基于IMFAC的无人艇抗干扰航向自适应控制

Anti- Disturbance Course Adaptive Control of USV based on IMFAC

摘要:

针对无人艇在航向控制中易受风浪流等环境干扰，导致控制效果下降的问题，提出一种结合细菌觅食算法的改进无模型自适应控制算法。文章首先分析了偏格式动态线性化方法在无人艇航向控制中的应用问题，并设计了虚拟输出项以满足无模型自适应控制假设条件，建立了基于偏格式动态线性化方法的无模型自适应航向控制器；针对无模型自适应控制算法参数初始值选取范围问题，设计了改进细菌觅食算法对参数初始值进行预整定，保证了算法的快速收敛。最后通过半物理仿真试验验证了所设计算法的有效性。试验表明，在模拟的3级海况干扰下，无人艇在30°阶跃航向控制和±30°方形航向控制中，相较于传统算法出现的较大稳态误差，使用无模型自适应控制算法能在经过10s左右调整后，将误差稳定趋近于零，实现无人艇的航向自适应控制。

Abstract:

In order to address the problem of decreased control effectiveness of Unmanned Surface Vessel(USV) due to environmental disturbances such as wind, waves, and currents in course control, an Improved Model-Free Adaptive Control (IMFAC) algorithm incorporating Bacterial Foraging Optimistic(BFO) algorithm is presented. Firstly, the application issues of Partial Form Dynamic Linearization-Model Free Adaptive Control(PFDL-MFAC) in course control of USV are analyzed. A virtual output is designed to meet the assumption conditions of IMFAC, and a model free adaptive course controller based on partial format dynamic linearization method is established. In view of the parameter initial value selection range problem 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 sea-state level 3, compared with the large steady-state errors resulting from traditional algorithms in 30° stepwise course control and ±30° square course control of USV, the IMFAC algorithm can steadily approach zero error after about 10 seconds of adjustment, achieving adaptive course control of unmanned vessels.