连续搅拌反应釜系统的非线性鲁棒控制

基于非线性鲁棒控制理论,针对一类单输入单输出连续搅拌反应釜（CSTR）模型设计了具有高增益观测器的非线性鲁棒控制器（ONRC）,并提出了一种简单的控制器参数整定方法。与非线性鲁棒控制器（NRC）和滑模控制方法（SMC）的仿真结果比较表明,ONRC对系统不确定性和干扰具有更好的抑制作用,通过Monte-Carlo试验检验显示,ONRC在模型参数摄动时具有更好的性能鲁棒性。     

基于微粒群优化的鲁棒PID控制器参数整定方法研究

针对复杂工业过程中存在的一类模型不确定问题,提出一种新的鲁棒PID控制器参数整定方法.通过将鲁棒PID控制器的参数整定问题转化成一个max-min优化问题,利用微粒群优化(PSO)的协作算法对之进行求解,能获得某种鲁棒性能指标最优的PID控制器.对连续搅拌反应釜系统的实例仿真结果表明:与其它典型鲁棒PID控制器设计方法相比,本方法得到的PID控制器具有更强的鲁棒性和抗干扰能力,当过程操作范围发生大的变化时,利用本方法设计的鲁棒PID控制器能获得满意的结果.     

一种新颖的鲁棒PID控制器设计方法

提出一种新颖的鲁棒PID控制器设计方法,基于最小最大原理寻找一组合理的PID控制器参数,使得闭环系统在一定模型变化范围内仍具有满意的控制品质,降低对模型不确定的敏感程度,提高系统的鲁棒性。鉴于直接求解min-max问题的困难,首先推导出不确定性最坏情况下性能指标的上界,然后将min-max问题转化为最坏性能指标上界的min问题,计算量小且求解容易。同时,证明了该算法的鲁棒稳定性。CSTR仿真例子表明了算法的有效性。     

SOPDT模型不确定性界及其在内模控制器设计中的应用

引　言内模控制 (ＩＭＣ)是一种在化工过程中有广泛应用前景的鲁棒控制 ,由于这一先进控制能保证系统的鲁棒稳定性和鲁棒性能 ,从而在过程控制界引起了极大的兴趣[1～ 8] .鲁棒控制器设计的必要条件之一 ,是首先要有实际过程的不确定信息 ,即模型 (或过程 )的不确定性界[7] .对于在过程控制中经常采用的一阶加纯滞后过程 (ＦＯＰＤＴ) ,其模型不确定性界的计算方法已有报道[5～ 7,9] .然而 ,过程控制中常常会遇到二阶加纯滞后过程 (ＳＯＰＤＴ) ,有时更高阶的过程采用ＦＯＰＤＴ模型来近似往往不能保证闭环系统的性能 ,需要采用ＳＯＰＤＴ模…     

连续搅拌釜式反应器的鲁棒最优控制

针对一类带不确定性的连续搅拌釜式反应器,提出基于滑模控制理论的鲁棒最优控制算法。输入输出线性化方法用于线性化对象模型,假设系统的不确定因素有界,滑模面采用积分型滑模面以确保系统稳态误差为零,将线性二次型理论用于等效控制律的设计中,保证了系统的性能指标最优,自适应滑模切换控制增益的选取在降低系统抖振的前提下补偿了系统的不确定因素及外部扰动,实现了控制器的鲁棒最优。通过仿真实验表明,提出的控制器对匹配的不确定性因素及外部扰动具有鲁棒性,且闭环系统的性能指标最优。     

基于LMI的鲁棒PID控制器设计及工业应用

针对工业广泛应用的PID控制器,提出一种PID控制器性能评价及鲁棒参数优化的设计方法,该方法将PID控制器性能评价与鲁棒参数整定问题转化为线性矩阵不等式(LMI)凸优化问题进行求解,并利用所提方法开发了工业应用注册软件.     

无模型控制器与PI控制器的性能对比分析

通过仿真，比较CyboCon无模型单输入单输出控制器、基于鲁棒性能指标设计的PI控制器和基于经验整定方法设计的PI控制器在对象模型精确和模型变化情况下的控制性能，得出CyboCon无模型单输入单输出控制器与基于经验整定方法设计的Pl控制器在性能上是相同的，基于鲁棒性能指标设计的PI控制器设计时应该考虑约束条件。     

一类非自衡化工过程的最优控制

对控制能量存在约束时一类模型不确定非自衡化工过程的最优控制问题作了探讨.首先针对控制对象的标称模型应用谱分解最小化一个包含跟踪误差和控制能量在内的积分平方性能指标,导出一个控制能量约束条件下的最优控制律;然后再针对模型不确定性应用谱分解最小化实际系统性能和标称性能在整个频段上的方差,得到一个鲁棒控制器的设计方法,可使系统的标称性能对模型误差具有最优的鲁棒性.     

化工双输入输出时滞过程解耦PID控制器解析设计

基于常规单位反馈控制结构,针对具有时滞特性的化工双输入输出过程提出一种解耦PID控制器的解析设计新方法.该解耦PID控制器的突出优点是控制器设计过程简单直观;控制器可以实现标称系统输出间的显著解耦;系统输出分别使用相应控制器对角元素中的调节参数进行单参数整定.同时,对于实际生产过程中常见的被控过程乘性不确定性,分析了系统保持鲁棒稳定的充要条件.仿真实例验证了该方法优于其它方法.     

