The paper addresses the problem to design a quadratic stable output/state feedback model predictive control for linear systems with input constraints. In the proposed design technique the model predictive control is designed for N2 step ahead prediction using the Lyapunov function approach with the cost function guaranteeing input constraints. Output gain matrix calculation is realized off line...

In a range of contexts, one comes across processes resembling inference, but where input propositions are not in general included among outputs, and the operation is not in any way reversible. Examples arise in contexts of conditional obligations, goals, ideals, preferences, actions, and beliefs. In a separate paper, we developed a general theory of such processes when they are applied without ...

We determine the maximum output amplitude of a system, when the input is bounded by certain constraints. In particular, amplitude and rate of change (i.e. the first derivative) have to be bounded. We show properties of the worst case input and present an algorithm that allows construction of this input and calculates the maximum amplitude of the output. The solution of this problem is a necessa...

Optimal feedback solutions to the in nite-horizon LQR problem with state and input constraints based on receding horizon real-time quadratic programming are well known. In this paper we develop an explicit solution to the same problem, eliminating the need for real-time optimization. A suboptimal strategy, based on a suboptimal choice of a nite horizon and imposing additional limitations on the...

This paper presents a design method of the reference governor for a stabilizable linear system with input constraints. When the input has a constraint, the input saturation might happen and thus, the controller cannot work correctly. Furthermore, the output might diverge in the case of an open loop unstable system. To remedy this problem, the reference governor is proposed. The set-point contro...

Robust stabilization of an uncertain system subject to input constraints is addressed without making open-loop stability assumptions. A local approach is taken to nd a robust control law and a set of initial conditions that can be stabilized. A piecewise-linear control law is described. It is generated by a parametrized algebraic Riccati equation of the H 1 control theory. Once system trajector...

The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is diffic...