A patterning strategy for large area arrays of high density nanostructures
A production-worthy method to implement high density arrays of sub-micron nanostructures at a wafer scale is proposed. It is based on a co-optimization of the layout design, the e-beam lithography exposure and the intermediate SiO2 hard mask used for the final silicon etch process. The great complexity of the e-beam lithography exposure required at this scale to directly obtain the desired geometries has been reduced by introducing an intermediate hard mask to get independent control of some of the pattern parameters through the fabrication process. Rather than using e-beam lithography to create a resist mask for the etch process, which results in unaffordable exposure times for complex geometries (large patterns of curved structures), a simplified e-beamlithography step has been developed to attain a primary resist mask with needed shape and periodicity, but smaller size.
Learning in Machines
Control of high-tech mechatronic systems traditionally involves feedback and feedforward control, and essentially only uses a few recent measurements. Here, we aim to explore what can be learned from all available sensor data. A general learning framework is developed that exploits the abundance of data of previously executed tasks. Both fundamental insight and experimental results show that such iterative learning control approaches enable substantial performance improvement compared to traditional control. Interestingly, traditional model-based control theory turns out to have an essential role for fast and safe learning from measured data.
A Fast Autotuning Method for Velocity Control of Mechatronic Systems
In this paper a fast automatic tuning methodology for velocity controllers of mechatronic systems is proposed. In order to be applicable in general, the method takes into account the position, velocity and torque constraints of the motion control system and it requires a minimum intervention of the operator. Further, it can be implemented also with small computational capabilities which makes it suitable for industrial drives. Simulation results show the effectiveness of the technique.
Simplified input-output inversion control of a double pendulum overhead crane for residual oscillations reduction
Marco Giacomelli, Fabrizio Padula, Luca Simoni, Antonio Visioli, “Simplified input-output inversion control of a double pendulum overhead crane for residual oscillations reduction” MECHATRONICS, DOI: 10.1016/j.mechatronics.2018.10.002
In this paper we present the application of an input-output inversion technique for the open-loop control of an overhead crane modelled as a double pendulum. The method is mathematically derived, obtaining a parametric trajectory that ensures reduced residual oscillations. Then, it is shown that the postactuation can be neglected so that the method can be implemented with standard industrial drives. The robustness of the method is evaluated by means of simulations, and the performance of the method is experimentally compared with the well-known input shaping technique. The advantages of using a double pendulum model instead of a simple pendulum one are also shown.
On the inclusion of temperature in the friction model of industrial robots
L. Simoni, M. Beschi, G. Legnani, A. Visioli, “On the inclusion of temperature in the friction model of industrial robots”, 20th IFAC World Congress, Tolouse (F), 2017, DOI: 10.1016/j.ifacol.2017.08.933
This paper deals with a modelling technique that takes into account the effects of the temperature in the joint friction of industrial robot manipulators. In particular, it is shown that a general friction model can be suitably modified by explicitly considering the temperature as a parameter. This allows to estimate the friction term accurately in different operating conditions without the direct measurement of the joint internal temperature, which makes the overall technique suitable to apply in practical cases. Experimental results show the effectiveness of the methodology.
Modelling the temperature in joint friction of industrial manipulators
In this paper, a new model for joint dynamic friction of industrial robot manipulators is presented. In particular, the effects of the temperature in the joints are considered. A polynomial-based model is proposed and the parameter estimation is performed without the need of a joint temperature sensor. The use of an observer is then proposed to compensate for the uncertainty in the initial estimation of the temperature value. A large experimental campaign show that the model, in spite of the simplifying assumptions made, is effective in estimating the joint temperature and therefore the friction torque during the robot operations, even for values of velocities that have not been previously employed.
On the use of a temperature based friction model for a virtual force sensor in industrial robot manipulators
L. Simoni, E. Villagrossi, M. Beschi, A. Marini, N. Pedrocchi, L. Molinari Tosatti, G. Legnani, A. Visioli, “On the use of a temperature based friction model for a virtual force sensor in industrial robot manipulators”, IEEE International Conference on Emerging Technologies and Factory Automation, Limassol (CY), 2017. DOI: 10.1109/ETFA.2017.8247655
In this paper we propose the use of a dynamic model in which the effects of temperature on friction are considered to develop a virtual force sensor for industrial robot manipulators. The estimation of the inertial parameters and of the friction model are explained. The effectiveness of the virtual force sensor has been proven in a polishing task. In fact, the interaction forces between the robot and the environment has been measured both with the virtual force sensor and a common load cell. Moreover, the advantages provided by considering the temperature dependency are highlighted.