Data-Driven Point-to-Point Finite-Iteration Learning Control for a Class of Nonlinear Systems With Output Saturation
针对未知非线性离散系统在输出饱和下的点对点跟踪控制问题，提出一种数据驱动有限迭代学习算法，通过时域递推和迭代域动态线性化建立模型，并利用误差信息分数幂实现有限迭代收敛。
This article considers the point-to-point tracking control problem for a class of unknown nonlinear discrete-time systems with output saturation. A novel data-driven finite-iteration learning control algorithm is proposed to achieve bounded tracking errors within limited iteration. First, considering the case that the model of the nonlinear discrete-time system is unknown, the relationship between the output of the system and the control inputs at these given points is derived using recursive evolution in the time domain. Then, the dynamic data-driven model of the system is established using iterative domain dynamic linearization techniques. Second, a finite finite-iteration learning algorithm based on the fractional power of error information is designed, and the finite-iteration convergence of the proposed algorithm is rigorously proven in theory. Finally, the effectiveness of the proposed method is validated by simulation results.