基于稀疏学习的连续型机械臂自适应控制器  

Sparse-learning-based adaptive controller for the space continuum manipulator

作　　者：江达 蔡志勤[1] 刘忠振 彭海军[1,2] 吴志刚[2] JIANG Da;CAI Zhi-qiny;LIU Zhong-zhen;PENG Hai-jun;WU Zhi-gang(Engineering Mechanics,Dalian University of Technology,Dalian 116024,China;State Key Laboratory of Structural Analysis for Industrial Equipment,Dalian University of Technology,Dalian 116024,China)

机构地区：[1]大连理工大学工程力学系,辽宁大连116024 [2]大连理工大学工业装备国家重点实验室,辽宁大连116024

出　　处：《控制与决策》2023年第9期2563-2568,共6页Control and Decision

基　　金：国家自然科学基金项目(91748203);国家自然科学基金优秀青年项目(11922203)。

摘　　要：探讨空间连续型机械臂执行在轨操作任务过程中的自适应轨迹跟踪控制器设计问题.首先,对于具有显著非线性特征的连续型机械臂动力学模型,考虑运动过程中存在的建模误差和外部干扰因素,设计变结构动力学控制器;然后,基于深度强化学习(deep reinforcement learning,DRL)对变结构控制器参数进行在线调整,实时优化控制器性能;最后,提出一种针对强化学习网络稀疏训练方法,训练过程中采用具有随机稀疏拓扑结构的稀疏连接层代替神经网络的全连接层,并以一定概率对连接薄弱的网络进行迭代剪枝,使得DRL的策略网络由初始稀疏拓扑结构演化为无标度网络,在不降低训练精度的基础上压缩网络规模.仿真结果表明,所提出基于强化学习的自适应控制器能够有效地进行连续型机械臂的跟踪控制,通过稀疏学习的方法,控制器在保证控制精度的同时,双隐层网络节点参数量下降99%,大幅降低了计算成本.In this paper,an adaptive tracking controller is designed for the space continuum manipulator in the on-orbit manipulation task.Firstly,considering the modeling error and external disturbance factors,a variable structure controller is designed for the continuum manipulator’s dynamic model with typical nonlinear characters.Then,a deep reinforcement learning(DRL)algorithm is adapted to adjust the controller’s parameters and to optimize the control performance online.In addition,a sparse training method for the DRL is proposed.In the training process,the origin network’s fully-connected layer is replaced with the sparsely-connected layer in a random sparse topology.The weak connections are iteratively pruned in a certain probability,which evolves the DRL’s policy network from an initial sparse topology into a scalefree network.Therefore,the network’s dimensions are significantly compressed without reducing the training accuracy.Simulation results show that the proposed DRL-based adaptive controller can effectively carry out the tracking control of the continuum manipulator.Through the sparse training method,the quantity of the two hidden layers’parameters is reduced by 99%on the premise of maintenance of the control accuracy.The calculation is effectively reduced.

关 键 词：空间机械臂 连续型机械臂 动力学控制 强化学习 稀疏学习 自适应控制 

分 类 号：TP273.2[自动化与计算机技术—检测技术与自动化装置]