Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor

Feasibility analysis of soft constraints for input and output variables is critical for model predictive control(MPC).When encountering the infeasible situation, some way should be found to adjust the constraints to guarantee that the optimal control law exists. For MPC integrated with soft sensor, considering the soft constraints for critical variables additionally makes it more complicated and difficult for feasibility analysis and constraint adjustment. Therefore, the main contributions are that a linear programming approach is proposed for feasibility analysis, and the corresponding constraint adjustment method and procedure are given as well. The feasibility analysis gives considerations to the manipulated, secondary and critical variables, and the increment of manipulated variables as well. The feasibility analysis and the constraint adjustment are conducted in the entire control process and guarantee the existence of optimal control. In final, a simulation case confirms the contributions in this paper.

Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.

Simulation on model predictive control for PMSM drive system based on double extended state observer

A novel double extended state observer(DESO)based on model predictive torque control(MPTC)strategy is developed for three-phase permanent magnet synchronous motor(PMSM)drive system without current sensor.In general,to achieve high-precision control,two-phase current sensors are necessary for successful implementation of MPTC.For this purpose,two ESOs are used to estimate q-axis current and stator resistance respectively,and then based on this,d-axis current is estimated.Moreover,to reduce torque and flux ripple and to improve the performance of the torque and speed,MPTC strategy is designed.The simulation results validate the feasibility and effectiveness of the proposed scheme.

Quality prediction of batch process using the global-local discriminant analysis based Gaussian process regression model

The conventional single model strategy may be ill- suited due to the multiplicity of operation phases and system uncertainty. A novel global-local discriminant analysis （GLDA） based Gaussian process regression （GPR） approach is developed for the quality prediction of nonlinear and multiphase batch processes. After the collected data is preprocessed through batchwise unfolding, the hidden Markov model （HMM） is applied to identify different operation phases. A GLDA algorithm is also presented to extract the appropriate process variables highly correlated with the quality variables, decreasing the complexity of modeling. Besides, the multiple local GPR models are built in the reduced- dimensional space for all the identified operation phases. Furthermore, the HMM-based state estimation is used to classify each measurement sample of a test batch into a corresponding phase with the maximal likelihood estimation. Therefore, the local GPR model with respect to specific phase is selected for online prediction. The effectiveness of the proposed prediction approach is demonstrated through the multiphase penicillin fermentation process. The comparison results show that the proposed GLDA-GPR approach is superior to the regular GPR model and the GPR based on HMM （HMM-GPR） model.

计及高渗透率光伏消纳与深度强化学习的综合能源系统预测调控

深度强化学习(DRL)是支撑园区综合能源系统(PIES)自适应调控其多能转换与存储设备,以消纳光伏发电及满足用户多能需求的重要技术。然而,DRL智能体通常利用其与PIES的实时环境-动作交互来调控其设备运行状态,难以在高渗透率光伏场景下考虑尖峰光伏发电并预留充足的储能资源。基于模型预测控制理论,该文提出了一种基于DRL与光伏发电区间预测的PIES优化调控方法。该方法面向电-气-热园区综合能源系统,利用时序卷积网络与核密度估计得到光伏发电区间预测结果,并采用柔性Actor-Critic(SAC)算法构建PIES预测优化调控模型。该模型将光伏发电预测区间构建为SAC智能体状态空间,通过迭代试错训练获得PIES多能存储与转换的动态调节策略,从而优化光伏消纳率和运行成本。仿真实验表明,所提方法通过动态调节电、气、热三种能源转换设备的运行功率和预留三种储能设备的储能量,可有效提升PIES在高渗透率光伏场景下的消纳率,优化其运行经济效益。

基于MPCC的PMSM电流传感器故障诊断与容错控制

针对电流传感器在永磁同步电机系统运行时发生的故障,提出一种基于模型预测电流控制的电流传感器故障诊断与容错控制方案。控制器依次遍历8种电压矢量,得出下一周期最优预测电流,并与下一周期的实测电流比较,以实现快速诊断。利用预测相电流代替故障相电流实现故障后的容错控制。仿真验证了系统在电流传感器突发故障时诊断与容错方法的有效性与可行性。

