多变量分数阶滞后系统预测控制参数解析调优

提出了一种针对各子系统由一阶加分数阶滞后模型描述的多变量系统模型预测控制参数解析调优方法.首先推导了多变量分数阶滞后系统的状态空间模型;其次,基于该模型构建模型预测控制优化问题,并获得了控制信号的解析表达式;再次,对闭环控制系统进行解耦分析,揭示了模型预测控制器参数与系统闭环性能间的定量关系,通过将参数调优问题转化为极点配置问题,得到能够保证闭环系统性能的模型预测控制器参数取值的解析表达式;最后通过仿真实验验证了本文所设计的参数解析调优算法的有效性.     

对角CARIMA模型多变量自适应约束广义预测控制

为了简化约束存在时多变量广义预测控制算法的设计与实现,依据对角CARIMA模型的结构特点,将多输入多输出对象的参数辨识和模型预报问题转化为一系列多输入单输出子对象的参数辨识和模型预报问题.推导了输入输出的约束形式及优化求解过程.简化了多变量对象的参数辨识、模型预报、目标函数和约束条件系数矩阵的计算.在由DCS控制的非线性液位装置上的对比实验结果表明了该方法的有效性.     

基于并行支持向量机的多变量非线性模型预测控制

提出一种基于并行支持向量机的多变量系统非线性模型预测控制算法．首先，通过考虑输入、输出间的耦合，建立基于并行支持向量机的多步预测模型；然后，将该模型用于非线性预测控制,提出新的适用于并行预测模型的反馈校正策略，得到最优控制律．连续搅拌槽式反应器（CSTR）的控制仿真结果表明,该算法的性能优于基于并行神经网络的非线性模型预测控制和基于集成模型的非线性模型预测控制．     

Tuning algorithms for fractional order internal model controllers for time delay processes

This paper presents two tuning algorithms for fractional-order internal model control (IMC) controllers for time delay processes. The two tuning algorithms are based on two specific closed-loop control configurations: the IMC control structure and the Smith predictor structure. In the latter, the equivalency between IMC and Smith predictor control structures is used to tune a fractional-order IMC controller as the primary controller of the Smith predictor structure. Fractional-order IMC controllers are designed in both cases in order to enhance the closed-loop performance and robustness of classical integer order IMC controllers. The tuning procedures are exemplified for both single-input-single-output as well as multivariable processes, described by first-order and second-order transfer functions with time delays. Different numerical examples are provided, including a general multivariable time delay process. Integer order IMC controllers are designed in each case, as well as fractional-order IMC controllers. The simulation results show that the proposed fractional-order IMC controller ensures an increased robustness to modelling uncertainties. Experimental results are also provided, for the design of a multivariable fractional-order IMC controller in a Smith predictor structure for a quadruple-tank system.     

Inverted decoupling internal model control for square stable multivariable time delay systems

This paper presents a new tuning methodology of the main controller of an internal model control structure for n × n stable multivariable processes with multiple time delays based on the centralized inverted decoupling structure. Independently of the system size, very simple general expressions for the controller elements are obtained. The realizability conditions are provided and the specification of the closed-loop requirements is explained. A diagonal filter is added to the proposed control structure in order to improve the disturbance rejection without modifying the nominal set-point response. The effectiveness of the method is illustrated through different simulation examples in comparison with other works.     

A sufficient condition for the stability of optimizing controllers with saturating actuators

The quadratic programme that must be solved with certain output–feedback model predictive controllers can be expressed as a continuous sector‐bounded nonlinearity together with two linear transformations. Thus, the multivariable circle criterion gives a simple test for stability, with or without model mismatch. In particular, it may be applied if the open‐loop plant is stable and the actuators are subject to simple saturation constraints. In the case of single horizon model predictive control, it suffices to check for positive realness a transfer function matrix whose dimension corresponds to the number of inputs. For an arbitrary length receding horizon it suffices to check the poles of a low dimension transfer function matrix and the eigenvalues (over an appropriate range of operator values) of a matrix whose dimension is independent of the horizon length. Copyright © 2005 John Wiley & Sons, Ltd.     

基于CARMA模型多变量系统的j步输出预估

用于广义预测控制（GPC）的单变量j步预估器传统上是在多项式域采用丢番图方程导出的，针对与其等价的状态空间形式预估器，对该预估器的多变量扩展进行了z域分析，得到一个多项式域的基于CARMA模型的多变量j步预估器．该预估器可以简化多变量系统的GPC算法设计，在单变量情形，该预估器即为传统的GPC预估器，最后提供了传统GPC预估器与状态空间形式预估器等价性的严格证明．     

阶梯式模块多变量模型算法控制器研究

针对多变量过程控制问题,将模块多变量与预测控制中的模型算法控制相结合,提出了模块多变量模型算法控制器.它将控制目标按照优先级排序,从高到低依次实现各个控制目标.算法中引入阶梯式控制策略,使得解决动态MMC问题成为可能.并用Shell标准问题作为研究对象,对算法作出仿真验证.实验证明该算法结构简单,可以解决动态模块多变量的问题,计算量较小,设计和改造相对容易.这些特点使模块多变量模型算法控制器具有很高的应用价值.     

