Sampled-data adaptive control for a class of nonlinear systems with parametric uncertainties

Abstract

Sampled-data control systems have been prevailing in various applications, in parallel with the development of digital computers and its applications in control activities. Moreover, adaptation has proved to improve performance of a control algorithm, particularly when uncertainties involve in the model of the systems. In this paper, a stabilization problem for a class of nonlinear systems with parametric uncertainties is addresses. A discrete-time adaptation algorithm constructed based directly on the discrete-time model of the system is proposed. This adaptation algorithm is then used for constructing a discrete-time controller to stabilize (in a semiglobal practical sense) the original continuous-time system in closed-loop, in a sampled-data set-up. This proposed direct discrete-time technique is shown to improves the closed-loop performance of the system, compared to applying a discrete-time adaptive control which is obtained through emulation design (by means of sample and hold). An example is presented to illustrate the result, to show the advantages of this direct discrete-time design for sampled-data implementation

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