Adaptive Tracking Control for Unknown Dynamics Systems with SINDYc-based Sparse Identification
作者机构：No.92941 Unit of PLAHuludao 125001P.R.China Unmanned System Research Institute Northwestern Polytechnical University Xi'an 710072P.R.China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University Xi'an 710072P.R.China
出 版 物：《Guidance, Navigation and Control》 (制导、导航与控制（英文）)
年 卷 期：2023年第3卷第2期
页 面：20-37页
学科分类：08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 080201[工学-机械制造及其自动化]
基 金：supported in part by the National Science Fund for Distinguished Young Scholarship of China(No.62025602) the National Natural Science Foundation of China(Nos.U22B2036 and 11931915) Fok Ying-Tong Education Foundationm China(No.171105) Technological Innovation Team of Shaanxi Province(No.2020TD013) Fundamental Research Funds for the Central Universities(No.D5000211001) the Tencent Foundation and XPLORER PRIZE
主 题：System identification tracking control input saturation
摘 要：Machine learning-based data-driven approaches have greatly improved system identification capabilities and facilitated the application of model-based control ***,tech-niques such as neural networks require significant amounts of training data and have limited generalization *** overcome this problem,we employ the sparse identification of nonlinear dynamics with control(SINDYc)for system identification,which considers both system states and control *** on the identified system,we design the controller using the backstepping control *** order to make the algorithm more practical in real-world scenarios,we introduce an input saturation compensation system into the controller ***,we apply a command filter into the method to avoid deriving a virtual control signal and reduce the computational complexity of the *** stability analysis,the proposed control algorithm ensures that the tracking error in the system is ***,we verify the effectiveness of the proposed SINDYc-Backstepping framework by conducting simulations using a single-link robot arm.
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