A Model-Based Control Scheme for General Nonlinear Plants with Sampled Information

This paper considers the problem of driving to zero by means of state feedback the state of a nonlinear, time-invariant plant, when only an approximate model of the plant is known and the information about its state is available, for control purposes, only at non equally spaced sampling instants. To this aim, a specific model-based control scheme is used and suitable hypotheses of Lipschitz type about the plant dynamics and the error between the model and the plant are considered. In case the modeling error satisfies a specific condition at the zero state of the plant, a sufficient condition for local asymptotic stability is found. Alternatively, a sufficient condition for assuring uniform boundedness of the state of the controlled plant is found. In both cases, the sufficient condition is expressed in terms of maximum length of the sampling interval and of other design parameters. The present results improve previous ones, found in the networked systems literature or due to the authors, by extending their applicability to general nonlinear plants in a broad range of situations.