NONLINEAR MODEL PREDICTIVE CONTROL FOR TRAJECTORY TRACKING OF A CLASS OF CONTINUUM ROBOTS

被引：0

作者：

Amouri, Ammar ^{[1
]}

Merabti, Halim ^{[2
]}

Cherfia, Abdelhakim ^{[1
]}

Laib Dit Leksir, Yazid ^{[3
]}

机构：

[1] Department of Mechanical Engineering, Laboratory of Mechanics, Frères Mentouri Constantine 1 University, Algeria

[2] Research Center in Industrial Technologies, CRTI, P. O. Box 64, Algiers, Cheraga,16014, Algeria

[3] Department of Mechanical Engineering, University of L’Arbi Ben M’hidi, Oum el Bouaghi, Algeria

来源：

UPB Scientific Bulletin, Series D: Mechanical Engineering | 2022年 / 84卷 / 03期

关键词：

Cable-driven - Cable-driven continuum robot - Continuum robot - Nonlinear model predictive control - Obstacles avoidance - Particle swarm - Particle swarm optimization - Swarm optimization - Trajectory-tracking;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This paper presents a Nonlinear Model Predictive Control (NMPC) scheme for solving the trajectory tracking and obstacle avoidance problems for a class of continuum robots with three actuators per bending section, namely Cable-Driven Continuum Robot (CDCR). Since, NMPC schemes were strongly limited by the computational burden associated with the optimization algorithms, the Knowledge-based Particle Swarm Optimization (KPSO) algorithm is used to solve the existing optimization problem in the NMPC, due to its simplicity and fast convergence. The proposed NMPC-KPSO has been applied to the kinematic models developed on the basis of kinematic equations of inextensible bending section and by using the Constant Curvature Kinematic Approach (CCKA). The proposed control scheme was evaluated via simulation examples with complex trajectories in a free and confined environment. The obtained results showed satisfactory performances in terms of tracking accuracy, computation time and obstacle avoidance. Considering the quality of solution and computation time, the proposed NMPC-KPSO can be considered as an alternative solution for real-time applications of this class of continuum robots. © 2022, Politechnica University of Bucharest. All rights reserved.

引用

页码：19 / 32