Practical solutions to multivariate feedback control performance assessment problem: reduced a priori knowledge of interactor matrices

The research on control loop performance monitoring and diagnostics has been and remains to be one of the most active research areas in process control community. Despite of numerous developments, it remains as a considerably challenging problem to obtain a minimum variance control benchmark from routine operating data for multivariable process since the solution relies on the interactor matrix (or inverse time delay matrix). Knowing the interactor matrix is tantamount to knowing a complete knowledge of process models that are either not available or not accurate enough for a meaningful calculation of the benchmark. However, the order of an interactor matrix (OIM) for a multivariable process, a scalar measure of multivariate time delay, is a relatively simple parameter to know or estimate a priori. This paper investigates the possibility to estimate a suboptimal multivariate control benchmark from routine operating data if the OIM is available. The relation between this suboptimal benchmark and the true multivariate minimum variance control benchmark is investigated. Analytical expressions for the lower and upper bounds of the true multivariate minimum variance are derived. Although not minimum variance control, this benchmark answers important practical questions like “at least how much potential of the improvement does the control have by tuning or redesigning?”. It is further shown that the proposed suboptimal benchmark is achievable by a practical control provided that the system of interest is minimum phase. Simulation examples illustrate the feasibility of the proposed approach.     

Minimum variance control and performance assessment of time-variant processes

This paper is concerned with (1) an explicit solution of a minimum variance control law for linear time-variant (LTV) processes in the transfer function form, and (2) performance assessment of LTV processes using minimum variance control as the benchmark. It is shown that there exists a time-variant, absolute lower bound of process variance that is achievable under LTV minimum variance control and can be estimated from routine operating data. This lower bound can subsequently be used to assess the benefit of implementing LTV control such as adaptive control. The proposed methods are illustrated through simulated examples and an industrial case study.     

Fuzzy Constrained Min-Max Model Predictive Control Based on Piecewise Lyapunov Functions

This paper proposes two novel stable fuzzy model predictive controllers based on piecewise Lyapunov functions and the min-max optimization of a quasi-worst case infinite horizon objective function. The main idea is to design state feedback control laws that minimize the worst case objective function based on fuzzy model prediction, and thus to obtain the optimal transient control performance, which is of great importance in industrial process control. Moreover, in both of these predictive controllers, piecewise Lyapunov functions have been used in order to reduce the conservatism of those existent predictive controllers based on common Lyapunov functions. It is shown that the asymptotic stability of the resulting closed-loop discrete-time fuzzy predictive control systems can be established by solving a set of linear matrix inequalities. Moreover, the controller designs of the closed-loop control systems with desired decay rate and input constraints are also considered. Simulations on a numerical example and a highly nonlinear benchmark system are presented to demonstrate the performance of the proposed fuzzy predictive controllers.     

基于历史性能基准的模型预测控制性能监控

针对实际工业过程中希望只利用过程的日常运行数据对控制系统进行监控的情况,提出了1种用历史性能基准对预测控制系统进行性能评估、监控以及分析的方法.利用运行状态良好的1段历史数据计算出历史性能基准,并将历史性能基准与实际性能的比值用于控制器性能的实时监控.根据历史性能指标的残差监控检测出性能变化时,进一步通过历史数据协方差监控及广义特征值分析,区分出性能显著变差或变好的方向和子空间.为现场工程师提供性能变化的一些原因,用于系统维护.最后通过Shell重油分馏塔仿真证明该方法可以获得很好的评估与监控结果.     

Fuzzy Predictive Functional Control in the State Space Domain

In the paper, a well-known predictive functional control strategy is extended to nonlinear processes. In our approach the predictive functional control is combined with a fuzzy model of the process and formulated in the state space domain. The prediction is based on a global linear model in the state space domain. The global linear model is obtained by the fuzzy model in Takagi–Sugeno form and actually represents a model with changeable parameters. A simulation of the system, which exhibits a strong nonlinear behaviour together with underdamped dynamics, has evaluated the proposed fuzzy predictive control. In the case of underdamped dynamics, the classical formulation of predictive functional control is no longer possible. That was the main reason to extend the algorithm into the state space domain. It has been shown that, in the case of nonlinear processes, the approach using the fuzzy predictive control gives very promising results.     

Predictive functional control based on fuzzy model forheat-exchanger pilot plant

In this paper, a new method of predictive control is presented. In this approach, a well-known method of predictive functional control is combined with fuzzy model of the process. The prediction is based on fuzzy model given in the form of Takagi-Sugeno type. The proposed fuzzy predictive control has been evaluated by implementation on heat-exchanger plant, which exhibits a strong nonlinear behavior. It has been shown that in the case of nonlinear processes, the approach using fuzzy predictive control gives very promising results. The proposed approach is potentially interesting in the case of batch reactors, heat-exchangers, furnaces, and all the processes that are difficult to model     

Optimal on–off control of refrigerated transport systems

This paper presents a hysteretic on–off control scheme with optimization algorithms for temperature regulation in refrigerated transport systems. A nonlinear dynamic refrigerated transport system model, which consists of a refrigeration unit and a cargo space, is developed. The model serves as an analytical tool for control design. An optimal on–off control strategy is developed based on the time domain analysis of the temperature oscillations. A cost function involving temperature variations from the set-point, energy consumption and average compressor on–off cycling frequency is introduced for minimization. Choices of weighting values in the cost function give the flexibility of the control approach to meet different requirements. Simulation examples are presented that demonstrate the ability and robustness of the proposed method in achieving tighter temperature regulation and higher system efficiency control in transport refrigeration systems. The simulations are augmented by experimental validation via a novel hardware-in-the-loop load emulation approach.     

