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.

Finite Set-Model Predictive Current Control of Three-Phase Voltage Source Inverter for RES （Renewable Energy Systems） Applications

This paper focuses on a combination of three-phase VSI （voltage source inverter） with a predictive current control to provide an optimized system for three-phase inverters that control the load current. A FS-MPC （finite set-model predictive control） strategy for a three-phase VSI for RES （renewable energy systems） applications is implemented. The renewable energy systems model is used in this paper to investigate the system performance when power is supplied to resistive-inductive load. With three different cases, the evaluation of the system is done. Firstly, the robustness of control strategy under variable DC-Link is done in terms of the THD （total harmonic distortion）. Secondly, with one prediction step, the system performance is tested using different sampling time, and lastly, the dynamic response of the system with step change in the amplitude of the reference is investigated. The simulations and result analyses are carried out using Matlab/Simulink to test the effectiveness and robustness of FS-MPC for two-level VSI with AC filter for resistive-inductive load supplied by a renewable energy system.

Multi-objective nonlinear model predictive control through switching cost functions and its applications to chemical processes

This paper proposes a switching multi-objective model predictive control(MOMPC) algorithm for constrained nonlinear continuous-time process systems.Different cost functions to be minimized in MPC are switched to satisfy different performance criteria imposed at different sampling times.In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system,the dual-mode control method is used to design the switching MOMPC controller.In this method,a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC.The correction term is computed if the states are out of the terminal set and the free-parameters of the local control law are computed if the states are in the terminal set.The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions.Finally,implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.

A Compensation Controller Based on a Nonlinear Wavelet Neural Network for Continuous Material Processing Operations

Continuous material processing operations like printing and textiles manufacturing are conducted under highly variable conditions due to changes in the environment and/or in the materials being processed.As such,the processing parameters require robust real-time adjustment appropriate to the conditions of a nonlinear system.This paper addresses this issue by presenting a hybrid feedforward-feedback nonlinear model predictive controller for continuous material processing operations.The adaptive feedback control strategy of the controller augments the standard feedforward control to ensure improved robustness and compensation for environmental disturbances and/or parameter uncertainties.Thus,the controller can reduce the need for manual adjustments.The controller applies nonlinear generalized predictive control to generate an adaptive control signal for attaining robust performance.A wavelet-based neural network model is adopted as the prediction model with high prediction precision and time-frequency localization characteristics.Online training is utilized to predict uncertain system dynamics by tuning the wavelet neural network parameters and the controller parameters adaptively.The performance of the controller algorithm is verified by both simulation,and in a real-time practical application involving a single-input single-output double-zone sliver drafting system used in textiles manufacturing.Both the simulation and practical results demonstrate an excellent control performance in terms of the mean thickness and coefficient of variation of output slivers,which verifies the effectiveness of this approach in improving the long-term uniformity of slivers.

Nonlinear model predictive control based on hyper chaotic diagonal recurrent neural network

Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window.In order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was obtained.The controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external disturbance.The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection.Parameter convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.

Model predictive control of three-level active front-end converter with low switching frequency

In medium voltage-high power(MV-HP)applications,the high switching frequency of power converter will result in unnecessary energy losses,which directly affect efficiency.To resolve this issue,a novel finite control set-model predictive control(FCS-MPC)with low switching frequency for three-level neutral point clamped-active front-end converters(NPC-AFEs)is proposed.With this approach,the prediction model of three-level NPC-AFEs is established inα-βreference frame,and the control objective of low average switching frequency is introduced into a cost function.The proposed method not only achieves the desired control performance under low switching frequency,but also performs the efficient operation for the three-level NPC-AFEs.The simulation results are provided to verify the effectiveness of proposed control scheme.

Nonlinear model predictive control with guaranteed stability based on pseudolinear neural networks

A nonlinear model predictive control problem based on pseudo-linear neural network (PNN) is discussed, in which the second order on-line optimization method is adopted. The recursive computation of Jacobian matrix is investigated. The stability of the closed loop model predictive control system is analyzed based on Lyapunov theory to obtain the sufficient condition for the asymptotical stability of the neural predictive control system. A simulation was carried out for an exothermic first-order reaction in a continuous stirred tank reactor.It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems.

Model Predictive Control for Discrete and Continuous Timed Petri Nets

The goal of this paper is to propose a unique control method that permits the evolution of both timed continuous Petri net （TCPN） and T-timed discrete Petri net （T-TDPN） from an initial state to a desired one. Model predictive control （MPC） is a robust control scheme against perturbation and a consistent real-time constraints method. Hence, the proposed approach is studied using the MPC. However, the computational complexity may prevent the use of the MPC for large systems and for large prediction horizons. Then, the proposed approach provides some new techniques in order to reduce the high computational complexity; among them one is taking constant control actions during the prediction.

