Advanced Optimal Control Methods for Non-Linear and Distributed-Parameter Processes

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To achieve top efficiencies amid increasing competition, reduced profit margins, increasing product quality expectations from consumers, and rising concerns to protect energy and the environment, modern industry demands an increasingly higher performance from control methods. This is a major challenge when dealing with chemical processes, which are ubiquitously non-linear and non-uniform, and are described by sophisticated mathematical models. Although the progress in control research, high-speed computing and computer hardware has helped in better control of industrial processes, there is considerable scope to develop control methods that are computationally more efficient, respond with significantly less time delay, and thus offer more effective real-time control of complex processes, which are highly relevant to industry. The proposed research program incorporates two major initiatives in the development of advanced optimal control methods. The first initiative involves the development and testing of high-performance optimal feedback control methods with greatly reduced computational delays for real-time control of nonlinear, distributed-parameter processes. This initiative incorporates developing fast-acting model predictive control strategies based on series transformation and homotopy continuation. The design of periodic optimal control strategies is also included to enhance the performance of continuous process operations. The second initiative exploits Artificial Intelligence including artificial neural networks, reinforced learning, and evolutionary computation to develop extremely efficient and resilient real-time control methods in conjunction with the proposed control strategies. By working on the above initiatives, the proposed research program will contribute to the development of advanced control methods for complex, non-linear, and non-uniform processes, which abound in industry. These methods will enable improved utilization of energy and resources, lower environmental impacts, and support production of consistently better quality products. Last but not least, the research program will provide valuable opportunities for the training of two PhD and two Master's students in the field of advanced optimal control and Artificial Intelligence. These students will develop knowledge and skills in high demand by industry.