Detection and management of actuator faults in controlled particulate processes using population balance models

This paper presents a methodology for the design of an integrated fault detection and fault-tolerant control (FD-FTC) architecture for particulate processes described by population balance models (PBMs) with control constraints, actuator faults and a limited number of process measurements. The architecture integrates model-based fault detection, state estimation, nonlinear feedback and supervisory control on the basis of an appropriate reduced-order model that captures the dominant dynamics of the process and is obtained through application of the method of weighted residuals. The architecture comprises a family of control configurations together with a fault detection filter and a supervisor. For each configuration, a stabilizing output feedback controller with well-characterized stability properties is designed through the combination of a state feedback controller and a state observer that uses the available measurements of principal moments of the particle size distribution (PSD) and the continuous-phase variables to provide appropriate state estimates. A fault detection filter that simulates the behavior of the fault-free, reduced-order model is designed, and its discrepancy from the behavior of the actual process state estimates is used as a residual for fault detection. Finally, a switching law based on the stability regions of the constituent control configurations is derived to reconfigure the control system in a way that preserves closed-loop stability in the event of fault detection. Appropriate fault detection thresholds and control reconfiguration criteria that account for model reduction and state estimation errors are derived for the implementation of the FD-FTC architecture on the particulate process. Finally, the methodology is applied to the problem of constrained, actuator fault-tolerant stabilization of an unstable steady-state of a continuous crystallizer.     

一类不确定多时滞系统的鲁棒容错控制研究

讨论一类线性不确定多时滞系统的鲁棒容错控制问题.基于Lyapunov稳定性理论和线性矩阵不等式方法（LMI）,针对一类参数有界不确定多时滞系统,给出了状态反馈鲁棒容错控制器设计方法,并且利用该方法得到的闭环控制系统,不仅在执行器失效情况下具有渐进稳定性,对参数不确定也具有良好的鲁棒性.最后,应用设计实例及仿真结果验证该设计方法的可靠性和有效性.     

Bounded robust control of constrained multivariable nonlinear processes

This work focuses on control of multi-input multi-output (MIMO) nonlinear processes with uncertain dynamics and actuator constraints. A Lyapunov-based nonlinear controller design approach that accounts explicitly and simultaneously for process nonlinearities, plant-model mismatch, and input constraints, is proposed. Under the assumption that all process states are accessible for measurement, the approach leads to the explicit synthesis of bounded robust multivariable nonlinear state feedback controllers with well-characterized stability and performance properties. The controllers enforce stability and robust asymptotic reference-input tracking in the constrained uncertain closed-loop system and provide, at the same time, an explicit characterization of the region of guaranteed closed-loop stability. When full state measurements are not available, a combination of the state feedback controllers with high-gain state observes and appropriate saturation filters, is employed to synthesize bounded robust multivariable output feedback controllers that require only measurements of the outputs for practical implementation. The resulting output feedback design is shown to inherit the same closed-loop stability and performance properties of the state feedback controllers and, in addition, recover the closed-loop stability region obtained under state feedback, provided that the observer gain is sufficiently large. The developed state and output feedback controllers are applied successfully to non-isothermal chemical reactor examples with uncertainty, input constraints, and incomplete state measurements. Finally, we conclude the paper with a discussion that attempts to put in perspective the proposed Lyapunov-based control approach with respect to the nonlinear model predictive control (MPC) approach and discuss the implications of our results for the practical implementation of MPC, in control of uncertain nonlinear processes with input constraints.     

An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller （ILRGCC）. A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H∞ performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities （LMIs）, and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.     

