内模统一预测控制的进一步分析

统一预测控制克服了一般预测控制器设计时难以比较每种控制器效果的缺点,将每个问题的设计统一在一种框架下进行,设计费用也显著降低.对单输入单输出系统,统一预测控制是一种优越的预测控制方法.采用内模结构就设计参数和模型匹配性对统一预测控制闭环系统的跟踪性能和鲁棒性能的影响作更为详细的分析.从中可以看出内模结构在预测控制中的独特优点.本文最后对一些结论给出了仿真结果.     

基于内模的机器人迭代学习控制

本文根据内模控制的概念，设计一个扰动控制器，使机器人系统表现为固定参数的解耦线性化系统．基于此线性系统，提出了一种迭代学习控制律，给出了算法收敛的充分条件．算法的参数选择非常简单，从而易于满足收敛条件．仿真结果表明了算法的有效性．     

预测函数控制的内模控制理论

内模控制是一种基于过程数学模型进行控制器设计的新型控制策略,是研究预测控制重要的理论基础。预测函数控制是一种控制量计算方程简单,实时控制效果好的新型预测控制算法。本文用内模控制理论研究预测函数控制,分析了系统的鲁棒性和稳定性,最后进行了参数设计和仿真研究。     

基于T-S模型的内模控制算法及其应用

针对模糊内模控制算法中模型的建立及模型求逆困难的问题,对一种模糊建模方法进行了改进,在此基础上提出了一种基于T—S模型的内模控制方法。采用启发性知识与复合非线性优化方法相结合的综合方法求解出模糊模型的结构,由模糊辨识获得过程的T—S模型和逆模型,并以此为基础建立内模控制算法。将该算法分别应用于慢时变非线性对象和具有大时延大惯性的热工系统主蒸汽温度的控制,仿真结果表明了该方法具有结构简单,计算效率高等优点,有利于在线应用。     

预测控制算法的内模结构及其统一格式

本文应用内模控制（ＩＭＣ）原理，研究了现有各类预测控制算法ＩＭＣ、ＭＡＣ、ＤＭＣ、ＧＰＣ、ＧＰＰ的控制器方程，闭环系统输入输出和误差方程，归纳出它们的统一算式，为理解各类预测控制算法的内在联系及进一步研究提供了方便．     

基于迭代学习与模型预测控制的交通信号混合控制方法

针对基于迭代学习控制的交通信号控制方法对于路网中存在的非重复性实时干扰不能进行有效处理的问题,本文在基于迭代学习控制的交通信号控制方法基础上,结合模型预测控制滚动优化和实时校正的特点,提出了一种基于迭代学习与模型预测控制的交通信号混合控制方法.该方法在有效利用交通流周期性特征改善路网交通状况的同时,可借助模型预测控制的...     

注塑机注射速度的模型预测迭代学习控制

针对注射过程具有重复运行和非线性的特性,在对预测控制与迭代学习控制进行综合应用并加以改进的基础上,给出一种模型预测迭代学习复合控制新算法,研究了控制器的设计方案。同时,将迭代学习思想引入到预测步长的在线调整,提出了预测步长的迭代学习方法。仿真结果表明,该方法是有效的,其控制性能优于PID送代学习控制系统。     

注塑机注射速度的模型预测迭代学习控制

针对注射过程具有重复运行和非线性的特性,在对预测控制与迭代学习控制进行综合应用并加以改进的基础上,给出一种模型预测迭代学习复合控制新算法,研究了控制器的设计方案.同时,将迭代学习思想引入到预测步长的在线调整,提出了预测步长的迭代学习方法.仿真结果表明,该方法是有效的,其控制性能优于PID迭代学习控制系统.     

