柔化系数在线调整改进广义预测控制在质子交换膜燃料电池供气系统中的应用

针对质子交换膜燃料电池中质子交换膜两侧的压力差易受负载电流变化的影响及压力差在常规控制中输出响应幅值过大等不足,本文提出了基于模糊推理的柔化系数在线调整改进广义预测控制算法来实现对压力差的控制。在预测控制的基础上,通过加入模糊推理对柔化系数进行实时调整和更新,来实现对供气系统中氢气分压和氧气分压输出响应速率的调节,以达到改善压力差动态响应过程的目的:。借助MATLAB进行仿真实验,验证了提出的控制算法在对压力差的控制具有调节时间短和输出响应幅值小等优点,其控制效果优于二固定柔化系数广义预测控制算法和PID控制算法。     

一种约束输入输出的隐式广义预测控制新算法

基于CARIMA模型提出了一种约束输入输出的隐式广义预测控制算法.针对广义预测控制问题,在整个预测时域和控制时域,对输入幅值,输入增量和输出幅值施加了约束,引入了输入输出柔化系数,从而简化了目标函数,减小了计算量,该算法不必求解逆矩阵;并采用了隐式广义预测自校正控制算法,利用并列预测控制器间的特点,直接辨识输出预测器中的参数,从而避免了在线求解Diophantine方程.该算法占用内存小,计算速度快,仿真结果表明该算法具有良好的控制性能.     

改进的广义预测算法在过热气温控制中的应用

针对传统广义预测控制算法（GPC）的计算量大这一缺陷,结合隐式广义预测算法（IGPC）和基于柔化矩阵的广义预测算法,通过辨识参数和输入增量引入柔化系数矩阵进行约束的方法对原算法进行改进,算法只精确计算当前时刻的控制作用而对未来时刻的控制序列进行离线近似计算.该算法简单,不必求解丢番图（Diophantine）方程和矩阵求逆,减小了在线计算量,确保了系统的快速性,并能够将输入很好地控制在约束范围之内,并具有良好的控制性能.同时隐式广义预测算法对模型的阶次、参数的变化都有较好的鲁棒性,能适应电厂过热汽温的控制.     

乙苯脱氢反应器系统的广义预测控制

本文详细介绍了在乙苯脱氢反应器计算机控制系统中引人多输入、单输出系统(MISO) 的广义预测控制算法,以及在扰动和负荷变化下在线控制情况.该算法简捷、直观,具有良好 的控制效果.文中还给出了算法的推导过程,讨论了柔化参数α和加权系数λ对控制质量的 影响.     

基于柔化矩阵的广义预测控制快速算法

本文针对输入要求平稳的系统设备提出了一种预测控制新算法.该算法通过对输入增量引入柔化系数矩阵从而进行约束,避免了矩阵求逆的计算,大大减少了计算量,确保了系统的快速性,同时保留了广义预测控制的基本特征.其次,控制算法中改善了传统广义预测控制算法的单一性,充分利用预测信息的补偿作用进行加权平均,很好得抑制了超调的出现.通过挤出机的压力系统做仿真试验,结果验证了该算法的有效性.     

滑模等式约束的时滞系统广义预测控制

提出了一种滑模等式约束的广义预测控制方法.该方法将广义预测控制与离散滑模控制结合起来用于具有大惯性、大时滞、时变和非线性的热力站换热机组的供水温度控制系统中,并采用柔化输入信号的方法,可避免广义预测控制算法中的矩阵求逆,有效缩短了预测时域,减小计算量.最后给出了稳定性分析并通过仿真验证了该方法的有效性.     

输入平稳的快速无超调预测控制算法

针对输入要求平稳的系统设备,提出了一种快速无超调预测控制新算法。该算法通过对输入增量引入柔化系数矩阵从而进行约束,防止了输入的剧烈变化;既达到了系统输入平稳的要求,又克服了传统控制律中求解逆矩阵的缺点,提高了系统响应的快速性。其次,控制算法中改善了传统广义预测控制算法的单一性,充分利用预测信息的补偿作用进行加权平均,很好地抑制了超调的出现。通过对水下航行器航向系统的仿真试验比较,试验结果验证了该算法的有效性。     

广义预测控制系统的闭环特性分析

从状态空间角度分析了广义预测控制的闭环特性,证明了广义预测控制是状态反馈控制。在控制量加权系数λ为0的条件下,分析了柔化因子α对系统闭环性能的影响,给出了柔化因子对系统闭环性能影响的关系式,证明了闭环系统稳定性与开环对象的稳定性无关。仿真验证了分析方法的有效性。     

广义预测控制器系数直接算法

为了简化广义预测控制算法的分析与设计，提出了广义预测控制器系数直接计算方法．该方法利用过程模型直接递推，把广义预测控制律表达成控制器系数与参考轨迹及过程历史信息乘积的形式．其控制器系数计算只与模型参数及设计参数有关，避免了在线求解Diophantine方程、输出预测表达式及自由响应项，简化了设计思路，减少了在线运算量．在一个DCS控制的非线性液位装置上得到的对比实验结果表明该方法是可行和有效的．     

超声电机参数摄动与广义预测控制

针对超声电机速度与驱动频率具有很强的非线性,理论建模困难,难以实现高精确控制的问题,采用辨识的方法建立了旋转行波超声电机的Hammerstein模型,并测量了该模型的参数摄动;针对电机参数在大范围内摄动,提出采用两步法广义预测控制方法,整定了广义预测控制算法的参数,并对超声电机两步法广义预测控制进行了稳定性分析和速度控制仿真。理论分析和仿真结果都表明,超声电机Hammerstein模型参数在大范围内摄动时,两步法广义预测控制器能够准确地跟踪速度设定值的变化,证明了超声电机广义预测控制的可行性,为超声电机高精度控制提供了控制模型和控制方法。     

基于状态空间模型广义预测控制的并行算法

本文首先基于脉动阵列经，提出了一种实时参数辨识的并行算法，然后推导出基于状态空间模型广义预测控制的两种新算法，这两种算法都可以通过阵列结构并行实现。     

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.     