狭小检测空间下多传感融合四足机器人自主导航策略

针对狭小检测空间的机器人巡检任务需求,提出多传感适时融合的小型四足机器人自主导航策略。采用惯性测量单元和激光测距传感器进行卡尔曼适时融合滤波获取机器人位姿;引入模型预测控制实现对期望路径的高精度鲁棒跟踪,完成机器人狭小空间内的自主导航目标。试验验证表明:机器人实际行进路径与期望路径的吻合度较好,基于该策略可以实现小型四足机器人在狭小空间的自主导航目标。

基于龙伯格观测器的PMSM双矢量模型预测控制

当永磁同步电机采用有限集模型预测控制时,虽动态性能优异,但存在稳态性能差、计算量大的问题。针对上述问题本文提出了一种基于龙伯格观测器的模型预测控制策略。首先,通过分析传统模型预测控制中占空比控制策略的次优电压矢量所在扇区的分布规律,构造新的电压矢量选择表降低了次优电压矢量的计算次数。此外,针对传统机械传感器的一系列弊端,设计了一种龙伯格观测器计算电机的反电动势,再由锁相环估计永磁同步电机转速和转子位置信息。最后,通过搭建实验平台,与传统有限集模型预测控制进行实验对比分析,实验结果表明本文所提控制策略具有更好的稳态性能和控制精度。

基于传感器融合技术的自动驾驶车辆轨迹跟踪与控制方案研究

文章旨在探讨基于传感器融合技术的自动驾驶车辆轨迹跟踪与控制方案。针对自动驾驶汽车在实际道路环境中需要准确跟踪车辆轨迹以实现安全、高效的驾驶需求,提出了一种综合利用多传感器数据的轨迹跟踪与控制方法。文章首先分析了传感器融合技术在自动驾驶领域的重要性和应用前景,并介绍了基于雷达、摄像头、激光雷达等传感器的数据融合方法。接着,提出了一种基于模型预测控制(MPC)的轨迹跟踪算法,利用多传感器数据实时更新车辆动态模型,并通过优化控制器参数以实现精确轨迹跟踪。最后,通过仿真实验和实际道路测试验证了所提方案的有效性和鲁棒性,结果表明该方法在各种道路场景下都能够实现稳定、准确的轨迹跟踪与控制。

Endpoint Prediction of EAF Based on Multiple Support Vector Machines

The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines （MSVM）. In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub- models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.

Approach of Synthesizing Model Predictive Control and Its Applications for Rotary Kiln Calcination Process

An actual control demand of rotary kiln is taken as background. By analyzing and improving approach of MPC （synthesizing model predictive control）, an effective strategy which applies complex S-MPC in actual industrial process is designed. Firstly, after analyzing the main components technology and calcination reaction mechanism in detail, the calcining belt state-space model of rotary kiln is built using PO-Moesp （past-output multivariable output error state space model identification） method. Then, calcining belt temperature predictive control system is de signed. The control system combines time-delay gain scheduled, output-tracking, recursive subspace adaptive and other methods, and forms the off-line/on-line predictive controller of rotary kiln. At last, MATLAB is applied for simulation, experiments run in constant value tracking and servo tracking situation. Simulation results show its ef- fectiveness and feasibility.

An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme

Purpose–Automated driving systems(ADSs)are being developed to avoid human error and improve driving safety.However,limited focus has been given to the fallback behavior of automated vehicles,which act as a fail-safe mechanism to deal with safety issues resulting from sensor failure.Therefore,this study aims to establish a fallback control approach aimed at driving an automated vehicle to a safe parking lane under perceptive sensor malfunction.Design/methodology/approach–Owing to an undetected area resulting from a front sensor malfunction,the proposed ADSfirst creates virtual vehicles to replace existing vehicles in the undetected area.Afterward,the virtual vehicles are assumed to perform the most hazardous driving behavior toward the host vehicle;an adaptive model predictive control algorithm is then presented to optimize the control task during the fallback procedure,avoiding potential collisions with surrounding vehicles.This fallback approach was tested in typical cases related to car-following and lane changes.Findings–It is confirmed that the host vehicle avoid collision with the surrounding vehicles during the fallback procedure,revealing that the proposed method is effective for the test scenarios.Originality/value–This study presents a model for the path-planning problem regarding an automated vehicle under perceptive sensor failure,and it proposes an original path-planning approach based on virtual vehicle scheme to improve the safety of an automated vehicle during a fallback procedure.This proposal gives a different view on the fallback safety problem from the normal strategy,in which the mode is switched to manual if a driver is available or the vehicle is instantly stopped.

