一类连续/间歇过程的迭代学习模型预测控制

An iterative learning model predictive control (ILMPC) technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control (ILC) handling repetitive signal and the flexibility of model predictive control (MPC). By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial appli-cation show that the proposed ILMPC method is effective for a class of continuous/batch processes.     

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Based on the two-dimensional (2D) systemtheory, an integrated predictive iterative learning control (2D-IPILC) strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control (ILC) andmodel predictive control(MPC) is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By minimizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type (Ptype) ILC despite the model error and disturbances.     

基于广义预测控制的间歇生产迭代优化控制

针对间歇生产,提出了一种基于广义预测控制的批次迭代优化控制策略--BGPC,在间歇过程中引入批次间优化的思想,将迭代学习控制ILC和广义预测控制GPC相结合,在GPC实时结构参数辨识的基础上利用前面批次的模型预测误差修正当前批次的模型预测值.该算法能够有效地克服模型失配、扰动和系统参数变化等情况.文章最后以一个数值例子和间歇反应器为对象进行仿真试验,验证了该算法是有效的.     

Repetitive model predictive control of a reverse flow reactor

This paper deals with the control of a catalytic reverse flow reactor (RFR) used for methane combustion. The periodic flow reversals effected on the system makes it both continuous and discrete in nature (i.e., a hybrid system). Control of this system is challenging due to the unsteady state behavior of the process along with its mixed discrete and continuous behavior. Although model predictive control (MPC) is proven to be a powerful technique for several processes it becomes less effective in systems such as the RFR where the model prediction errors and the effect of disturbances on the plant output repeat from time to time. In such cases, control can be improved if the repetitive error pattern is exploited. A novel repetitive model predictive control (RMPC) strategy, that combines the basic concepts of iterative learning control (ILC) and repetitive control (RC) along with the concepts of MPC, is proposed for such systems. In the proposed strategy, the state variables of the model are reset periodically along with predictive control action such that the process follows the reference trajectory as closely as possible. The results obtained prove that the RMPC approach provides an excellent performance for the control of the RFR.     

High-order iterative learning model predictive control for batch chemical processes

In order to address two-dimensional (2D) control issue for a class of batch chemical processes, we propose a novel high-order iterative learning model predictive control (HILMPC) method in this paper. A set of local state-space models are first constructed to represent the batch chemical processes by adopting the just-in-time learning (JITL) technique. Meanwhile, a pre-clustered strategy is used to lessen the computational burden of the modelling process and improve the modelling efficiency. Then, a two-stage 2D controller is designed to achieve integrated control by combining high-order iterative learning control (HILC) on the batch domain with model predictive control (MPC) on the time domain. The resulting HILMPC controller can not only guarantee the convergence of the system on the batch domain, but also guarantee the closed-loop stability of the system on the time domain. The convergence of the HILMPC method is ensured by rigorous analysis. Two examples are presented in the end to demonstrate that the developed method provides better control performance than its previous counterpart.     

Model predictive control with learning‐type set‐point: Application to artificial pancreatic β‐cell

A novel combination of model predictive control (MPC) and iterative learning control (ILC), referred to learning‐type MPC (L‐MPC), is proposed for closed‐loop control in an artificial pancreatic β‐cell. The main motivation for L‐MPC is the repetitive nature of glucose‐meal‐insulin dynamics over a 24‐h period. L‐MPC learns from an individual's lifestyle, inducing the control performance to improve from day to day. The proposed method is first tested on the Adult Average subject presented in the UVa/Padova diabetes simulator. After 20 days, the blood glucose concentrations can be kept within 68–145 mg/dl when the meals are repetitive. L‐MPC can produce superior control performance compared with that achieved under MPC. In addition, L‐MPC is robust to random variations in meal sizes within ±75% of the nominal value or meal timings within ±60 min. Furthermore, the robustness of L‐MPC to subject variability is validated on Adults 1–10 in the UVa/Padova simulator. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

A novel two-dimensional PID controller design using two-dimensional model predictive iterative learning control optimization for batch processes

It is known that the key indicators of batch processes are controlled by conventional proportional–integral–derivative (PID) strategies from the view of one-dimensional (1D) framework. Under such conditions, the information among batches cannot be used sufficiently; meanwhile, the repetitive disturbances also cannot be handled well. In order to deal with such situations, a novel two-dimensional PID controller optimized by two-dimensional model predictive iterative learning control (2D-PID-MPILC) is proposed. The contributions of this paper can be summarized as follows. First, a novel two-dimensional PID (2D-PID) controller is developed by combining the advantages of a PID-type iterative learning control (PIDILC) strategy and the conventional PID method. This novel 2D-PID controller overcomes the aforementioned disadvantages and extends the conventional PID algorithm from one-dimension to two-dimensions. Second, the tuning guidelines of the presented 2D-PID controller are obtained from the two-dimensional model predictive control iterative control (2D-MPILC) method. Thus, the proposed approach inherits the advantages of both PID control, PIDILC strategy, and 2D-MPILC scheme. The superiority of the proposed method is verified by the case study on the injection modelling process.     

