PUMA
Istituto di Studi sui Sistemi Intelligenti per l'Automazione     
Alessandri A., Cervellera C., Macciò D., Sanguineti M. Design of Parameterized State Observers and Controllers for a Class of Nonlinear Continuous-Time Systems. In: 45th IEEE Conference on Decision and Control (San Diego (CA), USA, 13-15 December 2006). Proceedings, pp. 5388 - 5393. IEEE, 2006.
 
 
Abstract
(English)
The design of observers and controllers for a class of continuous-time, nonlinear dynamic systems with Lipschitz nonlinearities is addressed. Observers and controllers that depend on a linear gain and a parameterized function implemented by a feedforward neural network are considered. The gain and the weights of the neural network are optimized in such way to ensure the convergence of the estimation error for the observer and the stability of the closed-loop system for the controller, respectively. This is achieved by constraining the derivative of a quadratic Lyapunov function to be negative definite on a grid of points, penalizing the constraints that are not satisfied. It is shown that suitable sampling techniques such as low-discrepancy sequences, commonly employed in quasi-Monte Carlo methods for high-dimensional integration, allow one to reduce the computational burden required to optimize the network parameters. Simulations results are presented to illustrate the effectiveness of the method.
DOI: 10.1109/CDC.2006.377136
Subject Lyapunov methods
closed loop systems
continuous time systems
control nonlinearities
feedforward neural nets
neurocontrollers
nonlinear control systems
nonlinear dynamical systems
observers
sampling methods
stability


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