To address the problem that control effectiveness of unmanned surface vessel(USV)decreases due to environmental disturbances such as winds,waves,and currents in course control,an improved model-free adaptive control(IMFAC)algorithm in-corporating bacterial foraging optimistic(BFO)algorithm is proposed.Firstly,this paper analyzes the partial form dynamic lineariza-tion-model free adaptive control(PFDL-MFAC)in USV heading control.designs the virtual output to meet the assumption conditions of IMFAC,and establishes the model free adaptive course controller based on partial format dynamic linearization method.In view of the parameter initial value selection range of PFDL-MFAC,an improved bacterial foraging algorithm is designed to pre-regulate the parameter initial values,ensuring rapid convergence of the algorithm.Finally,the effectiveness of the designed algorithm is verified through semi-physical simulation experiments.The results show that,under simulated interference from the sea conditions of level 3,compared with the large steady-state errors resulting from traditional algorithms for the USV with the stepwise course control of 30° and square course control of±30°,the IMFAC algorithm can steadily approach to zero error after the adjustment time of about 10 s,achieving adaptive course control of unmanned vessels.
关键词
改进无模型自适应控制算法/无人艇/航向自适应控制/抗干扰算法/半物理仿真试验
Key words
improved model free adaptive control algorithm/USV/course adaptive control/anti interference algorithm/semi-physical simulation experiments
维普
万方数据