基于线性时变扰动模型的间歇过程最优迭代学习控制

A batch-to-batch optimal iterative learning control (ILC) strategy for the tracking control of product quality in batch processes is presented. The linear time-varying perturbation (LTVP) model is built for product quality around the nominal trajectories. To address problems of model-plant mismatches, model prediction errors in the previous batch run are added to the model predictions for the current batch run. Then tracking error transition models can be built, and the ILC law with direct error feedback is explicitly obtained. A rigorous theorem is proposed, to prove the convergence of tracking error under ILC. The proposed methodology is illustrated on a typical batch reactor and the results show that the performance of trajectory tracking is gradually improved by the ILC.     

A unified data-driven design framework of optimality-based generalized iterative learning control

This paper proposes a unified design framework for data-driven optimality-based generalized iterative learning control (DDOGILC), including data-driven optimal ILC (DDOILC), data-driven optimal point-to-point ILC (DDOPTPILC), and data-driven optimal terminal ILC (DDTILC). First, a dynamical linearization in the iteration domain is developed. Then three specific DDOGILC approaches are proposed. Both design and analysis of the controller only require the measured I/O data without relying on any explicit model information. The optimal learning gain can be updated iteratively, which makes the proposed DDOGILC more adaptable to the changes in the plant. Furthermore, the proposed DDOPTPILC and DDOTILC only depend on the tracking error at specific points, and thus they can deal with the scenario when the system outputs are measured only at some time instants. Moreover, the proposed DDOPTPILC and DDOTILC approaches do not need to track the unnecessary output reference points so that the convergence performance is improved.     

基于输入轨迹参数化的间歇过程迭代学习控制

针对间歇过程的迭代学习控制问题,提出了一种基于输入轨迹参数化的迭代学习控制策略。根据最优输入轨迹的主要形态特征,将其参数化为较少量的决策变量,降低传统迭代学习控制复杂性的同时维持良好的优化控制效果。基于输入轨迹参数化的迭代学习控制策略能保持算法的简洁性和易实现性,在不确定扰动影响下逐步改善产品质量。对一个间歇反应器的仿真研究验证了本文方法的有效性。     

Iterative learning control for the systematic design of supersaturation controlled batch cooling crystallisation processes

The paper presents an approach to improve the product quality from batch-to-batch by exploiting the repetitive nature of batch processes to update the operating trajectories using process knowledge obtained from previous runs. The data based methodology is focused on using the linear time varying (LTV) perturbation model in an iterative learning control (ILC) framework to provide a convergent batch-to-batch improvement of the process performance indicator. The major contribution of this work is the development of a novel hierarchical ILC (HILC) scheme for systematic design of the supersaturation controller (SSC) of seeded batch cooling crystallizers. The HILC is used to determine the required supersaturation setpoint for the SSC and the corresponding temperature trajectory required to produce crystals with desired end-point property. The performance and robustness of these approaches are evaluated through simulation case studies. These results demonstrate the potential of the ILC approaches for controlling batch processes without rigorous process models.     

时变间歇过程的2D-PID自适应控制方法

针对间歇过程存在的参数时变问题,提出一种基于二维PID(2D-PID)迭代学习框架的自适应控制方法。首先,通过粒子群优化算法快速获取初始的2D-PID控制参数。在批次内,采用自调整神经元PID控制器对其进行在线自适应调节。进一步,考虑批次间的重复特性,通过PID型迭代学习控制,以利用历史批次的信息来修正当前批次的调节变量,最终提高控制性能。通过间歇发酵过程的仿真和比较研究,验证了所提出方法的有效性。     

Iterative learning reliable control of batch processes with sensor faults

An iterative learning reliable control (ILRC) scheme is developed in this paper for batch processes with unknown disturbances and sensor faults. The batch process is transformed into and treated as a two-dimensional Fornasini-Marchesini (2D-FM) model. Under the proposed control law, the closed-loop system with unknown disturbances and sensor faults not only converges along both the time and the cycle directions, but also satisfies certain H_∞ performance. For performance comparison, a traditional reliable control (TRC) law based on dynamic output feedback is also developed by considering the batch process in each cycle as a continuous process. Conditions for the existence of ILRC scheme are given as biaffine and linear matrix inequalities. Algorithms are given to solve these matrix inequalities and to optimize performance indices. Applications to injection packing pressure control show that the proposed scheme can achieve the design objectives well, with performance improvement along both time and cycle directions, and also has good robustness to uncertain initialization and measurement disturbances.     

