论文标题
具有有界噪声的H-内滤波器的加固求解器
Reinforcement Solver for H-infinity Filter with Bounded Noise
论文作者
论文摘要
H-Infinity滤波器已被广泛应用于工程领域，但是与有界噪声的应对仍然是一个开放的问题，难以解决。本文考虑了具有有界过程和测量噪声的线性系统的H-触发性过滤问题。该问题首先是作为零和游戏的配方，其中估计误差的动态相对于滤波器增益和测量噪声而言是无关的。然后，通过使用非二次成本来表征有界噪声的非季度汉密尔顿-Jacobi-ISAAC（HJI）方程来得出，由于其非伴随和非线性特性，这非常难以解决。接下来，提出了一种基于梯度下降方法的增强学习算法，该算法可以处理非线性，以更新增强过滤器的增益，其中固定测量噪声以应对非伴随性能并增加汉密尔顿的凸性。两个例子证明了所提出算法的收敛性和有效性。
H-infinity filter has been widely applied in engineering field, but copping with bounded noise is still an open problem and difficult to solve. This paper considers the H-infinity filtering problem for linear system with bounded process and measurement noise. The problem is first formulated as a zero-sum game where the dynamic of estimation error is non-affine with respect to filter gain and measurement noise. A nonquadratic Hamilton-Jacobi-Isaacs (HJI) equation is then derived by employing a nonquadratic cost to characterize bounded noise, which is extremely difficult to solve due to its non-affine and nonlinear properties. Next, a reinforcement learning algorithm based on gradient descent method which can handle nonlinearity is proposed to update the gain of reinforcement filter, where measurement noise is fixed to tackle non-affine property and increase the convexity of Hamiltonian. Two examples demonstrate the convergence and effectiveness of the proposed algorithm.