Performance assessment of control loops with time-variant disturbance dynamics

If a process is subject to time varying disturbance dynamics (or time varying disturbance models), the time invariant minimum variance control for one type of disturbance dynamics is no longer minimum variance control for another type of disturbance. An explicit solution to a time-invariant optimal control that can optimize overall performance of time-variant processes is derived in this paper and is used as a benchmark to assess control performance of time variant process under time invariant control. This work is a continuation of the work by Huang [Can. J. Chem. Eng. 77(5) (1999) 1044]. It is shown that this performance benchmark can be found from routine operating data through time series analysis and optimization technique. The developed performance assessment technique is illustrated by a simulated example and applied to an industrial process.     

Data-driven predictive control for solid oxide fuel cells

This paper is concerned with predictive control of solid oxide fuel cells (SOFC) based on a benchmark model commonly studied in the dynamic SOFC modeling/control literature. It has been shown in previous studies that control of SOFC is challenging owing to the slow response and tight operating constraints. In this paper, we apply a data-driven predictive control approach to solving the control problem of the SOFC system. The predictive control applied is completely data based. In addition, unlike other data-driven predictive control designs, the proposed approach can deal with systems without complete on-line measurement of all output variables. Simulation results have demonstrated the feasibility of the control application.     

Lower and upper bounds on time for multiprocessor optimal schedules

The lower and upper bounds on the minimum time needed to process a given directed acyclic task graph for a given number of processors are derived. It is proved that the proposed lower bound on time is not only sharper than the previously known values but also easier to calculate. The upper bound on time, which is useful in determining the worst case behavior of a given task graph, is presented. The lower and upper bounds on the minimum number of processors required to process a given task graph in the minimum possible time are also derived. It is seen with a number of randomly generated dense task graphs that the lower and upper bounds we derive are equal, thus giving the optimal time for scheduling directed acyclic task graphs on a given set of processors     

辅助动力装置负载压气机防喘控制研究

以带负载压气机的APU为对象,分析了负载压气机工作时喘振机理.根据负载压气机出口气流状态,采用可变极限流量法判别APU负载压气机喘振.根据流量平衡原理,进行负载压气机防喘放气控制,保证APU工作效率的同时,使APU负载压气机远离喘振边界稳定工作.运用硬件在回路仿真系统,通过逼喘仿真,结果表明该方法可以用来保证APU系统压气机的稳定工作.     

A performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control

This paper presents a performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control for large-scale systems. After the fault has been detected and diagnosed, several controller reconfigurations are proposed as candidate corrective actions for fault compensation. The solution of a set of constrained optimization problems with different actuator and setpoint reconfigurations is derived by means of an original approach, exploiting the information on the active constraints in the non-faulty subsystems. Thus, the global optimization problem is split into two optimization subproblems, which enable the online computational burden to be greatly reduced. Subsequently, the performances of different candidate controller reconfigurations are compared, and the better performing one is selected and then implemented to compensate the fault effects. Efficacy of the proposed approach has been shown by applying it to the benzene alkylation process, which is a benchmark process in distributed model predictive control.     

Fuel gas blending benchmark for economic performance evaluation of advanced control and state estimation

A simulation of a fuel gas blending process and its measurement system is proposed as a benchmark test case for advanced control and state estimation. The simulation represents an industrial facility and employs a well-established software environment. The objective is to maintain four controlled variables within specified bounds while minimizing an economic performance index. The controlled variables are the fuel gas pressure and three measures of gas quality. Six feed gas flow rates may be adjusted to achieve the objective. Each has a limited availability.The benchmark consists of three reproducible scenarios, each a 46-h period during which 23 discrete upsets occur and the feed gas compositions vary gradually with time. A benchmark multi-loop feedforward–feedback structure is described, tested, and compared to an estimate of optimal performance. The operating cost provided by the benchmark controller is from 1.19 to 1.71 times higher than the estimated minimum.Readers are challenged to download the simulation model, benchmark controller and estimated optimal performance from the URL given in this paper, and to devise case studies of advanced state estimation and control strategies to better the proposed benchmark controller.     

基于热负荷变化的后混合饮料机温度控制参数优化整定

介绍后混合饮料机的工作原理,分析CCDX型后混合饮料机温控参数设置的固有缺陷,通过改变糖浆的冷却方式,并利用NTC测得的温度和时间的比值(即升/降温速度)来反映热负荷的变化,从而设定不同的压缩机开/关机温度值.测试结果表明,本文所提的温度参数优化整定方法能有效地解决不能同时满足为了得到合格的饮料所需要的狭窄的温控范围和...     