Deterministic vs. stochastic performance assessment of iterative learning control for batch processes

The performance assessment of linear time‐invariant batch processes when iterative learning control (ILC) is implemented has been discussed. Previous literatures show that conventional performance assessment cannot be directly applied to batch processes due to the nature of batch operations. Chen and Kong have suggested a new method to assess the control performance of batch processes using optimal ILC as the benchmark. In their work, ILC controllers are assumed to affect either stochastic or deterministic performance but without considering their interaction. This work elaborates the controllers effects on both stochastic and deterministic control performance of batch processes. It is shown that the optimal solution based on the minimum variance control law has a trade‐off between deterministic and stochastic performance, which can be shown by a trade‐off curve. Furthermore, a method is proposed to estimate this curve from routine operating data, against which the performance of ILC controllers can be assessed. Simulation studies are conducted to verify the proposed method. © 2012 American Institute of Chemical Engineers AIChE J, 59: 457–464, 2013     

鲁棒模型预测控制系统的评估基准

在控制系统的性能评估中,基准的设计是个重要问题。将基本设计极限理论推广到模型预测控制系统(MPC),建立性能评估基准。直接考虑多输入多输出系统的频域扰动,建立输出反馈鲁棒模型预测控制器。此控制器仅仅依赖于过程参数,也是令闭环系统达到控制性能极限的基准控制器。建立了用于评估的性能指标,提出基于此基准的性能评估程序,用以评价其他模型预测控制系统的性能。数学算例证实了这一评估程序的有效性。     

Actuator stiction compensation via model predictive control for nonlinear processes

The problem of valve stiction is addressed, which is a nonlinear friction phenomenon that causes poor performance of control loops in the process industries. A model predictive control (MPC) stiction compensation formulation is developed including detailed dynamics for a sticky valve and additional constraints on the input rate of change and actuation magnitude to reduce control loop performance degradation and to prevent the MPC from requesting physically unrealistic control actions due to stiction. Although developed with a focus on stiction, the MPC‐based compensation method presented is general and has potential to compensate for other nonlinear valve dynamics which have some similarities to those caused by stiction. Feasibility and closed‐loop stability of the proposed MPC formulation are proven for a sufficiently small sampling period when Lyapunov‐based constraints are incorporated. Using a chemical process example with an economic model predictive controller (EMPC), the selection of appropriate constraints for the proposed method is demonstrated. The example verified the incorporation of the stiction dynamics and actuation magnitude constraints in the EMPC causes it to select set‐points that the valve output can reach and causes the operating constraints to be met. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2004–2023, 2016     

一种间歇过程产品质量迭代学习控制策略

针对基于迭代学习控制的间歇过程产品质量优化控制算法难以进行收敛性分析的难题,以数据驱动的神经模糊模型为基础,提出一种新颖间歇过程的产品质量迭代学习控制方法。通过在优化算法中加入了新的约束条件,改变了最优解的搜索空间范围,从而使产品质量在批次轴上收敛,并创新性地对优化问题的收敛性给出了严格的数学证明。在理论研究的基础上,将提出的算法用于间歇连续反应釜的终点质量控制研究,仿真结果验证了本文算法的有效性和实用价值,为间歇过程的优化控制提供了一条新途径。     

Disturbance rejection of dead-time processes using disturbance observer and model predictive control

This article mainly focuses on disturbance rejection of dead-time processes by integrating a modified disturbance observer (MDOB) with a model predictive controller (MPC). The effect caused by model mismatches is regarded as a part of the lumped disturbances. This means that the disturbances considered here include not only external disturbances, but also internal disturbances caused by model mismatches. Control structure of the proposed method includes two parts which can be designed separately. The MPC which acts as a prefilter, is employed to generate appropriate control actions such that a desired setpoint tracking response is achieved. The MDOB is employed to estimate the disturbances of the closed-loop system, and the estimation is used for feedforward compensation design to reject disturbances. Rigorous analysis of setpoint tracking and disturbance rejection properties of the closed-loop system are given in the presence of both model mismatches and external disturbances. The proposed scheme is applied to control the temperature of a simplified jacketed stirred tank heater (JSTH). Simulation results demonstrate that the proposed method possesses a better disturbance rejection performance than those of the MDOB-PI, MPC and PI methods in controlling such dead-time processes.     

A model predictive formulation for control of open-loop unstable cascade systems

Cascade control is commonly used in the operation of chemical processes to reject disturbances that have a rapid effect on a secondary measured state, before the primary measured variable is affected. In this paper, we develop a state estimation-based model predictive control approach that has the same general philosophy of cascade control (taking advantage of secondary measurements to aid disturbance rejection), with the additional advantage of the constraint handling capability of model predictive control (MPC). State estimation is achieved by using a Kalman filter and appending modeled disturbances as augmented states to the original system model. The example application is an open-loop unstable jacketed exothermic chemical reactor, where the jacket temperature is used as a secondary measurement in order to infer disturbances in jacket feed temperature and/or reactor feed flow rate. The MPC-based cascade strategy yields significantly better performance than classical cascade control when operating close to constraints on the jacket flow rate.