基于T-S模糊模型的间歇过程的迭代学习容错控制

间歇过程不仅具有强非线性,同时还会受到诸如执行器等故障影响,研究非线性间歇过程在具有故障的情况下依然稳定运行至关重要。针对执行器增益故障及系统所具有的强非线性,提出一种新的基于间歇过程的T-S模糊模型的复合迭代学习容错控制方法。首先根据间歇过程的非线性模型,利用扇区非线性方法建立其T-S模糊故障模型,再利用间歇过程的二维特性与重复特性,在2D系统理论框架内,设计2D复合ILC容错控制器,进而构建此T-S模糊模型的等价二维Rosser模型,接着利用Lyapunov方法给出系统稳定充分条件并求解控制器增益。针对强非线性的连续搅拌釜进行仿真,结果表明所提出方法具有可行性与有效性。     

Improved infinite horizon LQ tracking control for injection molding process against partial actuator failures

Focusing on injection molding processes with partial actuator failures, a new design of infinite horizon linear quadratic control is introduced. A new state space process model is first derived through input–output process data. Furthermore, an improved infinite horizon linear quadratic control scheme, whereby the process state variables and tracking error can both be regulated separately, is proposed to show enhanced control performance against partial actuator failures and unknown disturbances. Under the circumstances of actuator faults, the closed-loop system is indeed a process with uncertain parameters. Hence, a sufficient condition is proposed to guarantee robust stability is presented using Lyapunov theory. The proposed concepts are illustrated in an injection velocity control case study to show the effectiveness.     

Multi-objective nonlinear model predictive control through switching cost functions and its applications to chemical processes

This paper proposes a switching multi-objective model predictive control (MOMPC) algorithm for constrained nonlinear continuous-time process systems. Different cost functions to be minimized inMPC are switched to satisfy different performance criteria imposed at different sampling times. In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system, the dual-mode control method is used to design the switching MOMPC controller. In this method, a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC. The correction termis computed if the states are out of the terminal set and the free-parameters of the local control laware computed if the states are in the terminal set. The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions. Finally, implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.     

MULTI-OBJECTIVE RECONFIGURABLE OUTPUT FEEDBACK CONTROLLER FOR MIMO SYSTEM USING MOEAS

This article deals with the design of a parametric eigenstructure assignment (PEA)-based multi-objective reconfigurable output feedback controller for a multiple-input multiple-output (MIMO) system against partial actuator failure. The significance of the PEA technique is that it provides more design degrees of freedom to obtain multi-objective functions, namely robust stability and improved transient response. Robust stability and transient response are ensured by a minimum condition number of the eigenvector and a minimum norm of controller gain. These two objectives are conflicting in nature, and, hence, the main aim is to minimize both objectives simultaneously by means of a multi-objective evolutionary algorithm (MOEA). In this study, a multi-objective optimization problem is formulated and is solved by non-dominated sorting genetic algorithm-II (NSGA-II) and strength Pareto evolutionary algorithm-II (SPEA-II). Four indicators are used to evaluate each algorithm performance: spacing, error ratio, generational distance, and hypervolume. Then a reconfigurable controller is designed using PEA against actuator failure, which guarantees closed-loop stability. The effectiveness of the proposed controllers is demonstrated by implementing them in an interacting three-tank benchmark system.     

A unified framework for detection, isolation and compensation of actuator faults in uncertain particulate processes

This paper presents a methodology for the robust detection, isolation and compensation of control actuator faults in particulate processes described by population balance models with control constraints and time-varying uncertain variables. The main idea is to shape the fault-free closed-loop process response via robust feedback control in a way that enables the derivation of performance-based fault detection and isolation (FDI) rules that are less sensitive to the uncertainty. Initially, an approximate finite-dimensional system that captures the dominant process dynamics is derived and decomposed into interconnected subsystems with each subsystem directly influenced by a single manipulated input. The decomposition is facilitated by the specific structure of the process input operator. A robustly stabilizing bounded feedback controller is then designed for each subsystem to enforce an arbitrary degree of asymptotic attenuation of the effect of the uncertainty in the absence of faults. The synthesis leads to (1) an explicit characterization of the fault-free behavior of each subsystem in terms of a time-varying bound on an appropriate Lyapunov function and (2) an explicit characterization of the robust stability region in terms of the control constraints and the size of the uncertainty. Using the fault-free Lyapunov dissipation bounds as thresholds for FDI in each subsystem, the detection and isolation of faults in a given actuator is accomplished by monitoring the evolution of the system within the stability region and declaring a fault if the threshold is breached. The thresholds are linked to the achievable degree of asymptotic uncertainty attenuation and can therefore be properly tuned by proper tuning of the controllers, thus making the FDI criteria less sensitive to the uncertainty. The robust FDI scheme is integrated with a robust stability-based controller reconfiguration strategy that preserves closed-loop stability following FDI. Finally, the implementation of the fault-tolerant control architecture on the particulate process is discussed and the proposed methodology is applied to the problem of robust fault-tolerant control of a continuous crystallizer with a fines trap.     

