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Solving the inverse kinematics problem for sequential robot manipulators based on fuzzy numerical methods

Nowadays the introduction of robotic systems is one of the most common forms of the technological operations automation in various spheres of human activity. Among the robotic systems a special place is occupied by sequential multi-link robotic manipulators (SRM). SRM have become widespread due to relatively small dimensions and high maneuverability, which makes their use indispensable to solve various tasks. In practice, the effectiveness of the functioning of the SRM can be influenced by various types of external environment fuzzy factors. Among the external factors there is a group affecting the ability to determine the exact target position. Such factors often affect technical vision systems. This problem is especially relevant for special purpose mobile robots operating in aggressive environmental conditions. A situation similar to the described one also occurs when a medical robot manipulator is used for minimally invasive surgery, when the role of the control and monitoring system is assumed by an operator. In this regard, the organization of effective control taking into account influence of the external fuzzy factors, that prevent the correct recognition of the target position of the SRM instrument, is an urgent problem. The authors consider the solution of the inverse kinematics problem for SRM based on the use of fuzzy numerical methods, taking into account the possible occurrence of singular configurations in the process of solving. Read more...

Energy management of IIoT devices in electric power systems based on neuro-fuzzy models

The article presents the results of a study on optimizing the energy consumption of Industrial Internet of Things devices providing telemetry and included in the control loops of electric power systems. The relevance of the study lies in the emerging need to increase the battery life of mobile telemetry devices of industrial and technological systems, which helps to reduce the costs of their maintenance and support in working condition. An algorithm for controlling the parameters of the processor core of mobile devices with ARM architecture processors is proposed, which ensures higher energy efficiency of such devices used in the Industrial Internet of Things of the EPS. The novelty of the obtained results is the proposed algorithm for software control of the configuration of the ARM processor core parameters of mobile telemetry devices of the EPS, ensuring its higher energy efficiency, which is achieved by using data mining methods in its structure – a bidirectional neural network of long short-term memory and a fuzzy logical inference system. The choice of this network architecture is due to its ability to identify relationships in temporal sequences of device parameters by viewing the sequence in two directions at once – from the beginning to the end and vice versa. The network operates in the energy consumption plan classification mode, the results of which are then fed to the input of the fuzzy logic inference system to predict the optimal parameters of the ARM processor, which together forms a neuro-fuzzy model for managing the energy consumption of IIoT devices. Using machine learning libraries in the Python language in the Google Colab environment, model experiments were conducted, as a result of which the classification accuracy using a bidirectional neural network exceeded 0.8, and the standard deviation was 0.058 when predicting the parameters of the ARM processor based on the fuzzy logic inference system. Read more...