一类状态/输入受限的不确定非仿射非线性系统 鲁棒自适应backstepping控制

针对一类状态/输入受限的不确定严格反馈非仿射非线性系统跟踪控制问题,提出一种鲁棒自适应backstepping控制策略.在保证系统精度的前提下,对状态/输入受限的非仿射系统进行Taylor级数在线展开,得到其仿射形式;为保证系统复合扰动在线准确逼近,提出基于投影算子的递归扰动模糊神经网络干扰观测器(RPFNNDO);在考虑不确定系统存在状态受限和输入饱和等因素下,结合障碍Lyapunov函数、tanh函数及Nussbaum函数,利用backstepping方法设计控制器,并采用Lyapunov稳定理论分析闭环系统稳定性.应用于无人机航迹控制的仿真结果验证了所提方法的有效性.

Robust adaptive backstepping control for a class of non-affine nonlinear system with full states constraints and input saturation

A robust adaptive backstepping control scheme is proposed for a class of pure-strict non-affine nonlinear system with state constraint and input saturation. Taylor series expansion technique is applied to the non-affine system to convert it to affine-like expression with high accuracy. The recurrent perturbation fuzzy neural networks disturbance observer (RPFNNDO) based on the projection algorithm is designed to approximate the unknown compound disturbance online. Backstepping control is used with the barrier Lyapunov function, the tanh function and the Nussbaum function to design controllers, which handles states constraints, input saturation in the system. The stability of the closed loop system is analyzed by using the Lyapunov theory. Simulation results of the unmanned aerial vehicle track control show the effectiveness of the proposed method.