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 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, pendulum on cart balancing, and wheeled vehicle path following.

中文翻译：

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用于控制的神经识别

我们提出了一种新的控制律学习方法，可以在平衡点稳定未知的非线性动力系统。我们在自监督学习环境中制定系统识别任务，该任务联合学习控制器和相应的稳定闭环动态假设。未知动力系统在随机控制输入下的输入输出行为被用作监督信号来训练基于神经网络的系统模型和控制器。该方法依赖于李雅普诺夫稳定性理论来生成稳定的闭环动力学假设和相应的控制律。我们在各种非线性控制问题上演示了我们的方法，例如 n-Link 摆平衡、小车平衡摆和轮式车辆路径跟踪。