三端口柔性多状态开关有限集模型预测控制

柔性多状态开关(SOP)作为一种新型电力电子装置,可提升配电网运行的灵活性与稳定性。SOP通常采用双闭环控制策略,对于三端口SOP而言共需12个PI调节器,控制结构复杂。因此,本文提出一种基于有限集模型预测控制(FCS-MPC)的三端口SOP的内环电流控制方案。该方案无需PI调节器整定且控制灵活,易于实现。建立了三端口SOP的数学模型,分析其工作原理;接着分析了FCS-MPC和双闭环控制的工作原理;在稳态运行条件和动态运行条件下,用Matlab/Simulink对基于FCS-MPC的SOP的运行情况进行仿真,对比分析了双闭环控制和FCS-MPC时的负载电流响应速度。结果表明:FCS-MPC在电流波动时具有更快响应速度,在稳态时具有更好的稳定性和抗干扰性,验证了所提方案的优越性及有效性。

人机交互遥操作机器人软体手位置跟踪设计与实现

针对遥操作机器人在深海等未知环境中抓取软体物体或易碎物体的工作需求,提出了一种人机交互遥操作机器人的主从软体手位置跟踪方法。首先,介绍了主从软体手控制系统的构成与工作原理,该系统主要包括主端控制回路、通信链路和从端控制回路,用于控制软体手的抓取动作。然后,阐述了软体手指的建模过程与控制器设计,采用假设模态法进行建模,并在软体手指的位置跟踪控制中引入模型预测控制算法,以解决软体从手跟踪软体主手性能差的问题。最后,设计并研制了软体主手、软体从手及其控制系统,其中软体从手以硅胶为原料并嵌入固体材料来增大刚度,同时开展了软体从手跟踪软体主手抓握目标物体的实验。仿真结果表明,所设计的模型预测控制器能有效解决软体手指因模型失配而引起的控制精度下降问题;实验结果表明,所研制的软体从手能有效跟踪软体主手并实现目标物体的抓握,整个控制系统运行良好。研究结果为人机交互遥操作机器人软体手的跟踪控制应用提供了参考。

模块化多电平变换器的电容电压卡尔曼滤波预测方法

为避免子模块电压互感器发生故障对直流配电网模块化多电平变换器(modular multilevel converter,MMC)安全可靠运行造成威胁,需为MMC配置备用测量装置。工程上通常为所有子模块配置备用电压互感器以保证高可靠测量,但使得硬件结构较为复杂。为此,文中提出一种适用于中高压MMC测量系统备用模式的电容电压卡尔曼滤波预测方法,并融合快速模型预测控制(fast model predictive control,FMPC)策略提升电能变换稳定性。首先,为各桥臂仅配置一个电流互感器,无需任何电压互感器,并构建卡尔曼滤波观测器,提出卡尔曼滤波预测方法;进而根据电容电压预测值建立FMPC目标优化函数,确定桥臂最优电平数和子模块投切情况,在保证MMC输出电流和环流跟踪性能及电容电压预测准确性的前提下,显著简化测量系统硬件结构。最后,搭建21电平仿真模型和三电平实验平台,通过多个算例和工况验证所提方法的可行性和实用性。

