。在有扰动的模拟系统上的实验结果表明，GACN 优于其他比较控制方法。