Output constrained IMC controllers in control systems of electromechanical actuators

Electromechanical actuators are widely used in many industrial applications. There are usually some constraints existing in a designed system. This paper proposes a simple method to design constrained controllers for electromechanical actuators. The controllers merge the ideas exploited in internal model control and model predictive control. They are designed using the standard control system structure with unity negative feedback. The structure of the controllers is relatively simple as well as the design process. The output constraint handling mechanism is based on prediction of the control plant behavior many time steps ahead. The mechanism increases control performance and safety of the control plant. The benefits offered by the proposed controllers have been demonstrated in real-life experiments carried out in control systems of two electromechanical actuators: a DC motor and an electrohydraulic actuator.     

Nonlinear fuzzy model predictive iterative learning control for drum-type boiler–turbine system

Advanced control strategy is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. Model predictive control (MPC) has been widely used for controlling power plant. Nevertheless, MPC needs to further improve its learning ability especially as power plants are nonlinear under load-cycling operation. Iterative learning control (ILC) and MPC are both popular approaches in industrial process control and optimization. The integration of model-based ILC with a real-time feedback MPC constitutes the model predictive iterative learning control (MPILC). Considering power plant, this paper presents a nonlinear model predictive controller based on iterative learning control (NMPILC). The nonlinear power plant dynamic is described by a fuzzy model which contains local liner models. The resulting NMPILC is constituted based on this fuzzy model. Optimal performance is realized within both the time index and the iterative index. Convergence property has been proven under the fuzzy model. Deep analysis and simulations on a drum-type boiler–turbine system show the effectiveness of the fuzzy-model-based NMPILC     

一种基于最小二乘支持向量机的预测控制算法

针对工业过程中普遍存在的非线性被控对象，提出一种基于最小二乘支持向量机建模的预测控制算法．首先，用具有RBF核函数的LS-SVM离线建立被控对象的非线性模型；然后，在系统运行过程中，将离线模型在每一个采样周期关于当前采样点进行线性化，并用广义预测算法实现对被控系统的预测控制．仿真结果表明了该算法的有效性和优越性．     

Predictive Functional Control Based on Fuzzy Model: Comparison with Linear Predictive Functional Control and PID Control

The implementation of the fuzzy predictive functional control (FPFC) on the magnetic suspension system is presented in the paper. The magnetic suspension system was in our case the pilot plant for magnetic bearing and is an open-loop unstable process, therefore a lead compensator was used to stabilize it. The high quality control requirements were a-periodical step response and zero steady-state error. Adding the integrator to a feedback causes overshoot. The solution to the problem was cascade control with fuzzy predictive functional controller in the outer loop. To cope with the unknown model parameters and the nonlinear nature of the magnetic system, a fuzzy identification based on FNARX model was used. After successful validation the obtained fuzzy model was used for controller design. The FPFC is compared with a cascade linear predictive functional control (PFC) and PID control. The results we obtained with the FPFC are very promising and hardly comparable with conventional control techniques.     

Distributed model predictive control of an experimental four-tank system

A distributed model predictive control (DMPC) framework is proposed. The physical plant structure and the plant mathematical model are used to partition the system into self-sufficient estimation and control nodes. Local measurements at the nodes are used to estimate the relevant plant states. This information is then used in the model predictive control calculations. Communication among relevant nodes during estimation and control calculations provides improvement over the performance of completely decentralized controllers. The DMPC framework is demonstrated for the level control of an experimental four-tank system. The performance of the DMPC system for disturbance rejection is compared with other control configurations. The results indicate that the proposed framework provides significant improvement over completely decentralized MPC controllers, and approaches the performance of a fully centralized design.     

基于最小方差基准的软约束调整与经济性能协调

模型预测控制的经济性能主要通过减少关键工艺参数的方差,以及移动过程的操作点使其更接近约束边界来实现。另一方面,软约束边界需要经常的调整以有效地解决模型预测控制的优化不可行问题。在协调软约束调整与模型预测控制的经济性能的过程中,本文提出了将基于性能评估的最小方差引入到模型预测控制的稳态目标计算中,以保证模型预测控制能够更加合理地提高系统的经济性能。最后,以延迟焦化装置加热炉预测控制为例,讨论和分析了该方法的有效性。     

状态反馈预测控制的结构性能及应用实例

本文分析了状态反馈预测控制系统的结构与控制性能，基于状态空间模型，使用实 测状态变量反馈，状态反馈预测控制系统是状态变量动态反馈结构．对比动态矩阵控制（DM C）、广义预测控制（GPC）和内模控制（IMC）算法，由于使用可测状态变量动态反馈结构 ，提高了控制系统抑制不可测干扰能力，改善了控制系统的鲁棒性，预测模型可适用于较大 的操作范围．预测控制系统稳定时，对阶跃型给定值及干扰静态无偏差．给出了催化裂化装 置稳定吸收系统，稳定汽油饱和蒸汽压先进控制的应用实例，先进控制系统的设计功能全部 在DCS层实现，现场运行实测数据对比表明控制效果较好．     

