| 引用本文: | 廖列法,杨翌虢.机械臂的自适应径向基函数神经网络双二次泛函最优控制[J].控制理论与应用,2020,37(1):47~58.[点击复制] |
| LIAO Lie-fa,YANG Yi-guo.Adaptive radial basis function neural network bi-quadratic functional optimal control for manipulators[J].Control Theory and Technology,2020,37(1):47~58.[点击复制] |
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| 机械臂的自适应径向基函数神经网络双二次泛函最优控制 |
| Adaptive radial basis function neural network bi-quadratic functional optimal control for manipulators |
| 摘要点击 2910 全文点击 1117 投稿时间：2019-01-02 修订日期：2019-04-01 |
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| DOI编号 10.7641/CTA.2019.90004 |
| 2020,37(1):47-58 |
| 中文关键词 自适应控制 径向基函数网络 最优泛函 多关节机械臂 |
| 英文关键词 adaptive control radial basis function networks optimal function multi-joint manipulator |
| 基金项目 国家自然科学基金 |
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| 中文摘要 |
| 针对非线性机械臂系统中难以权衡控制能量与控制误差比重的最优控制问题,本文提出一种基于自适应RBF神经网络二阶段叠加优化的双二次泛函最优求解模型,实现在非线性机械臂控制系统中用不大的控制能量来保持较小的控制误差的综合最优控制.在本文所提模型中,首先设计一种线性误差函数, 作用于非线性控制方程,并采用自适应RBF网络逼近非线性控制方程中存在的不确定项, 构成闭环反馈系统,实现对非线性系统的最优控制; 其次, 将待求参数复合成双二次泛函的解域,并设计一种新型的类递归神经网络求解该带约束条件的双二次型模型,实现模型求解的快速收敛并得其解. 通过理论分析及数值仿真实例验证了所提模型能有效提高非线性系统的控制精度、稳定性、鲁棒性及自适应性, 从而实现非线性系统的综合最优控制. |
| 英文摘要 |
| In this paper, a double quadratic optimal functional solution model based on neural network two-stage superposition optimization is proposed to solve the optimal control problem in non-linear manipulator systems where it is difficult to balance the control energy and the proportion of control errors. In the non-linear manipulator control system, the comprehensive optimal control is realized, which uses little control energy to keep the smaller control error.In the model proposed in this paper,firstly, a linear error function is designed to act on the non-linear governing equation, and the uncertainties in the non-linear governing equation are approximated by RBF network adaptively to form a closed-loop feedback system to realize the optimal control of the non-linear system.Secondly, the parameters to be solved are combined into the solution domain of the bi-quadratic functional, and a new type of recursive neural network is designed to solve the bi-quadratic model with constraints, so as to realize the fast convergence and obtain the solution of the model. The results of theoretical analysis and numerical simulation show that the proposed model can effectively improve the control accuracy, stability, robustness and self-adaptability of the non-linear system, thus realizing the integrated optimal control of the non-linear system. |
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