质子交换膜燃料电池电堆的动态热模型及其温度控制

A dynamic thermal transfer model of a proton exchange membrane fuel cell (PEMFC) stack is developed based on energy conservation in order to reach better temperature control of PEMFC stack. Considering its uncertain parameters and disturbance, we propose a robust adaptive controller based on backstepping algorithm of Lyaponov function. Numerical simulations indicate the validity of the proposed controller.     

ADAPTIVE FUZZY MODEL BASED PREDICTIVE CONTROL OF AN EXOTHERMIC BATCH CHEMICAL REACTOR*

An adaptive fuzzy model based predictive control (AFMBPC) approach is presented to track the desired temperature trajectories in an exothermic batch chemical reactor. The AFMBPC incorporates an adaptive fuzzy modeling framework into a model based predictive control scheme to derive analytical controller output. This approach has the flexibility to cope with different fuzzy model structures whose choice also lead to improve the controller performance. In this approach, adaptation of fuzzy models using dynamic process information is carried out to build a predictive controller, thus eliminating the determination of a predefined fixed fuzzy model based on various sets of known input-output relations. The performance of the AFMBPC is evaluated by comparing to a fixed fuzzy model based predictive controller (FFMBPC) and a conventional PID controller. The results show the better suitability of AFMBPC for the control of highly nonlinear and time varying batch chemical reactors.     

Implementation of Multimodel-Based PID and Intelligent Controller for Simulated and Real-Time Temperature Control of Injection Molding Machine

The proposed real-time multimodel for the injection molding process mainly contributes to the barrel temperature control. Good control of the plastic melt temperature is very important for injection molding in reducing the operator setup time, ensuring product quality, and preventing thermal degradation of the melt. The controllability and set points of the barrel temperature also depend on the precise monitoring and control of the plastic melt temperature. Motivated by the practical temperature control of injection molding, this article proposes a multimodel-based proportional integral derivative (PID) control scheme in real-time and the simulation studies of the PID, fuzzy, and adaptive neuro fuzzy inference system (ANFIS) control schemes. The injection molding process consists of three zones, and the mathematical model for each zone is different. The control output for each zone controller is assigned a weight, based on the computed probability of each model, and the resulting action is the weighted average of the control moves of the individual zone controller.     

Development of a 3D Hybrid Intelligent‐Mechanistic Model for Simulation of Multiphase Chemical Reactors

A novel intelligent‐mechanistic model was developed to understand the behavior of multiphase chemical reactors. Computational fluid dynamics (CFD) and an intelligent algorithm were combined to predict different levels of 3D cylindrical bubble‐column reactors. An adaptive neuro‐fuzzy inference system (ANFIS) was used as the intelligence algorithm, and different ANFIS parameters were evaluated. With about one third of the training data the method can predict the overall behavior of the gas fraction in the reactor. A number of rules significantly influence the accuracy of the ANFIS method. After finding appropriate parameters, the method is applied for prediction of points which are not simulated with CFD, representing ANFIS mesh refinement. Also, bubble‐column reactors without training of exact values of measured data or numerical results can be predicted. Main advantages are time savings and reduction of computational expenses.     

质子交换膜燃料电池混合动力系统能量优化的仿真分析

分析了混合动力系统的控制目标及需要考虑的几个关键参数。以PEMFC混合动力系统为对象，分别针对给定负载模式和实时运行模式进行控制策略设计和能量优化仿真。对给定的负载模式，采用遗传算法对多约束组合优化问题进行求解，仿真结果证明了算法的可行性。对于实时运行模式，在原有模糊控制策略的基础上，引入燃料电池电压和电压变化量对模糊输出进行两级修正。仿真结果表明，改进后的模糊控制策略可以有效提高燃料电池运行效率，降低燃料电池功率波动。     