A computer-aided design tool for robustness analysis and control-relevant identification of Horizon Predictive Control with application to a binary distillation column

This paper describes a MATLAB-based computer-aided design tool, IRA-HPC, which accomplishes integrated system identification and robustness analysis for Horizon Predictive Control (HPC), a model predictive control algorithm implemented on the Application Module of the Honeywell TDC 3000 distributed control system. The tool addresses lifecycle as well as functional aspects of the technology, with the goal of making advanced control principles more accessible to the practising control engineer. IRA-HPC systematically performs the various stages of system identification in a control-relevant framework (addressing input design, parameter estimation, and model validation from the standpoint of the final purpose of the model, which is control system design), followed by robust HPC controller tuning using the Structured Singular Value (μ) paradigm as a basis. The benefits of the tool are shown experimentally in the modelling and control of a methanol/isopropanol pilot-scale distillation column, interfaced to an industrial-scale real-time computing testbed. The example demonstrates the practical feasibility of this tool and its benefits in terms of simplifying the choices of design variables in integrated identification and control design.     

MATLAB环境下的模型预测控制理论的应用

从模型预测控制的原理出发,介绍利用MATLAB模型预测控制工具箱进行模型预测控制器设计的全过程。就被控对象的不同模型,以及各类模型形式之间的转换做了具体的系统的阐述。在控制器的设计过程中,给出不同情况下的控制器的设计方法,并且对控制器设计中的参数选择对系统控制性能的影响进行了分析与总结。最后通过数值实例说明了如何进行了模型预测控制器的设计。     

Neural network modelling and control for underwater vehicles

Neural networks are currently finding practical applications ranging from ‘soft’ regulatory control in consumer products to accurate control of non-linear plant in the process industries. This paper describes the application of neural networks to modelling and control of a prototype underwater vehicle, as an example of a system containing severe non-linearities. The most common implementation strategy for neural control is model predictive control, where a model of the process is developed first and is used off-line to design an appropriate compensator. The accuracy and robustness of this control strategy relies on the quality of the non-linear process model, in particular its ability to predict the plant response accurately multiple-steps ahead. In this paper, several neural network architectures are used to evaluate a long-range model predictive control structure, both in simulation and for on-line control of vehicle depth, achieving accurate control with a smooth actuator signal.     

基于递归神经网络模型预测控制的模型平稳切换

不同生产条件下的控制系统可视多模型控制系统,但多模型控制在模型切换时会引起系统的瞬态响应。采用递归神经网络建立系统的多个模型,基于模型预测控制进行控制模型切换,克服了模型切换时引起的系统瞬态响应,实现系统的平稳切换。并通过仿真表明这种切换策略明显改善了模型切换过程的动态性能。     

Developing a robust model predictive control architecture through regional knowledge analysis of artificial neural networks

Chemical processes are nonlinear. Model based control schemes such as model predictive control are highly related to the accuracy of the process model. For a highly nonlinear chemical system, it is clear to implement a nonlinear empirical model, such as artificial neural network model, should be superior to a linear model such as dynamic matrix model. However, unlike linear systems, the accuracy of a nonlinear empirical model strongly depends on its original data or training data based on how the model is built up. A regional-knowledge index is proposed in this study and applied in the analysis of dynamic artificial neural network models in process control. New input patterns that imply extrapolations and thus unreliable prediction by an artificial neural network model can be recognized from a significant decrease in the regional-knowledge index. To tackle the extrapolation problem and assure stability of the control system, we propose to run a neural adaptive controller in parallel with a model predictive control. A coordinator weights the outputs of these two controllers to make the final control decision. The present state of the controlled process and the model fitness to the present input pattern determine the weightings of the controller's output. The proposed analysis method and the modified model predictive control architecture have been applied to a neutralization process and excellent control performance is observed in this highly nonlinear system.     

