论文标题
快速，最佳的自适应跟踪控制：通过有条件生成对抗网的新型荟萃方面学习
Fast and Optimal Adaptive Tracking Control: A Novel Meta-Reinforcement Learning via Conditional Generative Adversarial Net
论文作者
论文摘要
多年来，对具有未知动力学的非线性系统的控制一直是一个重要的研究领域。本文提出了一种新型的数据驱动的最佳自适应控制结构，其控制努力和更快的适应性比标准的自适应控制对应物。提出的控制结构利用系统的记录数据来大大提高适应性和性能的速度。在这项研究中，我们采用条件生成的对抗网（CGAN）作为一种新型的中央模式发生器，以重现控制信号的稳态谐波模式，使系统在广泛范围内与系统的不确定性相匹配。我们还可以将CGAN架构用作故障检测器。 CGAN提供了低维的不确定性潜在空间。当有许多参数不确定性时，尤其是对于大型系统时，它可以快速和方便的适应。然后，我们介绍了一个新颖的元强化学习框架，以使CGAN的潜在空间适应该系统的不确定性，作为一个没有任何系统标识符的最佳直接自适应控制器。控制结构的另一部分是使用Lyapunov稳定性分析来实现半全球渐近跟踪的调节剂。最后，通过一些模拟，我们在存在干扰和扰动的情况下评估了两个动力学系统，一个机器人操纵器和大规模肌肉骨骼结构的功能。
The control of nonlinear systems with unknown dynamics has been a significant field of research for many years. This paper presents a novel data-driven optimal adaptive control structure with less control effort and faster adaptation than standard adaptive control counterparts. The proposed control structure utilizes the system's recorded data to increase the speed of adaptation and performance dramatically. In this study, we employ a conditional generative adversarial net (CGAN) as a novel central pattern generator to reproduce the steady-state harmonic pattern of the control signals matching the system's uncertainties over a wide range. We can also use the CGAN architecture as a fault detector. The CGAN provides a low-dimensional latent space of uncertainties. It enables rapid and convenient adaptation when there are many parametric uncertainties, especially for large-scale systems. Then, we introduce a novel meta-reinforcement learning framework to adapt the latent space of CGAN to the system's uncertainties as an optimal direct adaptive controller without any system identifier. Another part of the control structure is a regulator that achieves semi-global asymptotic tracking using the Lyapunov stability analysis. Finally, via some simulations, we evaluate the capabilities of the proposed designs on two dynamical systems, a robot manipulator and a large-scale musculoskeletal structure, in the presence of disturbance and perturbation.