Predictive metamorphic control

In spite of its easy implementation, ability to handle constraints and nonlinearities, etc., model predictive control (MPC) does have drawbacks including tuning difficulties. In this paper, we propose a refinement to the basic MPC strategy by incorporating a tuning parameter such that one can move smoothly from an existing controller to a new MPC strategy. Each change of this tuning parameter leads to a new stabilising control law, therefore, allowing one to gradually move from an existing control law to a new and better one. For the infinite horizon case without constraints and for the general case with state and input constraints, stability results are established. We also examine the practical applicability of the proposed approach by employing it in the nominal prediction model of the tube-based output feedback robust MPC method. The merits of the proposed method are illustrated by examples.     

基于神经网络的时滞非线性系统的广义预测控制

针对一类时滞非线性被控对象,提出一种基于RBF神经网络的广义预测自校正控制方案,在广义预测控制中,采用RBF神经网络建立被控对象的多步预测模型,并不断修正预测输出,提高预测输出的精度.控制器则采用GPC隐式修正算法,不用辨识对象的模型参数,大大减少了计算量.经过仿真研究,与常规的PID自适应控制方法相比较,证明了该方法的优越性,预测控制误差小,实时性好,动态响应快.     

A Design Of Multiloop Predictive Self‐Tuning Pid Controllers

In this paper, a new design scheme of multiloop predictive self‐tuning PID controllers is proposed for multivariable systems. The proposed scheme firstly uses a static pre‐compensator as an approximately decoupling device, in order to roughly reduced the interaction terms of the controlled object. The static matrix pre‐compensator is adjusted by an on‐line estimator. Furthermore, by regarding the approximately decoupled system as a series of single‐input single‐output subsystems, a single‐input single‐output PID controller is designed for each subsystem. The PID parameters are calculated on‐line based on the relationship between the PID control and the generalized predictive control laws. The proposed scheme is numerically evaluated on a simulation example.     

A model predictive control approach with relevant identification in dynamic PLS framework

In this paper, a generalized predictive control (GPC) scheme under a dynamic partial least squares (PLS) framework is proposed. At the modeling stage, a model predictive control relevant identification (MRI) approach is used to improve the identification of the model. Within PLS framework, the MIMO system can be automatically decomposed into several SISO subsystems in the latent space. For each subsystem, MRI is implemented and GPC is designed independently. With the advantage of MRI and PLS framework, fewer parameters are needed to be estimated in the identification stage, nonsquare and ill-conditioned system can be handled naturally, control parameters tuning is easier and better control performance can be obtained. Furthermore, the computing time of control action which is very crucial for GPC on-line application decreases since each GPC is running in SISO subsystem in parallel. The results of two simulation examples and a laboratory experiment demonstrate the merit of the proposed method.     

A study on industrial robotic manipulator model using model based predictive controls

In this study, a single input single output (SISO) neural generalized predictive control (NGPC) was applied to a six joint robotic manipulator. The SISO generalized predictive control (GPC) was also used for comparison. Modeling of the dynamics of the robotic manipulator was made by using the Lagrange–Euler equations. The cubic trajectory principle is used for position reference and velocity reference trajectories. A simulation program was prepared by using Delphi 6.0. All computations for manipulator dynamics model, GPC-SISO, and NGPC-SISO were done on PC with 1.6 GHz Centrino CPUs by using this program. The parameter estimation algorithm used in the GPC-SISO is Recursive Least Squares. The minimization algorithm used in the NGPC-SISO is Newton–Raphson. According to the simulation results, the results of the NGPC-SISO algorithm were better than those of the GPC-SISO algorithm.     

Adaptive fuzzy identification and predictive control for industrial processes

This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T–S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T–S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm’s performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T–S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T–S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller.     

基于多变量广义预测控制的不稳定系统控制结构选择方法

对于多输入多输出系统, 在控制系统设计时首先要对被控变量和操纵变量进行控制结构选择. Bristol提出的相关增益矩阵(Relative gain array, RGA)法, 以及学者们后来提出的各种改进方法, 都只适用于稳定系统. 本文针对不稳定系统, 基于多变量广义预测控制(Generalized predictive control, GPC)的闭环控制律提出了一种控制结构的变量匹配准则. 通过对预测时域、控制时域等各个参数的优化选择, 使系统闭环稳定; 由闭环控制律得到被控变量期望值与操纵变量的相关性矩阵, 以此得出控制结构的变量配对方案. 通过实例研究表明, 对于开环不稳定系统, 该方法可以得出正确的变量配对结果.