Research at the Automatic Control Laboratory
- Hybrid models describe systems composed of both continuous and discrete components. For hybrid systems we study novel modelling methodologies and constrained optimal controllers which are able to stabilize hybrid systems on desired reference trajectories while obeying operating constraints. In addition to fundamental theoretical advances we focus on issues of complexity and computation, facilitating application of the new methods.
- Our group has been conducting research into the fundamental properties of non-deterministic and stochastic hybrid systems, computational methods, and applications to air traffic management and systems biology. Fundamental questions of interest include existence and uniqueness of solutions for hybrid and stochastic hybrid systems, stability and reachability questions. The computational methods we develop revolve primarily around randomized methods, such as Monte Carlo simulation, Markov chain Monte Carlo, and statistical learning theory in the context of neural networks.
- The field of System Identification addresses the derivation of dynamic models from experimental data. The research activity at the Automatic Control Laboratory covers the application of identification techniques to physical systems as well as the theoretical development of novel identification methods. In particular, optimisation based approaches for subspace identification, where state-space models are directly obtained from time or frequency domain data, are investigated.
- Safety and reachability are fundamental problems in systems and control. We develop the framework and algorithms to address safety and reachability of deterministic and stochastic hybrid dynamical systems, systems with multiple competing players, and systems in which the safe and target sets are dynamic and uncertain. The methods are applied to air traffic control, surveillance networks, power grid, and systems biology.
- Scalable control and optimization for large-scale multi agent systems is becoming increasingly important. In the lab we approach this topic from different angles: we study non cooperative approaches formulated as mean field games, cooperative approaches based on distributed optimization and more classical approaches based on decentralized linear feedback.
- Robust Model Predictive Control (RMPC) is a powerful methodology to design controller for uncertain systems in which state and input constrains must be satisfied for every possible disturbance realization. In certain situations, however, this requirement may significantly degrade the overall controller performance by the need to protect against low probability outliers. Stochastic Model Predictive Control (SMPC) is a relaxation of RMPC, in which the constraints are interpreted probabilistically via chance constraints, allowing for a (small) constraint violation probability. Unfortunately, chance constrained control problems are hard in general, and must often be approximated. In our work, we focus on sampling-based approximation methods for solving such problems, and derive efficient sample sizes for both linear and non-linear SMPC problems. The methods are numerically validated on energy-efficient building control problems.
- Predictive control is computationally intensive, while many embedded control platforms are limited in terms of their computational capabilities. With the goal of extending the scope of applications that can benefit from online optimization-based control and estimation, the group develops new optimization methods for convex and mixed-integer programming, as well as design automation tools and efficient software and hardware implementations for optimization solvers.
- Stochastic optimization arising from uncertain process data and forecasts presents fundamental research challenges. In presence of limited statistical information, or merely of empirical data, we propose novel randomized convex optimization approaches to compute probabilistically feasible solutions.
- The Multi-Parametric Toolbox is a collection of algorithms for modeling, control, analysis, and deployment of constrained optimal controllers developed under Matlab. The main focus of the toolbox is to provide code for high-speed hardware implementation of optimal controllers by means of explicit model predictive control. The toolbox features a powerful geometric library that extends the application of the toolbox beyond optimal control to various problems arising in computational geometry.
- IfA's research in this area focuses on model predictive control (MPC) of office buildings. Earlier work at IfA centered on the simulation-based assessment of MPC's energy savings and demand-response potential. In a recently finished project, MPC has been implemented on an occupied office building, completely controlling its building system over seven months. Within the project, a Matlab toolbox was developed that allows the fast generation of MPC suitable building models from construction and systems data. Current work aims at developing adaptive MPC formulations that are able to online improve uncertain model parameters.
- Airborne Wind Energy (AWE) Systems harvest wind energy by exploiting the aerodynamic forces generated by autonomous tethered wings, flying fast in crosswind conditions. This technology is able to reach higher altitudes, compared to conventional wind turbines, where the wind is generally stronger and more consistent. To achieve fully-autonomous, power-optimising flight of tethered wings, this projects focuses on advanced modelling and identification methods for unstable systems. This finally enables the design of optimal controllers for tracking periodic orbits in varying operating conditions.
