输入输出受限船舶的轨迹跟踪自适应递归滑模控制

Adaptive recursive sliding mode control for surface vessel trajectory tracking with input and output constraints

中文关键词  船舶轨迹跟踪  输入受限  输出受限  递归滑模控制  神经网络  控制系统稳定性

英文关键词  ship trajectory tracking  input constraints  output constraints  recursive sliding mode control  neural networks  control system stability

中文摘要

      针对输入输出受限, 模型部分不确定和受到未知海洋干扰的全驱动船舶的轨迹跟踪问题, 提出一种基于时 变非对称障碍李雅普诺夫函数的最小参数自适应递归滑模控制策略. 该策略首先设计障碍李雅普诺夫函数约束船 舶轨迹在有限区域内, 利用最小参数法神经网络逼近模型不确定项, 降低系统的计算复杂度, 然后采用指令滤波器 对输入信号进行幅值约束, 同时避免对因反步法导致的微分爆炸问题, 综合考虑船舶位置以及速度误差间的关系设 计递归滑模控制律, 提高系统的鲁棒性, 采用双曲正切函数和Nussbaum函数补偿由输入饱和引起的非线性项, 提高 系统稳定性. 最后通过Lyapunov理论分析证明了全驱动船舶闭环系统中所有信号是一致最终有界的. 仿真结果表 明, 本文所设计的船舶轨迹跟踪控制方案能有效处理船舶模型不确定部分以及未知外界干扰的问题, 能够实现船舶 在输入受限的情况下在有限区域内航行并准确的跟踪期望轨迹, 具有较强的鲁棒性.

英文摘要

      To solve the trajectory tracking problem of fully-actuated surface vessel with input and output constraints, a method of adaptive neural network recursive sliding mode dynamic surface control, based on time-varying asymmetric barrier Lyapunov function, is proposed in the presence of uncertain ship model parameters and unknown external environmental disturbances. In this strategy, the asymmetric barrier Lyapunov function is designed to restrict the actual ship trajectory within limited areas. An minimal learning parameter (MLP) neural network based is introduced to approximate the model uncertainty and to reduce the computational complexity of the system. Then the command filter is applied to restrain the amplitude of input signal and avoid the problem of complicated calculations caused by backstepping. Based on that, the recursive sliding mode method is incorporated into dynamic surface control to enhance the system robustness. The hyperbolic tangent function and the Nussbaum function are introduced to compensate for nonlinear terms caused by saturation function and ensure the system stability. Finally, the application of Lyapunov function proves that all signals in the closed-loop tracking system can be guaranteed the uniformly ultimate boundedness by the proposed control law. The simulation results show that the proposed controller can effectively solve the problem of control input constraints and output constraints, and enhance the system robustness against model uncertainty and unknown external environmental disturbances.