Sliding Mode Impedance Controlled Smart Fingered Microgripper for Automated Grasp and Release Tasks at the Microscale

Part 5: Gripping and Handling Solutions in AssemblyInternational audienceThe grasp and release of objects have been widely studied in robotics. At the microscale, this problem becomes more difficult due to the microscale specificities which are notably manifested by the high dynamics of microsystems, their small inertia, their fragility, the predominance of surface forces and the high complexity of integrating adapted sensors.In this paper, the problem of the grasp/release task is considered at the microscale. A new nonlinear controller design based on Sliding Mode Impedance Control (SMIC) is proposed to automate the grasp/release of the micropart. The proposed controller controls dexterously the dynamic interaction between the microgripper and the micropart and forces the system to follow the desired dynamic relation (impedance). To perform the grasp/release task, a new smart-fingered-microgripper is designed. The microgripper is composed of an active finger with integrated force sensor and a passive finger.The grasp/release of a micropart of size 50 µm $$\times$$ 350 µm $$\times$$ 2 mm is tested in experiments using the control scheme and the developed microgripper. The microgripper design and the control scheme tested show their effectiveness for the grasp/release at the microscale

Fuzzy modeling of a piezoelectric actuator

In this research, a piezoelectric actuator was modeled using fuzzy subtractive clustering and neuro-fuzzy networks. In the literature, the use of various modeling techniques (excluding techniques used in this article) and different arrangements of inputs in black box modeling of piezoelectric actuators for the purpose of displacement prediction has been reported. Nowadays, universal approximators are available with proven ability in system modeling; hence, the modeling technique is no longer such a critical issue. Appropriate selection of the inputs to the model is, however, still an unsolved problem, with an absence of comparative studies. While the extremum values of input voltage and/or displacement in each cycle of operation have been used in black box modeling inspired by classical phenomenological methods, some researchers have ignored them. This article focuses on addressing this matter. Despite the fact that classical artificial neural networks, the most popular black box modeling tools, provide no visibility of the internal operation, neuro-fuzzy networks can be converted to fuzzy models. Fuzzy models comprise of fuzzy rules which are formed by a number of fuzzy or linguistic values, and this lets the researcher understand the role of each input in the model in comparison with other inputs, particularly, if fuzzy values (sets) have been selected through subtractive clustering. This unique advantage was employed in this research together with consideration of a few critical but subtle points in model verification which are usually overlooked in black box modeling of piezoelectric actuators.Morteza Mohammadzaheri, Steven Grainger and Mohsen Bazghale