Volterra模型辨识及其在蒸馏塔预测控制中的应用

本文研究一类单输入单输出非线性系统的预测函数控制问题，这类系统能用有限阶离散Volterra级数模型表示，采用最小二乘法进行参数辨识，并通过求解高次方程得到控制律。针对化工过程蒸馏塔控制系统，通过仿真计算验证了该方法的有效性。     

一类非最小相位对象的内模预测控制：MIMO情形

针对一类多变量非最小相位受控对象,提出一种基于多道非因果ＦＩＲ型控制器的内模预测控制新算法,建立了控制器系数矢量与多变量受控对象输出的关系,并给出相应的控制器系数矢量估计法与仿真实例。     

A design method for indirect iterative learning control based on two-dimensional generalized predictive control algorithm

Indirect iterative learning control (ILC) facilitates the application of learning-type control strategies to the repetitive/batch/periodic processes with local feedback control already. Based on the two-dimensional generalized predictive control (2D-GPC) algorithm, a new design method is proposed in this paper for an indirect ILC system which consists of a model predictive control (MPC) in the inner loop and a simple ILC in the outer loop. The major advantage of the proposed design method is realizing an integrated optimization for the parameters of existing feedback controller and design of a simple iterative learning controller, and then ensuring the optimal control performance of the whole system in sense of 2D-GPC. From the analysis of the control law, it is found that the proposed indirect ILC law can be directly obtained from a standard GPC law and the stability and convergence of the closed-loop control system can be analyzed by a simple criterion. It is an applicable and effective solution for the application of ILC scheme to the industry processes, which can be seen clearly from the numerical simulations as well as the comparisons with the other solutions.     

Latent variable iterative learning model predictive control for multivariable control of batch processes

A latent variable iterative learning model predictive control (LV-ILMPC) method is presented for trajectory tracking in batch processes. Different from the iterative learning model predictive control (ILMPC) model built from the original variable space, LV-ILMPC develops a latent variable model based on dynamic partial least squares (DyPLS) to capture the dominant features of each batch. In each latent variable space, we use a state–space model to describe the dynamic characteristics of the internal model, and an LV-ILMPC controller is designed. Each LV-ILMPC controller tracks the set points of the current batch projection in the corresponding latent variable space, and the optimal control law is determined and the persistent process disturbances is rejected along both time and batch horizons. The proposed LV-ILMPC formulation is based on general LV-MPC and incorporates an iterative learning function into LV-MPC. In addition, the real physical input that drives the process can be reconstructed from the latent variable space. Therefore, this algorithm is particularly suitable for multiple-input, multiple-output (MIMO) systems with strong coupling and serious collinearity. Three studies are used to illustrate the effectiveness of the proposed LV-ILMPC .     

Single-cycle and multi-cycle generalized 2D model predictive iterative learning control (2D-GPILC) schemes for batch processes

In this paper, iterative learning control (ILC) system is modeled and designed from a two-dimensional (2D) system point of view. Based on a 2D cost function defined over a single-cycle or multi-cycle prediction horizon, two ILC schemes, referred respectively as single-cycle and multi-cycle generalized 2D predictive ILC (2D-GPILC) schemes, have been proposed and formulated in the GPC framework for the 2D system. Analysis shows that the resulted control schemes are the combination of a time-wise GPC and a cycle-wise ILC optimized in 2D sense. Guidelines for parameter tuning have been proposed based on the ultimate performance analysis for the control system. Simulation shows that the multi-cycle 2D-GPILC outperforms the single-cycle 2D-GPILC in term of cycle-wise convergence.     

