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When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
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Application of optimization algorithms to adaptive motion control for repetitive process.

Rafal Szczepanski1, Tomasz Tarczewski1, Lech M Grzesiak2

  • 1Department of Automatics and Measurement Systems, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Torun, Poland.

ISA Transactions
|January 16, 2021
PubMed
Summary

This study introduces an Adaptive Procedure for Optimization Algorithms (APOA) for adaptive motion control. The novel approach ensures consistent system response in electric drives despite parameter changes.

Keywords:
Adaptive controllerOptimization algorithmPMSMRepetitive processState feedback controller

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Area of Science:

  • Control Engineering
  • Robotics
  • Electrical Engineering

Background:

  • Adaptive motion control is crucial for optimal system response.
  • Existing optimization algorithms struggle with continuous, non-constant search spaces.
  • Controller coefficient adjustment is key for system optimization.

Purpose of the Study:

  • To introduce a novel Adaptive Procedure for Optimization Algorithms (APOA) for adaptive motion control.
  • To present a desired-response adaptive system (DRAS) for robust performance in repetitive processes.
  • To demonstrate the effectiveness of APOA and DRAS in modern electric drives.

Main Methods:

  • Development of the universally applicable Adaptive Procedure for Optimization Algorithms (APOA).
  • Integration of APOA with a novel desired-response adaptive system (DRAS).
  • Implementation and testing on a permanent magnet synchronous motor (PMSM) drive with adaptive speed control.

Main Results:

  • The APOA enables most optimization algorithms to function in continuous, non-constant search spaces.
  • The DRAS approach ensures unchanged system response despite plant parameter variations or disturbances.
  • The adaptive speed controller for PMSM demonstrated robust performance against moment of inertia variations.

Conclusions:

  • The proposed APOA offers a universal solution for applying optimization algorithms to adaptive control.
  • The DRAS approach provides model-free, disturbance-immune adaptation for enhanced system reliability.
  • The implemented adaptive speed controller effectively maintains desired performance in PMSM drives under varying conditions.