An economic model predictive control framework for mechanical pulping processes

We develop a multi-objective economic model predictive control (m-econ MPC) framework to control and optimize a nonlinear mechanical pulping (MP) process. M-econ MPC interprets economic MPC as a multi-objective optimization problem that trades off economic and set-point tracking performance. This interpretation allows us to construct a stabilizing constraint that guarantees closed-loop stability. The framework infers unmeasured states of the MP process (associated with product consistency) by using a moving horizon estimator (MHE). The MP process dynamics are described by using a nonlinear Wiener model. Examples from a two-stage high-consistency MP process are employed to demonstrate that significant improvements in economic performance are achievable.     

Convex output feedback model predictive control for mitigation of COVID-19 pandemic

In this paper, a model predictive control approach is proposed for epidemic mitigation. The disease spreading dynamics is described by an 8-compartment smooth nonlinear model of the COVID-19 pandemic in Hungary known from the literature, where the manipulable control input is the stringency of the introduced non-pharmaceutical measures. It is assumed that only the number of hospitalized people is measured on-line, and the other state variables are computed using a state observer which is based on the dynamic inversion of a linear sub-system of the model. The objective function contains a measure of the direct harmful consequences of the restrictions, and the constraints refer to input bounds and to the capacity of the healthcare system. By exploiting the special properties of the model, the nonlinear optimization problem required by the control design is reformulated to convex tasks, allowing a computationally efficient solution. Two approaches are proposed: the first finds a suboptimal solution by geometric programming, while the second one further simplifies the problem and transforms it to a linear programming task. Simulations show that both suboptimal solutions fulfill the design specifications even in the presence of parameter uncertainties.     

基于图像反馈的回转窑烧结温度智能预测控制

利用火焰图像作为回转窑烧结温度的反馈信号参与闭环预测控制,融合现场热工信号,设计了烧结温度的RBF网络预测模型,并利用遗传算法进行滚动优化,现场运行结果表明该方法可在正常工况下对烧结温度进行稳定控制.     

Comments on: “Efficient robust constrained model predictive control with a time varying terminal constraint set” by Wan and Kothare

We present an algorithm that modifies the original formulation proposed in Wan and Kothare [Efficient robust constrained model predictive control with a time-varying terminal constraint set, Systems Control Lett. 48 (2003) 375–383]. The modified algorithm can be proved to be robustly stabilizing and preserves all the advantages of the original algorithm, thereby overcoming the limitation pointed out recently by Pluymers et al. [Min–max feedback MPC using a time-varying terminal constraint set and comments on “Efficient robust constrained model predictive control with a time-varying terminal constraint set”, Systems Control Lett. 54 (2005) 1143–1148].     

Model predictive control: Recent developments and future promise

This paper recalls a few past achievements in Model Predictive Control, gives an overview of some current developments and suggests a few avenues for future research.     

Abnormal condition detection in a cement rotary kiln with system identification methods

In this paper, we use system identification methods for abnormal condition detection in a cement rotary kiln. After selecting proper inputs and output, an input–output model is identified for the plant’s normal conditions. A novel approach is used in order to estimate the delays of the input channels of the kiln before identification part. This method eases the identification since with determining the input channels delays, the dimension of search space in the identification part reduces. Afterward, to identify the kiln’s model, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOcally LInear MOdel Tree (LOLIMOT) algorithm which is an incremental tree-structure algorithm. Finally, with the model for normal condition of the kiln, the incident of abnormalities in output are detected based on the length of duration and magnitude of difference between the real output and model’s output. We distinguished three abnormal conditions in the kiln, two of which are known as common abnormal conditions and another one which was not characteristically known for cement experts either.     

On the finite time performance of model predictive control

This paper addresses the finite time performance of model predictive control (MPC) for linear-time-invariant (LTI) systems without constraints. The performance of MPC is compared with that of finite horizon optimal control to find out how well model predictive control can perform relative to the optimal performance with the same or different horizons. By exploring the properties of the Riccati difference equation (RDE), an upper and a lower bound of the ratio between the finite time performance of MPC and finite horizon optimal cost are obtained. It is possible to extend the obtained results to more complicated systems such as nonlinear dynamic systems with constraints with appropriate generalizations. Simulation example supports our results.     

回转窖无线测温系统的设计

介绍了回转窖无线测温系统的设计。热电偶测量窖内温度产生的电压信号,通过无线电传设备送至操作室,直接显示窖头、窖中和窖尾温度;同时将信号转发到中控室,并转换成4～20mA电流送其它仪表或控制系统显示,无线测温系统是回转窖无线测温的理想工具和最佳手段。     

Model predictive control of constrained LPV systems

This article considers robust model predictive control (MPC) schemes for linear parameter varying (LPV) systems in which the time-varying parameter is assumed to be measured online and exploited for feedback. A closed-loop MPC with a parameter-dependent control law is proposed first. The parameter-dependent control law reduces conservativeness of the existing results with a static control law at the cost of higher computational burden. Furthermore, an MPC scheme with prediction horizon ‘1’ is proposed to deal with the case of asymmetric constraints. Both approaches guarantee recursive feasibility and closed-loop stability if the considered optimisation problem is feasible at the initial time instant.     

Model predictive pressure control of steam networks

The control scheme of industrial power plants leads typically to a complex multivariable control structure with active constraints to be taken care of. Then Model Predictive Control method (MPC) handles multivariate control problems naturally and optimal control result is calculated considering actuator limitations and constraints of process variables. MPC is applied to control the pressure stability in a multilevel steam network. The system is demonstrated in a simulator environment. MPC can also be used as a convenient tool for analyzing and designing the structure of the steam network. A power plant simulator controlled by MPC helps to decide the location and the capacity of steam levelling components needed to stabilize the operation of the process.     

