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This paper presents an adaptive reference governor (RG) framework for alinear system with matched nonlinear uncertainties that can depend on both timeand states, subject to both state and input constraints. The proposed frameworkleverages an L1 adaptive controller (L1AC) that estimates and compensates forthe uncertainties, and provides guaranteed transient performance, in terms ofuniform bounds on the error between actual states and inputs and those of anominal (i.e., uncertainty-free) system. The uniform performance boundsprovided by the L1AC are used to tighten the pre-specified state and controlconstraints. A reference governor is then designed for the nominal system usingthe tightened constraints, and guarantees robust constraint satisfaction.Moreover, the conservatism introduced by the constraint tightening can besystematically reduced by tuning some parameters within the L1AC. Compared withexisting solutions, the proposed adaptive RG framework can potentially yieldless conservative results for constraint enforcement due to the removal ofuncertainty propagation along a prediction horizon, and improved trackingperformance due to the inherent uncertainty compensation mechanism. Simulationresults for a flight control example illustrate the efficacy of the proposedframework.

Integrated Adaptive Control and Reference Governors for Constrained Systems with State-Dependent Uncertainties