Finite-horizon LQR controller for partially-observed Boolean dynamical systems
Finite-horizon LQR controller for partially-observed Boolean dynamical systems
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DOI：
10.1016/j.automatica.2018.05.028
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发表时间：
2018-09-01
期刊：
影响因子：
6.4
通讯作者：
Braga-Neto, Ulisses M.
中科院分区：
文献类型：
--
作者：
Imani, Mandi;Braga-Neto, Ulisses M.
针对具有不确定连续控制输入和无限观测空间的部分可观测布尔动力系统，提出了一种有限时域控制方法。为了科普状态的部分可观测性，该方法首先将POBDS映射到一个未归一化的信念空间。在这个连续的信念空间的非线性动力学线性化的名义轨迹。然后，基于著名的线性二次型调节器（LQR），推导出最优反馈控制器，使系统跟踪标称轨迹。该标称轨迹在开始执行之前的规划阶段中计算，并且每当发现系统偏离标称轨迹时，在执行期间有效地更新。我们证明了，在温和的正则化条件下，所提出的控制器的名义轨迹的线性化误差接近零的成本。所提出的控制器的性能证明了通过噪声基因表达测量观察到的黑色素瘤基因调控网络的数值实验。(C)2018爱思唯尔有限公司版权所有
This paper proposes an approach for finite-horizon control of partially-observed Boolean dynamical systems (POBDS) with uncertain continuous control input and infinite observation space. To cope with the partial observability of states, the proposed method first maps the POBDS to an unnormalized belief space. The nonlinear dynamics in this continuous belief space are linearized over a nominal trajectory. Then, the optimal feedback controller is derived, based on the well-known linear quadratic regulator (LQR), to push the system to follow the nominal trajectory. This nominal trajectory is computed in a planning stage before starting execution, and updated efficiently during execution, whenever the system is found to deviate from the nominal trajectory. We prove that, under mild regularization conditions, the proposed controller approaches the cost of the nominal trajectory as the linearization error approaches zero. The performance of the proposed controller is demonstrated by numerical experiments with a Melanoma gene regulatory network observed through noisy gene expression measurements. (C) 2018 Elsevier Ltd. All rights reserved.