This paper describes a procedure to find the best economically controlled variables for the activated sludge process in a wastewater treatment plant despite the load disturbances. A further controllability analysis of those variables including a nonlinear model predictive controller (NMPC) has been performed. The self-optimizing methodology has been applied, considering the most important measu...

A new design of nonlinear model predictive controller (NMPC) is proposed for managed pressure drilling (MPD) system. The NMPC is based on output feedback control architecture and employs offset-free formulation proposed in [1]. NMPC uses active set method for computing control inputs. The controller implements an automatic switching from constant bottom hole pressure (CBHP) regulation to flow c...

The work presented in this thesis examines several aspects of Nonlinear Model Predictive Control (NMPC) that display and con rm its promising potentials as a powerful recon gurable control scheme. The e ects of signi cant nonlinearities and the intrinsically unstable nature of high performance ghter aircraft, among other challenges, have been shown to be well handled in the NMPC framework. This...

An adaptive version of growing and pruning RBF neural network has been used to predict the system output and implement Linear Model-Based Predictive Controller (LMPC) and Non-linear Model-based Predictive Controller (NMPC) strategies. A radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.An Unscented Kal...

−−This paper describes a procedure to find the best controlled variables in an economic sense for the activated sludge process in a wastewater treatment plant, despite the large load disturbances. A novel dynamic analysis of the closed loop control of these variables has been performed, considering a nonlinear model predictive controller (NMPC) and a particular distributed NMPC-PI control struc...

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 ...

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

this paper employs nonlinear disturbance observer (ndo) for robust trajectory-free nonlinear model predictive control (nmpc) of biped robots. the ndo is used to reject the additive disturbances caused by parameter uncertainties, unmodeled dynamics, joints friction, and external slow-varying forces acting on the biped robots. in contrary to the slow-varying disturbances, handling sudden pushing ...

In this work, we present a general nonlinear model predictive control (NMPC) framework for low-density polyethylene (LDPE) tubular reactors. The framework is based on a first-principles dynamic model able to capture complex phenomena arising in these units. We first demonstrate the potential of using NMPC to simultaneously regulate and optimize the process economics in the presence of persisten...