Related papers: Model Predictive Control of Diesel Engine Emission…
This paper presents the results of developing a multi-layer Neural Network (NN) to represent diesel engine emissions and integrating this NN into control design. Firstly, a NN is trained and validated to simultaneously predict oxides of…
This paper presents the results of a model predictive controller (MPC) development for diesel engine air-path regulation. The control objective is to track the intake manifold pressure and exhaust gas recirculation (EGR) rate targets by…
A fuel cell system must output a steady voltage as a power source in practical use. A neural network (NN) based model predictive control (MPC) approach is developed in this work to regulate the fuel cell output voltage with safety…
The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and…
The high thermal efficiency and reliability of the compression-ignition engine makes it the first choice for many applications. For this to continue, a reduction of the pollutant emissions is needed. One solution is the use of machine…
This paper investigates options to complement a diesel engine airpath feedback controller with a feedforward. The control objective is to track the intake manifold pressure and exhaust gas recirculation (EGR) rate targets by manipulating…
Diesel airpath controllers are required to deliver good tracking performance whilst satisfying operational constraints and physical limitations of the actuators. Due to explicit constraint handling capabilities, model predictive controllers…
Machine learning (ML) and a nonlinear model predictive controller (NMPC) are used in this paper to minimize the emissions and fuel consumption of a compression ignition engine. In this work machine learning is applied in two methods. In the…
Model Predictive Control (MPC) provides an optimal control solution based on a cost function while allowing for the implementation of process constraints. As a model-based optimal control technique, the performance of MPC strongly depends…
A significant challenge in the development of control systems for diesel airpath applications is to tune the controller parameters to achieve satisfactory output performance, especially whilst adhering to input and safety constraints in the…
This work presents a physics-based machine learning framework to predict and analyze oxides of nitrogen (NOx) emissions from compression-ignition engine-powered vehicles using on-board diagnostics (OBD) data as input. Accurate NOx…
Fuel efficiency is a crucial aspect of long-distance cargo transportation by oil-powered trucks that economize on costs and decrease carbon emissions. Current predictive control methods depend on an accurate model of vehicle dynamics and…
Control of non-condensing non-ideal-gas power cycles is challenging because their output power dynamics depend on complex system interactions, non-ideal-gas effects complicate turbomachinery behavior, and state constraints must be…
This paper considers an application of model predictive control to automotive air conditioning (A/C) system in future connected and automated vehicles (CAVs) with battery electric or hybrid electric powertrains. A control-oriented…
In this paper, we show the implementation of deep neural networks applied in process control. In our approach, we based the training of the neural network on model predictive control. Model predictive control is popular for its ability to…
The control of complex systems is of critical importance in many branches of science, engineering, and industry. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy…
This paper presents a robust Model Predictive Control (MPC) scheme that provides offset-free setpoint tracking for systems described by Neural Nonlinear AutoRegressive eXogenous (NNARX) models. The NNARX model learns the dynamics of the…
The stringent regulatory requirements on nitrogen oxides (NOx) emissions from diesel compression ignition engines require accurate and reliable models for real time monitoring and diagnostics. Although traditional methods such as physical…
Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output LC filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Although it is…
Model predictive control (MPC) is an industry standard control technique that iteratively solves an open-loop optimization problem to guide a system towards a desired state or trajectory. Consequently, an accurate forward model of system…