In this thesis a nonlinear model predictive control (NMPC) scheme is presented that is based on the concept of passivity. The proposed NMPC scheme takes advantage of the relationship between optimal control and passivity and the relationship between optimal control and model predictive control. This novel NMPC scheme, guaranteeing closedloop stability through the incorporation of a passivity-ba...

In the field of path tracking for car-like robots, although nonlinear model predictive control (NMPC) can handle system constraints well, its real-time performance is poor. To solve this problem, a neural network method with NMPC as learning sample proposed. The design process includes establishing controller based on time-varying local model, generating samples controller, and training to obta...

Robustness and offset-free tracking remains a challenge in the field of nonlinear model predictive control (NMPC) in the presence of plant-model mismatch. This paper focuses on offset-free tracking with robust constraint satisfaction using NMPC. In the proposed approach, the discrepancy between the plant and a fundamental model is captured using a model-error model. The fundamental model is aug...

Constrained Nonlinear Predictive Control of Gasoline Low-Temperature Combustion — Combustion of a gasoline fuel with a high amount of recirculated exhaust gas is increasingly gaining interest. Such in part load conditions, a low combustion peak temperature can be achieved, which yields lowest emissions but suffers from instabilities of the process and a highly nonlinear behavior. These properti...

This paper presents an adaptive high performance control method for autonomous miniature race cars. Racing dynamics are notoriously hard to model from first principles, which is addressed by means of a cautious nonlinear model predictive control (NMPC) approach that learns to improve its dynamics model from data and safely increases racing performance. The approach makes use of a Gaussian Proce...

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 Kalman Filter (UKF) algo...

A drift-driving maneuver is a control technique used by an expert driver to vehicle along sharply curved path or slippery road. This study develops nonlinear model predictive (NMPC) method for the autonomous perform drift and generate datasets necessary training deep neural network(DNN)-based controller. In general, NMPC based on numerical optimization which difficult run in real-time. By repla...

Nonlinear Model Predictive Control (NMPC) algorithms are based on various nonlinear models. Among others, an on-line optimization approach for NMPC based on neural network models can be found in the literature. Nevertheless, NMPC with on-line optimization is time consuming. On the other hand, an explicit solution to the NMPC problem would allow efficient on-line computations as well as verifiab...

Abstract: This paper proposes a nonlinear model based predictive control (NMPC) algorithm for nonlinear systems by using multiple models approach. To have a less complexity model we expand each linear sub-model on an orthogonal Laguerre basis, the characteristic pole of which should be optimized. In this paper we propose a pole optimization algorithm based on the Gauss-Newton method and we use ...

In this paper, we apply model predictive control (MPC) for control of blood glucose in people with type 1 diabetes. The two first control strategies are based on nonlinear model predictive control (NMPC). The first control strategy is based on meal announcement in advance, while the second one considers meal announcement at mealtimes only. They give a quantitative upper bound on the achievable ...