Anemia management with erythropoiesis stimulating agents is a difficult task in hemodialysis patients since their response to treatment varies highly. The aim is to stabilize hemoglobin levels within a narrow target window while keeping drug doses low to mitigate side effects and further reduce costs. Based on a model of erythropoiesis, which contains a number of personalized parameters, we pre...

Autonomous vehicle's technology is expected to be disruptive for automotive industry in next years. This paper proposes a novel real-time trajectory planner based on Nonlinear Model Predictive Control (NMPC) algorithm. A nonlinear single track vehicle model with Pacejka's lateral tyre formulas has been implemented. The numerical solution of the NMPC problem obtained by means implementation gene...

Currently, the most advanced method to realize trajectory control is to use Nonlinear Model Predictive Control (NMPC). The NMPC also needs an accurate estimate of the state of the controlled system. An Extended Kalman filter (EKF) is the best choice to realize the state estimation. Normally the tuning of these methods requires high expertise from the control engineer as well as knowledge of the...

Industrial Batch Polymerization System Zoltan K. Nagy, Ruediger Franke, Bernd Mahn, and F. Allgoewer Nowadays chemical and related processing industries are faced with the need of increasingly precise control of product properties to meet specific demands. It is recognized that nonlinear model predictive control (NMPC) is an excellent candidate as a key enabling technology for the continued suc...

Model predictive control (MPC) has made a significant impact on control engineering. It has been applied in almost all of industrial fields such as petrochemical, biotechnical, electrical and mechanical processes. MPC is one of the most applicable control algorithms which refer to a class of control algorithms in which a dynamic process model is used to predict and optimize process performance....

In this paper, a deep neural network (DNN)-based nonlinear model predictive controller (NMPC) is demonstrated using real-time experimental implementation. First, the emissions and performance of 4.5-liter 4-cylinder Cummins diesel engine are modeled DNN with seven hidden layers 24,148 learnable parameters created by stacking six Fully Connected one long-short term memory (LSTM) layer. This then...

This article presents a novel algorithm for blending human inputs and automatic controller commands, guaranteeing safety in mixed-initiative interactions between humans quadrotors. The is based on nonlinear model predictive control (NMPC) involves using the state solution to assess whether safety- and/or task-related rules are met mix authority. mixing attained through convex combination of act...

This chapter proposes a reliable fault-tolerant model predictive control applied to drinking water transport networks. After a fault has occurred, the predictive controller should be redesigned to cope with the fault effect. Before starting to apply the fault-tolerant control strategy, it should be evaluated whether the predictive controller will be able to continue operating after the fault ap...

Abstract— Adaptive Nonlinear Model Predictive Controller (ANMPC) can estimate system’s parameters and detect a fault of actuators based on relationship between past control inputs and states variables of systems, and estimated parameters are need for calculating a control input in next step. In this paper, for applying this ANMPC to real systems, performance verification of ANMPC using an appro...

Autonomous surface vehicles are with increasing popularity being seen in various applications where automatic control plays an important role. In this paper the problem of twodimensional trajectory tracking for autonomous marine surface vehicles is addressed using Model Predictive Control (MPC). At each time step, the reference trajectories of a vessel are assumed to be known over a finite time...