论文标题
输出反馈随机MPC带有数据包损失
Output feedback stochastic MPC with packet losses
论文作者
论文摘要
本文考虑了有限的线性系统，具有随机的加性干扰和噪声测量，并在有损耗的通信渠道上传播。我们提出了一项模型预测控制（MPC）定律，该法律将折扣成本降至最低，但受期望约束的约束。假定传感器数据以已知概率丢失，并且通过将预测的控制策略表示为未来观察结果的仿射功能来解释数据丢失，从而导致凸出最佳控制问题。一种在线约束密度的技术可确保在线优化和期望约束满意度的递归可行性，而无需在噪声和干扰输入的分布方面界定。沿封闭环系统的轨迹评估的成本显示出受最佳预测成本的限制。给出了一个数字示例来说明这些结果。
The paper considers constrained linear systems with stochastic additive disturbances and noisy measurements transmitted over a lossy communication channel. We propose a model predictive control (MPC) law that minimizes a discounted cost subject to a discounted expectation constraint. Sensor data is assumed to be lost with known probability, and data losses are accounted for by expressing the predicted control policy as an affine function of future observations, which results in a convex optimal control problem. An online constraint-tightening technique ensures recursive feasibility of the online optimization and satisfaction of the expectation constraint without bounds on the distributions of the noise and disturbance inputs. The cost evaluated along trajectories of the closed loop system is shown to be bounded by the optimal predicted cost. A numerical example is given to illustrate these results.
