This paper provides an overview of commercially available Model Predictive Control (MPC) technology, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented rst, followed by results of our vendor survey of MPC control and identiication technology. A general MPC control algorithm is presented, and approaches taken by each vendor for the diierent...

Adaptive Horizon Model Predictive Control (AHMPC) is a scheme for varying as needed the horizon length of Model Predictive Control (MPC). Its goal is to achieve stabilization with horizons as small as possible so that MPC can be used on faster or more complicated dynamic processes. Beside the standard requirements of MPC including a terminal cost that is a control Lyapunov function, AHMPC requi...

This paper proposes an efficient and online learning control system that uses the successful Model Predictive Control (MPC) method in a model based locally weighted learning framework. The new approach named Locally Weighted Learning Model Predictive Control (LWL-MPC) has been proposed as a solution to learn to control complex and nonlinear Elastic Joint Robots (EJR). Elastic Joint Robots are g...

In this paper, we study a tracking control problem for linear time-invariant systems, with model parametric uncertainties, under input and states constraints. We apply the idea of modular design introduced in [1], to solve this problem in the model predictive control (MPC) framework. We propose to design an MPC with input-to-state stability (ISS) guarantee, and complement it with an extremum se...

This paper proposes an MPC - based (model predictive control) scheme to control active and reactive powers of DERs (distributed energy resources) in a grid - connected mode (either through a bus with its associated loads as a PCC (point of common coupling) or an MG (micro - grid)). DER may be a DG (distributed generation) or an ESS (energy storage system). In the proposed scheme, the set - poin...

During the last decade, there has been a growing interest in developing nonlinear model-based control methods. This interest has led to substantial progress mainly within the two frameworks of model predictive control (MPC) and differential geometric control. MPC is an optimization-based control methodology which explicitly accounts for process constraints and in general leads to a controller w...

In a multi input and output (MIMO) process mathematical modeling of the physical systems has gained importance due to the complexity of interactions within the system. All the parameters used in a model cannot be determined accurately. The major problem in a multivariable process is that loop interaction can arise and cause difficulty in feedback control design. This problem can be solved using...

VOLUME 85, AUGUST 2007 INTRODUCTION Model predictive control (MPC) has been the most successful advanced control technique applied in the process industries. The formulation naturally handles time-delays, multivariable interactions and constraints. Particularly in the petrochemical industry, MPC has often been tuned for robustness rather than a high level of dynamic performance. In addition to ...

Figure 1. Reduction in variance after MPC is applied. M odel-based predictive control (MPC) is a valuable, proven advanced process control (APC) approach that has been used for some time, especially in the chemical and hydrocarbon processing industries. It has been underutilized until recently in the power industry, and in that segment, MPC is now also showing dramatic results. The strength of ...

This paper presents modelling of internal combustion (IC) engine with adaptive neural networks. A radial basis function network model with both centres and weights adapted and a model with only weights adapted are compared with a fixed parameter model. The developed models are used in model based predictive control (MPC) to form an adaptive nonlinear MPC scheme and applied to engine speed track...