Filter-based iterative learning control for linear large-scale industrial processes

In the procedure of the steady-state hierarchical optimization with feedback for large-scale industrial processes, a sequence of set-point changes with different magnitudes is carried out on the optimization layer. To improve the dynamic performance of transient response driven by the set-point changes, a filter-based iterative leaning control strategy is proposed. In the proposed updating law, a local-symmetric-integral operator is adopted for eliminating the measurement noise of output information, a set of desired trajectories are specified according to the set-point changes sequence, the current control input is iteratively achieved by utilizing smoothed output error to modify its control input at previous iteration, to which the amplified coefficients related to the different magnitudes of set-point changes are introduced. The convergence of the algorithm is conducted by incorporating frequency-domain technique into time-domain analysis. Numerical simulation demonstrates the effectiveness of the proposed strategy.     

Application of nonlinear model predictive control to an industrial induction heating furnace

Induction Heating Furnaces are used extensively in industry. The basic principle is that induced eddy currents are used to heat a ferromagnetic material as it passes through a series of coils. Because of the importance of such systems, there has been on-going interest in their design and operation. Past work includes model development from physical principles and optimal design of operational practices. However, previous work has invariably been based on open-loop strategies. Our work is aimed at the design of a closed-loop control strategy incorporating feedback from the available measurements. This paper reports initial work including model development and calibration together with preliminary control system design. Proposed future work includes full scale industrial implementation.     

T-S模型的窑炉压力无超调快速广义预测控制设计

窑压是玻璃窑炉运行过程中重要的被控指标之一,直接影响窑炉能耗、寿命及产品成品率,优化窑炉压力控制具有重要的经济意义。由于受到众多因素的影响,窑压具有典型的非线性特性,现有方法的控制效果还有很大的提升空间。本文针对窑压设计了一套新型无超调快速模糊广义预测控制方法(NFGPC),先利用"离线+在线"组合辨识方法得到窑压对象的T-S模型;然后基于该模型对系统进行分片线性化得到时变CARIMA模型,以设计窑压广义预测控制律;结合柔化理论与新型滚动优化目标函数设计一种广义预测控制律,该方法无需求解逆矩阵即可得到控制输出,计算量更小;由于新目标函数的应用,该方法还能够克服传统GPC引起的超调效应。仿真结果表明该方法能够很好的处理窑压非线性系统建模问题,与PID控制、线性GPC(LGPC)以及模糊广义预测控制(FGPC)以及快速模糊广义预测控制(FFGPC)等方法控制效果的对比表明,NFFGPC在处理非线性系统控制问题上具有一定的优越性。     

Support vector machine based predictive functional control design for output temperature of coking furnace

A new support vector machine based nonlinear predictive functional control design method has been developed and applied to an industrial coking furnace, which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection compared with traditional PFC and PID control strategies. The nonlinear process is first treated into a linear part plus a nonlinear part, then a convergent overall linear predictive functional control law is designed. The method gives a direct and effective multi-step predicting method and uses linear methods to get the control law which avoids the complicated nonlinear optimization. Comparison results and application to the temperature control of the industrial heavy oil coking furnace are presented in the article showing the efficiency of the method.     

An iterative approach to the optimal co-design of linear control systems

This paper investigates the optimal co-design of both physical plants and control policies for a class of continuous-time linear control systems. The optimal co-design of a specific linear control system is commonly formulated as a nonlinear non-convex optimisation problem (NNOP), and solved by using iterative techniques, where the plant parameters and the control policy are updated iteratively and alternately. This paper proposes a novel iterative approach to solve the NNOP, where the plant parameters are updated by solving a standard semi-definite programming problem, with non-convexity no longer involved. The proposed system design is generally less conservative in terms of the system performance compared to the conventional system-equivalence-based design, albeit the range of applicability is slightly reduced. A practical optimisation algorithm is proposed to compute a sub-optimal solution ensuring the system stability, and the convergence of the algorithm is established. The effectiveness of the proposed algorithm is illustrated by its application to the optimal co-design of a physical load positioning system.     

A convex optimization approach to robust iterative learning control for linear systems with time-varying parametric uncertainties

In this paper, we present a new robust iterative learning control (ILC) design for a class of linear systems in the presence of time-varying parametric uncertainties and additive input/output disturbances. The system model is described by the Markov matrix as an affine function of parametric uncertainties. The robust ILC design is formulated as a min–max problem using a quadratic performance criterion subject to constraints of the control input update. Then, we propose a novel methodology to find a suboptimal solution of the min–max optimization problem. First, we derive an upper bound of the worst-case performance. As a result, the min–max problem is relaxed to become a minimization problem in the form of a quadratic program. Next, the robust ILC design is cast into a convex optimization over linear matrix inequalities (LMIs) which can be easily solved using off-the-shelf optimization solvers. The convergences of the control input and the error are proved. Finally, the robust ILC algorithm is applied to a physical model of a flexible link. The simulation results reveal the effectiveness of the proposed algorithm.     