Predictive control of an activated sludge process: An application to the Viikinmäki wastewater treatment plant

In this work, we discuss the application of multivariable predictive control for the activated sludge process in a full-scale municipal wastewater treatment plant. Emphasis is given to the selection of a control configuration that contributes to minimising the economic costs while improving the removal efficiency of the nitrogen compounds. For this task, a simple dynamic matrix control algorithm is favoured for controlling the nitrogen concentrations at the end of the biological process. The behaviour of the activated sludge process is reproduced in a commercial simulator that acts as a real-time testing platform and that is also used for identifying the multivariable input–output models for the predictive controller. For demonstrating the effectiveness of the proposed approach, different control configurations are considered and compared against the aeration control strategies currently used at the plant. Based on the simulation results, this work shows the potentiality of the dynamic matrix control which is able to decrease the energy consumption costs and, at the same time, reduce the ammonia peaks and nitrate concentration in the effluent.     

Robust output tracking of constrained perturbed linear systems via model predictive sliding mode control

A robustifying strategy for constrained linear multivariable systems is proposed. A combination of tracking model predictive control with output integral sliding mode techniques is used to completely reject bounded matched perturbations. It can be guaranteed that all constraints on inputs, states, and outputs are satisfied although only output information is used. Finally, real‐world experiments with an unstable plant are presented in order to demonstrate the validity and the effectiveness of the proposed approach.     

Unscented model predictive control of chance constrained nonlinear systems

This paper introduces an unscented model predictive approach for the control of constrained nonlinear systems under uncertainty. The main contribution of this paper is related to incorporation of statistical linearization, rather than commonly used analytical linearization, of the process and measurement models to provide a closer approximation of belief space propagation. Specifically, the state transition is approximated using an unscented transform to obtain a Gaussian belief space. This approximation allows for realization of closed-form solutions, which are otherwise available to linear systems only. Subsequently, the proposed approach is used to develop a model predictive motion control scheme that yields optimal control policies in presence of nonholonomic constraints as well as state estimation and collision avoidance chance constraints. As an example, successful kinematic control of a two-wheeled mobile robot is demonstrated in unstructured environments. Finally, the superiority of the proposed unscented model predictive control (MPC) over the traditional linearization-based MPC is discussed.     

模糊广义预测控制及其应用

本文将单变量广义预测控制原理应用到多变量模糊系统,提出了一种基于辨识模糊模型 的多变量预测控制方法.仿真研究表明,该模糊广义预测控制方法适用于工业过程的动态辨 识和控制,交能取得良好的效果.     

Robust nonlinear MPC based on Volterra series and polynomial chaos expansions

A robust nonlinear model predictive controller (NMPC) based on a Volterra series is proposed. Polynomial chaos expansions (PCE) are used to represent the uncertainty in the Volterra series coefficients and this uncertainty is then propagated onto the output predictions. The key advantage of the PCE is that it provides an analytical expression to compute the L²-norm of the output prediction error resulting in computational savings, compared to previously proposed techniques, which are essential for real time implementation. Terminal and input constraints based on Structured Singular Value based-norms are used to ensure convergence to a set-point and compliance with constraints in manipulated variables. The algorithm is applied to a multivariable pH neutralization system. A comparative study shows superior closed loop performance and computational efficiency of the proposed technique as compared to previously proposed algorithms.     

A predictive functional control algorithm for multivariable systems with time delay

To improve the dynamic characteristics and the coupling capability, a new predictive functional control algorithm is proposed for strong coupling multivariable systems with time delay, which combines predictive functional control and decoupliug control. First, a decoupling control algorithm is proposed, in which first-order models with time delay are established by analyzing the amplitude-frequency and phase-frequency characteristics of the decoupled subject. Then, a controller is designed for the single-variable subjects after decoupling based on the principles of predictive functional control. The simulation results show that this proposed algorithm has less online computation time and faster tracking. It can provide a more effective control for complex multivariable systems.     

混合约束过程的多变量协调预测控制

研究了工业生产过程存在着输入、输出软硬混合约束的优化控制技术。给出了基于约束的预测控制算法，提出了用动态优先级对有硬约束的操作变量进行在线协调，当协调过程找不到满足所有约束条件的可行解时，对被控变量进行约束软化，本文采取的线性和二次型相结合的惩罚函数对预测时域上每个时刻的激活值进行惩罚，不仅可以保证可行解的存在，而且能使系统处于动态和稳态的优化性能。     

大射电望远镜馈源轨迹跟踪自适应控制

针对大射电望远镜悬索馈源为一非线性慢时变多变量耦合的系统特点, 提出用多变量极点配置自校正预测控制器 (MPSTPC)来实现馈源轨迹的跟踪控制. 建立了悬索 馈源系统的数学模型, 在有随机风扰动和无随机风扰动的情况下, 进行了数字仿真. 结果表明, 该控制算法可以很好地满足馈源系统轨迹跟踪高精度要求.     

多变量双线性广义预测极点配置自校正解耦控制器（续）

对一类多变量双线性系统提出了一种基于预测状态空间实现的GPP自校正控制算法,建立了预测状态与模型结构参数和输入输出信息之间的直接关系,给出了含有多个加权矩阵的多变量二次型性能指标。增加了系统设计的自由度。由于加权因子可以根据闭环系统稳定性要求以及系统动态特性、前馈零点增补、输出滤波和跟踪要求分别加以选取,可以保证闭环系统稳定并改善了系统动态特性增强了鲁棒性,仿真结果表明了该算法具有GPP的诸多优点。     

基于反步设计的构造性非线性预测控制算法

针对具有状态和输入约束的严格反馈非线性系统,提出一种反步设计构造性非线性预测控制算法.利用反步设计法离线构造系统的控制李亚普诺夫函数,进而得到系统的镇定可调控制器即稳定控制类.基于性能指标,滚动优化控制器可调参数,计算满足系统约束的预测控制量.进一步,运用控制李亚普诺大函数的性质建立闭环系统的稳定性.最后,应用轮式移动机器人的优化控制验证本文结果的有效性.     

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.