稳态目标优化的稳定MIMO约束预测控制

A two-stage multi-objective optimization model-predictive control algorithms(MPC) strategy is presented. A domain MPC controller with input constraints is used to increase freedom for steady-state objective and enhance stabilization of the controller. A steady-state objective optimization algorithm oriented to transient process is adopted to realize optimization of objectives else than dynamic control. It is proved that .the stabilization for both dynamic control and steady-state objective optimization can be guaranteed. The theoretical results are demonstrated and discussed using a distillation tower as the model. Theoretical analysis and simulation results show that this control stratek--v is efl$cient and provides a good strategic solution to uractical urocess control.     

适应多负载电池储能系统的拓扑结构优化重构方法

可重构技术应用于电池储能系统是退役电池回收再利用的有效方法之一。然而，由于储能系统供给侧和需求侧的多样性、退役电池包的拆解成本高且易引发环境和安全问题等问题，退役电池重组再利用构成储能系统非常困难。为了解决这一问题，本文采用电池重构对电压不一致的电池包进行重组再利用，以满足多负载需求为目标，在保证能源效率和系统安全的前提下，提出了一种面向多负载需求的低拓扑复杂交换度的退役电池组拓扑重构优化设计方法。通过构建多目标优化模型，并采用改进的二进制纵横交叉算法求解模型，获得了满足多负载需求的最优拓扑结构，并通过案例分析验证了本文所提方法的有效性。研究表明，通过调整多目标间的权重，可提高可重构电池储能系统拓扑结构的设计弹性。     

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.     

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     

Multi-objective optimization of drainage-plates in wave-plate mist eliminators using experiment and data-driven modelling for lower water loss and energy requirement

Wave-plate mist eliminators are widely employed as gas–liquid separation devices to prevent the liquid escaping from thermal power plants or other cooling towers. In this study, the wave-plate mist eliminator with drainage plates was numerically analyzed and the effects between geometrical variables on two objectives, namely, pressure drop (ΔP) and separation efficiency (η), were revealed. Plate spacing, width, and length, as well as the relative position of the drainage plate, were thoroughly investigated. A combined strategy was developed for multi-objective optimization of the wave-plate mist eliminator by integrating computational fluid dynamics (CFD) simulation, response surface methodology (RSM), non-dominated sorting genetic algorithm-II (NSGA-II), and a technique for order of preference by similarity to ideal solution (TOPSIS) method. The results demonstrated that the relative position of drainage plates has a greater impact on the overall performance, whereas the width of drainage plates has the minimum effect. With the implementation of NSGA-II and the TOPSIS method, an optimal solution for the design of the mist eliminator was obtained. After comparing with the baseline case, the optimized case presents promising characteristics with high separation efficiency (enhanced by 3.6%~9.06%) and a low energy consumption coefficient (reduced by 72.30% at η = 45%).     

An archive-based multi-objective evolutionary algorithm with adaptive search space partitioning to deal with expensive optimization problems: Application to process eco-design

In eco-design, the integration of environmental aspects into the earliest stage of design is considered with the aim of reducing adverse environmental impacts throughout a product's life cycle. An eco-design problem is therefore multi-objective, where several objectives (environmental, economic, and technological) are to be simultaneously optimized.The optimization of industrial processes usually requires solving expensive multi-objective optimization problems (MOPs). Aiming to solve efficiently MOPs, with a limited computational budget, this paper proposes a new framework called AMOEA-MAP. The framework relies on the structure of the NSGAII algorithm and possesses two novel operators: a memory-based adaptive partitioning strategy, which provides an adaptive reticulation of the search space for a quick identification of optimal zones with less computational effort; and a bi-population evolutionary algorithm, tailored for expensive optimization problems.To ascertain its generality, the framework is first tested on several tough benchmarks. Its performance is subsequently validated on a real-world eco-design problem.     

Tuning of NMPC controllers via multi-objective optimisation

Nonlinear Model Predictive Control (NMPC) is a powerful technique that can be used to control many industrial processes. Different and often conflicting control objectives, e.g., reference tracking, disturbance rejection and minimum control effort, are typically present. Most often these objectives are translated into a single weighted sum (WS) objective function. This approach is widespread because it is easy to use and understand. However, selecting an appropriate set of weights for the objective function is often non-trivial and is mainly done by trial and error. The current study proposes a systematic procedure for tuning Nonlinear MPC based on multi-objective optimisation methods. Advanced methods allow an efficient solution of the multi-objective problem providing a systematic overview of the controller behaviour. Moreover, through analytic relations it is possible to link a solution obtained with these novel methods to a set of weights for a weighted sum objective function. Applying this set of weights causes the WS to generate the same solution as obtained with the advanced method. Hence, an appropriate controller can be selected based on the alternatives generated by the advanced method, while the corresponding weights for a WS can be derived for implementing the controller in practice. The procedure is successfully tested on two benchmark applications: the Van de Vusse reactor and the Tennessee Eastman plant.     

