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

非线性和分布式参数过程的先进优化控制方法

• 批准号: RGPIN-2020-04352
• 负责人: Upreti, Simant
• 金额: $ 2.04万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739911
• 关键词: Advanced; Optimal; Control; Methods; Non

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.

为了在竞争日益激烈、利润率下降、消费者对产品质量的期望不断提高以及对能源和环境保护的日益关注的情况下实现最高效率，现代工业对控制方法的性能要求越来越高。在处理化学过程时，这是一个重大挑战，这些过程普遍是非线性和非均匀的，并且由复杂的数学模型描述。尽管控制研究、高速计算和计算机硬件的进步有助于更好地控制工业过程，但仍有相当大的空间来开发计算效率更高、响应时间延迟显著更少的控制方法，从而为与工业高度相关的复杂过程提供更有效的实时控制。拟议的研究计划包括两个主要举措，在先进的最优控制方法的发展。第一项倡议涉及开发和测试高性能的最佳反馈控制方法，大大减少计算延迟，用于实时控制非线性分布参数过程。该计划包括开发基于级数变换和同伦连续的快速作用模型预测控制策略。周期性的最优控制策略的设计也包括以提高连续过程操作的性能。第二项计划利用人工智能，包括人工神经网络，强化学习和进化计算，结合所提出的控制策略，开发非常有效和有弹性的实时控制方法。通过上述举措的工作，拟议的研究计划将有助于开发先进的控制方法的复杂，非线性和非均匀的过程，这在工业中比比皆是。这些方法将能够提高能源和资源的利用率，降低环境影响，并支持生产质量更高的产品。最后但并非最不重要的是，该研究计划将为高级最优控制和人工智能领域的两名博士生和两名硕士生的培训提供宝贵的机会。这些学生将培养行业高需求的知识和技能。