Robust Adaptive Step-Tracking with Exponential Stability and Convolution Bounds Using Supervisory Control

Supervisory Control has been shown to be a very effective approach to adaptive control which ensures step-tracking, exponential stability, and a degree of robustness to unmodeled dynamics. Here we apply the technique in the discrete-time setting and prove a new linear-like convolution bound on the effect of the noise/disturbance. This property is then leveraged to prove robustness to slow time-variations.