Model Predictive Control for Vapor Compression Cycle of Refrigeration Process

A model predictive controller based on a novel structure selection criterion for the vapor compression cycle (VCC) of refrigeration process is proposed in this paper. Firstly, those system variables are analyzed which exert significant influences on the system performance. Then the structure selection criterion, a trade-off between computation complexity and model performance, is applied to different model structures, and the results are utilized to determine the optimized model structure for controller design. The controller based on multivariable model predictive control (MPC) strategy is designed, and the optimization problem for the reduced order models is formulated as a constrained minimization problem. The effectiveness of the proposed MPC controller is verified on the experimental rig.     

ARX-Laguerre模型PID预测控制及其在过热汽温控制中的应用

针对火电厂过热汽温控制的特点,提出了一种基于ARX-Laguerre模型的PID预测控制算法(ALMPCPID),该算法使用增量式的ARX-Laguerre模型作为预测模型,将滚动优化的性能指标改为PID控制器的形式,并利用带遗忘因子的最小二乘递归方法对模型参数进行在线辨识.将ALMPCPID算法应用于过热汽温控制系统...     

数据中心制冷系统非线性模型预测控制

数据中心制冷系统具有非线性、强耦合和大滞后特性,目前常用的PID方法无法实现系统整体能效提升,而现有非线性优化算法计算量大,不易工程实现.鉴于此,提出一种数据中心制冷系统模型预测控制策略,上层优化层设计预测控制器,其目标为在满足制冷要求的前提下降低系统能耗,优化层采用神经网络作为反馈控制器,将系统整体优化目标函数作为神经网络控制器优化性能指标,结合变分法与随机梯度下降法,通过滚动优化求取下层各回路被控变量最优设定值,算法占用存储区适中、计算量小;下层现场控制层通过实时控制使各回路被控变量跟踪最优设定值,可以在不破坏原有现场控制系统的情况下实现性能优化.构建Trnsys-Matlab联合仿真平台,针对系统夏季、过渡季和冬季的控制策略进行仿真实验.结果表明,所提出控制策略能够在满足数据中心安全运行的前提下,实现系统整体能效提升,且具有良好的鲁棒性.     

制冷站双目标权重自适应非线性预测控制

针对传统制冷站控制系统易产生振荡, 且无法实现系统性能整体优化的问题, 本文提出一种制冷站非线性 预测控制策略, 优化目标函数设计为满足建筑冷量需求的同时, 尽可能提高系统整体能效. 为解决上述两个优化目 标之间的矛盾关系, 本文采用模糊逻辑设计了优化目标权重自适应模块, 实时求取权重因子最优解; 针对非线性系 统在线优化求解困难问题, 本文提出了基于神经网络的非线性滚动优化算法, 采用神经网络作为反馈优化控制器, 并将系统优化目标函数作为在线寻优性能指标, 结合Euler-Lagrange方法和随机梯度下降法对控制器权值和阈值进 行在线寻优, 算法计算量小, 占用存储空间适中, 便于采用低成本的现场控制器实现制冷站预测控制. 仿真实验结果 表明, 本文所提出的预测控制策略与PID控制相比, 在未加入优化目标函数权重自适应模块情况下, 系统平均能效 比提高约32.5%; 进行优化目标函数权重自适应寻优后, 系统平均能效提高约39.43%.     

网路化双线性系统的预测控制优化算法研究

本文对一类离散时间双线性系统进行网络化预测控制研究.针对控制系统网络信道传输引起的前向通道和反馈通道时延问题,基于双线性系统结构特性提出2种逐步优化算法对非凸优化问题进行求解,进而得到未来时刻的预测控制序列.仿真实例说明所求预测控制序列可以主动补偿网络引起的时延问题,从而说明所提出预测控制算法的有效性.     

Adaptive UKF-based model predictive control of a Fresnel collector field

One of the ways to improve the efficiency of solar energy plants is by using advanced control and optimization algorithms. In particular, model predictive control strategies have been applied successfully in their control.The control objective of this kind of plant is to regulate the solar field outlet temperature around a desired set-point. Due to the highly nonlinear dynamics of these plants, a simple linear controller with fixed parameters is not able to cope with the changing dynamics and the multiple disturbance sources affecting the field.In this paper, an adaptative model predictive control strategy is designed for a Fresnel collector field belonging to the solar cooling plant installed at the Escuela Superior de Ingenieros in Sevilla. The controller changes the linear model used to predict the future evolution of the system with respect to the operating point.Since only the inlet and outlet temperatures of the heat transfer fluid are measurable, the intermediate temperatures have to be estimated. An unscented Kalman filter is used as a state estimator. It estimates metal-fluid temperature profiles and effective solar radiation.Simulation results are provided comparing the proposed strategy with a PID + feedforward series controller showing better performance. The controller is also compared to a gain scheduling generalized predictive controller (GS-GPC) which has previously been tested at the actual plant with a very good performance. The proposed strategy outperforms these two strategies.Furthermore, two real tests are presented. These tests show that the proposed controller achieves adequate set-point tracking in spite of strong disturbances.     

