论文标题
控制神经识别
Neural Identification for Control
论文作者
论文摘要
我们提出了一种学习控制法律的新方法，该方法可以在平衡点稳定未知的非线性动力学系统。我们在自制的学习环境中制定了系统识别任务，该设置共同学习控制器和相应的稳定闭环动力学假设。随机控制输入下未知动力学系统的输入输出行为用作训练基于神经网络的系统模型和控制器的监督信号。提出的方法依赖于Lyapunov稳定理论来产生稳定的闭环动力学假设和相应的控制定律。我们演示了有关各种非线性控制问题的方法，例如N-Link Pendulum平衡和轨迹跟踪，推车平衡上的摆和随后的车辆路径。
We present a new method for learning control law that stabilizes an unknown nonlinear dynamical system at an equilibrium point. We formulate a system identification task in a self-supervised learning setting that jointly learns a controller and corresponding stable closed-loop dynamics hypothesis. The input-output behavior of the unknown dynamical system under random control inputs is used as the supervising signal to train the neural network-based system model and the controller. The proposed method relies on the Lyapunov stability theory to generate a stable closed-loop dynamics hypothesis and corresponding control law. We demonstrate our method on various nonlinear control problems such as n-link pendulum balancing and trajectory tracking, pendulum on cart balancing, and wheeled vehicle path following.
