Optimal Design of Multiple Robust Control Systems

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Robust control theory is a tool to analyze and design feedback control systems by explicitly taking into account plant dynamics variations, such as changes of parameter values and operating conditions. In robust control, the larger the size of plant dynamics variations is, the worse the achievable robust performance becomes. Although efforts to reduce dynamics variations are always made in the design, manufacturing, and modeling stages, such variations are often impossible to eliminate completely. In applications that need high control performance, it is necessary to overcome the performance limitations imposed by the unavoidable dynamics variations. The multiple robust control method, which divides the variation space into subregions and assign one robust controller to each subregion, is a promising approach to overcome the limitations of existing theories. The objective of the proposed program is to develop optimal design methodologies of multiple robust control systems. The novelty of the program is the focus on the optimality of the control performance. For a wide applicability of the methods developed in this program, various situations are considered for the type of plant dynamics variations (time-invariant or time-varying, deterministic or stochastic), the type of controllers (linear time-invariant or linear parameter-varying, full-order or fixed-structure), the performance measures (frequency domain or time domain), and the shape of subregions (rectangles or general polytopes). The developed optimal methodologies of multiple robust control systems are significant in applications, e.g., where extremely high control accuracy and control speed are required, or where plant dynamics vary to a large extent during plant operations. In the present program, the methods will be applied to automotive engine control and wind turbine control problems to demonstrate their impact on mechatronic and energy systems. It is also expected that the outcome of this program will have a great impact on control applications in mechanical, electrical, aerospace, chemical, and biomedical engineering fields, by providing a way to improve performance of control systems in these fields.