Distributed model predictive control over network information exchange for large-scale systems

A class of large scale systems, which is naturally divided into many smaller interacting subsystems, are usually controlled by a distributed or decentralized control framework. In this paper, a novel distributed model predictive control (MPC) is proposed for improving the performance of entire system. In which each subsystem is controlled by a local MPC and these controllers exchange a reduced set of information with each other by network. The optimization index of each local MPC considers not only the performance of the corresponding subsystem but also that of its neighbours. The proposed architecture guarantees satisfactory performance under strong interactions among subsystems. A stability analysis is presented for the unconstrained distributed MPC and the provided stability results can be employed for tuning the controller. Experiment of the application to accelerated cooling process in a test rig is provided for validating the efficiency of the proposed method.     

非线性系统的蚁群优化预测PID控制

针对非线性、时变及大惯性系统的控制问题,提出了一种基于蚁群算法的预测PID控制算法。该算法以神经网络作为预测模型,将预测控制和PID控制相结合,并用蚁群算法在线优化控制器参数,其中以常规的Ziegler-N ichols方法整定的控制器参数为基础,选取蚁群优化变量的动态搜索区间。该算法考虑了控制能量受限情况下,非线性系统的预测控制问题。计算机仿真结果表明,该非线性控制方案具有较好的鲁棒性,相对传统PID控制策略还表现出了良好的动态性能,能够满足对再热汽温对象的控制要求。     

An integrated robust iterative learning control strategy for batch processes based on 2D system

This paper studies the problem of integrated control in the 2-dimensional (2D) system with parameter uncertainties for batch processes. An integrated iterative learning control (ILC) strategy based on quadratic performance for batch processes is proposed. It realizes comprehensive control by combining robust ILC in batch-axis with model predictive control (MPC) in time-axis. The design of quadratic-criterion-based ILC for the system can be converted into a min-max problem. Then a model predictive controller with time-varying prediction horizon is designed based on a quadratic cost function. For an uncertain model, a novel integrated robust ILC scheme based on a nominal model is further proposed. As a result, the control law of the 2D system can be regulated during one batch, which leads to good tracking performance and strong robustness against the disturbance and the uncertainties. Moreover, the analyses of the convergence and tracking performance are given. The proposed methods are applied to batch reactor, and results demonstrate that the system has good robustness and convergence. This paper provides a new way for batch processes control.     

A recursive modified partial least square aided data-driven predictive control with application to continuous stirred tank heater

In this paper, a data-driven predictive control strategy for nonlinear system is proposed and testified on a continuous stirred tank heater (CSTH) benchmark. A recursive modified partial least square (RMPLS) algorithm is employed to regress the local linear model. The algorithm of locally weighted projection regression (LWPR) is then leveraged to build the predictive model, based on which a novel data-driven predictive control strategy is put forward. The proposed predictive controller has the ability to deal with changing working conditions, benefiting from the incremental learning ability of RMPLS and LWPR. The performance of the proposed control strategy is demonstrated with the CSTH while the superiority is illustrated by comparison with an existing model-free adaptive control approach.     

SOPDT对象的预测PID控制及稳定性分析

提出了基于二阶非振荡及振荡加纯滞后的预测PID控制器的结构形式。这种控制器既具有PID控制器的优点;简单的结构形式、良好的鲁棒性和可靠性,又具有预测的功能;即可以根据以前的控制作用来预测以后的控制作用。通过仿真表明：在干扰、噪音存在和模型失配的情况下,预测PID控制器具有良好的控制性能,特别适合大纯滞后系统的控制。同时运用Monte-Carlo方法分析了其鲁棒稳定性,结果表明：它是一种值得在实际工程中推广应用的新型控制器。     

ILC strategy for progress improvement of economic performance in industrial model predictive control systems

A novel approach to progress improvement of the economic performance in model predictive control (MPC) systems is developed. The conventional LQG based economic performance design provides an estimation which cannot be done by the controller while the proposed approach can develop the design performance achievable by the controller. Its optimal performance is achieved by solving economic performance design (EPD) problem and optimizing the MPC performance iteratively in contrast to the original EPD which has nonlinear LQG curve relationship. Based on the current operating data from MPC, EPD is transformed into a linear programming problem. With the iterative learning control (ILC) strategy, EPD is solved at each trial to update the tuning parameter and the designed condition; then MPC is conducted in the condition guided by EPD. The ILC strategy is proposed to adjust the tuning parameter based on the sensitivity analysis. The convergence of EPD by the proposed ILC has also been proved. The strategy can be applied to industry processes to keep enhancing the performance and to obtain the achievable optimal EPD. The performance of the proposed method is illustrated via an SISO numerical system as well as an MIMO industry process.     

基于MPC的无传感器永磁同步电机NFTSMC仿真研究

为了提高注塑机中永磁同步电机控制系统的运行可靠性,优化永磁同步电机的调速系统动态性能,提出了一种基于模型预测电流控制的无速度传感器永磁同步电机非奇异快速终端滑模控制策略.以模型预测电流控制作为电流控制内环,取代传统的PI调节器,能够有效地抑制电流纹波,提高电流的动态跟踪性能.根据非奇异终端滑模的设计原理,构造外环速度控制器,从而生成期望的q轴电流,提高了系统的稳定性.设计无速度传感器对电机运行转速进行在线辨识,实现对转速和转子位置准确地估计.并与传统的PI调节器进行对比,仿真与实验结果表明该控制策略具有较高的可靠性和快速性.     

