数据驱动控制理论及方法的回顾和展望

给出了数据驱动控制理论和方法相关问题的定义, 从控制理论、实际应用和历史发展趋势三个角度阐述了数据驱动控制的存在背景, 说明了数据驱动控制理论和方法的适用条件. 综述了已有数据驱动控制方法的本质内容和发展历程, 从数据利用的角度指出了已存在的数据驱动控制方法的区别和应用环境, 并对数据驱动控制理论的发展进行了展望.  

非线性离散时间系统的最优终端迭代学习控制

仅利用系统的终端输出误差而不是整个输出轨迹,提出了一种最优终端迭代学习控制方法.控制信号可直接通过终点的误差信息进行更新.主要创新点在于控制器的设计和分析只利用系统量测的I/O数据而不需要关于系统模型的任何信息,并可实现沿迭代轴的单调收敛.在此意义上,所提出的控制器是数据驱动的无模型控制方法.严格的数学分析和仿真结果均表明了所提出方法的适用性和有效性.  

基于即时学习的MIMO系统滑模预测控制方法

针对MIMO非线性系统的控制问题，采用数据驱动的控制策略，将具有本质自适应能力的即时学习算法与具有强鲁棒性的滑模预测控制相结合，设计了一种基于即时学习的滑模预测（LL-SMPC）控制方法．该方法在在线局部建模的基础上，采用滑模预测控制律求取最优控制量，具有较强的自适应和抗干扰能力，并避免TDiophantine方程的求解，有效减少了计算量．通过仿真研究，验证了算法的有效性．  

水厂混凝投药大滞后过程的数据驱动直接控制方法

以自来水厂混凝投药大时滞过程为研究对象,在迭代反馈整定(IFT)方法的基础上,结合Smith预估控制结构,提出了一种混凝投药过程数据驱动直接控制算法.着重针对过程大时滞的特点,在性能指标中加入了预估误差惩罚因子.提出了一种新的步长设置方法,使得步长的下降速率可调.设计了3个闭环实验来求取性能指标梯度向量的无偏估计,完全利用输入输出数据对广义控制器参数实现了自整定.通过水厂混凝投药实测模型仿真验证了该方法在抗滞后方面表现出较强的收敛性和稳定性,降低了对迭代初值的要求.该方法既可以用于初始控制器的整定,还可以用于投药过程中控制器的在线自整定.  

用数据驱动控制技术提高船舶CAD软件的可重用性

在高速客船重量及重心计算系统开发实践中，探索出了一项简单快捷且具通用性的技术——通过对对象进行数据驱动控制，以提高软件的可重用性。它能大大缩短软件的开发周期，减少软件对硬件资源的占用量。  

WASMII: An MPLD with data-driven control on a virtual hardware

Through the use of an extended field programmable gate array (FPGA) technology, a large digital circuit can be realized on a relatively small amount of real hardware. Several configuration RAM modules are provided inside the FPGA chip, and the configuration of the gate array can be rapidly changed by replacing the active module. Data for configuration are transferred from an off-chip backup RAM to an unused configuration RAM module. A novel computation mechanism called the WASMII, which executes a target dataflow graph directly, can be proposed on the basis of this virtual hardware. A WASMII chip consists of the FPGA for virtual hardware and an additional mechanism to replace configuration RAM modules in a data-driven manner. Configuration data are preloaded in the order assigned in advance by a static scheduling preprocessor. By connecting a number of WASMII chips, a highly parallel system can be easily constructed.  

Application of multivariate virtual reference feedback tuning for wastewater treatment plant control

The objective of this paper is to apply the Virtual Reference Feedback Tuning (VRFT) to Multiple-Input Multiple-Output (MIMO) control strategies in wastewater treatment plants (WWTPs). Using the Benchmark Simulation Model No. 1 (BSM1) as a case study, the proposed data-driven approach provides reduced-order controllers only using a batch of input-output data points (i.e. manipulated variable—controlled variable), obtained from an open-loop experiment. The methodology also includes a pre-processing step that subtracts the impact of influent variability and sensor noise from the output signals. The possible interactions amongst different control loops are handled using a decoupling approach where each control signal is computed depending on the error signal of all the loops at the same time. In order to test the methodology, several control strategies are evaluated via simulation. The results show that substantial improvements in the plant performance can be obtained when controllers are implemented.  

Data-driven nonparametric model adaptive precision control for linear servo systems

Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control (NMAC) method is proposed in this paper to control the position of a linear servo system. The controller design requires no information about the structure of linear servo system, and it is based on the estimation and forecasting of the pseudo-partial derivatives (PPD) which are estimated according to the voltage input and position output of the linear motor. The characteristics and operational mechanism of the permanent magnet synchronous linear motor (PMSLM) are introduced, and the proposed nonparametric model control strategy has been compared with the classic proportional-integral-derivative (PID) control algorithm. Several real-time experiments on the motion control system incorporating a permanent magnet synchronous linear motor showed that the nonparametric model adaptive control method improved the system’s response to disturbances and its position-tracking precision, even for a nonlinear system with incompletely known dynamic characteristics.  

基于数据驱动的风力发电系统一步超前预测转速控制

针对风力发电系统转速控制系统的特点,提出了一种基于数据驱动的一步超前预测控制方法.该方法先利用动态线性化技术,采用投影算法在线估计伪偏导数,然后通过最小化一步超前加权性能指标获得广义最小方差预测控制算法;为了取得更好的跟踪效果,引入一个自适应项用于增强输出跟踪能力,使得系统输出响应具有更强的跟踪能力.仿真结果验证了该方法的有效性.  

一种离-在线混合数据驱动控制方法及其在直流电动机中的应用

针对离线和在线数据驱动控制方法各自存在的不足,提出一种离-在线混合数据驱动控制方法。首先给出一种基于最小二乘支持向量机和虚拟目标值反馈整定的离线数据驱动控制方法;其次在二自由度控制系统框架下,结合无模型自适应控制,给出一种离-在线混合数据驱动控制方法的结构和设计步骤。该方法跳过被控对象建模过程,大大降低了控制器设计成本,且可避免引入模型误差。将该方法应用于直流电动机离-在线数据驱动控制中,并进行了仿真实验,结果表明,离-在线混合数据驱动控制方法可以有效地实现目标信号跟踪和电动机系统的不确定(外部扰动)抑制。