基于T-S模糊模型与粒子群优化的非线性预测控制

引言模型预测控制属于一种基于模型的多变量的控制算法,发展至今已在化工过程控制方面得到了广泛的应用[1-5]。状态反馈预测控制[6-8]是模型预测控制技术的一种,基于状态空间模型,采用实测状态     

自动纤维铺丝机的设计与实验研究

自动纤维铺丝是一种非常重要的复合材料自动化成型方法。提出了一种新型的纤维铺丝机的总体机构和控制系统方案,利用气缸为执行元件带动切刀和压辊,实现纤维的剪切和夹紧,构建以直线位移传感器为反馈元件的新型张力控制系统,同时设计一种基于主流的工控机(IPC)+运动控制器(UMAC)相结合的开放式控制系统方案,最后进行相关的铺丝实验。运行结果表明,各轴运动情况良好,控制系统整体运行稳定,各模块设计合理,达到了预期效果。     

Double-command feedforward-feedback control of a nonlinear plant

A design approach is proposed for feedforward-feedback control systems. The basis of the proposed approach is a steady state control law which maintains the desired control output of the system and is employed as the feedforward controller. With this feedforward controller, for a wide class of systems, the stability of the control system is proved if the feedback controller is a gain with an arbitrarily high value; that is, the only limit for the feedback (transient) control command is the actuator’s practical limit. Moreover, in continuous domain, there will be no overshoot. In this article, the proposed method has been applied to a catalytic stirred tank reactor (CSTR) to control the output concentration through adjusting the flow of two valves simultaneously and resulted in an excellent control response.     

化工过程多回路PID控制系统模式切换参数自整定

化工过程一般为多变量系统,但其主要控制方案为分散多回路PID常规控制。由于多变量系统内部存在不同程度的耦合作用,各控制回路之间存在相互影响,当其他回路进行手动/自动模式切换时,本回路等效被控对象将会发生突变,导致本回路的原有控制参数不能适应等效被控对象的变化,造成控制性能下降,甚至闭环系统不稳定。为避免这种情况的发生,从整个系统的角度研究控制回路模式切换时的稳定性,采用多变量频域Nyquist阵列设计法。基于对角优势下正Nyquist稳定性判据,从Gershgorin圆边界点的角度定量分析各个控制回路在模式切换前后的稳定性变化程度,从而确定各回路控制器增益的调整方向及程度,实现各回路的控制器参数在控制回路模式切换瞬间的自动整定,尽可能抵消控制回路模式切换对整个系统的扰动,保证整个系统的闭环稳定性。以Shell公司重油分馏塔的多回路PID控制系统为例,将3个PID控制回路依次投用时,根据Gershgorin圆边界点进行控制参数的自整定,闭环系统仍能保持一定的控制性能,否则闭环系统将不稳定。     

Nonlinear adaptive switching control for a class of non-affine nonlinear systems

An improved nonlinear adaptive switching control method is presented to relax the assumption on the higher order nonlinear terms of a class of discrete-time non-affine nonlinear systems. The proposed control strategy is composed of a linear adaptive controller, a neural network (NN) based nonlinear adaptive controller and a switching mechanism. An incremental model is derived to represent the considered system and an improved robust adaptive law is chosen to update the parameters of the linear adaptive controller. A new performance criterion of the switching mechanism is designed to select the proper controller. Using this control scheme, all the signals in the system are proved to be bounded. Numerical examples verify the effectiveness of the proposed algorithm.     

