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For the purpose of controlling industrial robots attached to various types of technological equipment the configuration structures and unification principles were investigated and the unified controlling systems of positional and cyclic type developed.

The paper aims to substantiate optimization directions of resettable fuses parameters to protect solar arrays from overcurrent.

The method of modeling the electrophysical characteristics of resettable fuses is used.

Resettable fuses currently produced are of little use for protecting photovoltaic cells (PVC) in solar arrays from overcurrent. The volume fraction of the conductive filler should be about 0.15, near the percolation threshold. Thus, reducing the resistance by increasing the amount of filler is not possible. The matrix of the composite should consist of a material with a significant proportion of the crystalline phase to ensure a sharp increase in the composite's volume near the melting point. Using a polymer with a lower melting point instead of polyethylene can reduce the power required to switch a resettable fuses.

The possibility of using resettable fuses based on polymer composite materials with a positive temperature coefficient of resistance to protect photovoltaic solar cells from current overloads is considered. Modeling of the electrophysical characteristics of modern industrial fuses of this type based on polyethylene-nanocarbon composites has been carried out. The limits of their applicability for the protection of photovoltaic solar cells are analyzed. On the basis of the obtained results, the optimization directions of the resettable fuses parameters for use in the protection circuits of PVC of solar array are determined.

Aiming at the problem that the transmission mechanism of the assembly error of the multi-stage rotor with saddle surface type is not clear, the purpose of this paper is to propose a deep belief network to realize the prediction of the coaxiality and perpendicularity of the multi-stage rotor.

First, the surface type of the aero-engine rotor is classified. The rotor surface profile sampling data is converted into image structure data, and a rotor surface type classifier based on convolutional neural network is established. Then, for the saddle surface rotor, a prediction model of coaxiality and perpendicularity based on deep belief network is established. To verify the effectiveness of the coaxiality and perpendicularity prediction method proposed in this paper, a multi-stage rotor coaxiality and perpendicularity assembly measurement experiment is carried out.

The results of this paper show that the accuracy rate of face type classification using convolutional neural network is 99%, which meets the requirements of subsequent assembly process. For the 80 sets of test samples, the average errors of the coaxiality and perpendicularity of the deep belief network prediction method are 0.1 and 1.6 µm, respectively.

Therefore, the method proposed in this paper can be used not only for rotor surface classification but also to guide the assembly of aero-engine multi-stage rotors.