CAJ | 학술논문

对于Buck变换器系统，考虑到实际应用中负载变动引起系统参数的不确定性，且不确定性上界无法测量的情况，本文拟采用 RBF神经网络对不确定性上界进行自适应学习。针对 Buck变换器输出电压的控制问题，为了避免普通滑模控制跟踪误差渐进收敛的问题，改善其动态响应速度和稳态性能，本文拟设计一种基于 RBF神经网络的上界自适应的终端滑模控制器，并通过 Simulink 仿真验证这种方法的可行性。
대우Buck변환기계통，고필도실제응용중부재변동인기계통삼수적불학정성，차불학정성상계무법측량적정황，본문의채용 RBF신경망락대불학정성상계진행자괄응학습。침대 Buck변환기수출전압적공제문제，위료피면보통활모공제근종오차점진수렴적문제，개선기동태향응속도화은태성능，본문의설계일충기우 RBF신경망락적상계자괄응적종단활모공제기，병통과 Simulink 방진험증저충방법적가행성。
In Buck converter system,considering the uncertainty of the system parameter caused by load change in prac-tical application,and the uncertain up-bound value cannot be measured properly,RBF neural network is planned to be adopt-ed to learn the uncertain up0bound value.For the control problem of the output voltage of Buck converter,in order to avoid asymptotic convergence of the tracking error in conventional sliding mode control,and improve the speed of dynamic re-sponse and steady state performance,a terminal sliding mode controller which is based on RBF neural network to learn the uncertain up-bound value will be designed.At last,simulations are used to verify the feasibility of the algorithm.