摘要
针对一类模型不精确的非线性系统,提出了一种RBF神经网络与滑模控制策略。RBF神经网络在一定条件下可以任意精度逼近非线性函数,且具有较强的自学习、自适应和组织能力。因此,将其与滑模变结构控制策略相结合,应用于非线性系统中。实验结果表明:其克服了传统滑模变结构控制中的振颤问题,同时,继承了滑模变结构控制所具有的快速性能好、鲁棒性强和抗干扰性能优良的特点。
Aiming at a type of imprecise nonlinear system,the paper develops a kind of tactics composed of radical basis function（RBF） neural networks and slide mode control（SMC）.The RBF neural networks can approach any nonlinear system with arbitrary precision under the given conditions.It also has a strong ability of adaptive,self-study and organization.So the tactics of combining RBF NN with SMC applied to nonlinear system can overcome tremble problem other than the traditional SMC.And the experiment results that the tactics has fast performance,good robustness and excellent anti-jamming.
出处
《江苏技术师范学院学报》
2011年第2期27-33,共7页
Journal of Jiangsu Teachers University of Technology
