Robust Fixed-Time Model Reference Adaptive Control
This work addresses robust adaptive control for unknown systems, enhancing practicality by relaxing excitation requirements, but it is incremental as it builds on existing MRAC frameworks.
The paper tackles the problem of achieving fixed-time convergence for parameter estimation and tracking errors in unknown linear time-invariant systems using a Model Reference Adaptive Control strategy, without needing persistence of excitation, and simulation results validate its effectiveness compared to existing methods.
This article proposes a Model Reference Adaptive Control (MRAC) strategy to achieve fixed-time convergence of parameter estimation and tracking errors for unknown linear time-invariant systems, without relying on the persistence of excitation condition. Instead, it employs a less restrictive initial/interval excitation condition on the regressor matrix, enhancing practicality and ease of implementation in real-world scenarios. Our primary contribution is a novel parameter update law within the indirect MRAC framework, ensuring that parameter estimates converge within a fixed time, once the initial/interval excitation condition is met. This approach simplifies the practical requirements for adaptive control while guaranteeing robust performance against parameter uncertainty and external disturbances. Simulation results provide a comparison with the current literature to validate the effectiveness of this approach.