Feedback controller design and process modeling methods using machine learning

The recent advances in the field of machine learning, the availability of powerful computing resources, and growing data collection capabilities across industries present new opportunities to upgrade the existing feedback control and automation methods implemented in applications. This thesis presents novel approaches and methods to use machine learning algorithms to develop feedback controllers and process models for dynamical systems. In the first half of this thesis, we develop approaches to use reinforcement learning and neural networks to design feedback controllers for multivariable systems. We develop a novel model-free Q-learning approach suitable to estimate linear, unconstrained feedback controllers from noisy process data. We present a neural network (NN) design approach to approximate the model predictive control (MPC) feedback law for large-scale applications that may be out of reach with available QP solvers. The proposed NN design approach is applied to a large industrial crude distillation unit model, and we demonstrate that NNs can be used to execute MPC orders of magnitude faster compared to an available QP solver.The next half of this thesis focuses on developing hybrid model identification approaches that utilize both the advantages of neural networks and some first principles process knowledge usually available in applications. We consider building systems affected by large occupancy induced heat disturbances. For these systems, we develop a novel two step grey-box dynamic and NN disturbance model identification framework. We use a NN to model the heat disturbance so that it can be used to provide feedforward predictions of the disturbance in an MPC controller for improved energy cost optimization. We also present a hybrid modeling approach for nonlinear chemical engineering processes. For this class of systems, we use NNs to approximate some functions in the overall dynamic model, e.g, reaction kinetics, which may be challenging to parameterize using the available domain knowledge. The estimated hybrid models are used for steady-state economic optimization at the real time optimization layer. Throughout this thesis, we present examples with heating, ventilation, and air-conditioning and chemical engineering systems to demonstrate the effectiveness of the proposed controller design and process modeling methods. We compare the proposed methods with existing approaches and illustrate their potential to design high-performance control systems.

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