Magnus Gäfvert

Friction Models and Friction Compensation

This paper reviews friction phenomena and friction models of interest for automatic control. Particular emphasis is given to two recently developed dynamic friction models: the Bliman-Sorine model and the LuGre model. These models capture many frictional phenomena observed in laboratory experiments. The behaviours of the models in different situations are discussed in detail. Methods for friction compensation are presented and illustrated with results from practical experiments.

Modeling and Optimization with Optimica and JModelica.org—Languages and Tools for Solving Large-Scale Dynamic Optimization Problems

The Modelica language, targeted at modeling of complex physical systems, has gained increased attention during the last decade. Modelica is about to establish itself as a de facto standard in the modeling community with strong support both within academia and industry. While there are several tools, both commercial and free, supporting simulation of Modelica models few efforts have been made in the area of dynamic optimization of Modelica models. In this paper, an extension to the Modelica language, entitled Optimica, is reported. Optimica enables compact and intuitive formulations of optimization problems, static and dynamic, based on Modelica models. The paper also reports a novel Modelica-based open source project, JModelica.org, specifically targeted at dynamic optimization. JModelica.org supports the Optimica extension and offers an open platform based on established technologies, including Python, C, Java and XML. Examples are provided to demonstrate the capabilities of Optimica and JModelica.org.

Interactive tools for education in automatic control

Experiments have shown that the time is now ripe for a new generation of interactive learning tools for control. The tools are based on objects which admit direct graphical manipulation. During manipulations, objects are updated instantaneously, so that relations between objects are maintained all the time. The tools are natural complements to traditional education, and allow students to quickly gain insight and motivation. A high degree of interactivity has been found to be a key issue in the design. Together with a high bandwidth in the man-machine interaction, this enhances learning significantly. Another nice feature is the possibility to hide minor issues and focus on the essentials. It is not easy to describe the power of these tools adequately in text. The best way to appreciate them is simply to use them. We believe that there is a strong pedagogical potential for the type of tools that we have described. We are also of the opinion that we are only at the very beginning in the development of learning tools of this type. The addition of sound and animation are interesting avenues that should be pursued.

Control of GDI engines using torque feedback exemplified by simulations

A novel approach to the control of a gasoline direct injection (GDI) engine is presented. The controller consists of a combination of sub-controllers, where torque feedback is a central part. The sub-controllers are with a few exceptions designed using simple linear feedback and feedforward control-design methods, in contrast to traditional table-based engine control. A switching strategy which maintains driving comfort during combustion-mode changes is also proposed. A novel silent extremum-controller is presented, and used to minimize the fuel consumption in stratified combustion mode. The controller has been evaluated with good results on the European driving cycle using a dynamic simulation model.

Simple Feedback Control and Mode Switching Strategies for GDI Engines

A novel approach to the control of a GDI engine is presented. The controller consists of a combination of sub-controllers, where torque feedback is a central part. The sub-controllers are with a few exceptions designed using simple linear feedback and feedforward control design methods. Special mode switch strategies are used to minimize the torque bumps during combustion mode changes. The controller has been evaluated on the European driving cycle using a dynamic simulation model, including a power train model and a driver model, with good results.