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

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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.