基于线性二次型高斯基准的多变量预测控制技术经济性能评估

由于受控过程参数的漂移及缺乏维护,令采用的控制器性能逐渐降低,需要做经济性能评估,以确保其最佳运行状态.因为目前最小方差评估算法没有考虑控制器的约束条件,对此我们采用线性二次型高斯(linearquadratic Gaussian,LQG)基准的模型预测控制(model predictive control,MPC)双层优化控制结构,将控制和输出的加权值引入上层经济性能指标,通过求解LQG问题获取控制与输出方差关系的离散点集,进一步拟合Pareto最优曲面方程,建立优化命题并求解最优经济指标及设定值.对延迟焦化加热炉的多变量MPC控制进行了性能评估及分析,证明该方法可以改进控制器设计,提高经济效益.     

基于模糊强化学习的空气压缩机群组协调预测控制

空气压缩机在提供动力的同时消耗着大量的电力能源,其节能增效备受关注.空气负荷需求受生产节奏、计划排程等影响呈现阶段性、间歇性的特点,导致空气压缩机组在大范围内变工况运行,人工调节速度慢,机组能耗高.针对此问题,以国内工业园区空气压缩机群组系统为背景,提出一种基于模糊强化学习的空气压缩机群组协调预测控制方法.首先利用工业现场的历史数据和设备运行机理建立基于模糊辨识的空气压缩机群组多工况模型;在此基础上,以最小化生产过程能耗为目标,结合生产工艺条件、设备安全等约束条件,建立基于模糊强化学习的空气压缩机组变负荷协调预测控制方法,保证系统在复杂工况下安全稳定运行;最后,将所提出方法应用于工业园区空气压缩仿真系统进行性能测试,取得较好的控制效果.     

Passivity, feedback equivalence, and the global stabilization ofminimum phase nonlinear systems

Conditions under which a nonlinear system can be rendered passive via smooth state feedback are derived. It is shown that, as in the case of linear systems, this is possible if and only if the system in question has relative degree one and is weakly minimum phase. It is proven that weakly minimum phase nonlinear systems with relative degree one can be globally asymptotically stabilized by smooth state feedback, provided that suitable controllability-like rank conditions are satisfied. This result incorporates and extends a number of stabilization schemes recently proposed for global asymptotic stabilization of certain classes of nonlinear systems     

基于PSO的预测控制及在聚丙烯中的应用

输入输出受限非线性系统的预测控制问题,可以看作是一个难以直接求解的约束非线性优化问题。针对预测控制在解决此类优化问题时,存在易收敛到局部极小或者非可行解,对初始值敏感等缺点,提出了一种基于微粒群优化方法的非线性预测控制算法。采用微粒群优化算法（PSO）作为模型预测控制的滚动优化方法,在线实时求解最优控制律。将PSO与序贯二次规划（SQP）算法进行对比仿真实验,求解两个标准函数优化问题,结果表明PSO能够快速有效地求得全局最小点,而SQP则很容易陷入局部极小点。将该算法应用于丙烯聚合反应过程的温度控制中,仿真结果显示了该方法的有效性。     

Using hash tables to manage the time-storage complexity in a point location problem: Application to explicit model predictive control

The online computational burden of linear model predictive control (MPC) can be moved offline by using multi-parametric programming, so-called explicit MPC. The solution to the explicit MPC problem is a piecewise affine (PWA) state feedback function defined over a polyhedral subdivision of the set of feasible states. The online evaluation of such a control law needs to determine the polyhedral region in which the current state lies. This procedure is called point location; its computational complexity is challenging, and determines the minimum possible sampling time of the system. A new flexible algorithm is proposed which enables the designer to trade off between time and storage complexities. Utilizing the concept of hash tables and the associated hash functions, the proposed method solves an aggregated point location problem that overcomes prohibitive complexity growth with the number of polyhedral regions, while the storage–processing trade-off can be optimized via scaling parameters. The flexibility and power of this approach is supported by several numerical examples.     

On the practice of artificial intelligence based predictive control scheme: a case study

This paper describes a novel artificial intelligence based predictive control scheme for the purpose of dealing with so many complicated systems. In the control scheme proposed here, the system has to be first represented through a multi-Takagi-Sugeno-Kang (TSK) fuzzy-based model approach to make an appropriate prediction of the system behavior. Subsequently, a multi-generalized predictive control (GPC) scheme, which is organized based on a number of GPC schemes, is realized in line with the investigated model outcomes, at chosen operating points of the system. In case of the proposed control strategy realization, the investigated multi-GPC scheme is instantly updated to handle the system by activating the best control scheme through a new GPC identifier, while the system output is suddenly varied with respect to time. To present the applicability of the proposed control scheme, an industrial tubular heat exchanger system and also a drum-type boiler-turbine system have been chosen to drive through the proposed strategy. In such a case, the simulations are carried out and the corresponding results are compared with those obtained using traditional GPC scheme in addition to nonlinear GPC (NLGPC) scheme, as benchmark approaches, where the acquired results of the proposed control scheme are desirably verified.