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Fixed-Time System Identification Using Concurrent Learning
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DOI：10.1109/TNNLS.2021.3125145.png)
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Abstract
En 中文
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.
Keywords:
Convergence
System dynamics
Estimation error
Parameter estimation
Upper bound
Stability analysis
Control design
Fixed-time concurrent learning (FxTCL)
nonlinear systems
system identification
unknown dynamics
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