Data-Driven Tracking Control for Uncertain Linear Systems Using a Dual-System Approach


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This letter proposes a dual-system approach to designing tracking controllers for uncertain linear time-invariant systems by explicitly combining prior and data-based knowledge about the uncertainty. The tracking controller is transformed into a static state-feedback controller for an augmented system. We derive data-driven synthesis conditions that robustly guarantee stability and H-2 performance for all systems consistent with prior knowledge and gathered data. Importantly, the synthesis conditions are jointly linear with respect to the prior and data-based multipliers, which significantly reduces conservatism and improves performance. Additionally, we further develop the data-driven regional pole placement approach to fine-tune the closed-loop dynamic response. A numerical example illustrates the effectiveness of our method.
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Key words
Linear systems,Uncertainty,Data models,Optimization,Uncertain systems,Transient analysis,Transfer functions,Data-driven control,uncertain linear time-invariant systems,regional pole placement
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