An artificial pancreas system in android phones: A dual app architecture

An Artificial Pancreas (AP) system centered around an Android Smartphone is proposed in this paper. This unique architecture involves two separate but interacting modular Android applications (Apps) that are designed for unique functionalities. Even though an artificial pancreas system is a safety-critical system that demands a complex architecture and careful coding process, owing to their modular nature, these apps could be designed and developed through rapid prototyping processes. The first app (App-A), which runs in the front-end, serves as the user interface and acts as a connection hub for the hardware devices, was developed on the Android studio platform. Necessary communication protocols to enable communication with the hardware devices are incorporated into this app. The second app (App-B), which runs in the back-end, is developed using Simulink’s Android support package. It contains a safety-critical model predictive control algorithm with appropriate constraints to compute the required insulin rate, augmented with a Kalman Filter for estimating the states. There is an additional safety logic as well to prevent insulin over-dosage, thereby augmenting to the cause of a safety-critical control algorithm. The contribution of this work is a modular software framework and prototyping methodology that can be used for rapid development, testing and deployment of Android based AP-systems.     

Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters

A multi-parametric model predictive control (mpMPC) algorithm for subcutaneous insulin delivery for individuals with type 1 diabetes mellitus (T1DM) that is computationally efficient, robust to variations in insulin sensitivity, and involves minimal burden for the user is proposed. System identification was achieved through impulse response tests feasible for ambulatory conditions on the UVa/Padova simulator adult subjects with T1DM. An alternative means of system identification using readily available clinical parameters was also investigated. A safety constraint was included explicitly in the algorithm formulation using clinical parameters typical of those available to an attending physician. Closed-loop simulations were carried out with daily consumption of 200 g carbohydrate. Controller robustness was assessed by subject/model mismatch scenarios addressing daily, simultaneous variation in insulin sensitivity and meal size with the addition of Gaussian white noise with a standard deviation of 10%. A second-order-plus-time-delay transfer function model fit the validation data with a mean (coefficient of variation) root-mean-square-error (RMSE) of 26 mg/dL (19%) for a 3 h prediction horizon. The resulting control law maintained a low risk Low Blood Glucose Index without any information about carbohydrate consumption for 90% of the subjects. Low-order linear models with clinically meaningful parameters thus provided sufficient information for a model predictive control algorithm to control glycemia. The use of clinical knowledge as a safety constraint can reduce hypoglycemic events, and this same knowledge can further improve glycemic control when used explicitly as the controller model. The resulting mpMPC algorithm was sufficiently compact to be implemented on a simple electronic device.     

An open-closed-loop iterative learning control approach for nonlinear switched systems with application to freeway traffic control

For nonlinear switched discrete-time systems with input constraints, this paper presents an open-closed-loop iterative learning control (ILC) approach, which includes a feedforward ILC part and a feedback control part. Under a given switching rule, the mathematical induction is used to prove the convergence of ILC tracking error in each subsystem. It is demonstrated that the convergence of ILC tracking error is dependent on the feedforward control gain, but the feedback control can speed up the convergence process of ILC by a suitable selection of feedback control gain. A switched freeway traffic system is used to illustrate the effectiveness of the proposed ILC law.     

一种间歇过程的综合预测迭代学习控制方法

为了提高迭代学习控制方法在间歇过程轨迹跟踪问题中的收敛速度,本文将批次间的比例型迭代学习控制与批次内的模型预测控制相结合,提出了一种综合应用方法.首先根据间歇过程的线性模型,预测出比例型迭代学习控制的系统输出,然后在批次内采用模型预测控制,通过极小化一个二次型目标函数来获得控制增量.该方法可使系统输出跟踪期望轨迹的速度比比例型迭代学习控制方法更快些.最后通过仿真实例验证了该方法的有效性.     

电流型变流器的改进模型预测控制

传统的模型预测控制(MPC)采用脉冲响应的非参数模型作为系统的预测控制模型,计算量大,很难直接应用于实时控制系统.本文提出将传统的MPC进行改进,应用到电流型变流器(CSC)功率因数校正系统中.改进的MPC根据CSC控制量与被控制量的传递函数得出CSC的一阶差分方程作为预测控制模型,同时保留传统MPC反馈校正、动态优化等优点.将此改进的MPC与传统的PID控制进行对比研究,结果表明:改进的MPC用于电流型变流器,比传统的PID控制具有更好的鲁棒性和更快速的动态响应特性.