Finite-time path tracking control of unmanned vehicles based on multi-dimensional Taylor network
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摘要:
针对含有模型不确定和测量噪声条件下的无人车路径跟踪控制问题，提出基于多维泰勒网(MTN)的有限时间路径跟踪控制方案。利用MTN模型刻画无人车模型的不确定性，改进的反向传递(BP)算法作为其学习算法；设计自适应MTN滤波器来滤除测量噪声，MTN作为滤波器，最小均方(LMS)算法作为其学习算法；设计MTN有限时间控制器对无人车进行精确路径跟踪控制，其可以快速准确跟踪参考路径；根据有限时间控制理论，给出了收敛性证明。通过无人车仿真实验验证了所提方法的有效性。
Abstract:In this paper, a finite-time tracking control scheme based on the multi-dimensional Taylor network (MTN) is proposed for path tracking control of unmanned vehicles with model uncertainty and measurement noise. First, the MTN model is used to characterize the uncertainties of the unmanned vehicle model, and the improved back propagation (BP) algorithm is adopted as its learning algorithm. Second, an adaptive MTN filter is designed to suppress the measurement noise. MTN serves as the filter, and the least mean square (LMS) algorithm is employed as its learning algorithm. Then, the MTN finite-time controller is designed for precise path tracking control of the unmanned vehicle, which can track the reference trajectory quickly and accurately. Based on finite-time control theory, the convergence of the system is proved. Finally, unmanned vehicle simulation experiments are conducted to verify the effectiveness of the proposed method.
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图 6 模型不确定性和测量噪声情况下跟踪轨迹
Figure 6. Tracking trajectory under model uncertainty and measurement noise
图 7 模型不确定性和测量噪声情况下跟踪误差
Figure 7. Tracking error under model uncertainty and measurement noise
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