M4: Efficient and Accurate State Estimation and Feedback Control under Uncertainties

该项目侧重于分布式过程的高质量估计和控制，基于M2的学习模型和M3的优化前馈控制轨迹。我们假设状态是隐藏的或仅部分可用的，因此我们必须在系统地考虑观测中的不确定性的同时估计分布式过程状态的信念。基于RL和MPC的方法将被开发用于改善受控过程的动态（稳定性、速度、衰减、准确性），并且将允许更有目的地操纵该过程。重点是：（i）分布式非线性过程的置信状态的表示，在复杂性和表示能力之间具有可调节的折衷;（ii）在具有可微系综流滤波器的大型分布式状态空间中用于随机不确定性传播和滤波的方法，其中状态的数量可以是几千个;（iii）模块化传感器建模框架，其允许在传感器模型之间快速切换，而无需针对成熟的不同阶段重新学习;（iv）非线性分布过程的随机反馈控制，基于从M2学习的分布过程模型，具有基于MIMO的渐进随机MPC，以科普作用于过程的模型不确定性和噪声;以及（v）利用给定生产过程的分布式状态和动作空间的基于模型的策略优化技术。研究的挑战包括高维的分布式过程模型，需要开发分布式状态和动作表示的过程中，强非线性的状态演化模型，非线性传感器模型，和观察不同的维度。

This project focuses on high-quality estimation and control of the distributed processes, based on the learned models from M2 and the optimised feedforward control trajectories from M3. We assume the state to be hidden or only partially available, so we have to estimate beliefs over the distributed process state while systematically considering uncertainties in observations. Methods based on RL and MPC will be developed for improving the dynamics (stability, speed, attenuation, accuracy) of the controlled process, and will allow the process to be steered more purposively. The focus is on: (i) representation of belief states of distributed nonlinear processes with an adjustable tradeoff between complexity and representation capacity; (ii) methods for stochastic uncertainty propagation and filtering in large, distributed state spaces with differentiable ensemble flow filters, where the number of states may be several thousand; (iii) a modular sensor modelling framework that allows quick switching between sensor models without relearning for the different phases of the maturation; (iv) stochastic feedback control of nonlinear distributed processes, based on the learned distributed process models from M2, with scenario-based progressive stochastic MPC to cope with model uncertainties and noise acting upon the process; and (v) model-based policy optimisation techniques exploiting the distributed state and action spaces of the given production process. Research challenges include the high dimensionality of the distributed process models, the need for exploitation of distributed state and action representations of the process, strong nonlinearities in the state evolution models, nonlinear sensor models, and observations of disparate dimensionalities.