This paper focuses on motion control problems of an omnidirectional robot based on the Nonlinear Model Predictive Control (NMPC) method. Although NMPC has been studied in many mobile robots applications due to the advantages of taking the robot constraints into account and increasing the robot performance with future information, the high computational requirement makes NMPC difficult to be uti...

This paper provides an overview of nonlinear model predictive control (NMPC) applications in industry, focusing primarily on recent applications reported by NMPC vendors. A brief summary of NMPC theory is presented to highlight issues pertinent to NMPC applications. Five industrial NMPC implementations are then discussed with reference to modeling, control , optimization, and implementation iss...

An adaptive radial basis function (RBF) neural network model is developed in this paper for nonlinear systems using the recursive orthogonal least squares (ROLS) algorithm. The model is used in a nonlinear model predictive control (NMPC). The developed adaptive NMPC is applied to a chemical reactor rig. On-line control performance is presented and it demonstrates superiority over the fixed para...

This dissertation addresses two issues that arise in the field of Nonlinear Model Predictive Control (NMPC): computational delay and stability of economically oriented NMPC. NMPC has gained wide attention through the application of dynamic optimization. It has the ability to handle variable bounds and multi-input-multi-output systems. However, computational delay caused by large size of nonline...

2 Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran [email protected] Abstract: This paper proposes an adaptive Nonlinear Model Predictive Controller (NMPC) for hybrid position/velocity control of robot manipulators. Robot dynamics have generally uncertainties, including parameters variations, unknown nonlinearities of the robot, payload var...

A non-iterative Nonlinear Model Predictive Controller (NMPC) for formation control of helicopters is proposed and validated through simulations. The method is based on minimizing the error of geometrical formation parameters specifically designed for helicopters. These parameters are used to form desired three-dimensional (3D) configurations among members of a helicopter group. This approach is...

Machine learning (ML) and a nonlinear model predictive controller (NMPC) are used in this paper to minimize the emissions fuel consumption of compression ignition engine. In work machine is applied two methods. first application, ML identify for implementation control optimization problems. second as replacement NMPC where learns optimal action by imitating or mimicking behavior controller. stu...

surge is one of the two destructive factors in compressors. surge is the stream instability phenomenon in compressor that imposes severe damages to the compressors. nowadays, suppressingsurge phenomenon is one of the most important issues in oil and gas industries, especially when flow reduction or gas reflux is considered. according to moore-greitzer compressor model, this paper designs an act...

We propose two different nonlinear model predictive control (NMPC) schemes (with and without terminal cost constraints) for stabilizing the translational dynamics of thrust-propelled vehicles. Both approaches make use an elaborated feedback linearization controller its associated ellipsoidal invariant set under restrictive input constraints, hence guaranteeing closed-loop asymptotic stability. ...

Nonlinear model predictive control (NMPC) is an appealing control method as it allows to control multi-input multi-output processes while taking constraints into account. It is based on successively solving open-loop optimal control problems. Although NMPC schemes may naturally exhibit a certain degree of inherent robustness, an explicit consideration of process uncertainties is preferable, par...