Optimal control and learning for cyber‐physical systems

Yan Wan, Tao Yang, Ye Yuan, Frank L. Lewis
<span title="2021-02-18">2021</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/uibkgtipardftespo5cqapdguy" style="color: black;">International Journal of Robust and Nonlinear Control</a> </i> &nbsp;
Modern systems are becoming increasingly complex in their functionality, structure, and dynamics. A successful management of these systems requires enhanced performance in terms of robustness, safety, resiliency, scalability, and usability. To achieve these performance requirements, it is important to adopt cyber-physical system (CPS) design techniques. A CPS features tightly coupled physical and cyber components. The physical components include system dynamics, sensors, controllers, and the
more &raquo; ... ertain environment in which the system operates. The cyber components include data, communication, control, and computation. The CPS co-design principle suggests that these physical and cyber components should be designed as an integrated whole. CPS studies cross boundaries of multiple science and engineering disciplines and require deep domain knowledge. CPS applications span intelligent transportation, unmanned systems, smart grids, smart homes, smart health care, smart materials, and intelligent civil infrastructures. Developing practical closed-loop optimal decisions is a common and pivotal task for these CPS applications. The optimal control theory finds its significant value in developing such solutions. However, the traditional optimal control theory cannot be directly used because it was developed for systems that do not have the complexity level of modern systems we observe today. Significant challenges exist in developing practical optimal control solutions for real CPSs, considering the increased level of complexity and challenging performance requirements aforementioned. Addressing these challenges requires a seamless integration of the optimal control theory with advances from learning and other science and engineering domains. The performance of such integration or co-design is not fully understood or developed. This special issue focuses on the optimal control theory and learning for CPSs. The papers received span broad topics including learning and data-driven optimal control to address physical unknowns and disturbances, estimation techniques to deal with uncertainties; secure and resilient solutions that account for disturbances, faults, and attacks; control solutions subject to physical constraints on energy, actuation, communication, and computation; and CPS applications toward robotics and power grids. The 18 accepted papers are categorized into five directions and summarized as follows:
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