Optimization Transmission Efficiency with Driver Intention for Automotive Continuously Variable Transmission under Slip Mode

This study proposes and experimentally validates an optimal integrated system to control the automotive continuously variable transmission(CVT)by Model Predictive Control(MPC)to achieve its expected transmission efficiency range.The control system framework consists of top and bottom layers.In the top layer,a driving intention recognition system is designed on the basis of fuzzy control strategy to determine the relationship between the driver intention and CVT target ratio at the corresponding time.In the bottom layer,a new slip state dynamic equation is obtained considering slip characteristics and its related constraints,and a clamping force bench is established.Innovatively,a joint controller based on model predictive control(MPC)is designed taking internal combustion engine torque and slip between the metal belt and pulley as optimization dual targets.A cycle is attained by solving the optimization target to achieve optimum engine torque and the input slip in real-time.Moreover,the new controller provides good robustness.Finally,performance is tested by actual CVT vehicles.Results show that compared with traditional control,the proposed control improves vehicle transmission efficiency by approximately 9.12%-9.35%with high accuracy.

感应电机损耗最小化预测控制策略研究

针对传统感应电机效率优化算法在复杂工况下存在鲁棒性和动态性能差的问题,提出了一种基于损耗模型控制算法的感应电机效率优化预测控制策略。搭建考虑铁损的电机功率损耗模型,包含铁损、铜损以及漏感等因素,提高了损耗模型的精度。采用模型参考自适应观测器估计损耗模型中的定子电阻,设计电流模型为参考模型,电压模型为可调模型。基于电机铁损模型构建连续集模型预测电流控制器,提升系统的动态性能。实验结果验证了所提算法的可行性和有效性。

神经网络类机理建模下的持续自学习控制

针对未知、时变复杂动力学系统在基于模型的控制中的动态建模问题,本文采用前向全连接神经网络对动力学系统进行数据驱动下的非机理拟合建模.通过动态线性化和归一化/反归一化数据处理,基于前向传播算法,将神经网络的网络拓扑计算过程转化成动力学系统机理模型的同构等价表达形式.与基于模型的预测与反演控制相结合,提出了神经网络类机理建模下的持续自学习控制方法,探索了神经网络在动力学系统建模与控制中的可解释性问题.以机械臂为控制对象的仿真结果表明,神经网络类机理模型与机理模型在形式上同构,在参数上近似或等价,可用于控制系统控制品质的定性、定量分析.持续自学习控制对非线性未知、时变复杂系统具有较好的动态适应能力.

非理想条件下基于SAADR-PI的MMC型电力电子变压器CCS-MPC控制策略

基于模块化多电平换流器(modular multilevel converter,MMC)的电力电子变压器(power electronic transformer,PET)在非理想工况下易发生故障及扰动,严重影响系统电能质量。针对传统控制方法在非理想工况下存在的动稳态性能差等问题,在MMC-PET整流级提出了基于自适应自抗扰比例积分控制器的连续控制集模型预测控制策略。首先,设计了自适应自抗扰比例积分控制器用于电压外环,解决了电压外环信号跟踪及扰动抑制能力差等问题。其次,电流内环使用连续控制集模型预测控制方法以提高系统的响应速度及稳态性能,引入改进型载波移相调制策略解决桥臂电流畸变问题。最后,在网侧负载突变、网压不平衡、输出级负载投入等非理想工况下对MMC-PET系统进行对比仿真和实验,验证了所提控制策略的优越性。

汽车CVT夹紧力紧急制动控制及传动性能分析

无级变速器(CVT)在汽车传动系统上得到广泛的应用,CVT夹紧力直接影响传动效率及整车燃油经济性。采用模型预测控制实现系统夹紧力参数计算,防止夹紧力误差过大而降低传动效率。先通过CVT动态方程构建状态空间函数,设计了夹紧力预测模型,再以控制模型对各时刻下参数采集实时分析,得到夹紧力最佳输入。相对传统控制模式,模型预测控制模型表现出了更优控制性能,可以使从动缸压力减小近9.2%,使系统传动效率提升近9.5%。该研究有助于提高紧急制动工况下汽车的运行稳定性,保障安全效率。

基于终端代价函数的连续搅拌反应釜无穷时域经济性预测控制优化

针对连续搅拌反应釜,设计一种基于不变集技术和终端代价函数的无穷时域经济性预测控制优化算法。在经济指标中引入动态指标,构造基于寻优动态指标加权矩阵系数的二次复合性能指标,进而通过不变集技术离线计算终端约束集和终端代价函数;将有限时域内的复合性能指标附加终端代价函数,构造无穷时域性能指标,对连续搅拌反应釜进行无穷时域优化控制,改善经济性能;仿真验证了所设计算法的有效性。

Reduced precision solution criteria for nonlinear model predictive control with the feasibility-perturbed sequential quadratic programming algorithm

