Adaptive dynamic programming-based controller with admittance adaptation for robot–environment interaction

Hong Zhan,Zhaopeng Chen,Min Wang,Dianye Huang,Chenguang Yang

doi:10.1177/1729881420924610

Abstract

The problem of optimal tracking control for robot–environment interaction is studied in this article. The environment is regarded as a linear system and an admittance control with iterative linear quadratic regulator method is obtained to guarantee the compliant behaviour. Meanwhile, an adaptive dynamic programming-based controller is proposed. Under adaptive dynamic programming frame, the critic network is performed with radial basis function neural network to approximate the optimal cost, and the neural network weight updating law is incorporated with an additional stabilizing term to eliminate the requirement for the initial admissible control. The stability of the system is proved by Lyapunov theorem. The simulation results demonstrate the effectiveness of the proposed control scheme.

Highlights

Robot applications are becoming more and more widespread, such as rehabilitation therapy, assembly automation and surgery.[1,2,3,4] They can either work independently to accomplish tasks or cooperate with their human partners for certain tasks
The optimal control of robots interacting between unknown environment was studied in this article
The unknown environment was regarded as a linear system and a compliant behaviour was guaranteed by the admittance adaptation control

Summary

Introduction

Robot applications are becoming more and more widespread, such as rehabilitation therapy, assembly automation and surgery.[1,2,3,4] They can either work independently to accomplish tasks or cooperate with their human partners for certain tasks. For robot system (7), the optimal control Ã should guarantee system stability and can make the cost function finite, that is, the control law should be in the admissible control set which defined as .

Results

Conclusion