A survey of industrial model predictive control technology

This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology. A general MPC control algorithm is presented, and approaches taken by each vendor for the different aspects of the calculation are described. Identification technology is reviewed to determine similarities and differences between the various approaches. MPC applications performed by each vendor are summarized by application area. The final section presents a vision of the next generation of MPC technology, with an emphasis on potential business and research opportunities.     

A hybrid approach for supervisory control of furnace temperature

The temperature control of the reheat furnace is very difficult due to its complex characteristics. Based on expert knowledge and pyrology mechanism, a hybrid supervisory control system (an optimal setting model) has been developed innovatively to control its temperature. With the help of a statistical process controller, this hybrid supervisory control system can replace the human operator for most of operations in the process. Since an expert compensator is designed to suppress the external disturbance, the proposed model can automatically update the optimal set-point value for the furnace temperature of each zone under the varying boundary conditions. Both simulation and industrial experiment show the viability and effectiveness of the suggested model and its bright application foreground in thermal process.     

A simplified approach to force control for electro-hydraulic systems

In this paper, a Lyapunov-based control algorithm is developed for force tracking control of an electro-hydraulic actuator. The developed controller relies on an accurate model of the system. To compensate for the parametric uncertainties, a Lyapunov-based parameter adaptation is applied. The adaptation uses a variable structure approach to account for asymmetries present in the system. The coupled control law and the adaptation scheme are applied to an experimental valve-controlled cylinder. Friction modeling and compensation are also discussed. The experimental results show that the nonlinear control algorithm, together with the adaptation scheme, gives a good performance for the specified tracking task. The original adaptive control law is then simplified in several stages with an examination of the output tracking at each stage of simplification. It is shown that the original algorithm can be significantly simplified without too significant a loss of performance. The simplest algorithm corresponds to an adaptive velocity feedback term coupled with a simple force error feedback.     

一种间歇过程的综合预测迭代学习控制方法

为了提高迭代学习控制方法在间歇过程轨迹跟踪问题中的收敛速度,本文将批次间的比例型迭代学习控制与批次内的模型预测控制相结合,提出了一种综合应用方法.首先根据间歇过程的线性模型,预测出比例型迭代学习控制的系统输出,然后在批次内采用模型预测控制,通过极小化一个二次型目标函数来获得控制增量.该方法可使系统输出跟踪期望轨迹的速度比比例型迭代学习控制方法更快些.最后通过仿真实例验证了该方法的有效性.     

Improved PI controller based on predictive functional control for liquid level regulation in a coke fractionation tower

Due to limitations of hardware, cost and so on, the application of proportional-integral-derivative (PID) control is more convenient than predictive control. However, predictive control usually has better performance than traditional PID control, thus it is important to combine the advantages of these two control algorithms. A novel PI controller optimized by predictive functional control (PFC) is proposed and tested on liquid level in the industrial coke fractionation tower in this paper. Since this kind of process always shows the integrating behavior, a P controller is first used for it to generate a self-balancing generalized process, then the PFC based PI control is designed for the generalized process. The resulting controller displays the performance of both PFC and PI control with easy implementation in practice. The performance of the proposed PI controller is compared with traditional PI controller in terms of regulatory/servo set-point tracking, disturbance rejection and measurement noise issues, from which results show that the proposed PI controller provides better performance than traditional PI controller.     

Rail pressure controller design of GDI basing on predictive functional control

Gasoline direct injection (GDI) is a pivotal technique for a highly efficient engine. However, how to maintain a stable rail pressure which offers good fuel economy and low emissions, is still a challengeable work. In this paper, a rail pressure controller is designed basing on predictive functional control (PFC), a model predictive control (MPC) method, to surmount the nonlinearity and discontinuity brought by the common rail pressure system (CRPS). A control-oriented piecewise linear model is presented to simplify the CRPS. The simulation results on a benchmark show that rail pressure tracks the setpoint accurately even with some perturbations. Profiting from the conciseness of PFC algorithm, the controller can compute the online solution in a short time, which makes it possible to realize the strategy on a fast response system.     

A design method for indirect iterative learning control based on two-dimensional generalized predictive control algorithm

Indirect iterative learning control (ILC) facilitates the application of learning-type control strategies to the repetitive/batch/periodic processes with local feedback control already. Based on the two-dimensional generalized predictive control (2D-GPC) algorithm, a new design method is proposed in this paper for an indirect ILC system which consists of a model predictive control (MPC) in the inner loop and a simple ILC in the outer loop. The major advantage of the proposed design method is realizing an integrated optimization for the parameters of existing feedback controller and design of a simple iterative learning controller, and then ensuring the optimal control performance of the whole system in sense of 2D-GPC. From the analysis of the control law, it is found that the proposed indirect ILC law can be directly obtained from a standard GPC law and the stability and convergence of the closed-loop control system can be analyzed by a simple criterion. It is an applicable and effective solution for the application of ILC scheme to the industry processes, which can be seen clearly from the numerical simulations as well as the comparisons with the other solutions.     