Feedforward model predictive control

This paper examines the role played by feedforward in model predictive control (MPC). We contrast feedforward with preview action. The latter is standard in model predictive control, whereas feedforward has been rarely, if ever, used in contemporary formulations of MPC. We argue that feedforward can significantly improve performance in the presence of measurement noise and certain types of model uncertainty.     

预测控制优化变量的集结策略

为减少预测控制的在线计算量，提出了对优化变量进行集结的优化策略，通过一个集结矩阵将原优化变量与集结后的变量联系起来，从而为多种预测控制算法建立一个统一的框架，分析了现有的几种典型优化策略的集结表述，说明了所提出的集结优化框架具有一定的普适性，并在此框架下提出一种新的具有输出衰减形式的集结优化算法。     

Model predictive control suitable for closed-loop re-identification

The main problem of a closed-loop re-identification procedure is that, in general, the dynamic control and identification objectives are conflicting. In fact, to perform a suitable identification, a persistent excitation of the system is needed, while the control objective is to stabilize the system at a given equilibrium point. However, a generalization of the concept of stability, from punctual stability to (invariant) set stability, allows for a flexibility that can be used to avoid the conflict between these objectives. Taking into account that an invariant target set includes not only a stationary component, but also a transient one, the system could be excited without deteriorating the stability of the closed-loop. In this work, a MPC controller is proposed that ensures the stability of invariant sets at the same time that a signal suitable for closed-loop re-identification is generated. Several simulation results show the propose controller formulation properties.     

广义预测自适应控制的双重控制算法

提出了一种基于双重控制思想的广义预测自适应控制算法,该算法在模型辨识和控制的过程中,采用谨慎控制和探测控制相结合的双重控制,充分考虑估计参数的误差,在使系统状态最优地沿预定轨线运动的同时最大限度的积累被估计参数的信息,以便最快地降低系统的不确定性。仿真结果表明,该控制算法比普通的广义预测自适应控制具有更好的控制品质。     

Identification, prediction and detection of the process fault in a cement rotary kiln by locally linear neuro-fuzzy technique

In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, locally linear neuro-fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 min prediction horizon. The other two models are presented for the two faulty situations in the kiln with 7 min prediction horizon. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used in this study.     

Generalized terminal state constraint for model predictive control

A terminal state equality constraint for Model Predictive Control (MPC) laws is investigated, where the terminal state/input pair is not fixed a priori but it is a free variable in the optimization. The approach, named “generalized” terminal state constraint, can be used for both tracking MPC (i.e. when the objective is to track a given steady state) and economic MPC (i.e. when the objective is to minimize a cost function which does not necessarily attains its minimum at a steady state). It is shown that the proposed technique provides, in general, a larger feasibility set with respect to the existing approaches, given the same prediction horizon. Moreover, a new receding horizon strategy is introduced, exploiting the generalized terminal state constraint. Under mild assumptions, the new strategy is guaranteed to converge in finite time, with arbitrarily good accuracy, to an MPC law with an optimally-chosen terminal state constraint, while still enjoying a larger feasibility set. The features of the new technique are illustrated by an inverted pendulum example in both the tracking and the economic contexts.     

On optimality of nonlinear model predictive control

In this note the optimality property of nonlinear model predictive control (MPC) is analyzed. It is well known that the MPC approximates arbitrarily well the infinite horizon (IH) controller as the optimization horizon increases. Hence, it makes sense to suppose that the performance of the MPC is a not decreasing function of the optimization horizon. This work, by means of a counterexample, shows that the previous conjecture is fallacious, even for simple linear systems.     

Interactions between control and process design under economic model predictive control

Economic model predictive control (EMPC) is a model-based control scheme that integrates process control and economic optimization, which can potentially allow for time-varying operating policies to maximize economic performance. The manner in which an EMPC operates a process to optimize economics depends on the process dynamics, which are fixed by the process design. This raises the question of how process and EMPC designs interact. Works which have addressed process and control design interactions for steady-state operation have sought to simultaneously develop process designs and control law parameters to find the most profitable way to operate a process that is able to prevent process constraints from being violated and to optimize capital costs in the presence of disturbances. Because EMPC has the potential to operate a process in a transient fashion, this work first focuses on how EMPC and process design interact in the absence of disturbances. Using small-scale process examples, we seek to understand the fundamental nature of the interactions between EMPC and process design, including how these interactions can impact computational complexity of the controller and the design procedure. We subsequently utilize the insights gained to suggest controller design variables which might be considered as decision variables for a simultaneous process and control design problem when disturbances are considered.     

Constrained multivariable control of a distillation column using a simplified model predictive control algorithm

Distillation columns are important process units in petroleum refining and need to be maintained close to optimum operating conditions because of economic incentives. Model predictive control has been used for control of these units. However, the constrained optimization problem involved in the control has generally been solved in practice in a piece-meal fashion. To solve the problem without decomposition, the use of a linear programming (LP) formulation using a simplified model predictive control algorithm has been suggested in the literature. In this paper, the LP approach is applied for control of an industrial distillation column. The approach involved a very small size optimization problem and required very modest computational resources. The control algorithm eliminated the large cycling in the product composition that was present using SISO controllers. This resulted in a 2.5% increase in production rate, a 0.5% increase in product recovery, and a significant increase in profit.