广义预测控制中闭环系统稳定新的充分条件

基于被控对象的离散差分方程推导出广义预测控制中闭环系统特征多项式的阶数,然后给出一个新的保证闭环系统稳定的充分条件。     

非线性模型预测控制的现状与问题

模型预测控制的主要优点是能显式并优化处理控制量和状态量的约束。为此,主要围绕非线性预测控制的算法、稳定性鲁棒性、对偶问题和流动时域估计的最新研究成果进行综述,并阐述了理论与应用方面有待进一步研究的几个主要问题。     

双值预测函数控制

针对典型工业过程－－一阶加纯滞后对象跟踪设定曲线提出了双值预测函数控制。在给出控制算法的基础上,对系统稳定性和鲁棒性进行了分析,给出了增益失配的鲁棒性条件。     

加权多步预报控制

本文提出了一类加权多步预报控制(WLPC)算法.这种算法是由极小化一个很一般的加权二次型性能指标得到的.由于权因子可以根据闭环极点配置、前馈零点增补和动态性能要求任意选取,所以可保证闭环系统的稳定性和对建模误差的鲁棒性.文中给出了这方面结果的理论证明和仿真实例.     

预测函数控制系统的闭环性能分析

分析了单变量预测函数控制系统的闭环稳定性、跟踪性能、鲁棒性等问题,并在此基础上讨论了控制参数的调节方法,通过理论分析和仿真表明预测函数控制方法是一种具有计算简单、鲁棒性较强、抑制干扰能力好、控制精度高的控制方法。     

冷连轧机张力系统的预测函数控制

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，可以处理不稳定，时滞，带约束等系统，尤其适用于快速系统的控制。本文将预测函数控制用于冷连轧机张力控制系统中，分析了系统的鲁棒性，稳定性和快速性，仿真结果表现预测函数控制具有良好的控制性能。     

预测控制应用于工业过程的若干问题

本文论述了广义预测自校正控制器在工业过程应用中的一些问题：建模问题、辨识问题以及参数与目标函数的选取问题。并给出了三个具体应用的实例。     

双值预测函数控制

针对典型工业过程——一阶加纯滞后对象跟踪设定曲线提出了双值预测函数控制。在给出控制算法的基础上,对系统稳定性和鲁棒性进行了分析,给出了增益失配的鲁棒性条件。     

预测控制性能研究的新进展

随着预测控制在工业过程中的广泛应用,近年来,预测控制理论研究也取得了很大进展。本文系统地综述和评析了在预测控制性能方面理论研究的新进展,主要内容包括控制系统的稳定性,鲁棒性,可行性等性能以及对非线性系统的研究。     

Advanced-multi-step nonlinear model predictive control

Nonlinear model predictive control (NMPC) has gained widespread attention due to its ability to handle variable bounds and deal with multi-input, multi-output systems. However, it is susceptible to computational delay, especially when the solution time of the nonlinear programming (NLP) problem exceeds the sampling time. In this paper we propose a fast NMPC method based on NLP sensitivity, called advanced-multi-step NMPC (amsNMPC). Two variants of this method are developed, the parallel approach and the serial approach. For the amsNMPC method, NLP problems are solved in background multiple sampling times in advance, and manipulated variables are updated on-line when the actual states are available. We present case studies about a continuous stirred tank reactor (CSTR) and a distillation column to show the performance of amsNMPC. Nominal stability properties are also analyzed.     

Handling communication disruptions in distributed model predictive control

In this work, we study distributed model predictive control (DMPC) of nonlinear systems subject to communication disruptions - communication channel noise and data losses - between distributed controllers. Specifically, we focus on a DMPC architecture in which one of the distributed controllers is responsible for ensuring closed-loop stability while the rest of the distributed controllers communicate and cooperate with the stabilizing controller to further improve the closed-loop performance. To handle communication disruptions, feasibility problems are incorporated in the DMPC architecture to determine if the data transmitted through the communication channel is reliable or not. Based on the results of the feasibility problems, the transmitted information is accepted or rejected by the stabilizing MPC. In order to ensure the stability of the closed-loop system under communication disruptions, each model predictive controller utilizes a stability constraint which is based on a suitable Lyapunov-based controller. The theoretical results are demonstrated through a nonlinear chemical process example.     

Nonlinear model predictive control for the ALSTOM gasifier

In this work a nonlinear model predictive control based on Wiener model has been developed and used to control the ALSTOM gasifier. The 0% load condition was identified as the most difficult case to control among three operating conditions. A linear model of the plant at 0% load is adopted as a base model for prediction. A nonlinear static gain represented by a feedforward neural network was identified for a particular output channel—namely, fuel gas pressure, to compensate its strong nonlinear behaviour observed in open-loop simulations. By linearising the neural network at each sampling time, the static nonlinear model provides certain adaptation to the linear base model at all other load conditions. The resulting controller showed noticeable performance improvement when compared with pure linear model based predictive control.     

