The paper studies dependability of software implementation of the explicit DMC (Dynamic Matrix Control) Model Predictive Control (MPC) algorithm applied for a rectification column. The process with two inputs and two outputs with strong cross-couplings and significant time delays is studied. The algorithm’s control law is calculated off-line. Dependability is evaluated experimentally using soft...

The constrained infinite-horizon LQG control problem can be solved via semidefinite programming if the state-control constraints are given by variance-bounds on linear functions of the state and control input. Given a nonzero initial state covariance matrix, each suboptimal dynamic output feedback controller is initially time-varying but reaches time-invariance after a finite number of time ste...

In this paper we present the new development of a semiautonomous response module for the NOISA system. NOISA is an interactive music system that predicts performer’s engagement levels, learns from the performer, decides what to do and does it at the right moment. As an improvement for the above, we implemented real-time adaptive features that respond to a detailed monitoring of the performer’s ...

We consider the problem of synthesizing optimal linear feedback policies subject to arbitrary convex constraints on the feedback matrix. This is known to be a hard problem in the usual formulations (H2,H∞,LQR) and previous works have focused on characterizing classes of structural constraints that allow efficient solution through convex optimization or dynamic programming techniques. In this pa...

In this paper we extend standard dynamic programming results for the risk sensitive optimal control of discrete time Markov chains to a new class of models. The state space is only ®nite, but now the assumptions about the Markov transition matrix are much less restrictive. Our results are then applied to the ®nancial problem of managing a portfolio of assets which are a ̈ected by Markovian micro...

Optimization problems in discrete time can be modeled more flexibly by extended linearquadratic programming than by traditional linear or quadratic programming, because penalties and other expressions that may substitute for constraints can readily be incorporated and dualized. At the same time, dynamics can be written with state vectors as in dynamic programming and optimal control. This sugge...