In this paper, a multivariable control strategy based on a parameterized Nonlinear Model Predictive Control (NMPC) approach is proposed for a Diesel engine air path. The aim is to control the level of pollutant emissions by coordinating the Exhaust Gas Recirculation (EGR) and the Variable Geometry Turbocharger (VGT) valves simultaneously. The main feature of the proposed controller lies in its ...

This paper presents a nonlinear model based predictive controller (NMPC) for trajectory tracking of the Middle-size Omnidirectional Robots from the 5DPO Robotic Soccer team. The strategy proposed uses methods of numerical optimization to perform real time nonlinear minimization of the cost function. The cost function penalizes the robot position error, the robot orientation angle error and the ...

Nonlinear Model predictive control (NMPC) is a popular control strategy for highly nonlinear chemical processes. The ability to handle safety and environmental constraints along with the use of an economic objective makes NMPC highly appealing to industries. The performance of NMPC depends strongly on the accuracy of the model. In reality, there always are plant-model mismatch and state estimat...

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...

Model predictive control (MPC) anticipates future events to take appropriate control actions. Nonlinear MPC (NMPC) describes systems with nonlinear models and/or constraints. Continuation MPC, suggested by T. Ohtsuka in 2004, uses Krylov-Newton iterations. Continuation MPC is suitable for nonlinear problems and has been recently adopted for minimum time problems. We extend the continuation MPC ...

In this work, the pH neutralization process is described by a neural network Wiener (NNW) model. A nonlinear Model Predictive Control (NMPC) is established for the considered process. The main difficulty that can be encountered in NMPC is solving the optimization problem at each sampling time to determine an optimal solution in finite time. The aim of this paper is the use of global optimizatio...

Nonlinear Model Predictive control (NMPC) is one of the advanced control strategies for multi-dimensional nonlinear systems with constraints. With uncertainties present in the model, robust NMPC strategies are proposed in order to counteract the effects of the uncertainties and have a safe operation of the plant. Multi-stage NMPC offers a non-conservative alternative as it models the feedback i...

In this research, an intelligent multi-objective nonlinear model predictive control (NMPC) scheme is proposed for its application in the ‘on-line’ optimization of dynamical gas turbine model. The scheme proposed belongs to the sub-optimal NMPC strategies where near-optimal, instead of global optimal, control solutions are obtained at each control sampling time. The complexity of NMPC implementa...

This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electrom...

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 ...