执行器饱和受限约束下锅炉燃烧系统的鲁棒预测控制

针对执行器饱和受限锅炉燃烧系统,提出一种鲁棒预测控制方法。首先,建立燃烧过程的线性参数变化系统模型,将执行器饱和受限转变成凸包形式描述;进而,设计执行器饱和受限的鲁棒预测控制器;最后,以某电站300MW机组锅炉控制为实例,对所提出的方法进行验证。结果表明:该方法可以在满足执行器饱和受限约束的同时获得满意的性能。     

A robustly stabilizing fault-tolerant MPC: A performance improvement-based approach

In terms of model predictive control (MPC) performance degradation caused by operational faults, in this article, a robust MPC strategy with active fault tolerance properties is proposed. The proposed strategy incorporates a fault supervision layer into the structure of conventional cost-contracting formulation-based robust MPC for the online update of the nominal controller model in the event of faults. The robust MPC is based on multiplant uncertainty, while the supervisory layer consists of a bank of unknown input observers and a decision-making algorithm. Simulation results in a nonlinear polymerization reactor subject to process faults demonstrate that the proposed approach offers superior performance compared to the conventional strategy.     

LMI-based robust model predictive control for a continuous MMA polymerization reactor

A linear matrix inequality (LMI)-based robust model predictive control (MPC) is applied to a continuous stirred-tank reactor for the polymerization of methyl methacrylate (MMA). The polytopic model is constructed to predict the responses to various control input sequences by using Jacobians of uncertain nonlinear model at several operating points and the controller design is characterized as the problem of minimizing an upper bound on the ‘worst-case’ infinite horizon objective function subject to constraints on the control input and plant output. Simulation studies under different conditions are conducted to validate the feasibility of the optimization problem and evaluate the applicability of such a control scheme. Simulation results show that, despite the model uncertainty, the LMI-based robust model predictive controller performs quite satisfactorily for the property control of the continuous polymerization reactor and guarantees the robust stability.     

Safe-parking of nonlinear process systems: Handling uncertainty and unavailability of measurements

This work considers the problem of handling actuator faults in nonlinear process systems subject to input constraints, uncertainty and availability of limited measurements. A framework is developed to handle faults that preclude the possibility of continued operating at the nominal equilibrium point using the existing robust or reconfiguration-based fault-tolerant control approaches. The key consideration is to operate the plant using the depleted control action at an appropriate ‘safe-park’ point to prevent onset of hazardous situations as well as enable smooth resumption of nominal operation upon fault-repair. First, we consider the presence of constraints and uncertainty and develop a robust Lyapunov-based model predictive controller that enhances the set of initial conditions from which closed-loop stability is achieved. The stability region characterization provided by the robust predictive controller is subsequently utilized in a safe-parking algorithm that appropriately selects ‘safe-park’ points from the safe-park candidates (equilibrium points subject to failed actuators) to preserve closed-loop stability upon fault-repair. Specifically, a candidate parking point is termed a safe-park point if (1) the process state at the time of failure resides in the stability region of the safe-park candidate (subject to depleted control action and uncertainty) and (2) the safe-park candidate resides within the stability region of the nominal control configuration. Then we consider the problem of availability of limited measurements. An output feedback Lyapunov-based model predictive controller, utilizing an appropriately designed state observer (to estimate the unmeasured states), is formulated and its stability region explicitly characterized. An algorithm is then presented that accounts for the estimation errors in the implementation of the safe-parking framework. The proposed framework is illustrated using a chemical reactor example and demonstrated on a styrene polymerization process.     

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。     

Robust model predictive control of nonlinear process systems: Handling rate constraints

This work considers the problem of stabilization of control affine nonlinear process systems subject to constraints on the rate of change and magnitude of control inputs in the presence of uncertainty. We first handle rate constraints within a soft constraints framework. A new robust predictive controller formulation that minimizes rate constraint violation while guaranteeing stabilization and input constraint satisfaction from an explicitly characterized stability region is designed. We then derive conditions that allow for guaranteed satisfaction of hard rate constraints. Subsequently, a predictive controller is designed that ensures rate constraints satisfaction when the required conditions are satisfied, relaxing them otherwise to preserve feasibility and robust stability. The implementation of the proposed predictive controllers is illustrated via a chemical reactor example.     

Internal model control design for input‐constrained multivariable processes

Multivariable plants under input constraints such as actuator saturation are liable to performance deterioration due to control windup and directionality change. A two‐stage internal model control (IMC) antiwindup design for open loop stable plants is presented. The design is based on the solution of two low‐order quadratic programs at each time step, which addresses both transient and steady‐state behaviors of the system. For analyzing the robust stability of such systems against any infinity‐norm bounded uncertainty, stability test have also been developed. In particular, we note that the controller input‐output mappings satisfy certain integral quadratic constraints. Simulated examples show that the two‐stage IMC has superior performance when compared with other existing optimization‐based antiwindup methods. The stability test is illustrated for a plant with left matrix fraction uncertainty. A scenario where the proposed two‐stage IMC competes favorably with a long prediction horizon model predictive control is described. © 2011 American Institute of Chemical Engineers AIChE J, 2011     

Adaptive type-2 fuzzy logic control of a bioreactor

Two adaptive type-2 fuzzy logic controllers with minimum number of rules are developed and compared by simulation for control of a bioreactor in which aerobic alcoholic fermentation for the growth of Saccharomyces cerevisiae takes place. The bioreactor model is characterized by nonlinearity and parameter uncertainty. The first adaptive fuzzy controller is a type-2 fuzzy-neuro-predictive controller (T2FNPC) that combines the capability of type-2 fuzzy logic to handle uncertainties, with the ability of predictive control to predict future plant performance making use of a neural network model of the nonlinear system. The second adaptive fuzzy controller is instead a self-tuning type-2 PI controller, where the output scaling factor is adjusted online by fuzzy rules according to the current trend of the controlled process. The performance of a type-2 fuzzy logic controller with 49 rules is used as reference.