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Optimal robust motion controller design using multiobjective genetic algorithm.

Andrej Sarjaš1, Rajko Svečko1, Amor Chowdhury2

  • 1Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia.

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Summary
This summary is machine-generated.

This study introduces a robust motion controller design using a multiobjective genetic algorithm. The approach enhances system stability and performance by simultaneously optimizing robustness and time-response criteria.

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Area of Science:

  • Control Systems Engineering
  • Robotics
  • Optimization Algorithms

Background:

  • Robust motion controller design is crucial for systems operating under uncertainties.
  • Existing methods often struggle to balance robustness and performance objectives.
  • The Reusable Intelligent Controller (RIC) framework offers a structured approach to controller synthesis.

Purpose of the Study:

  • To develop a novel multiobjective optimization strategy for robust motion controller design.
  • To integrate disturbance observer-based control within the RIC framework.
  • To simultaneously assess and optimize robustness, stability, and time-performance metrics.

Main Methods:

  • Utilized a multiobjective genetic algorithm (MOGA) for controller optimization.
  • Employed a disturbance observer within a Reusable Intelligent Controller (RIC) structure with internal and external feedback loops.
  • Implemented regional pole placement for controller structure simplification and arbitrary selection.
  • Defined objective functions based on polynomial conditions for robustness and performance deviation.

Main Results:

  • Successfully designed controllers optimizing multiple, often conflicting, objectives simultaneously.
  • Demonstrated the effectiveness of the MOGA-differential evolution approach in achieving robust stability and desired time-performance.
  • The regional pole placement method facilitated simplified and adaptable controller structures.

Conclusions:

  • The proposed multiobjective optimization framework effectively addresses robust motion controller design challenges.
  • The integration of disturbance observers and RIC offers a systematic approach to enhance system resilience.
  • This method provides a powerful tool for simultaneously improving stability and performance in complex control systems.