基于均匀分布NSGAII算法的污水处理多目标优化控制

针对污水处理过程控制中能耗过大、出水水质超标严重等问题，提出了一种基于均匀分布的NSGAII(non-dominated sorting genetic algorithm II based on uniform distribution, UDNSGAII)多目标优化智能控制系统。首先，该方法以污水处理能耗和出水水质作为优化目标，建立多目标优化模型。其次，为了获得溶解氧和硝态氮的优化设定值，提高Pareto解的性能，该算法将种群映射到目标函数对应的超平面, 并在该平面上进行聚类以增加解的多样性。此外，加入分布性判断模块和分布性加强模块提高解的分布性。最后，采用比例积分微分(proportional integral derivative, PID)控制器对溶解氧和硝态氮的优化设定值进行底层跟踪控制。为了验证该算法的有效性，采用国际基准的污水处理仿真平台(benchmark simulation model No.1, BSM1)来进行实验。结果显示，所提出的UDNSGAII多目标优化控制方法能够在满足出水水质达标的同时，有效地降低污水处理过程能耗。     

Multi-objective optimization of seeded batch crystallization processes

The optimization of a batch cooling crystallizer has been traditionally sought with respect to the cooling profile and seeding characteristics that keep supersaturation at an optimum level throughout the operation. Crystallization processes typically have multiple performance objectives and optimization using different objective functions leads to significantly different optimal operating conditions. Thus different temperature profiles and seeding characteristics impose a complex interplay on the crystallizer behavior and there is a trade-off between the performance objectives. Therefore, a multi-objective approach should be adopted for optimization of a batch crystallizer for best process operation. This study presents the solution of various optimal control problems for a seeded batch crystallizer within a multi-objective framework. A well known multi-objective evolutionary algorithm, the elitist Nondominated Sorting Genetic Algorithm, has been adapted here to illustrate the potential for the multi-objective optimization approach.     

A Web Server for Designing Molecular Switches Composed of Two Interacting RNAs

The programmability of RNA–RNA interactions through intermolecular base-pairing has been successfully exploited to design a variety of RNA devices that artificially regulate gene expression. An in silico design for interacting structured RNA sequences that satisfies multiple design criteria becomes a complex multi-objective problem. Although multi-objective optimization is a powerful technique that explores a vast solution space without empirical weights between design objectives, to date, no web service for multi-objective design of RNA switches that utilizes RNA–RNA interaction has been proposed. We developed a web server, which is based on a multi-objective design algorithm called MODENA, to design two interacting RNAs that form a complex in silico. By predicting the secondary structures with RactIP during the design process, we can design RNAs that form a joint secondary structure with an external pseudoknot. The energy barrier upon the complex formation is modeled by an interaction seed that is optimized in the design algorithm. We benchmarked the RNA switch design approaches (MODENA+RactIP and MODENA+RNAcofold) for the target structures based on natural RNA-RNA interactions. As a result, MODENA+RactIP showed high design performance for the benchmark datasets.     

基于NSGA-Ⅱ的模拟移动床色谱分离过程多目标操作优化

多目标优化策略被应用于模拟移动床过程的操作优化中，采用一种基于Pareto最优解的多目标优化算法——NSGA-Ⅱ算法，以分离联萘酚对映体的模拟移动床色谱分离过程作为研究对象，利用模拟移动床TMB数学模型，以分离性能指标作为目标函数进行了多目标操作优化设计。优化结果表明，NSGA-Ⅱ算法得到的非劣解在目标空间分布均匀，算法收敛性和鲁棒性好。基于NSGA-Ⅱ算法的面向分离性能多目标优化设计方法为模拟移动床分离过程的工艺设计和操作指导提供了有效的工具。     

