FocusResearch in the Robotics and Industrial Automation group spans a variety of topics related to applications of control engineering. The common denominator of the research addressed in this group is in fact the focus on those applications where the added value of control techniques is tangible. The research approach is then to derive beneficial feedbacks on new control methodologies, whenever possible. Application domains include mechatronics (with special emphasis on motion control), robotics (both industrial and outdoor robotics), process modeling, simulation and control (with use of object- oriented modeling and simulation techniques for power generation plants), automation of manufacturing systems and industrial plants, with applications to batch production and packaging systems, power transmission grids, and energy management systems.
Most relevant research achievements
Control of electrical motors with low resolution position sensing
The goal of this research was characterization of dynamic behaviour and control methodologies for electrical (brushless and asynchronous) motors for applications in home appliances, characterized by use of low-cost components. A first scientific result is a methodology to characterize the estimation error made by non model based velocity estimators. A second result concerned the study of the effects of the velocity estimation made with low resolution sensors in the performance of a current and velocity control of an asynchronous motor.
New methodologies for the control of robotic manipulators
One important result of this line of research has been the revision of robust control methods for robotic manipulators, based on a reformulation of the equations of the dynamic model of the manipulator, where the uncertainty of the model is structured in terms which depend from the control action and terms which are independent. Other significant results concern the development of a general user-oriented framework for holonomic redundancy resolution in robotic manipulators using task augmentation and of a method for controller design in human-robot interaction.
Mechatronic analysis of complex systems
Making reference to a complex transmission chain for an axis of a machine tool, a methodology has been derived, based on modal analysis, to quantify the sensitivity of each vibration mode to the elasticity of each component of the structure. Detailed simulation models of the transmission have been developed as well, which allow to reproduce with high fidelity the experimental results. As another example of mechatronic analysis of a complex system, a simulation model for the dynamic behaviour of a motorcycle based on the object-oriented modelling paradigm developed in Modelica, within the Dymola environment, has been obtained.
A novel approach is proposed to the design of computing system components. Instead of directly devising them in algorithmic form and then possibly try to adapt their parameters, components are entirely conceived as controllers. The approach - and the underlying modelling framework - is very general, yielding significant simplicity and performance advantages. A notable application example referring to process scheduling has been described.
O.O. modelling framework to support control studies in power plants and energy conversion systems
Power plants and energy conversions systems are operated in an increasingly dynamic way, making advanced, model-based control increasingly strategic. A framework for flexible and efficient dynamic modelling of such systems for control purposes has been developed using the object-oriented modelling paradigm. It has been successfully applied to a variety of systems, including combined-cycle plants, coal-fired plants, nuclear plants, organic Rankine cycle plants and nuclear fusion plants.
Predictive control in power transmission systems
The goal of this research was to design an innovative control system for power lines able to account for thermal loads and at the same increase the overall transportation capacity. Novel approaches have been proposed to the design such control systems, based on both deterministic and statistic, linear and non-linear models. Basic results show great improvement with respect to literature results.