Fixed-Time System Identification Using Concurrent Learning

This article presents a fixed-time (FxT) system identifier for continuous-time nonlinear systems. A novel adaptive update law with discontinuous gradient flows of the identification errors is presented, which leverages concurrent learning (CL) to guarantee the learning of uncertain nonlinear dynamics in a fixed time, as opposed to asymptotic or exponential time. More specifically, the CL approach retrieves a batch of samples stored in a memory, and the update law simultaneously minimizes the identification error for the current stream of samples and past memory samples. Rigorous analyses are provided based on FxT Lyapunov stability to certify FxT convergence to the stable equilibria of the gradient descent flow of the system identification error under easy-to-verify rank conditions. The performance of the proposed method in comparison with the existing methods is illustrated in the simulation results.