Parameter Estimation in Adaptive Control of Time-Varying Systems Under a Range of Excitation Conditions

This article presents a new parameter estimation algorithm for the adaptive control of class time-varying plants. The main feature this is matrix learning rates, which enables error trajectories to tend exponentially fast toward compact set whenever excitation conditions are satisfied. employed in large problems where unknown parameters present and time-varying. It shown that guarantees global boundedness state errors system, avoids an often used filtering approach constructing key regressor signals. In addition, intervals time over these provided, both presence finite persistent excitation. A projection operator ensure rate matrix, as compared forgetting factor. Numerical simulations provided complement theoretical analysis.