In their recent paper [2], the authors provided a tool for obtaining the explicit solution of constrained model predictive control (MPC) problems by showing that the control law is a continuous piecewise affine (PWA) function of the state vector. Therefore, the feedback interconnection between the MPC controller and a linear system, or a PWA system (e.g., a PWA approximation of a nonlinear syst...

Model Predictive Control (MPC) is currently the accepted standard in the process industries for handling complex constrained multivariable control problems. Here at each sampling time, starting at the current state, an open-loop optimal control problem is solved over a finite horizon. The optimal command signal is applied to the process only during the following sampling interval. At the next t...

We survey some recent research directions within the field of approximate dynamic programming (ADP), with a particular emphasis on rollout algorithms and model predictive control (MPC). We argue that while motivated by different concerns, these two methodologies are closely connected, and the mathematical essence of their desirable properties (cost improvement and stability, respectively) is co...

To reach an acceptable controller strategy and tuning it is important to state what is considered “good”. To do so one can set up a closed-loop specification or formulate an optimal control problem. It is an interesting question, if the two can be equivalent or not. In this article two controller strategies, model predictive control (MPC) and constrained direct inversion (CDI) are compared in c...

Linear Model Predictive Control (MPC) is an efficient control technique that repeatedly solves online constrained linear programs. In this work we propose an economic linear MPC strategy for operation of energy systems consisting of multiple and independent power units. These systems cooperate to meet the supply of power demand by minimizing production costs. The control problem can be formulat...

Vehicle-mounted 2-axis turrets are widely used in high bandwidth tracking systems, frequently encountered in air-to-ground, ground-to-air and air-to-air targeting. Existing controllers for these systems are generally implemented in classical proportional-integral-derivative (PID) form. The objective of this paper is to examine the novel application of constrained model predictive control (MPC) ...

Controlling a mobile vehicle to navigate in GPS-denied environments introduces a challenging partially observable control problem with complex constraints. This report presents a combination of various suboptimal control schemes such as open loop feedback control (OLFC), certainty equivalent control (CEC), model predictive control (MPC), and using expected values of estimates as full states to ...

Abstract This manuscript addresses the problem of controlling a bioreactor to maximize production desired product while respecting constraints imposed by nature bio‐process. The approach is demonstrated first building data‐driven model and then formulating predictive controller (MPC) with results illustrated implementing detailed monoclonal antibody (the test bed) created Sartorius Inc. In part...

Model predictive control (MPC) is one of the few techniques which is able to handle with constraints on both state and input of the plant. The admissible evolution and asymptotically convergence of the closed loop system is ensured by means of a suitable choice of the terminal cost and terminal contraint. However, most of the existing results on MPC are designed for a regulation problem. If the...