Research on Robot Attitude Control Strategy Based on Model Predictive Control

doi:10.3901/JME.2024.19.088

Abstract

Abstract: For the complex and flexible tasks of robots, robots are required to have the ability of smooth motion, large range positioning and high precision tracking.In order to solve the problem that low real-time performance of offline trajectory planning and low precision numerical solution of inverse kinematics lead to robot jitter, a robot attitude control strategy based on model predictive control is proposed.Firstly, the forward kinematics model of the robot is established and expressed as a state space model.Secondly, considering the two constraints of the ultimate joint position and motion smoothness of the robot, and it is constructed as a compact and convex set.Then, the inverse kinematics of the robot is transformed into the constrained optimal control problem, and the optimal control action is obtained.Finally, the stability of the controller is proved theoretically.Simulation results show that compared with the classical control strategy, the proposed control strategy has better positioning ability in a large range.Apart from this, large range positioning experiments and complex trajectory tracking experiments based on six degree of freedom robot are designed, which confirm the good tracking and positioning ability of the proposed control strategy.

Key words: robot attitude control, model predictive control, system limitations, trajectory tracking

CLC Number:

TG242

YAO Jianjun, ZHANG Yikun, KE Yun, QIAN Chen, WANG Chao. Research on Robot Attitude Control Strategy Based on Model Predictive Control[J]. Journal of Mechanical Engineering, 2024, 60(19): 88-100.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://www.cjmenet.com.cn/EN/10.3901/JME.2024.19.088

http://www.cjmenet.com.cn/EN/Y2024/V60/I19/88

References

[1] MUELLER E，CHEN X L，RIEDEL R. Challenges and requirements for the application of industry 4.0：A special insight with the usage of cyber-physical system[J]. Chinese Journal of Mechanical Engineering，2017，30(5)：1050-1057.
[2] XIAO F，LI G F，JIANG D，et al. An effective and unified method to derive the inverse kinematics formulas of general six-DOF manipulator with simple geometry[J]. Mechanism and Machine Theory，2021，159：104265.
[3] THANH P H，NGUYEN V H，KONIGORSKI U. A time-optimal trajectory generation approach with non-uniform B-splines[J]. International Journal of Control Automation and Systems，2021，19(12)：3947-3955.
[4] PHAM Q C. A general，fast，and robust implementation of the time-optimal path parameterization algorithm[J]. IEEE Transactions on Robotics，2014，30(6)：1533-1540.
[5] YAO J J，QIAN C，ZHANG Y K，et al. Multi-objective redundancy optimization of continuous-point robot milling path in shipbuilding[J]. Cmes-Computer Modeling in Engineering & Sciences，2023，134(2)：1283-1303.
[6] 杨真真，李明富，张黎明，等. 基于模型预测控制的机器人工业机器人曲面跟踪方法研究[J]. 机械工程学报，2022，58(19)：24-33. YANG Zhenzhen，LI Mingfu，ZHANG Liming，et al. Research on surface tracking method of industrial robot based on model predictive control[J]. Journal of Mechanical Engineering，2022，58(19)：24-33.
[7] TIAN X Y，XU Q H，ZHAN Q. An analytical inverse kinematics solution with joint limits avoidance of 7-DOF anthropomorphic manipulators without offset[J]. Journal of the Franklin Institute-Engineering and Applied Mathematics，2021，358(2)：1252-1272.
[8] KUCUK S，BINGUL Z. Inverse kinematics solutions for industrial robot manipulators with offset wrists[J]. Applied Mathematical Modelling，2014，38(7-8)：1983-1999.
[9] ZHANG Q Q，WANG D Q，GAO L F. Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm[J]. International Journal of Advanced Robotic Systems，2021，18(3)：17298814211014413.
[10] LIU F，HUANG H，LI B，et al. A parallel learning particle swarm optimizer for inverse kinematics of robotic manipulator[J]. International Journal of Intelligent Systems，2021，36(10)：6101-6132.
[11] LOPEZ-FRANCO C，HERNANDEZ-BARRAGAN J，ALANIS A Y，et al. A soft computing approach for inverse kinematics of robot manipulators[J]. Engineering Applications of Artificial Intelligence，2018，74：104-120.
[12] 陶永，兰江波，刘海涛，等. 基于滑膜和模糊算法相融合的人机协作机器人轨迹跟踪方法[J]. 机械工程学报，2022，58(18)，181-191. TAO Yong，LAN Jiangbo，LIU Haitao，et al. Cooperative robot trajectory tracking control method based on the fusion of sliding mode control and fuzzy algorithm[J]. Journal of Mechanical Engineering，2022，58(18)：181-191.
[13] 席裕庚，李德伟，林姝. 模型预测控制——现状与挑战[J]. 自动化学报，2013，39(3)：222-236. XI Yugeng，LI Deiwei，LIN Shu. Model predictive control-status and challenges[J]. Acta Automatica Sinica，2014，39(3)：222-236.
[14] WANG X Y，DING Y M. Adaptive real-time predictive compensation control for 6-DOF serial arc welding manipulator[J]. Chinese Journal of Mechanical Engineering，2010，23(3)：361-366.
[15] MAYNE D Q. Model predictive control：recent developments and future promise[J]. Automatica，2014，50(12)：2967-2986.
[16] GRIMM G，MESSINA M J，TUNA S E，et al. Examples when nonlinear model predictive control is nonrobust[J]. Automatica，2004，40(10)：1729-1738.
[17] STELLATO B，BANJAC G，GOULART P，et al. OSQP：an operator splitting solver for quadratic programs[J]. Mathematical Programming Computation，2020，12(4)：637-672.
[18] SCOKAERT P O M，RAWLINGS J B. Stability of model predictive control under perturbations[J]. IFAC Proceedings Volumes，1995，28(14)：19-24.
[19] BEESON P，AMES B. TRAC-IK：an open-source library for improved solving of generic inverse kinematics[C]// 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids)，Seoul：IEEE，2015，928-935.
[20] GONG Z，TAO B，YANG H，et al. An uncalibrated visual servo method based on projective homography[J]. IEEE Transactions on Automation Science and Engineering，2018，15(2)：806-817.
[21] HAO M，SUN Z Q. A universal state-space approach to uncalibrated model-Free visual dervoing[J]. IEEE-ASME Transactions on Mechatronics，2012，17(5)：833-846.