Lazy-Learning-Based Data-Driven Model-Free Adaptive Predictive Control for a Class of Discrete-Time Nonlinear Systems
Lazy-Learning-Based Data-Driven Model-Free Adaptive Predictive Control for a Class of Discrete-Time Nonlinear Systems
复制标题
一类离散时间非线性系统的基于惰性学习的数据驱动无模型自适应预测控制
DOI：
10.1109/tnnls.2016.2561702
复制
发表时间：
2017-08-01
影响因子：
10.4
通讯作者：
Tian, Taotao
中科院分区：
文献类型：
--
作者：
Hou, Zhongsheng;Liu, Shida;Tian, Taotao
针对一类离散单输入单输出非线性系统，提出了一种基于懒惰学习技术的数据驱动无模型自适应预测控制方法。该方法的特点是仅使用系统的输入输出（I/O）测量数据，通过一种新的动态线性化技术和伪梯度（PG）的新概念来设计控制器。此外，该预测函数在控制器中采用基于lazy-learning （LL）的PG预测算法实现，使得控制器不仅具有良好的鲁棒性，而且能够实现对期望信号突变的无模型自适应预测效果。此外，由于LL技术具有数据库查询的特点，因此在控制过程中，在线和离线的I/O测量数据都被充分、同时地利用来实时调整控制器参数。通过严格的数学分析，保证了所提方法的稳定性。同时，在一个实际的三水箱水位控制系统上进行了数值模拟和室内实验，验证了该方法的有效性。
In this paper, a novel data-driven model-free adaptive predictive control method based on lazy learning technique is proposed for a class of discrete-time single-input and single-output nonlinear systems. The feature of the proposed approach is that the controller is designed only using the input-output (I/O) measurement data of the system by means of a novel dynamic linearization technique with a new concept termed pseudogradient (PG). Moreover, the predictive function is implemented in the controller using a lazy-learning (LL)-based PG predictive algorithm, such that the controller not only shows good robustness but also can realize the effect of model-free adaptive prediction for the sudden change of the desired signal. Further, since the LL technique has the characteristic of database queries, both the online and offline I/O measurement data are fully and simultaneously utilized to real-time adjust the controller parameters during the control process. Moreover, the stability of the proposed method is guaranteed by rigorous mathematical analysis. Meanwhile, the numerical simulations and the laboratory experiments implemented on a practical three-tank water level control system both verify the effectiveness of the proposed approach.