基于SDP的信息正则化最优LQG控制传感器与控制器联合设计
SDP-based Joint Sensor and Controller Design for Information-regularized Optimal LQG Control
摘要：
考虑线性高斯随机系统的传感器与控制器联合设计问题，其中二次控制代价与传感器获取的信息量的加权和最小.这个问题的制定动机的情况下，必须设计一个控制律的存在感测，通信和隐私的限制。我们表明，最佳的联合传感器控制器的设计是相对容易的传感器策略被限制为线性时。也就是说，一个显式形式的最佳线性传感器方程，卡尔曼滤波器，和确定性等价控制器，共同解决问题，可以有效地找到半定规划（SDP）。在我们的设计中，线性假设是否是限制性的，目前是一个悬而未决的问题。
英文摘要：
We consider a joint sensor and controller design problem for linear Gaussian stochastic systems in which a weighted sum of quadratic control cost and the amount of information acquired by the sensor is minimized. This problem formulation is motivated by situations where a control law must be designed in the presence of sensing, communication, and privacy constraints. We show that the optimal joint sensor-controller design is relatively easy when the sensing policy is restricted to be linear. Namely, an explicit form of the optimal linear sensor equation, the Kalman filter, and the certainty equivalence controller that jointly solves the problem can be efficiently found by semidefinite programming (SDP). Whether the linearity assumption in our design is restrictive or not is currently an open problem.
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