摘要
在控制理论的研究中,离散系统的滑模变结构控制是控制理论所关注的重要问题,其中趋近律与控制器的设计直接影响系统稳定性及动态性能。为了解决传统趋近律存在系统抖振、动态过程过渡较慢等问题,提出一种基于神经网络的离散系统双幂次趋近律,并设计了相应滑模控制器。经理论证明,该趋近律可以有效抑制系统抖振、加快动态趋近速度并增强系统鲁棒性。通过与单幂次趋近律以及传统双幂次趋近律仿真对比,趋近律具有较快收敛速度并且系统在基于趋近律设计的控制器控制下,动态性能得到明显改善。
Sliding mode variable structure control for discrete system is an important problem in control theory, and its reaching law election and controller design influence the stability and dynamic performances of discrete sys- tem. In order to reduce system chattering and fasten the dynamic reaching rate caused by traditional reaching laws, a double power reaching law based on neural network was proposed, and a sliding mode controller was designed. Theory proves that this method can eliminate the chattering, increase the reaching speed and improve the robustness. Simulation results show that compared with exponential reaching law, single power reaching law and traditional double power reaching law, the proposed reaching law has faster convergence speed and better dynamic performance.
出处
《计算机仿真》
CSCD
北大核心
2014年第8期416-420,共5页
Computer Simulation
基金
国家自然科学基金(51379049)
中央高校基本科研业务专项资金资助(HEUCF110419)
黑龙江省留学归国人员科学基金(LC2013C21)
关键词
双幂次趋近律
神经网络
轨迹跟踪
离散系统
Double power reaching law
Neural network
Trajectory tracking
Discrete system
