Lino Guzzella: Efficient Monitoring and Control of Complex Systems

From heat pumps to engines, "smart" buildings and even the human body, research in dynamics and control is crucial to the efficient monitoring, control and design of complex systems. Building on first principles in mathematics and physics, we bring a model-based approach to a wide range of environmental, commercial, social, and biomedical design challenges.

Control-oriented systems modeling and dynamic optimization and feedback control design are our main areas of research. One of our primary objectives is to combine application relevance with scientific depth, and to bridge the gap between system theory and engineering.

 

For example ...

Model-Based Adaptive and Cylinder Individual Air/Fuel Ratio Control

As it ages over the course of its lifetime, an engine’s air/fuel ratio sensor undergoes a substantial change in its dynamics. To some extent, a robust controller can mitigate this change. The resulting increased demands on the system, however, can reduce its overall performance. The goal of this project is to design a controller capable of dealing with changes of the air/fuel sensor dynamics without sacrificing overall system performance. Our control strategy identifies changes in sensor dynamics and adapts its parameters accordingly, ultimately superceding the need for global robustness and circumventing the system performance problem. 

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Emission-Controlled Diesel Engine

Diesel engines are more fuel efficient than other engines, but their pollutant emissions must be substantially reduced to meet future environmental standards. To achieve this aim, robust and accurate control of the engine is critical. Engine variability and ageing must also be dealt with, and information about exhaust gas must be integrated into the emission control loop. This project focuses on the development of control-oriented engine models and flexible control structures that use integrated sensor information to minimize emissions.

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Optimized Control of Standard and Plug-In HEVs

Hybrid electric vehicles (HEVs) promise excellent fuel-efficiency without sacrificing vehicle performance or passenger comfort. Because they have more than one power source, however, these vehicles are complex and, from a controls perspective, pose significant a design challenge. This project focuses on the development of control strategies capable of managing complex systems such as these. Our strategy also makes use of dynamic programming to handle issues such as component sizing.

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Pneumatic Hybrid Engine for Fuel Consumption Reduction

Here at IDSC we have built the world’s first fully-functional hybrid pneumatic engine. Using compressed air as an energy buffer, our engine is substantially smaller than a conventional internal combustion engine with the same power. The result: a 30% improvement in fuel efficiency over conventional engines, and excellent driveability. Because they require no batteries or electric motors, hybrid pneumatic vehicles offer an affordable alternative to the hybrid electric vehicle.

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Monte Rosa Hut

The Monte Rosa Hut is a collaborative project designed with sustainability in mind. Using photovoltaic systems with large battery capacity, solar panels with hot-water reservoirs, and a cogeneration power plant, the Monta Rosa Hut can host up to 125 guests with minimal environmental impact. With so many interacting energy and storage systems, however, the Monta Rosa hut poses a significant control challenge. At IMRT we are developing ways to optimize the interactions of the various energy systems, storage systems, and the people that use them.

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Optimal Control of Cables

Because of their low damping and long length, cables on large span cable-stayed bridges are prone to potentially damaging vibrations. Fortunately, promising new feedback-controlled magnetorheological damping systems are under development to prevent premature material failure within cable anchor systems. The behaviour of both damper and cable are complex, however, and an intelligent damping system is needed. The goal of this project is to create a robust and accurate non-linear model of such a system.

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Smart-Shunt Project

This transdisciplinary project aims at conducting the basic research necessary for the subsequent development of a smart cerebrospinal fluid (CSF) shunt for normal pressure hydrocephalus (NPH). NPH is most commonly treated by the surgical placement of a ventriculoperitoneal shunt that drains CSF from the patient’s ventricular space to the peritoneal area. The IDSC contributes to the project with the design of a SmartShunt functional model, and the development of a control algorithm that regulates CSF drainage.

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