多阶段间歇过程无穷时域优化线性二次容错控制

针对具有输入时滞的多阶段间歇过程,考虑执行器故障影响,提出了无穷时域优化混杂容错控制器设计方法。该方法首先将给定具有输入时滞的模型转化为新的无时滞的状态空间模型,接着再将此模型转换为包含状态变量误差和输出跟踪误差的扩展状态空间模型,并用切换系统模型表示,然后引入有限时域的二次目标函数,利用最优控制理论,设计出在无穷时域中容错控制器。为获得最小运行时间,针对不同阶段设计依赖于Lyapunov函数的驻留时间方法。创新之处在于,控制律设计简单,计算量小,且每一阶段时间求取不需要引用任何其他变量,简单易行。最后,以注塑成型过程为例,仿真结果证明所提出方法具有可行性和有效性。     

双时滞线性切换正系统的鲁棒L_1控制器设计

设计了一类正系统在任意切换信号下的鲁棒L1控制器。针对一类带有状态时滞和输入时滞的线性切换正系统,基于线性余正Lyapunov-Krasovskii泛函和平均驻留时间理论,推导出在任意切换信号下使系统指数稳定且具有干扰抑制能力γ的充分条件,进一步设计了鲁棒L1控制器,保证系统不仅是指数稳定且具有干扰抑制能力γ。最后通过数值仿真示例证明了控制器的有效性。     

具有输入时滞的不确定时滞系统的积分控制

针对具有输入时滞的不确定时滞系统,利用Lyapunov稳定性定理和一个特殊的引理,设计了带记忆的积分控制器,使得系统在有限时间内渐近稳定,并给出时滞依赖的稳定性判据.数值算例表明了该设计方法的有效性.     

基于自适应谱方法的裂解炉烧焦模型化简策略

炉管烧焦是乙烯裂解炉的重要操作，准确的烧焦模型是优化控制烧焦过程、提高烧焦效率的重要前提。常见的烧焦过程机理模型为偏微分方程的形式，具有无穷维、时空耦合的特点，计算复杂度高，难以满足实时优化和控制对轻量级模型的需求。为此提出一种基于自适应谱方法的乙烯裂解炉烧焦过程轻量级模型化策略。首先，针对炉管生焦与烧焦过程焦层变化特点，将炉管自适应划分为有焦段和无焦段。其次，使用Legendre多项式作为空间基函数分别对有焦段和无焦段分布参数近似降维，建立烧焦过程控制输入与炉管内焦层厚度分布、温度分布的时间维系数的集中参数模型，避免因直接使用谱方法近似拟合带局部特征的分布参数系统而产生较大误差的问题。     

陶瓷取坯机械手H∞鲁棒自适应跟踪控制

针对具有不确定和干扰输入的二自由度电动陶瓷取坯机械手系统，基于李雅普诺夫函数递推设计方法设计了H∞鲁棒自适应跟踪控制器，该控制器不仅可保证跟踪误差闭环系统的一致有界稳定性，而且使得由干扰力短到跟踪误差评价信号的L2增益小于给定的值，同时本文也提出了不同求解HJI不等式设计陶瓷取坯机械手H∞鲁棒控制器的方法。仿真结果表明，所设计的控制器具有良好的跟踪性能和较强的鲁棒自适应性。     

多故障并发不确定系统的鲁棒完整性容错控制

针对传统容错控制方法难以保证非线性系统在执行器和传感器多故障并发情形下的稳定性问题,研究了一类时滞不确定模糊系统的鲁棒完整性容错控制方法。建立了基于T-S模糊逻辑的不确定非线性模型,定义执行器和传感器故障阵的标准归一化形式,在利用Newton-Leibniz公式变换系统结构的基础上,根据线性矩阵不等式技术给出了鲁棒容错控制器存在的时滞相关性充分条件,以保证整个闭环系统在执行器和(或)传感器发生故障时的稳定性,同时满足给定的广义鲁棒性能约束,联合抑制扰动、初始状态和时滞状态对系统性能的影响。最后仿真结果验证了方法的必要性和可行性。     

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。     

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。     

时滞系统的稳定性分析与控制器设计

给出时滞系统的定义、分类、数学描述和求解，介绍时滞系统性分析的频域法和时域法，概述控制器设计的发展概况，重点介绍混合灵敏度问题的有限谱配置、鲁棒整定问题的Bezout分解、时滞系统的H∞控制、保成本控制、鲁棒可靠控制和容错控制、无源控制和耗散控制以及自整定PID控制等重要的控制方法，最后介绍当前的一些研究热点。     

带有执行器故障和分布时滞的网络控制系统容错H_∞控制

考虑带有执行器故障和分布时滞的网络控制系统(NCSs),研究了一类鲁棒容错H_∞控制器的设计问题。首先,把通信网络中的时延和丢包现象处理为等效时滞,再结合执行器的故障矩阵,建立闭环控制系统。然后,根据Lyapunov稳定性理论,得到保证闭环NCSs渐近稳定的容错H_∞性能判据;利用线性矩阵不等式技术(LMI),给出控制器参数的求解方法。最后,仿真验证了所提控制器设计方法的有效性。     

