CAJ | 학술논문

为了解决机械手系统模型存在参数变化、强耦合、高度非线性等不确定性因素，提出基于RBF神经网络机械手自适应控制方法。该方法利用RBF神经网络的自适应、容错、并行处理及非线性映射能力，从而实现了无需机械手精确模型信息的控制。通过Matlab/Simulink环境下的仿真实验表明，该方法可实现对SCARA机械手的位置跟踪控制，通过控制算法适时地修正网络参数，实现对非线性系统任意轨迹的轨迹跟踪控制，具有良好的控制品质。
위료해결궤계수계통모형존재삼수변화、강우합、고도비선성등불학정성인소，제출기우RBF신경망락궤계수자괄응공제방법。해방법이용RBF신경망락적자괄응、용착、병행처리급비선성영사능력，종이실현료무수궤계수정학모형신식적공제。통과Matlab/Simulink배경하적방진실험표명，해방법가실현대SCARA궤계수적위치근종공제，통과공제산법괄시지수정망락삼수，실현대비선성계통임의궤적적궤적근종공제，구유량호적공제품질。
A self-adaptive control method based on RBF neural network for manipulator was put forward in order to solve parame-ters time-variation,strong coupling and high nonlinear of manipulator system model as uncertain factors. This method was made use of self-adaptability,fault-tolerant,parallel processing and nonlinear mapping ability of RBF neural network,so as to realize the control without accurate manipulator model information. According to the simulation experiment under Matlab/Simulink environment,it shows that this method can realize the position tracking control of SCARA manipulator. By timely correction of network parameters through the control algorithm,the arbitrary trajectory tracking control of the nonlinear system is realized with good control quality.