A Neurocontrol Paradigm for Intelligent Process Control using Evolutionary Reinforcement Learning

ii DECLARATION I, the undersigned, hereby declare that the work contained in this thesis is my own original work and has not previously in its entirety or in part been submitted at any university for a degree. iii Now to Him who is able to keep us from stumbling, And to bring us faultless Before the presence of His glory with exceeding joy, To God our Saviour, Who alone is wise, Be glory and majesty, Dominion and power, Both now and forever. Amen. iv ACKNOWLEDGEMENTS My Father in heaven for refusing to let me go. Professor Risto Miikkulainen, for his immeasurable guidance at the University of Texas at Austin. Friends and family, for their love, kindness and patience. Loving thanks to my wife, Anje, who put up with "Alex world" for long enough. Balancing multiple business and operational objectives within a comprehensive control strategy is a complex configuration task. Non-linearities and complex multiple process interactions combine as formidable cause-effect interrelationships. A clear understanding of these relationships is often instrumental to meeting the process control objectives. However, such control system configurations are generally conceived in a qualitative manner and with pronounced reliance on past effective configurations (Foss, 1973). Thirty years after Foss' critique, control system configuration remains a largely heuristic affair. Biological methods of processing information are fundamentally different from the methods used in conventional control techniques. Biological neural mechanisms (i.e., intelligent systems) are based on partial models, largely devoid of the system's underlying natural laws. Neural control strategies are carried out without a pure mathematical formulation of the task or the environment. Rather, biological systems rely on knowledge of cause-effect interactions, creating robust control strategies from ill-defined dynamic systems. Dynamic modelling may be either phenomenological or empirical. Phenomenological models are derived from first principles and typically consist of algebraic and differential equations. First principles modelling is both time consuming and expensive. Vast data warehouses of historical plant data make empirical modelling attractive. Singular spectrum analysis (SSA) is a rapid model development technique for identifying dominant state variables from historical plant time series data. Since time series data invariably covers a limited region of the state space, SSA models are almost necessarily partial models. Interpreting and learning causal relationships from dynamic models requires sufficient feedback of the environment's state. Systemisation of the learning task is imperative. Reinforcement learning is a computational approach to understanding and automating goal-directed learning. This thesis aimed …