安全最优跟踪控制算法与机械手仿真
Safety Optimal Tracking Control Algorithm and Robot Arm Simulation
陈文杰 1崔小红 1王斌锐1
作者信息
- 1. 中国计量大学机电工程学院,浙江杭州 310018
- 折叠
摘要
为确保安全关键型系统在安全区域内运行并保持最佳性能,提出一种基于强化学习的安全最优控制跟踪算法.通过在评价函数中加入控制屏障函数,兼顾系统的安全性和最优性;通过在控制屏障函数中加入阻尼系数,指定其对评价函数的相对支配能力.引入强化学习的思想,实现系统动力学未知系统的安全最优跟踪控制,证明跟踪控制系统能在安全区域内能实现最优性和稳定性.利用二连杆平面机械手仿真验证所提算法的有效性,仿真实验结果表明:机械手末端位置被控制在安全范围内的同时,也实现稳定下的最优性能表现;仿真结果证明所提算法能实现系统状态安全下的最优的跟踪控制效果.
Abstract
A safety optimal tracking control algorithm based on reinforcement learning is proposed to ensure that safety-critical systems operate within a safe area and maintain optimal performance.Both the safety and optimality of the system are considered by adding a control barrier function in the evaluation function.The relative dominance of the control barrier function over the evaluation function is specified by adding damping coefficients to the control barrier function.The idea of reinforcement learning is introduced to realize the safety optimal tracking control of the system with unknown system dynamics.It has been proven that the tracking control system can achieve optimality and stability within a safe region.The effectiveness of the proposed algorithm is verified through simulation of a two-link planar manipulator.The experimental results show that the end position of the manipulator is controlled within a safe range,while also achieving optimal performance during stabilization.The simulated results demonstrate that the proposed algorithm can achieve safe and optimal tracking control effects.
关键词
强化学习/安全最优跟踪控制/控制屏障函数/阻尼系数Key words
reinforcement learning/safety optimal tracking control/control barrier function/damping co-efficient引用本文复制引用
基金项目
国家自然科学基金项目(61903351)
浙江省自然科学基金项目(LY22F030009)
浙江省重点研发计划项目(2023C03186)
浙江省基本科研业务费项目(2022YW20)
浙江省基本科研业务费项目(2022YW84)
浙江省普通本科高校"十四五"教学改革项目(jg20220265)
出版年
2024
NETL
NSTL
万方数据