A new real-time perspective on non-linear model predictive control

This work presents a new formulation of continuous-time non-linear model predictive control (NMPC) in which the parameters defining the input trajectory are adapted continuously in real time. Continuous implementation of the control as the input parameterization is being optimized reduces the impact of computational delay, in particular in response to process disturbances. By eliminating the typical correspondence between the time partitions used for input parameterization and implementation, and instead parameterizing the input over arbitrary intervals of variable length, a means is provided to reduce the overall number of optimization parameters (and hence the dimension of the required gradient and Hessian calculations) without adversely affecting stability or optimality.     

Spacecraft attitude control using explicit model predictive control

In this paper, an explicit model predictive controller for the attitude of a satellite is designed. Explicit solutions to constrained linear MPC problems can be computed by solving multi-parametric quadratic programs (mpQP), where the parameters are the components of the state vector. The solution to the mpQP is a piecewise affine (PWA) function, which can be evaluated at each sample to obtain the optimal control law. The on-line computation effort is restricted to a table-lookup, and the controller can be implemented on inexpensive hardware as fixed-point arithmetics can be used. This is useful for systems with limited power and CPU resources. An example of such systems is micro-satellites, which is the focus of this paper. In particular, the explicit MPC (eMPC) approach is applied to the SSETI/ESEO micro-satellite, initiated by the European Space Agency (esa). The theoretical results are supported by simulations.     

Model predictive control using an extended ARMarkov model

The original ARMarkov identification method explicitly determines the first μ Markov parameters from plant input–output data and approximates the slower dynamics of the process by an ARX model structure. In this paper, the method is extended to include a disturbance model and an ARIMAX structure is used to approximate the slower dynamics. This extended ARMarkov model is then used to formulate a predictive controller. As the number of Markov parameters in the model varies from one to P (prediction horizon)+1, the controller changes from generalized predictive control (GPC) to dynamic matrix control (DMC). The advantages of the proposed ARM-MPC are the consistency of the Markov parameters estimated by the ARMarkov method, independent tuning of the controller for servo and regulatory responses and the ability to combine the characteristics of GPC and DMC. The theoretical results are illustrated through simulation examples.     

Constrained robust model predictive control based on periodic invariance

In this paper, a receding-horizon control method for input/state constrained systems with polyhedral uncertainties is proposed. The dual-mode prediction strategy is adopted to deal with the constraints and periodically-invariant sets are used to derive a target invariant set of the dual-mode prediction strategy. The proposed control method is shown to have novel characteristics earlier approaches do not have i.e.: (i) the convex-hull of all the periodically invariant sets are invariant in the sense that there are feasible feedback gains guaranteeing invariance for any elements of the convex-hull and it provides larger target sets than other methods based on ordinary invariant sets. (ii) A particular convex-hull of periodically invariant sets, that is computable off-line, can be used as an invariant target set. In this case the number of on-line variables is only equal to the period of invariance and thus the proposed algorithm is computationally very efficient. These on-line variables provide interpolation between different feedback gains to yield best performance.     

A formulation of nonlinear model predictive control using automatic differentiation

An efficient algorithm is developed to alleviate the computational burden associated with nonlinear model predictive control (NMPC). The new algorithm extends an existing algorithm for solutions of dynamic sensitivity from autonomous to non-autonomous differential equations using the Taylor series and automatic differentiation (AD). A formulation is then presented to recast the NMPC problem as a standard nonlinear programming problem by using the Taylor series and AD. The efficiency of the new algorithm is compared with other approaches via an evaporation case study. The comparison shows that the new algorithm can reduce computational time by two orders of magnitude.     

基于Laguerre模型的预测函数控制及其稳定性分析

基于Laguerre模型设计了一种自适应预测函数控制器.通过对闭环系统状态方程的稳定性分析.依据Lyapunov稳定性定理和奇异值理论得到控制系统稳定的必要条件.提出一种衰减因子校正方法,并使用遗传算法在线寻优调节衰减因子,以提高控制品质.仿真研究表明该控制算法稳定性强、在线计算量小.     

Efficient building energy management using distributed model predictive control

This work presents a distributed model predictive (DMPC) scheme for the efficient management of energy distribution in buildings. The energy demanded by the building's residents is supplied by a renewable power system whose capacity is limited and sometimes cannot fulfill the energy requirements of the residents, depending on the availability of renewable resources. Extensions are proposed for the distributed controllers aiming to overcome difficulties that arise from the direct application of a standard DMPC formulation. The alternative formulation retains desirable features like the ability to perform energy saving, when demand does not exceed supply, and to effectively distribute energy without disproportionally harming any of the building users, when the system experiences a shortage of energy supply. Simulation and experimental results obtained in a solar energy research center located in Almería, Spain, are reported and discussed, showing promising results for the proposed control strategy.