基于LPV模型的燃料电池空气进气系统控制

质子交换膜燃料电池是一种通过氢气和氧气的电化学反应将化学能直接转化为电能的装置。提出一种改进的四阶燃料电池进气系统模型,分析了系统的约束性。针对系统模型所具有的非线性特性,提出建立线性变参数（LPV）模型用于对系统的控制。针对状态变量不可测的问题引入卡尔曼滤波器,同时通过可观性分析得出系统所需测量的最佳变量。在符合约束条件下设计基于线性变参数模型的状态空间模型预测控制器,控制空压机的工作电压保证氢气燃料的充分反应。仿真结果表明,基于LPV模型的模型预测控制器能够对空气进气系统进行有效的控制,且满足空压机喘振和阻塞边界等约束条件,与单模型预测控制相比具有更好的控制效果。     

APPLICATION OF FUZZY ADAPTIVE CONTROLLER IN NONLINEAR PROCESS CONTROL

In general, physical processes are usually nonlinear and control system design based on the linearization technique cannot control the process well for a wide range of operation. Use of the variable transformation method may not always solve the problem. In this paper, a fuzzy adaptive controller is proposed to control the nonlinear process. The CSTR control problem has also been considered. The results are compared with the method of nonlinear model predictive control (NMPC) with constrained and unconstrained control variables. A fuzzy model-following control system scheme is also proposed. The results show that the proposed controller is a feasible control structure for a nonlinear or parameter-variations process control.     

Identification and control of a riser‐type FCC unit

This paper addresses the use of feedforward neural networks for the steady‐state and dynamic identification and control of a riser type fluid catalytic cracking unit (FCCU). The results are compared with a conventional PI controller and a model predictive control (MPC) using a state space subspace identification algorithm. A back propagation algorithm with momentum term and adaptive learning rate is used for training the identification networks. The back propagation algorithm is also used for the neuro‐control of the process. It is shown that for a noise‐free system the adaptive neuro‐controller and the MPC are capable of maintaining the riser temperature, the pressure difference between the reactor vessel and the regenerator, and the catalyst bed level in the reactor vessel, in the presence of set‐point and disturbance changes. The MPC performs better than the neuro controller that in turn is superior to the conventional multi‐loop diagonal PI controller.     

基于自适应模糊神经网络的非线性系统模型预测控制

针对非线性动态系统的控制问题，提出了一种基于自适应模糊神经网络(adaptive fuzzy neural network, AFNN)的模型预测控制(model predictive control, MPC)方法。首先，在离线建模阶段，AFNN采用规则自分裂技术产生初始模糊规则，采用改进的自适应LM学习算法优化网络参数；然后，在实时控制过程，AFNN根据系统输出和预测输出之间的误差调整网络参数，从而为MPC提供一个精确的预测模型；进一步，AFNN-MPC利用带有自适应学习率的梯度下降寻优算法求解优化问题，在线获取非线性控制量，并将其作用到动态系统实施控制。此外，给出了AFNN-MPC的收敛性和稳定性证明，以保证其在实际工程中的成功应用。最后，利用数值仿真和双CSTR过程进行实验验证。结果表明，AFNN-MPC能够取得优越的控制性能。     

Modeling and predictive control using fuzzy logic: Application for a polymerization system

In this study, a predictive control system based on type Takagi‐Sugeno fuzzy models was developed for a polymerization process. Such processes typically have a highly nonlinear dynamic behavior causing the performance of controllers based on conventional internal models to be poor or to require considerable effort in controller tuning. The copolymerization of methyl methacrylate with vinyl acetate was considered for analysis of the performance of the proposed control system. A nonlinear mathematical model which describes the reaction plant was used for data generation and implementation of the controller. The modeling using the fuzzy approach showed an excellent capacity for output prediction as a function of dynamic data input. The performance of the projected control system and dynamic matrix control for regulatory and servo problems were compared and the obtained results showed that the control system design is robust, of simple implementation and provides a better response than conventional predictive control. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Distributed model predictive control based on adaptive sampling mechanism

In this work, an adaptive sampling control strategy for distributed predictive control is proposed. According to the proposed method, the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation function. Then, the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller, and the sampling interval of the controller is changed accordingly before the next sampling period begins.In the nextcontrolperiod, the adaptive sampling mechanism recalculates the sampling rate of each subsystem's measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system, and this process is repeated. Such an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled object. It can also accurately capture dynamic changes, meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment, significantly improving the performance of distributed model predictive control (DMPC). A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.     

