A System-Level Approach to Controller Synthesis

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Abstract
Biological and advanced cyberphysical control systems often have limited, sparse, uncertain, and distributed communication and computing in addition to sensing and actuation. Fortunately, the corresponding plants and performance requirements are also sparse and structured, and this must be exploited to make constrained controller design feasible and tractable. We introduce a new system level (SL) approach involving three complementary SL elements. System Level Parameterizations (SLPs) provide an alternative to the Youla parameterization of all stabilizing controllers and the responses they achieve, and combine with System Level Constraints (SLCs) to parameterize the largest known class of constrained stabilizing controllers that admit a convex characterization, generalizing quadratic invariance. We further provide a catalog of useful SLCs, including sparsity and delay constraints on both communication and computing internal to the controller, and external system performance. Finally, we formulate System Level Synthesis problems, which define the broadest known class of constrained optimal control problems that can be solved using convex programming.
Funding Information
  • Air Force Office of Scientific Research
  • National Science Foundation

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