论文标题
稳定复发平衡网络控制器的合成
Synthesis of Stabilizing Recurrent Equilibrium Network Controllers
论文作者
论文摘要
我们提出了基于经常性平衡网络的非线性动态控制器的参数化，这是复发性神经网络的概括。我们对控制器保证具有扇形界限非线性的部分动态系统的指数稳定性的参数化受到限制。最后，我们提出了一种使用投影策略梯度方法合成该控制器的方法，以最大程度地利用任意结构来奖励功能。投影步骤涉及凸优化问题的解决方案。我们通过模拟控制非线性植物的示例（包括用神经网络建模的植物）演示了提出的方法。
We propose a parameterization of a nonlinear dynamic controller based on the recurrent equilibrium network, a generalization of the recurrent neural network. We derive constraints on the parameterization under which the controller guarantees exponential stability of a partially observed dynamical system with sector bounded nonlinearities. Finally, we present a method to synthesize this controller using projected policy gradient methods to maximize a reward function with arbitrary structure. The projection step involves the solution of convex optimization problems. We demonstrate the proposed method with simulated examples of controlling nonlinear plants, including plants modeled with neural networks.