We propose a novel kind of termination criteria, reduced precision solution (RPS) criteria, for solving optimal control problems (OCPs) in nonlinear model predictive control (NMPC), which should be solved quickly for new inputs to be applied in time. Computational delay, which may destroy the closed-loop stability, usually arises while non-convex and nonlinear OCPs are solved with differential equations as the constraints. Traditional termination criteria of optimization algorithms usually involve slow convergence in the solution procedure and waste computing resources. Considering the practical demand of solution precision, RPS criteria are developed to obtain good approximate solutions with less computational cost. These include some indices to judge the degree of convergence during the optimization procedure and can stop iterating in a timely way when there is no apparent improvement of the solution. To guarantee the feasibility of iterate for the solution procedure to be terminated early, the feasibility- perturbed sequential quadratic programming (FP-SQP) algorithm is used. Simulations on the reference tracking performance of a continuously stirred tank reactor (CSTR) show that the RPS criteria efficiently reduce computation time and the adverse effect of computational delay on closed-loop stability.

分布式储能型MMC电池荷电状态均衡优化控制策略

为提高电池的能量利用率和解决电池因制造工艺、循环充放电次数不同以及老化程度不一致等因素导致的荷电状态(SOC)极度不均衡问题,提出一种兼顾电流波动抑制的分布式储能型模块化多电平换流器的电池SOC均衡优化控制策略。为准确控制充放电功率,采用双环控制:外环针对相间、桥臂间和子模块间电池SOC差异,建立离散时域预测功率模型,通过负反馈控制生成动态电流参考值;内环设计了模型预测优化控制策略,准确追踪动态电流参考值,实现电池SOC均衡、提高电池能量利用率,并提高系统的动态响应能力以及抑制电池电流纹波,延长电池使用寿命。最后通过在PSCAD/EMTDC中构建仿真模型对所提出的控制器性能进行验证。

考虑可控负荷的多区域电力系统分布式模型预测负荷频率控制

由于同步发电机的惯性较大,导致传统的集中式负荷频率控制模式反应不够迅速,而用户侧具有快速响应能力的可控负荷资源为系统的频率调节提供了新机遇。研究了考虑用户侧可控负荷资源主动参与系统频率调节的多区域互联电力系统分布式模型预测负荷频率控制问题。通过建立的含可控负荷的多区域互联电力系统负荷频率响应模型及自动发电控制模型,基于连续时域交替方向乘子法和分布式模型预测控制方法,提出了一种用户侧可控负荷资源主动参与的多区域互联电力系统分布式模型预测最优负荷频率控制模型。基于修改的IEEE39节点三区域互联电力系统进行仿真验证,结果表明所提考虑可控负荷的分布式模型预测控制策略可显著减少系统恢复至稳态所需的时间。分布式控制策略的控制自由度更高,增强了系统的可控性。

基于改进STO的IPMSM退磁故障模型预测MTPA容错控制

针对内嵌式永磁同步电机发生退磁故障时系统模型和参数发生改变,控制器控制性能严重下降的问题,提出基于改进STO的退磁故障模型预测MTPA容错控制策略。首先针对电机发生退磁故障,分析模型参数变化,重新构建故障模型,并且求解了考虑故障状态的MTPA曲线。然后针对模型预测控制对参数变化的敏感性问题,构建改进的STO观测器,对永磁体磁链在线识别。最后设计电流模型预测控制器,对退磁故障的IPMSM进行容错控制。通过实验对比,构建的观测器对退磁故障情况的永磁体磁链有更好的观测性能,并且容错控制策略也更优秀。

多电平变换器连续集模型预测控制研究

为解决有限集模型预测控制(FCS-MPC)下变换器输出波形频谱发散、纹波过大的问题,在此提出一种基于矢量分析的连续集模型预测控制(CCS-MPC)策略。该策略采用通过多个基本状态矢量实时合成目标矢量的方式,实现了四开关状态变换器的无差拍控制。同时由于单周期内各矢量作用时间可通过几何方法简便求得,因此该策略便于现有微处理的实现。与FCS-MPC相比,新型CCS-MPC策略能够在不显著增加计算负担的前提下获得优异的频谱特性。该策略在100 kHz氮化镓(GaN)三电平飞跨电容(FC)变换器上进行了实验验证。实验结果显示,与FCS-MPC相比,新型CCS-MPC下变换器的电感电流总谐波失真(THD)降低了60%以上。

基于NGSIM的连续换道轨迹规划与跟踪控制

连续换道是一种危险的驾驶行为,易导致交通效率降低,甚至诱发交通事故,因此有必要对连续换道进行研究。首先,基于NGSIM数据对有等待时间和无等待时间的连续换道轨迹分别拟合,建立了换道轨迹模型;其次,基于LTV-MPC算法设计了控制器,通过CarSim与Simulink联合仿真实验平台进行算法验证。结果表明,对于无等待时间的连续换道,跟踪效果及稳定性均优于有等待时间的连续换道;所设计的控制器对所有参考轨迹的跟踪效果较好,横向载荷率均小于风险阈值,具有较好的稳定性和鲁棒性,所提出的规划控制器可为道路几何设计提供理论依据。