This paper presents a Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithm to achieve high-performance path tracking in challenging off-road terrain through learning. The LB-NMPC algorithm uses a simple a priori vehicle model and a learned disturbance model. Disturbances are modelled as a Gaussian Process (GP) as a function of system state, input, and other relevant variables. ...

In this paper the application of Nonlinear Model Predictive Control (NMPC) to a chemical reactor, namely the Open Plate Reactor(OPR), is considered. It is designed a control strategy which allows a safe start-up operation of the reactor and provides an easy handling of system restrictions and nonlinearities originating from the process dynamics. An efficient parameterization of control signals ...

In the 27 km circumference Large Hadron Collider, the temperature of more than 1600 main superconducting magnets is stabilized below 2 K by the Superfluid Helium Cryogenic Circuit. The key component of the circuit’s Standard Cell is an over 100 m long bayonet heat exchanger with two phase flow of superfluid helium, He II, that is integrated into the magnets submerged in a static bath of He II. ...

In situ adaptive tabulation (ISAT) is applied to store and retrieve solutions of nonlinear model predictive control (NMPC) problems. ISAT controls approximation error by adaptively building the database of NMPC solutions with piecewise linear local approximations. Unlike initial state or constraint parameterized constrained quadratic programming (QP) solutions, ISAT approximates NMPC solutions ...

In this study, the design, optimization and dynamic modelling of a milk pasteurization unit have been developed, using pseudo-component approach for describing properties. The fluid has regarded as mixture five major categories, namely water, fats, proteins, carbohydrates, minerals. Exploiting optimal pasteurizer configuration, selected based on total annualized cost, model process also derived...

This study applies nonlinear model predictive control (NMPC) to the torque-vectoring (TV) and front-to-total anti-roll moment distribution of a four-wheel-drive electric vehicle with in-wheel-motors, brake-by-wire system, active suspension actuators. The NMPC cost function formulation is based on energy efficiency criteria, strives minimize power losses caused by longitudinal lateral tire slips...

Model Predictive Control (MPC) has become one of the most popular control techniques in the process industry mainly because of its ability to deal with multiple-input-multipleoutput plants and with constraints. However, its performance can deteriorate in the presence of model uncertainties and disturbances. In the last years, the development of robust MPC techniques has been widely discussed, b...

In this article, we describe the design and implementation of a current controller for reluctance synchronous machine (RSM) based on continuous control set nonlinear model predictive (NMPC). A computationally efficient gray box flux linkage map, Gaussian-linear-arctangent (GLA) model, is proposed employed in tracking formulation, which implemented using high-performance framework NMPC <monospac...

This paper studies the reduction of the conservativeness of robust nonlinear model predictive control (NMPC) via the reduction of the uncertainty range using guaranteed parameter estimation. Optimal dynamic experiment design is formulated in the framework of robust NMPC in order to obtain probing inputs that maximize the information content of the feedback and simultaneously to guarantee the sa...

Any proper operation could be translated as a constrained optimization problem inside a WWTP, whose nonlinear behavior renders its control problems quite attractive for performance of multivariable optimization–based control technique algorithms, such as NMPC. The main advantage of this control technique lies in its ability to handle model nonlinearity as well as various types of constraints on...