基于干扰观测器的一类不确定仿射非线性系统有限时间收敛backstepping控制

Finite-time convergence backstepping control for a class of uncertain affine nonlinear systems based on disturbance observer

中文关键词  小脑模型  干扰观测器  backstepping  有限时间收敛  障碍型函数

英文关键词  cerebellar model articulation controller  disturbance observer  backstepping  finite-time convergence  barrier function

中文摘要

      针对一类不确定仿射非线性系统的跟踪控制问题, 提出一种基于干扰观测器的有限时间收敛backstepping 控制方法. 为增强小脑模型(cerebellar model articulation controller，CMAC)泛化和学习能力, 将非对称高斯函数和模糊理论相结合, 给出非对称模糊CMAC结构, 设计干扰观测器实现系统未知复合干扰在线准确逼近; 基于非对称模糊CMAC干扰观测器, 给出有限时间收敛backstepping 控制器设计步骤, 利用Lyapunov 稳定理论证明闭环系统稳定性, 其中采用非线性微分器获取虚拟控制量滤波和微分信息以避免backstepping 设计中的微分“膨胀问题”, 设计辅助系统修正因微分器带来的误差对系统跟踪性能影响, 引入基于障碍型函数的自适应滑模鲁棒项抑制复合干扰估计偏差对跟踪误差的影响; 将所提方法应用于无人机飞行控制仿真实验, 结果表明所提方法有效性.

英文摘要

      This work studies an observer-based finite-time tracking control technique for a class of uncertain affine nonlinear system. To improve generalization and learning ability of cerebellar model articulation controller (CMAC), asymmetrical fuzzy CMAC is presented based on asymmetrical Gaussian function and fuzzy logic, and disturbance observer is proposed to estimate unknown compound disturbance. Furthermore, finite-time backstepping controller is designed to force compensation errors and tracking errors to zero in finite time, where a nonlinear differentiator is employed to avoid “explosion of complexity” and an adaptive barrier function-based sliding mode robust term is proposed to compensate disturbance estimation error. The stability of closed-loop system is proved by Lyapunov theory. Finally, the control scheme is applied to an unmanned aerial vehicle, and the simulation results illustrate the effectiveness.