Model predictive control (MPC) for uncertain systems in the presence of hard constraints on state and input is a non-trivial problem, challenge increased manyfold absence measurements. In this paper, we propose an adaptive output feedback MPC technique, based novel combination observer robust MPC, single-input single-output discrete-time linear time-invariant systems. At each time instant, prov...

The objective of this study is to design a robust direct model reference adaptive controller (DMRAC) for a nonlinear cardiovascular model over a range of plant parameters representing a variety of physical conditions. The direct adaptive controllers used in thisd study require the plant to be almost strictly positive real (ASPR) that is, for a plant to be controlled there must exist a feedback ...

up to three decades ago, economic statistics could systematically meet the statistical needs and economic views quantitative analyses requirements (known as growth – centered views) in the framework of national accounts and input-output tables. however, owing to the insensitivity of these views to simultaneous survey of socio-economic issues, no attention has been paid to systematizing social s...

Abstract We propose a computationally efficient Learning Model Predictive Control (LMPC) scheme for constrained optimal control of class nonlinear systems where the state and input can be reconstructed using lifted outputs. For considered systems, we show how to use historical trajectory data collected during iterative tasks construct convex value function approximation along with safe set in s...