Parameter tuning of modified adaptive backstepping controller for strict-feedback nonlinear systems☆

被引:4
|
作者
Liu, Xiaoping [1 ,2 ]
Li, Ning [2 ]
Liu, Cungen [2 ]
Fu, Jun [3 ]
Wang, Huanqing [4 ]
机构
[1] Lakehead Univ, Fac Engn, Thunder Bay, ON P7B 5E1, Canada
[2] Shandong Jianzhu Univ, Sch Informat & Elect Engn, Jinan 250101, Peoples R China
[3] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang, Peoples R China
[4] Bohai Univ, Dept Math, Jinzhou 121013, Peoples R China
关键词
Parameter tuning; Backstepping; Stabilization; Adaptive control; Nonlinear systems; BARRIER LYAPUNOV FUNCTIONS; PRESCRIBED PERFORMANCE; TRACKING CONTROL; FUNNEL CONTROL;
D O I
10.1016/j.automatica.2024.111726
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Most research on control of nonlinear systems using backstepping concentrates on construction of feedback controllers to achieve stability and/or steady-state tracking performance. Little research considers systematic methods for tuning nonlinear feedback controllers to guarantee the transient performance. This paper investigates how parameters of a backstepping controller can be determined to achieve the predefined settling time and overshoot, which are basic criteria for the transient performance. First, some modifications are made for the backstepping design method by introducing more parameters so that there are more degrees of freedom for parameter tuning. Second, the pole-placement method is used for the linear part of the closed-loop system corresponding to a backstepping controller. Third, an objective function is defined by summing the square of errors between the corresponding coefficients of the desired and real characteristic polynomials. Fourth, constraints are introduced to guarantee the negative semi-definiteness of the time derivative of the Lyapunov function. Fifth, the controller parameters can be found by minimizing the objective function. Finally, simulation and experimental results show that, compared with the existing method, the proposed method is more effective and efficient for fine tuning backstepping controllers. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
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页数:10
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