基于数据驱动的非线性网络系统自适应迭代学习控制

Data driven adaptive learning control of nonlinear network system

针对非线性网络控制系统中测量数据的量化及随机丢包问题,给出一种基于数据驱动的自适应迭代学习控制算法.该算法能够保证系统在数据量化、随机丢包以及不确定迭代学习长度等因素的影响下,经过有限次迭代后输出轨迹跟踪误差收敛到零;借助伪偏导线性化方法,将非线性系统转换为线形时变系统形式;在线性系统框架下利用前一批次的系统输出信息更新自适应学习增益.与传统迭代学习控制算法不同的是,该算法无需预知迭代长度的先验信息和控制系统模型信息.最后通过Matlab仿真实验验证所提出算法的有效性.

Aiming at the quantization of measurement data and random packet loss problems in nonlinear network control systems, this paper presents a data-driven based adaptive iterative learning control algorithm. This algorithm can ensure that the output tracking error can converge to zero after a limited number of iterations, although the system suffers from factors such as data quantification, random packet loss, and uncertainties. Resorting to a pseudo partial derivative based linearization method, the nonlinear system is converted into a linear time-varying system form. Under the framework of linear systems, the adaptive learning gain is updated by the previous batch outputs. Different from the traditional iterative learning control algorithm, the proposed one has no need to predict a priori iteration length and the control system model.Finally, the effectiveness of the proposed algorithm is verified by simulations.