Acknowledgments I would like to express my appreciation to my advisor, Dr. James B. Rawl-ings. His diicult questions have pushed this work forward when it stalled and his insight has allowed me to progress when it seemed impossible. His vision of computing in this profession has provided his research group with the best computer facilities at the University. The work reported in this dis-sertat...

This article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (MPC) of a chemical plant. A combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (MIMO) process with time delays.  An optimization procedure for a neural MPC algorithm based on this model is then developed. T...

This paper presents a globally optimal nonlinear Model Predictive Control (NMPC) algorithm. Utilizing local techniques on nonlinear nonconvex problems leaves one susceptible to suboptimal solutions. In complex problems, local solver reliability is difficult to predict and often highly dependent upon the choice of initial guess. For the purpose of NMPC, local solvers can cause the algorithm to f...

Nonlinear Model Predictive Control (NMPC) has gained wide attention through the application of dynamic optimization. However, this approach is susceptible to computational delay, especially if the optimization problem cannot be solved within one sampling time. In this paper we propose an advanced-multi-step NMPC (amsNMPC) method based on nonlinear programming (NLP) and NLP sensitivity. This met...

In this note the Infinite Horizon (IH) optimality property of Nonlinear Model Predictive Control (MPC) is analysed. In particular it is shown with a contra example that the conjecture that the IH cost of the closedloop system controlled with a stabilizing MPC controller is a monotonic decreasing function of the optimization horizon is fallacius.

This paper presents a novel method for designing robust MPC schemes that are self-optimizing in terms of disturbance attenuation. The method employs convex control Lyapunov functions and disturbance bounds to optimize robustness of the closed-loop system on-line, at each sampling instant a unique feature in MPC. Moreover, the proposed MPC algorithm is computationally efficient for nonlinear sys...

While linear model predictive control is popular since the 70s of the past century, the 90s have witnessed a steadily increasing attention from control theoretists as well as control practitioners in the area of nonlinear model predictive control (NMPC). The practical interest is driven by the fact that today’s processes need to be operated under tighter performance specifications. At the same ...

This paper discusses an algorithm for efficiently calculating the control moves for constrained nonlinear model predictive control. The approach focuses on real-time optimization strategies that maintain feasibility with respect to the model and constraints at each iteration, yielding a stable technique suitable for suboptimal model predictive control of nonlinear process. We present a simulati...