- The accommodation of uncertain forecasts is one of the most pressing challenges in the control of power systems with a high penetration of intermittent renewables, such as wind and solar power. Our recent work has demonstrated the use of robust optimization with multi-stage recourse policies to provide reserves much less conservatively than with existing means. Our approach is particularly attractive for ensuring that devices such as plug-in electric vehicles, pumped hydro plants, batteries, and building HVAC can participate in reserve provision while still making guarantees on future availability of stored energy.
- Through controlling the flexibility of demand side resources, we address grid security in the presence of renewable sources of energy. Population control, stochastic optimal control, and game theoretic methods are being developed for control of an aggregation of a large number of distributed loads, such as household appliances or electric vehicles, and an aggregation of a small number of office buildings.
- Power electronics deals with controlled electrical energy conversion. Two key aspects that characterize the performance of a power conversion system are its efficiency of the conversion and its dynamic performance. By dynamic performance, it is meant how fast the power conversion system can adapt to fluctuations and also how efficient is the efficiency during fluctuations. As power conversion systems operate from below the kHz to above the MHz, another crucial aspect is the real-time performance. Our research deals with the optimization of the static and dynamic performance of power conversion systems using advanced modelling, optimization and control techniques as well as real-time implementation of these solutions.
- The optimization of the operations of electrical drives is a topic of paramount importance when considering energy efficiency of industrial plants. An electrical drive consists of a converter and a motor: this architecture allows one for variable speed control of these machines. These devices convert the electric energy from the grid into mechanical energy in the load. The goal of the research activity is the minimization of the power losses in the transformation. In particular, we focus on accurate modeling and control based on mathematical optimization to improve the performance of the drive. The activity is in the framework of the Energy SmartOps project.
- The primary concern of Air Traffic Control (ATC) is to maintain safe separation between the aircraft in the airspace. ATC is currently very much a human-in-the-loop process, with air traffic controllers ultimately responsible for safety. The aim of our research in this area is to facilitate the work of human controllers by introducing automation and decision support tools, based on modeling, estimation, distributed control, and optimization methods.
- Networked control systems consist of many spatially distributed sensors, actuators, and processors that can communicate with one another over a wired or wireless computer network. Rapid advances in sensing technologies are revealing unprecedented dynamic data streams about diverse physical, technological, and social network dynamics, and new actuation technologies are increasing the ability to manipulate network architecture and dynamics. Traditional methods from control theory are not suitable for analyzing and designing such networks due to computational complexity and inability to deal with constraints imposed by the network such as structural information flow constraints, data loss and corruption, bandwidth limits, and variable delays. Research in this area brings together results from control, optimization, information and communication theory, computer science, data mining and statistics, and others to form a theoretical foundation.
- The chemical industry, a pillar of the world economy, has been known to profit from modern control techniques, such as model predictive control, for decades; however, the complex and highly nonlinear nature of certain processes has so far eluded useful quantitative description and hence has been difficult to control. The field of separation processes contains many such problems, and in recent years, the automatic control laboratory has focused its efforts on these types of systems:
Apart from several contributions in the field of chromatography, in particular the control of simulated moving bed (SMB) processes, current research is mostly connected to the CrystOCAM project, a collaboration between the laboratoire automatique at EPF Lausanne and the separation process laboratory at ETH Zurich, that aims at developing a new generation of tools to model, to measure and monitor online, to optimize and control the distribution of sizes and shapes of crystals of a suspension of particles and crystals during the crystallization from solution.