A multi-objective iterative learning control approach for additive manufacturing applications

Iterative learning control (ILC) is a method for improving the performance of stable, repetitive systems. Standard ILC is constructed in the temporal domain, with performance improvements achieved through iterative updates to the control signal. Recent ILC research focuses on reformulating temporal ILC into the spatial domain, where 2D convolution accounts for spatial closeness. This work expands spatial ILC to include optimization of multiple performance metrics. Performance objectives are classified into primary, complementary, competing, and domain specific objectives. New robustness and convergence criteria are provided. Simulation results validate flexibility of the spatial framework on a high-fidelity additive manufacturing system model.     

基于广义预测控制的常压加热炉先进控制技术应用研究

常减压蒸馏装置是石化行业的龙头装置,同时也是能耗大户。因此,有效优化常减压装置,优化常压炉温度控制对提高经济效益,节能减排具有重要意义。本文根据某石化公司常减压蒸馏装置的生产实际,提出了一种基于广义预测控制技术的先进控制算法。广义预测控制算法是一种基于模型的控制算法。首先通过收集分析历史数据,测试各数据的关联规律来建立系统的数学模型。运用所建数学模型,通过对支路温度、流量,终温等变量的过去值、现在值的计算,得出被控变量的未来预测值,以此达到预测控制的目的。同时,多支路换热往往会出现各支路温差较大的现象。通过支路平衡技术,利用对各支路流量和瓦斯加热气的控制,实现各支路温度相近,温差极小的目的。为预测控制的有效实施,提供更加稳定的工况环境。通过对广义预测控制、提降量控制技术以及支路平衡控制技术等先进控制技术的工程应用,提高了常压加热炉装置的生产性能。效果表明该方法提高了常压加热炉的鲁棒性,减小了各换热支路的温差,降低了常压加热炉流量波动对生产的影响,有效促进了生产稳定和效益提升。     

An iterative model predictive control algorithm for constrained nonlinear systems

An iterative model predictive control (MPC) scheme for constrained nonlinear systems is presented. The idea of the method is to detour from the solution of a non‐convex optimization problem using a time‐variant linearization of the nonlinear system model that is adjusted iteratively by solving an iterative quadratic programming optimization problem at each sampling time. The main advantage is the faster resolution of the optimization problem by using quadratic programming instead of non‐convex programming and yet, properly describing the nonlinear dynamics of the process being controlled. In this article, a general framework of the method is presented together with a discussion on the conditions under which the iterations converge and on the uncertainty of its results due to the linearization used, as well as some practical considerations about its implementation. The performance of the proposed controller is illustrated via two examples.     

Partially decoupled approach of extended non-minimal state space predictive functional control for MIMO processes

This paper presents a partially decoupled design of the state space predictive functional control for MIMO processes. The multivariable process is first treated into MISO process by a simple Cramer's rule solution to linear equations which provides a balance between model complexity and control system design, and then the derived MISO process based extended state space predictive functional control is presented. The overall design of the controller enables the controller to consider both the process state dynamics and the output dynamics, thus improved control performance for tracking set-points and disturbance rejection is resulted. The proposed controller is tested on both model match and model mismatch cases to demonstrate its superiority. In addition, a closed-form of transfer function representation that facilitates frequency analysis of the control system is provided to give further insight into the proposed method.     

Latent variable iterative learning model predictive control for multivariable control of batch processes

A latent variable iterative learning model predictive control (LV-ILMPC) method is presented for trajectory tracking in batch processes. Different from the iterative learning model predictive control (ILMPC) model built from the original variable space, LV-ILMPC develops a latent variable model based on dynamic partial least squares (DyPLS) to capture the dominant features of each batch. In each latent variable space, we use a state–space model to describe the dynamic characteristics of the internal model, and an LV-ILMPC controller is designed. Each LV-ILMPC controller tracks the set points of the current batch projection in the corresponding latent variable space, and the optimal control law is determined and the persistent process disturbances is rejected along both time and batch horizons. The proposed LV-ILMPC formulation is based on general LV-MPC and incorporates an iterative learning function into LV-MPC. In addition, the real physical input that drives the process can be reconstructed from the latent variable space. Therefore, this algorithm is particularly suitable for multiple-input, multiple-output (MIMO) systems with strong coupling and serious collinearity. Three studies are used to illustrate the effectiveness of the proposed LV-ILMPC .