The wide adoption of wireless devices in the Internet Things requires controllers that are able to operate with limited resources, such as battery life. Operating these robustly an uncertain environment, while managing available increases difficultly controller design. This paper proposes a robust self-triggered model predictive control approach optimize objective resource consumption. In parti...

Conditions for robust input-output stability of barrier-based model predictive control linear systems with and convex nonlinear (hard or soft) constraints are established through the construction integral quadratic (IQCs). The IQCs can be used to determine sufficient conditions global closed-loop stability. In particular, obtained in presence unstructured uncertainty. both static dynamic multip...

this article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (mpc) of a chemical plant. a combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (mimo) process with time delays.  an optimization procedure for a neural mpc algorithm based on this model is then developed. the p...

This paper presents a novel optimal control approach for systems represented by multi-model, i.e., finite set of models, each one corresponding to different operating point. The proposed scheme is based on the combined use model predictive (MPC) and first order integral sliding mode control. component plays important role rejecting matched uncertainty terms possibly affecting plant, thus making...

This paper provides a strategy for the problem of robust model predictive control constrained, discrete-time systems with state and output disturbances. Using linear matrix inequality (LMI) method, nested geometric proportion asymptotically stable ellipsoid (GPASE) is designed off-line, then shrinking ellipsoids assures system converges on equivalent an exponential convergence velocity. The big...