基于递归式多目标协同进化遗传算法的电厂负荷分配研究

提出以多目标优化方法来完成电厂负荷调度分配。综合考虑了经济性、快速性和稳定性的要求,建立多目标优化的数学模型,采用递归式多目标协同进化遗传算法求解负荷分配问题。与传统多目标算法相比,该方法减少了各个目标之间可能产生的偏移,并且进化过程简单,具有较强的灵活性和实用性。     

一阶时滞不稳定对象的PID控制器多目标优化设计

针对一阶时滞（FOPDT）不稳定对象,提出了一种基于参数稳定空间的多目标遗传算法进行PID控制器的优化设计。该算法首先采用两级控制结构,以保证PID控制器参数稳定空间的存在性,然后,基于该稳定空间提出了一种多目标遗传算法,用来求解带约束的多目标PID控制器参数整定问题。数值例子和生物反应器仿真研究表明所提方法的有效性。     

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.     

Fault-tolerant control of fluid dynamic systems via coordinated feedback and switching

This work addresses the problem of designing a fault-tolerant control system for fluid dynamic systems modeled by highly-dissipative partial differential equations (PDEs) with constrained control actuators. The proposed approach is predicated upon the idea of coordinating feedback controller synthesis and switching between multiple, spatially-distributed control actuator configurations. Using appropriate finite-dimensional approximations of the PDE system, a stabilizing feedback controller is designed for a given actuator configuration, and an explicit characterization of the constrained stability region is obtained. Switching laws are then derived, on the basis of these stability regions, to orchestrate the switching between the control actuator configurations, in a way that guarantees constraint satisfaction and preserves closed-loop stability of the infinite-dimensional system in the event of actuator failures. The results are demonstrated through an application of the proposed methodology to the suppression of wave formation in falling liquid films via the stabilization of the zero solution of the one-dimensional Kuramoto–Sivashinsky equation (KSE), with periodic boundary conditions, subject to actuator constraints and failures.     

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.     

Towards a better understanding of the epoxy-polymerization process using multi-objective evolutionary computation

The epoxy-polymerization process can be better understood by investigating the underlying optimization problem involving a number of conflicting objectives and more than 20 decision parameters. A combination of minimization or maximization of objectives, such as the number average molecular weight, polydispersity index and reaction time, are considered in this paper. The first two objectives are related to the properties of a polymer, whereas the third objective is related to productivity of the polymerization process. The decision variables are addition quantities of various reactants, e.g. the amount of addition for bisphenol-A (a monomer), sodium hydroxide and epichlorohydrin at different time steps (modeled in a semi-batch operation), whereas the satisfaction of all species balance equations is treated as constraints. A multi-objective evolutionary algorithm (the elitist non-dominated sorting genetic algorithm or NSGA-II) is used to obtain a set of non-dominated solutions in a single simulation run. The results show a substantial improvement (with about 300% more productivity) over the benchmark condition (reported by performing a one-time addition of reactants in the beginning in a batch process). Importantly, this study brings out a salient aspect of using an evolutionary approach to multi-objective problem solving. The availability of multiple optimal trade-off solutions allows a process engineer to have salient information about the polymerization process. Changes in the distribution of various polymer species in the course of polymerization process as observed among various Pareto-optimal solutions are identified and explained for this purpose. Such information provide important information about optimal operating conditions corresponding to different trade-offs among objectives, which are otherwise difficult to obtain. The systematic approach of starting from the two-objective problems to capture the essential features of interesting optimal operating conditions to finally solving the three-objective problem associated with the epoxy-polymerization problem in discovering the optimal trade-off interactions should motivate further such studies on other chemical process optimization problems. Overall, this paper demonstrates how fundamental optimization principles can be used systematically and reliably to find optimum operating conditions for complex chemical process operations.     

DESIGNING PID CONTROLLERS WITH A MINIMUM IAE CRITERION BY A DIFFERENTIAL EVOLUTION ALGORITHM

Although the integral of absolute error (IAE) has long been used as a performance measure for controller evaluation, a systematic approach is still lacking for the design of optimal PID controller with the minimum IAE performance criterion. This may be ascribed to the nonsmoothness of the IAE objective function and the lack of closed-form expression for the IAE performance index. In this paper, we propose the use of a differential evolution algorithm (DEA) to design a minimum-IAE PID controller to overcome the nondifferentiability of the IAE objective function. To achieve also a specified least relative stability margin and least damping ratio, the D-partition technique is used to identify the allowable domain in the tuning parameter space. Three examples are provided to illustrate the effectiveness of the DEA.     

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。     

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.