基于单母线电流传感器的三相永磁同步电机驱动方法

该文研究基于单母线电流传感器的三相永磁同步电机驱动方法,无需使用相电流传感器而是通过单母线电流传感器重构三相电流信号,为消除母线电流传感器重构死区的影响,提出基于两步预测控制的三态脉宽调制方法,利用3个相邻的开关状态构建参考电压,通过两步预测计算下一个周期的参考电压,避免了逆变器多相桥臂同时动作。所提出的单母线电流传感器驱动方法具有共模电压峰-峰值低、电流谐波含量小、采样点固定的优点。最后,通过实验验证了重构电流的准确性和所提出驱动方法的有效性。

虚拟同步机电流传感器故障容错模型预测控制

虚拟同步机(virtual synchronous generator,VSG)交流电流传感器容错控制中存在不可测区域,影响VSG容错运行。为此,提出基于电流重构的故障容错模型预测控制(model predictive control,MPC)策略,消除不可测区域,实现全域内的电流重构。首先,该策略分析不同开关状态下直流电流测量值对应的相电流,设计交流电流传感器故障判据。其次,分析不可测区域成因并根据可测量相电流的数量不足和矢量持续时间过短将其分为两类。为消除因可测量相电流不足造成的不可测区域,设计可测量两相电流的虚拟矢量,通过直流电流重构三相电流。为消除因矢量持续时间过短造成的不可测区域,建立LCL型VSG输出电流预测模型,通过直流电流和单电压矢量的预测电流重构三相电流。最后,通过故障容错MPC策略从虚拟矢量和单电压矢量中选择最优矢量,实现全域内的电流重构和VSG的容错运行。实验验证了所提方法的有效性,说明该方法提高了VSG的可靠性和容错能力。

基于STA-BiLSTM-LightGBM组合模型的多步预测软测量建模

在复杂工业生产过程中,为提高产品质量,建立关键变量多步预测模型非常必要,但传统软测量建模方法难以聚焦工业数据复杂特性,导致预测不准。本文提出一种基于时空注意力机制的双向长短时记忆网络与轻量级梯度提升机(spatial-temporal attention mechanism bi-directional long short-term memory network and light gradient boosting machine,STA-BiLSTM-LightGBM)的多步预测软测量模型。首先训练STA-BiLSTM,时空注意力机制从时间和空间维度为输入特征分配权重,BiLSTM捕捉数据时序特征;其次使用BiLSTM最后一个时间步的隐状态扩充原始输入数据后,训练LightGBM,利用弱学习器迭代训练得到最优模型;进而将STA-BiLSTM和LightGBM的预测输出按照误差倒数法变权求和得到预测结果。最后将该方法在工业数据集上仿真验证,结果表明组合模型预测效果优于BiLSTM和LightGBM,且随着预测步数增大,仍保持较高的预测精度。

化工自动化仪表的自动控制过程研究

研究了化工自动化仪表的自动控制过程,强调了实时监测和数据采集在化工自动化中的作用,深入探讨了反馈控制和前馈控制两种常用的控制策略,特别关注了模型预测控制(MPC)这一高级多变量控制策略。结果表明,MPC能够满足化工自动化仪表自动控制需要,并为优化化工自动化仪表的自动控制过程提供有力支持。

基于时序迁移与双流加权的ONLSTM软测量建模

实际化工过程建模具有多变量、非线性和动态性等特点,会导致模型复杂度提高且提取特征时产生冗余信息和时序分布漂移问题,因此提出一种基于时序迁移和双流加权的有序神经元长短时记忆网络(ONLSTM)模型。首先,利用时序迁移对特征分布进行匹配以自适应表征特征分布信息,采用划分特征分布差异最大时间域进行训练,减小时序分布失配,从而解决时序分布漂移问题;其次,在时序迁移框架内嵌入双流加权ONLSTM模型,通过对ONLSTM主遗忘门和主输入门分别加权,更精确控制传递信息;进一步结合双流结构设计双信息流控制相应门控单元,减小参数调节过程中的耦合影响,降低模型复杂度,提高其预测性能;最后,将所提模型应用于硫回收过程以及某火电厂脱硫过程排放烟气SO_(2)浓度软测量建模,并与其他深度学习网络进行对比,验证了模型有效性。