摘要
针对无人驾驶车辆在轨迹跟踪控制中由于模型不精确和内外干扰使得跟踪精度下降的问题,提出模型预测控制(MPC)和改进型自抗扰控制补偿相结合的控制算法。先建立MPC控制器,为了提升计算的实时性,将非线性模型预测控制转化为线性时变模型预测控制。由于模型不精确和内外扰动会使车辆产生轨迹偏差,构建了一种更加平滑的非线性函数,进而设计改进型自抗扰控制,以横向偏差和横摆角偏差趋于零为目标来补偿车辆的前轮角,提升无人驾驶车辆的轨迹跟踪性能和抗干扰性能。不同工况下的仿真结果表明:此方法可以有效地完成轨迹跟踪,且跟踪偏差较小,并对外界干扰和车辆参数变化具有较强的鲁棒性。
Aiming at the problem that the tracking accuracy of unmanned vehicles in trajectory tracking control decreases due to imprecision of the model and internal and external interference,this paper proposes a control algorithm combining model predictive control(MPC)and improved active disturbance rejection control compensation.Firstly,an MPC controller is established.In order to improve the real-time performance of the calculation,a nonlinear model predictive control is transformed into linear time-varying model predictive control.Due to trajectory deviations caused attributed to the inaccuracy of the model and internal and external disturbance,a smoother nonlinear function is constructed,and then the improved active disturbance rejection control is designed.With the lateral deviation and the angular deviation tending to be zero as the goal to compensate the front wheel angles of the vehicle,this paper enhances the unmanned vehicle trajectory tracking performance and anti-jamming performance.The simulation results under different working conditions show that this method effectively completes trajectory tracking with small tracking deviations,and has strong robustness to external interference and vehicle parameter change.
作者
秦超
魏民祥
QIN Chao;WEI Minxiang(College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《重庆理工大学学报（自然科学）》
北大核心
2023年第7期51-61,共11页
Journal of Chongqing University of Technology：Natural Science
基金
国家重点研发计划重点专项资助项目(2018YFB2002300)。
关键词
轨迹跟踪精度
模型预测控制
改进型自抗扰控制
非线性函数
抗干扰
trajectory tracking accuracy
model predictive control
improved active disturbance rejection control
nonlinear function
anti-interference
