Abstract

This article aims to improve the accuracy of each joint in a manipulator and to ensure the high-speed and real-time requirements. A method called the variational method genetic algorithm radial basis function, which is based on a combination feedback controller, is proposed to solve the optimal control problem. It is proposed a combined feedback with a linear part and a nonlinear part. We reconstruct the manipulator’s kinematics and dynamics models with a feedback control. In this model, the optimal trajectory, which was solved by the variation method, is regarded as the desired output. The other one is also established an improved genetic algorithm radial basis function neural network model. The optimal trajectory is rapidly solved by using the desired output and the improved genetic algorithm radial basis function neural network. This method can greatly improve the speed of the calculation and guarantee real-time performance while simultaneously ensuring accuracy.

Highlights

  • Mechanization, automation, and intelligence are ultimate goals in the development of industrial production

  • The linkage manipulator can be used in harsh environments that are unsuitable for manual operation, such as unknown environment exploration, aerial work, and risk investigation

  • The accuracy of the genetic algorithm (GA)-radial basis function (RBF) algorithm is improved by 18.62% compared with that of the RBF algorithm

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Summary

Introduction

Mechanization, automation, and intelligence are ultimate goals in the development of industrial production. The linkage manipulator can be used in harsh environments that are unsuitable for manual operation, such as unknown environment exploration, aerial work, and risk investigation. The optimal control problem of the link mechanism has already exceeded the requirements of mechanization and automation. Direct or indirect methods can be used to solve the optimal trajectory.

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