Adaptive speed control based on just-in-time learning technique for permanent magnet synchronous linear motor

In this paper, an adaptive two degrees of freedom (2Dof) PI controller based on a just-in-time learning (JITL) method is proposed for predictive speed control of permanent magnet synchronous linear motor (PMSLM). Firstly, to guarantee the high identification accuracy and high real-time performance simultaneously, an improved JITL method is proposed to estimate the controlled model parameters of speed control system. Then, based on the dynamic controlled model, a simplified generalized predictive control (GPC) supplies a 2Dof proportional integral (PI) controller with suitable control parameters to follow a sinusoid-type speed command in operating conditions. The main motivation of this paper is the extension of the predictive controller to replace traditional PI controller in industrial applications. Finally, the efficacy and usefulness of the proposed controller are verified through the experimental results.     

地铁站台空调系统非线性预测控制策略

地铁站台空调系统回路众多且具有强耦合和非线性特性,PID控制方法参数整定困难,无法兼顾乘客舒适性和能效最优,由于系统建模困难,非线性优化算法计算量大,智能控制方法难以实现工程应用.对此,提出一种地铁站台空调系统预测控制策略.首先,根据热湿负荷平衡和能量守恒定律建立地铁站台热动态特性预测模型;然后,将满足乘客舒适性并节省能耗作为系统优化目标,使用神经网络作为优化反馈控制器,将系统优化目标函数作为控制器优化性能指标,结合变分法和随机梯度下降法,对神经网络控制器的权值和阈值进行在线滚动优化,算法计算量小,占用存储空间适中.仿真实验结果表明,所提出的预测控制策略与传统PID控制方法相比,在满足乘客舒适性要求的前提下,系统响应时间可缩短约39.6%,末端风机能耗降低约73.39%.     

变频电网三相有源电力滤波器重复控制策略

为了在变频环境下,提高三相并联型有源电力滤波器的电流跟踪性能和补偿效果,提出了一种基于并联通用内模,具有频率自适应性的复合重复控制策略.提出的并联通用内模能依据指令电流谐波成份的变化动态调节内模的补偿范围和延迟时间,并进一步将延迟时间缩短至同等补偿范围内模的一半,显著提高了重复控制器动态性能和适应能力.内模中由电网频率变化引起的分数阶延时环节,采用线性插值法近似,使基于并联通用内模的重复控制器具有频率自适应性.采用插入式结构设计了复合重复控制系统.详细分析了复合控制系统的稳定条件和收敛性.以飞机变频电网并联型有源电力滤波器系统为应用环境,将提出的复合重复控制策略与其他控制策略进行了对比.仿真和实验结果验证了提出的复合重复控制策略的有效性和优越性.     

基于预估观测器的二自由度速度控制器设计

高速高精度伺服控制系统中, 预估观测器可以消除反馈信号的相位延迟和采样噪声. 但由于伺服控制系统通常采用比例-积分-微分(PID) 控制器进行反馈调节, 即使与预估观测器结合使用, 仍不能满足高速高精度系统环路性能的需要. 针对此问题, 引入一种基于预估器观测器的二自由度控制器算法, 并给出其在焊线机??-?? 平台直线电机速度控制器中的设计方法. 仿真和实验结果表明, 所提出的算法不仅可以保证系统控制的精度, 而且能够提高系统的速度和位置跟随特性.

     

基于累积平方误差–总平方波动指标的模型预测控制器性能评价及自愈

本文针对模型预测控制器实际投运中遇到性能下降问题,提出了一种基于累积平方误差(ISE)–总平方波动(TSV)指标的模型预测控制器性能评价及自愈方法.先基于累积平方误差(ISE)和总平方波动(TSV)指标对模型预测控制器进行实时性能评价,再根据无限时域模型预测控制器(MPC)的逆特性,基于ISE–TSV指标的分析,提出了...     

Neural Approximation-based Model Predictive Tracking Control of Non-holonomic Wheel-legged Robots

This paper proposes a neural approximation based model predictive control approach for tracking control of a nonholonomic wheel-legged robot in complex environments, which features mechanical model uncertainty and unknown disturbances. In order to guarantee the tracking performance of wheel-legged robots in an uncertain environment, effective approaches for reliable tracking control should be investigated with the consideration of the disturbances, including internal-robot friction and external physical interactions in the robot’s dynamical system. In this paper, a radial basis function neural network (RBFNN) approximation based model predictive controller (NMPC) is designed and employed to improve the tracking performance for nonholonomic wheel-legged robots. Some demonstrations using a BIT-NAZA robot are performed to illustrate the performance of the proposed hybrid control strategy. The results indicate that the proposed methodology can achieve promising tracking performance in terms of accuracy and stability.