Adaptive Control By Regulation-Triggered Batch Least-Squares Estimation of Non-Observable Parameters
For control engineers, this provides a robust adaptive control method for systems with non-observable parameters, ensuring guaranteed convergence properties.
The paper extends an event-triggered adaptive control scheme to handle non-observable parameters using a novel Batch Least-Squares Identifier, achieving global asymptotic regulation with uniform exponential convergence rate. The method is validated on the wing-rock model, outperforming conventional adaptive schemes.
The paper extends a recently proposed indirect, certainty-equivalence, event-triggered adaptive control scheme to the case of non-observable parameters. The extension is achieved by using a novel Batch Least-Squares Identifier (BaLSI), which is activated at the times of the events. The BaLSI guarantees the finite-time asymptotic constancy of the parameter estimates and the fact that the trajectories of the closed-loop system follow the trajectories of the nominal closed-loop system ("nominal" in the sense of the asymptotic parameter estimate, not in the sense of the true unknown parameter). Thus, if the nominal feedback guarantees global asymptotic stability and local exponential stability, then unlike conventional adaptive control, the newly proposed event-triggered adaptive scheme guarantees global asymptotic regulation with a uniform exponential convergence rate. The developed adaptive scheme is tested to a well-known control problem: the state regulation of the wing-rock model. Comparisons with other adaptive schemes are provided for this particular problem.