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A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot

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  • 1School of Information Engineering, Yangzhou University, Yangzhou 225127, China; Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225127, China.

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A novel direct discrete technique offers a more concise and efficient method for solving discrete time-variant nonlinear optimization (DTVNO) problems. This new approach avoids indirect continuous-time derivations, demonstrating superior performance in robot manipulator control.

Keywords:
ConvergenceDirect discrete techniqueDiscrete time-variant nonlinear optimization (DTVNO)Discrete-time recurrent neurodynamics (DTRN)Robot manipulators

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Area of Science:

  • Robotics and Control Systems
  • Optimization Theory
  • Computational Neuroscience

Background:

  • Discrete time-variant nonlinear optimization (DTVNO) problems are prevalent in science and engineering.
  • Existing discrete-time recurrent neurodynamics (DTRN) methods for DTVNO often use an indirect approach, requiring conversion between discrete-time and continuous-time derivations.
  • This indirect methodology can lead to inefficiencies in solving DTVNO problems.

Purpose of the Study:

  • To develop a novel DTRN method for DTVNO problems using a direct discrete technique.
  • To enhance conciseness and efficiency in solving DTVNO problems compared to traditional methods.
  • To apply and validate the proposed method in the context of discrete-time robot manipulator tracing control.

Main Methods:

  • Abstracted and summarized the mathematical definition of DTVNO problems, focusing on applications in robot manipulator tracing control.
  • Defined a corresponding error function for the DTVNO problem.
  • Developed a novel DTRN method by directly applying a second-order Taylor expansion, bypassing continuous-time derivations.

Main Results:

  • The proposed DTRN method was theoretically analyzed, and its convergence was demonstrated.
  • Numerical experiments confirmed the effectiveness and superiority of the new DTRN method.
  • Application experiments with robot manipulators further validated the superior performance of the developed DTRN method.

Conclusions:

  • The novel DTRN method based on direct discrete technique provides a more concise and efficient solution for DTVNO problems.
  • The method eliminates the need for intermediate continuous-time derivations, simplifying the solution process.
  • The proposed approach shows significant promise for applications like robot manipulator control.