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This paper provides an exponential stability result for the adaptiveanti-unwinding attitude tracking control problem of a rigid body with uncertainbut constant inertia parameters, without requiring the satisfaction ofpersistent excitation (PE) condition. Specifically, a composite immersion andinvariance (I&I) adaptive controller is derived by integrating aprediction-error-driven learning law into the dynamically scaled I&I adaptivecontrol framework, wherein we modify the scaling factor so that the algorithmdesign does not involve any dynamic gains. To avoid the unwinding problem, abarrier function is introduced as the attitude error function, along with thetactful establishment of two crucial algebra properties for exponentialstability analysis. The regressor filtering method is adopted in combinationwith the dynamic regressor extension and mixing (DREM) procedure to acquire theprediction error using only easily obtainable signals. In particular, aiding bya constructive liner time-varying filter, the scalar regressor of DREM isextended to generate a new exciting counterpart. In this way, the derivedcontroller is shown to permit closed-loop exponential stability without PE, inthe sense that both output-tracking and parameter estimation errorsexponentially converge to zero. Further, the composite learning law isaugmented with a power term to achieve synchronized finite/fixed-time parameterconvergence. Numerical simulations are performed to verify the theoreticalfindings.

Composite Adaptive Control for Anti-Unwinding Attitude Maneuvers: An Exponential Stability Result Without Persistent Excitation