This paper presents an online scheduling of the parameter ensuring, in addition to closed-loop stability, the fastest possible transient between two extreme values of ε, chosen for stability and performance, respectively.
That is, the probabilistic setting does not need a gridding of the set of scheduling parameters or approximations such as a linear fractional transformation of the state space matrices.
Although our method requires a line search to obtain suboptimal controllers, it produces practical GSOF controllers, being independent of the derivatives of scheduling parameters.
Samples for determination of plasma glucose and serum insulin concentrations were drawn at 2, 3, 4, 5, 6, 8, 10, 12, 14, 16, 19, 22, 23, 24, 25, 27, 30, 35, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 220, and 240 min. This sampling schedule allows for the identification of parameters using the minimal model of glucose kinetics (14) and the acute insulin response to glucose (AIRg).
Another disadvantage is that the interpolation increases in complexity as number of scheduling parameters increases.
It is assumed that the vector of scheduling parameters in LPV models is not available for measurement.
The utilization of scheduling parameters can be generalized to accommodate more sophisticated workload characterizations and more complicated server environments.
This paper addresses the problem of designing output-feedback switching linear parameter-varying (LPV) controllers under inexact measurement of scheduling parameters.
This work is devoted to interval observer design for Linear Parameter-Varying (LPV) systems under assumption that the vector of scheduling parameters is not available for measurements.
The second approach is based on reformulating the nonlinear model so that it provides an LPV model with a fewer number of scheduling parameters but preserves the same input output behavior.
This paper develops synthesis conditions for Static Output-Feedback (SOF) Gain-Scheduling (GS) control with guaranteed upper bound of H2 performance for continuous-time Linear Parameter Varying (LPV) systems, where measurements of scheduling parameters are affected by uncertainties or measurement noise.
