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• SIMULATION OF A HYBRID CONTROLLER FOR CONTROLLING PLASMA WELDING PARAMETERS

  Al-Shamki Amir Abdulkadim Ouda, V.V. Shadrina, V.G. Galalu

  2022-08-09

  Abstract ▼

  One of the most common technological operations is welding of individual parts and blocks.
  Welding is widely used in shipbuilding, aviation, defense and chemical industries, in the construction
  of oil and gas pipelines. At the same time, very strict requirements are imposed on the quality of the
  weld in terms of strength, absence of voids and cavities, operability at high pressures (up to 100 kGf /
  cm2) and in a wide temperature range (± 50 ° C). Plasma (argon) welding meets these requirements
  most fully. A brief analytical review on the research topic was carried out. It is shown that a promising
  direction for the development of plasma welding control systems is the use of hybrid regulators
  created on the basis of classical automatic control methods and fuzzy control, formalizing the average
  knowledge of experts. The fuzzy component (expert knowledge) should be available for quick and
  easy input into the controller. A block diagram and a model of a single channel of a hybrid controller
  was developed in the Matlab Simulink environment. The current control channel was modeled using
  a fuzzy controller from the Fuzzy Logic library, using the Mamdani fuzzy output algorithm. 19 variants
  of linguistic and fuzzy variables were set, the surface of the variable membership function was
  obtained. It should be noted that it is possible to quickly enter linguistic assessments of experts into
  the memory of the hybrid controller. The behavior of hybrid controller models and standard PI and
  PID controllers under a single step action was analyzed. The hybrid regulator provides significantly
  better quality indicators (2.5-3 times) than standard regulators. The hybrid controller enters the
  steady-state mode after 6s, the PID controller – after 13s, the PI controller - after 15s, and the standard
  regulators have an overshoot (first emission) of up to 50%. Thus, the real possibility of constructing
  a fuzzy hybrid controller with specified characteristics is shown. It is possible to implement a
  hybrid controller in the form of an FPGA.

• METHOD OF AUTOMATIC OPTIMIZATION OF THE FUZZY RULE BASE OF AN INTELLIGENT CONTROLLER BASED ON SUBTRACTIVE CLUSTERING

  А.S. Ignatyeva , V.V. Shadrina , D.S. Ignatyev , А.V. Maksimov

  181-197

  2025-07-24

  Abstract ▼

  The aim of the work is to develop a method for optimizing the fuzzy rule base of an intelligent controller for controlling a technical object using subtractive clustering. The article provides an overview and a brief analysis of the state of affairs in the field of optimizing the operation of intelligent control systems. To achieve the goal of the study, a hybrid model has been developed in which the technical object is controlled using a classical PI controller and a fuzzy PI controller with a generated structure of a Cygeno-type fuzzy inference system and a developed model of an adaptive neuro-fuzzy inference system. This configuration of the model allows you to form a fuzzy rule base that does not depend on the expert's knowledge in the subject area. The article proposes a new method for optimizing the fuzzy controller rule base based on clustering methods, in particular subtractive clustering, which allows you to reduce the number of fuzzy logical inference rules and increase the performance of the technical object control system. First, a hybrid model synthesized on the basis of the values of the fuzzy and classical controllers before applying subtractive clustering was simulated. The application of subtractive clustering according to the method developed in the study for the values of the classical and fuzzy controllers allowed us to achieve their quantitative reduction by 1.7 and 5.25 times, respectively. Then, the hybrid model synthesized on the basis of the values of the fuzzy and classical controllers after applying subtractive clustering was simulated. The results obtained in the process of simulation showed high efficiency of the proposed method for optimizing the fuzzy controller rule base. Due to the application of subtractive clustering in the hybrid model for the intelligent controller, it was possible to significantly reduce the number of membership functions required to describe the input linguistic variables (from five to four) and reduce the number of fuzzy logical inference rules (from twenty-five to sixteen). The analysis of the resulting graphs of transient processes obtained for the hybrid models before and after applying subtractive clustering showed that the main indicators of the quality of the control process remain unchanged with a significant reduction in the calculations performed.