网络控制系统在线时延预估策略与实现

分析网络时延的产生以及影响其取值规律的原因,提出网络时延在线预估法以预估将来时刻可能产生的控制器与执行器之间的时延.在CAN bus网络控制实验平台上对该预估法的有效性进行验证,实验结果表明,该方法准确地预估出下一时刻网络时延的大小,并且基于预估到的网络时延设计的控制器很好地保证了控制系统的稳定性.     

Generic model controller tuning for chemical processes with input saturation

Control in the face of process input constraints is very common and of great practical importance in the processing industries. Generic Model Control (GMC) is a model‐based control framework for both linear and nonlinear systems. In this paper, a constrained GMC controller tuning approach using a nonlinear least squares technique is proposed. This tuning approach is simple to apply. For a SISO GMC control system with input saturation, the tracking performance is significantly improved by adding a simple heuristic switching strategy. The effectiveness of the proposed controller tuning approach is demonstrated using dynamic simulations and MIMO real‐time experiments.     

基于控制器切换的模糊不确定网络化系统的稳定性

基于T-S模糊模型建立模糊网络化控制系统模型,研究了一类存在不确定参数的网络时滞系统在单包传输且没有丢包情况下的稳定性.为了增强其诱导时延的动态性能,假设存在有限个备选的控制增益己知的模糊状态反馈控制器,在每个备选的控制器均不能镇定系统的情况下,使用控制器切换技术及Lyapunov函数方法,设计切换律,得到了系统渐进稳定的一个充分条件,并且此条件可转化为求解线性矩阵不等式(LMIS)问题.最后仿真结果表明所设计策略的可行性和有效性.     

Integrated nonlinear model predictive fault tolerant control and multiple model based fault detection and diagnosis

In this paper, a new fault-tolerant control approach is presented for a class of nonlinear systems, which preserves system stability despite a time delay in fault detection. The faults are assumed to occur in the actuators and are modeled for the general form of affine nonlinear systems. A fault detection and diagnosis (FDD) block is designed based on the multiple model method. The bank of extended Kalman filters (EKF) is used to detect predefined actuator faults and to estimate the unknown parameters of actuator position. The estimated parameters are then used to correct the model of the faulty system and to reconfigure the controller. The reconfigurable controller is designed based on the stabilizing nonlinear model predictive control (NMPC) scheme. On the other hand, in the duration between fault occurrence and fault detection, because of the mismatch between the process and the model, the system states may go off the attraction region. The proposed method is based on designing multiple local controllers for individual predefined faults. Depending on the value of a system variable at the moment of fault detection, one of these controllers will operate. This leads to a stability region of a set of auxiliary equilibrium points (AEPs), which is larger than the attraction region. Moreover, a framework for preserving system stability is presented. Finally, a practical chemical process example is presented to illustrate the effectiveness of this method.     

Robust control of a reverse-flow reactor

This paper is focused on the design of a robust controller for a catalytic fixed-bed reactor with periodical inversion of the flow direction (reverse-flow reactor, RFR). The analogy between the RFR operated at infinite switching frequency and the countercurrent reactor is the basis of the simplified mathematical model of the reactor.The control system uses dilution and internal electric heating to ensure complete conversion of the reactants and to prevent overheating of the catalyst. As the state of the system is not fully available, apart from some temperature measurements, an observer is designed and used in the control algorithm. This is a typical case of nonlinear system with uncertainties. Following the procedure described in detail by Fissore [2008. Robust control in presence of parametric uncertainties: observer-based feedback controller design. Chemical Engineering Science, in press, doi:10.1016/j.ces.2007.12.019.], the extended model for the process is setup, thus taking into account all the simplifications of the model and linking performance and robustness to the control law, which is a simple state feedback. Simulations with randomly varying feeding concentration have been carried out in order to demonstrate the effectiveness of the proposed control system.