Conference Proceeding

A new pointwise adaptive control approach for time-varying parameters with known periodicity

National University of Singapore;
02/2003; ISBN: 0-7803-7896-2 pp.1596- 1601 In proceeding of: American Control Conference, 2003. Proceedings of the 2003, Volume: 2
Source: IEEE Xplore

ABSTRACT Not Available

0 0
 · 
0 Bookmarks
 · 
17 Views
  • Source
    Article: On Repetitive Learning Control for Periodic Tracking Tasks
    [show abstract] [hide abstract]
    ABSTRACT: In this note, a repetitive learning control (RLC) approach is proposed to deal with periodic tracking tasks for nonlinear dynamical systems with nonparametric uncertainties. We address two fundamental issues associated with the learning control methodology: The existence of the solution, and learning convergence property. Applying the existence theorem of the neutral differential difference equation, and using Lyapunov-Krasovskii functional, the existence of the solution and learning convergence can be proven rigorously. A further extension of the RLC to cascade systems is also explored
    IEEE Transactions on Automatic Control 12/2006; · 2.11 Impact Factor
  • Source
    Conference Proceeding: Repetitive learning control: existence of solution, convergence and robustification
    [show abstract] [hide abstract]
    ABSTRACT: In this paper, we propose a repetitive learning control (RLC), which deals with nonlinear dynamical systems with non-parametric uncertainties. We address three fundamental issues associated with the new learning control methods: the existence of the solution, learning convergence property and robustification, which are indispensable for the learning control methods to evolve to a new paradigm. Applying the existence theorem of the differential difference equation of neutral type, and using Lyapunov-Krasovskii functional, the existence of solution and the learning convergence can be proven rigorously. To enhance the robustness of the repetitive learning control, we further develop two kinds of robustification methods with projection and damping respectively to ensure the boundedness of the learning signals
    American Control Conference, 2006; 07/2006