Batch or semi-batch processing is becoming more prevalent in industrial chemical
manufacturing but it has not benefited from advanced control technologies to a same degree …

Process systems are characterized by nonlinearity, uncertainty, large scales, and also the
need of pursuing both safety and economic optimality in operations. As a result they are …

A supervised learning framework is proposed to approximate a model predictive controller
(MPC) with reduced computational complexity and guarantees on stability and constraint …

Explicit model predictive control (MPC) addresses the problem of removing one of the main
drawbacks of MPC, namely the need to solve a mathematical program on line to compute …

In this paper we study the effect of false data injection attacks on state estimation carried
over a sensor network monitoring a discrete-time linear time-invariant Gaussian system. The …

This paper proposes to use Nesterov's fast gradient method for the solution of linear
quadratic model predictive control (MPC) problems with input constraints. The main focus is …

This paper describes nonlinear methods in model building, dynamic data reconciliation, and
dynamic optimization that are inspired by researchers and motivated by industrial …

In this paper relations between model predictive control and reinforcement learning are
studied for discrete-time linear time-invariant systems with state and input constraints and a …

In the Proportional-Integral-Derivative (PID) controllers the control signal is a linear function
of: the current control error (the proportional part), the past errors (the integral part) and the …

This article investigates the problem of efficient computation of physically consistent
multicontact behaviors. Recent work showed that under mild assumptions, the problem …