Explicit distributed model predictive control design for chemical processes under constraints and uncertainty

In multivariable industrial processes, the common distributed model predictive control strategy is usually unable to deal with complex large-scale systems efficiently, especially under system constraints and high control performance requirements. Based on this situation, we use the distributed idea to divide the large-scale system into multiple subsystems and transform them into the state space form. Combined with the output tracking error term, we build an extended non-minimal state space model that includes output error and measured output and input. When dealing with system constraints, the new constraint matrix is divided into range and kernel space by using the explicit model predictive control algorithm, which reduces the difficulty of solving constraints in the extended system and further improves the overall control performance of the system. Finally, taking the coke furnace pressure control system as an example, the proposed algorithm is compared with the conventional distributed model predictive control algorithm using non-minimal state space, and the simulation results show the feasibility and superiority of this method.     

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.     

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.     

Two-degree-of-freedom output feedback controllers for discrete-time nonlinear systems

A discrete-time, model-based output feedback control structure for nonlinear processes is developed in the present work. The structure makes use of a closed-loop observer, while at the same time it guarantees that the overall feedback controller possesses integral action. An algebraic transformation is applied on the observer states to insure that the input/output gain of the observer matches the model upon which the static state feedback control law is based. The resulting control algorithm is a two-degree-of-freedom control law, in the sense that the output and the set point are processed in different ways. The control structure is shown not only to have the same properties as the standard model-state feedback structure, but also that it emerges from a model algorithmic control framework. Finally, a simulation example using an exothermic CSTR operating at an open-loop unstable steady state is used to evaluate the closed-loop performance of the proposed method.     

Boundary control synthesis for a lithium‐ion battery thermal regulation problem

The thermal regulation problem for a lithium ion (Li‐ion) battery with boundary control actuation is considered. The model of the transient temperature dynamics of the battery is given by a nonhomogeneous parabolic partial differential equation (PDE) on a two‐dimensional spatial domain which accounts for the time‐varying heat generation during the battery discharge cycle. The spatial domain is given as a disk with radial and angular coordinates which captures the nonradially symmetric heat‐transfer phenomena due to the application of the control input along a portion of the spatial domain boundary. The Li‐ion battery model is formulated within an appropriately defined infinite‐dimensional function space setting which is suitable for spectral controller synthesis. The key challenges in the output feedback model‐based controller design addressed in this work are: the dependence of the state on time‐varying system parameters, the restriction of the input along a portion of the battery domain boundary, the observer‐based optimal boundary control design where the separation principle is utilized to demonstrate the stability of the closed loop system, and the realization of the outback feedback control problem based on state measurement and interpolation of the temperature field. Numerical results for simulation case studies are presented. © 2013 American Institute of Chemical Engineers AIChE J, 59: 3782–3796, 2013     

Two-degree-of-freedom output feedback controllers for nonlinear processes

In this work, a nonlinear output feedback control algorithm is proposed, in the spirit of model-state feedback control. The structure provides state estimates using a process model, the measured output, and the residual between the model output and the measured output. These estimates will track the process states at a rate determined by a set of tunable parameters. An algebraic transformation of the state estimates is incorporated in the control structure to ensure that the input/output gain of the observer matches the model upon which the static state feedback control law is based. The transformed states are then used in the control law. This leads to a controller of minimal order possessing integral action. The control structure is shown to have the same properties as the standard model-state feedback structure. The resulting algorithm is a two-degree of freedom control law, in the sense that the control action is not a function of the error only, but the output and the set point are processed in different ways. Finally, a simulation example using an exothermic CSTR operating at an open-loop unstable steady state is used to demonstrate the closed-loop performance of the proposed method.     

Robust continuous velocity control of convective spatially distributed systems

In this work a robust nonlinear scheme is proposed to control spatially distributed convective systems described by first-order hyperbolic partial differential equations by manipulating the flow velocity. The proposed scheme is designed after the method of characteristics is used to establish key structural properties of the system dynamics. The resulting feedback control, which can be seen as a proportional integral controller with variable integration time, does not require measurements for several axial points nor infinite dimensional state estimations. The proposed controller is applied successfully to two heat exchange simulation examples and a nonisothermical plug flow reactor. It is shown that it is robust in the face of uncertain parameters and load disturbances. Finally, the performance of the robust controller is compared to other control applications.     

A Simplified Numerical Approach to Feedback Controller Design

A generalized parameter optimization method for computing feedback controller parameters is proposed. The method utilizes the downhill simplex method (DSM), a pattern search algorithm, to determine the optimal parameters that minimize an objective function or performance index. The system model, expressed in terms of state‐space equations is integrated with respect to time at each DSM iteration in order to determine the states. A fourth‐order Runge‐Kutta scheme is used for integrating the state equations. A penalty function approach is used for problems with inequality constraints on the state variables or controls. Though relatively inefficient in terms of the number of function evaluations, DSM requires only that the user provide the model equations, and not their derivatives. Additionally, the DSM code is very compact. Thus, a small and straightforward program allows for controller parameter determination for a variety of state‐space and classical PID feedback control design problems.     

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.