基于状态观测器的约束鲁棒模型预测控制

模型不确定情况下的鲁棒问题是模型预测控制的一个根本问题。本文采用线性矩阵不等式(LMI)，研究多模型不确定性描述情况下的鲁棒模型预测控制问题。在输入输出约束条件下，最小化最坏情况下的无穷时域目标函数，获得保证系统稳定的基于状态观测器的状态反馈增益并且给出观测器增益的设计方法。实例说明算法可行且保证闭环系统渐近稳定。     

基于LS-SVM预报的动态矩阵预测控制

针对实际工业过程中的非线性及时变特性,传统预测控制算法就难于建立精确的数学模型,从而提出了一种基于最小二乘支持向量机预报的动态矩阵预测控制模型.在整个过程中,首先建立基于最小二乘支持向量机的非线性动态矩阵预测控制结构,通过利用最小二乘支持向量机辨识被控对象模型,同时预测对象的未来输出,然后用动态矩阵控制算法进行滚动优化和反馈校正.仿真实例表明该模型对预测结果有很好的控制作用,有效消除输入干扰的影响,从而提高了预测精度.     

A multiple model approach for predictive control of nonlinear hybrid systems

This paper presents modeling and control of nonlinear hybrid systems using multiple linearized models. Each linearized model is a local representation of all locations of the hybrid system. These models are then combined using Bayes theorem to describe the nonlinear hybrid system. The multiple models, which consist of continuous as well as discrete variables, are used for synthesis of a model predictive control (MPC) law. The discrete-time equivalent of the model predicts the hybrid system behavior over the prediction horizon. The MPC formulation takes on a similar form as that used for control of a continuous variable system. Although implementation of the control law requires solution of an online mixed integer nonlinear program, the optimization problem has a fixed structure with certain computational advantages. We demonstrate performance and computational efficiency of the modeling and control scheme using simulations on a benchmark three-spherical tank system and a hydraulic process plant.     

焦炉加热智能控制系统的研究与应用

焦炉是具有大时滞、强非线性、多变量耦合、变参数的复杂对象，直行温度受多种因 素的影响，采用常规的控制方法难以将直行温度控制到要求的精度范围内．焦炉生产过程既 受连续时间信号驱动，又受离散事件信号驱动，本文将焦炉及其操作过程作为一类混杂系统 ，研究并开发了焦炉加热智能控制系统．系统采用神经网络建模、多变量模糊控制、专家控 制和预测控制等多种算法，构造了切换系统模型，在北京炼焦化学厂投入生产运行后，取得 良好控制效果．该系统对提高焦炭质量，降低能耗和延长炉体使用寿命都有重要的意义．     

火电机组多变量预测控制系统的开发与应用

针对目前我国火电机组对于先进控制软件的迫切需求,结合子空间模型辨识和预测控制技术开发出一种基于状态空间模型的火电机组多变量预测控制系统;该系统采用子空间辨识方法离线辨识状态空间模型,利用多模型切换适应火电机组部分控制回路的非线性特征,最后使用预测控制器完成对象的在线控制;在电厂协调控制回路的实际应用中,负荷升降速率4%,压力偏差小于0.18MPa。所以该系统能够有效提高控制回路的响应速度和稳定性,具有一定的实用性和推广价值。     

基于跟踪误差调节的非线性自适应模糊预测控制

将模糊逻辑系统引入预测控制,对一类非线性离散系统提出了直接自适应模糊预测控制的方法,此方法首先建立被控对象的预测模型;然后基于此模型直接利用模糊逻辑系统设计预测控制器,并基于跟踪误差对控制器参数中的未知向量进行自适应调整;最后证明了此方法可使跟踪误差收敛到原点的一个小邻域内。     

A robust/cautious model predictive controller

In process control, a significant number of problems are encountered where there are hard and soft constraints on the measured process variables and/or on the value and rate of change of the manipulated variables. On-line implementation of traditional control strategies becomes unfeasible since they cannot explicitly deal with process constraints. Model predictive control offers a viable alternative; however, a prominent issue is the behaviour of these algorithms when the prediction model does not match the actual plant. A possible solution is to formulate the problem in a linear/non-linear programming framework, using the cutting plane technique to locate the ‘worst’ plant/model mismatch at every time interval. This results in a very practical cautious predictive controller that computes the next controller action based on expected model/plant mismatch. Control of a stirred tank reactor illustrates the method, using a one-step ahead predictive controller.     

Predictive controller with estimation of measurable disturbances. Application to an olive oil mill

This paper describes the application of a predictive controller that deals with measurable disturbances in the extraction process in an olive oil mill. The work focuses on the thermal part of the process, where the raw material is prepared for the mechanical separation. The system under consideration can be viewed as composed of several changing-level stirred tanks. The paper shows the development of the controller based upon a model obtained from first principles combined with experimental results and validated with real data. Strong disturbances and large time delays appear in the process, so predictive control strategies have been tested under linear and nonlinear simulation. Finally, they have been implemented on the real plant. A study about the consideration of different models for the estimation of measurable disturbances along the prediction horizon has been carried out, showing that a good performance can be obtained by the use of an appropriate model. A new idea that can improve periodic disturbance rejection in Model Based Predictive controllers is presented.