Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Nonlinear control design: geometric, adaptive and robust
Nonlinear control design: geometric, adaptive and robust
Adaptive algorithms for the rejection of sinusoidal disturbances with unknown frequency
Automatica (Journal of IFAC)
Active Noise Control Systems: Algorithms and DSP Implementations
Active Noise Control Systems: Algorithms and DSP Implementations
Iterative Learning Control for Deterministic Systems
Iterative Learning Control for Deterministic Systems
Brief paper: Adaptive learning control of linear systems by output error feedback
Automatica (Journal of IFAC)
Paper: The internal model principle of control theory
Automatica (Journal of IFAC)
Global learning controls for uncertain relative degree one linear systems: a comparative study
ACC'09 Proceedings of the 2009 conference on American Control Conference
Brief Robust adaptive compensation of biased sinusoidal disturbances with unknown frequency
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Robust design of nonlinear internal models without adaptation
Automatica (Journal of IFAC)
Hi-index | 22.15 |
The design of an adaptive learning regulator is addressed for uncertain minimum phase linear systems (with known bounds, known upper bound on system order, known relative degree, known high frequency gain sign) and for unknown exosystems (with unknown order, uncertain frequencies). On the basis of a known bound on system uncertainties and a known bound on the modeled exosystem frequencies, a new adaptive output error feedback control algorithm is proposed which guarantees exponential convergence of both the output and the control input errors into residual bounds which decrease as the exosystem modeling error decreases. Exponential convergence of both errors to zero is obtained when the regulator exactly models all exosystem excited frequencies, while asymptotic convergence of both errors to zero is achieved when the actual exosystem is overmodeled by the regulator. The new algorithm generalizes existing learning controllers since, in the case of periodic references and/or disturbances, the knowledge of the period is not required.