- This interdisciplinary research project focuses on assessing the potential of wing morphing in improving the characteristics of airplanes. Conventional rigid-wing airplane designs are the result of a tradeoff between different requirements arising from diverse operating points within their typical mission. Morphing wings have the potential of adapting to different flight conditions in an optimal way (e.g. minimal drag at each operating point). The research carried out at the Automatic Control Lab as part of this project consists of parameter identification from free flight data, closed-loop control of Macro Fiber Piezo actuators, attitude stabilization for a flying wing and aims at showcasing closed-loop control of the span-wise lift-distribution on a 3m-span flying wing prototype with smart actuators integrated in a selectively compliant lifting surface.
- Biochemical reaction networks can be understood and controlled with the help of stochastic models. These models have to be identified from measurements of the abundance of chemical compounds inside single cells. For a successful identification it is usually critical to perturb the system in order to induce dynamic changes in the molecule abundances. These perturbations can be planned such that they optimally excite the system and lead to maximally informative data.
- In a free-electron laser (FEL), the accelerated electron beam is passed through a periodic magnet array, called undulator, causing transverse acceleration of the electrons which results in the release of photons. The SwissFEL project at Paul Scherrer Institute will develop a free-electron laser which provides a source of very bright and short X-ray pulses. The major control contributions are in the area of: RF system modeling and identification; repetitive robust control theory; repetitive learning algorithms; and distributed control and information architectures for high performance systems.
- Thermoacoustics deals with reversible energy conversion and interaction between heat oscillations and sound waves in gases and involves research in acoustic, thermodynamic, fluid dynamical phenomena and system dynamics. Thermoacoustic machines convert fluctuating low grade thermal energy into sound waves of high amplitude which in turn can drive thermoacoustic refrigerators and heat pumps or can be directly converted into electrical energy using an electrodynamic or piezoelectric transduction mechanism. Such machines have a high potential for the development of sustainable and alternative energy systems by utilizing low temperature waste heat recovery, biomass and gas combustion heat and solar energy, respectively. Promising features of thermoacoustic machines are high reliability, lack of moving parts (with the exception of the acoustic resonator), quietness, low cost and generally environmental friendliness.
- This project investigates the use of robotics in architecture and construction. Our objectives are to design and analyse control systems for cooperative robots engaged in building construction. These robotic systems will allow the construction of architectural designs that are not currently possible due to cost or precision constraints.
- The Distributed Flying and Localization Laboratory (D-FaLL) creates a space where students can learn, experiment, and develop new ideas on the topics of Flying Machines and their Localization. The projects pursued by the student-teams always maintain a focus on how to handle the challenges of a distributed architecture when there are many agents simultaneously flying and localizing.
- The RoboCup promotes research in cooperative multi-robot and multi-agent systems through the game of soccer. University teams from all around the globe compete in various leagues and the Standard Platform League has been using Aldebaran Robotics since 2008. The team Z-Knipser is a collaboration between the Computer Vision Group and the Automatic Control Lab. Bachelor and master students are working on projects in perception and behavior programming that is train the NAO to perceive his environment and act on it.
- The Automatic Control Laboratory is developing a fully-autonomous model sail boat. This is a multi-disciplinary project that includes challenging tasks in the fields hardware and software integration, system identification, and advanced control design. The goal is the construction of a testbed for novel control algorithms and the participation in international robotic-sailing competitions.
- The ORCA (Optimal RC Racing) Project developed (and improves) a test bed consisting of a race track, a infrared camera based tracking system and modified 1:43 dnano RC cars, in order to study control algorithms allowing high-speed, real-time control.
On the test bed different fast MPC algorithms are implemented, allowing the cars to online plan their trajectory only based on the track layout and avoid other cars. To allow competitive racing between automatically controlled cars, game theoretical methods are used to derive new control strategies, suited for competitive racing.
- The solar powered vehicle project has as its aim the realization and the investigation of a reduced scale autonomously-driving solar electrical vehicle. It covers different engineering disciplines including automatic control, power electronics, electric drives, mechatronics and telecommunication. The project provides an attractive platform that allows graduate students acquire knowledge in the fields of automatic control and power electronics, and provides a research platform to investigate, implement and validate advanced concepts related to autonomous electric vehicles.
Project titles or images are links to detailed project webpages.