Output-Feedback Control of Unknown Linear Discrete-Time Systems With Stochastic Measurement and Process Noise via Approximate Dynamic Programming
Output-Feedback Control of Unknown Linear Discrete-Time Systems With Stochastic Measurement and Process Noise via Approximate Dynamic Programming
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DOI：
10.1109/tcyb.2017.2726004
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发表时间：
2018-07
影响因子：
11.8
通讯作者：
Jun-Sheng Wang;Guang‐Hong Yang
中科院分区：
文献类型：
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作者：
Jun-Sheng Wang;Guang‐Hong Yang
本文研究了具有随机测量和过程噪声的未知线性离散时间系统的最优输出反馈控制问题。通过以有限求和的形式给出的期望算子构造具有新息协方差矩阵的抖动贝尔曼方程。在此基础上，提出了一种基于输出反馈的近似动态规划方法，其中借助预先确定的新息协方差矩阵可以获得取决于新息协方差矩阵的项。因此，通过迭代贝尔曼方程，得到的值函数可以在存在上述噪声的情况下收敛到最优值函数，并给出接近最优的控制律。为了展示所提出方法的有效性和优点，采用了直流电机上的仿真示例和速度控制实验。
This paper studies the optimal output-feedback control problem for unknown linear discrete-time systems with stochastic measurement and process noise. A dithered Bellman equation with the innovation covariance matrix is constructed via the expectation operator given in the form of a finite summation. On this basis, an output-feedback-based approximate dynamic programming method is developed, where the terms depending on the innovation covariance matrix are available with the aid of the innovation covariance matrix identified beforehand. Therefore, by iterating the Bellman equation, the resulting value function can converge to the optimal one in the presence of the aforementioned noise, and the nearly optimal control laws are delivered. To show the effectiveness and the advantages of the proposed approach, a simulation example and a velocity control experiment on a dc machine are employed.