摘要
针对传统离散滑模控制采用趋近律方法存在较大抖振以及对非匹配不确定性不具有鲁棒性等问题,提出一种改进的扰动补偿的离散趋近律。并利用径向基函数神经网络调节参数,设计径向基函数神经网络参数调节的船舶航向离散滑模控制器,进行仿真研究。仿真结果表明:经改进的趋近律不仅能直接平滑地预测内部参数的摄动和外部扰动,而且不必满足匹配条件,提高了系统的动态品质和鲁棒性。该控制器在有干扰状况下取得了较好的控制效果。
A mathematical model of ship course locomotion with autopilot restrictions and wave disturbances was presented.To solve this problem,a kind of dynamic sliding mode control method based on RBFNN parameter adjustment was proposed.For the second order ships maneuvering mode,without considering steering engine characteristics,and considering the uncertainty of the system,the ship course dynamic sliding mode controller was designed and a lot of simulations were made.In the situation of uncertainty and the case of all kinds of disturbances,the simulation results show that the RBFNN dynamic sliding mode controller has excellent performance in heading track and heading keeping,and the rudder angle output chattering can also be further reduced.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第S1期12-15,共4页
Journal of Central South University:Science and Technology
基金
山东省高等学校科技计划项目(J12LN29)
山东省自然科学基金资助项目(ZR2010FL014)
山东交通学院"船舶安全与低碳智能控制科技创新团队"资助项目
山东交通学院科研基金资助项目(Z201208)
关键词
船舶航向控制
RBFNN
离散滑模
趋近律
ship course control
RBFNN
discrete-time sliding mode
reaching law