On‐Line PEMFC Control Using Parameterized Nonlinear Model‐Based Predictive Control

In this work, a fast nonlinear model‐based predictive control (NMPC) strategy is designed and experimentally validated on‐line on a real fuel cell. Regarding NMPC strategies, the most challenging part remains to achieve on‐line implementation, especially when dealing with fast dynamic systems. As previously demonstrated in a recent work, the proposed control strategy is ideally suited to address this problem. Indeed, it is 30 times faster than classical NMPC controllers. This strategy relies on a specific parameterization of the control actions to reduce the computational time and achieve on‐line implementation. Due to its short computational time compared to mechanistic models, an artificial neural network model is designed and experimentally validated. This model is employed as internal model in the NMPC controller to predict the system behavior. To confirm the applicability and the relevance of the proposed NMPC controller varying control scenarios are investigated on a test bench. The built‐in controller is overridden and the NMPC controller is implemented externally and executed on‐line. Experimental results exhibit the outstanding tracking capability and robustness against model‐process mismatch of the proposed strategy. The parameterized NMPC controller turns out to be an excellent candidate for on‐line applications.     

基于ANFIS与多模型的一类非线性系统的自适应控制方法

针对一类不确定的离散时间零动态不稳定非线性系统,提出了一种基于自适应神经模糊推理系统(ANFIS)与多模型的非线性自适应控制方法。该方法由线性鲁棒自适应控制器,基于ANFIS的非线性自适应控制器以及切换机制组成。线性控制器用来保证闭环系统输入输出信号有界,非线性控制器用来改善系统性能。切换机制通过对上述两种控制器的切换,保证闭环系统输入输出有界的同时,改善系统性能。在采用ANFIS作为系统未建模动态补偿器时,首先用一个连续、单调、可逆的一一映射把可能无界的未建模动态的定义域转化成一个有界闭集,保证了ANFIS的万能逼近特性成立的前提条件。而且,ANFIS能减小BP神经网络收敛速度慢和容易陷入局部极小的问题,改善了控制效果。建立了保证系统稳定性的引理,并给出了闭环系统的稳定性和收敛性分析。通过仿真比较,说明了所提方法的有效性。     

Numerical modeling and optimization of wastewater treatment using porous polymeric membranes

A new modeling approach was developed for prediction of ammonia removal from water by means of porous membranes. The model was based on adaptive neuro‐fuzzy interface system (ANFIS) to simulate ammonia stripping from water by means of hollow‐fiber membrane contactors. The predictions aimed to obtain optimum conditions for ammonia stripping using the Taguchi method. The initial concentrations of ammonia, pH of the ammonia solution, velocity of the feed, and the presence of excess ions in the ammonia feed solution were considered as the input properties. On the other hand, mass transfer coefficient was considered as output. The prediction results revealed that the pH of the ammonia feed has a significant effect on the separation of ammonia from water. The results also showed that the prediction of ANFIS model and experimental data match well and that the model can be used for prediction of porous membranes. Furthermore, simulated annealing was also used to determine controllable conditions to find the highest mass transfer coefficient. POLYM. ENG. SCI., 2013. © 2012 Society of Plastics Engineers     

The Performance of SOFC Model Under Different Three‐phase Load Conditions

In this paper, the dynamic performance of a modified thermal based dynamic model of a solid oxide fuel cell (SOFC) is presented under different three‐phase load conditions. The modified thermal based fuel cell model contains ohmic, activation and concentration voltage losses, thermal dynamics, methanol reformer and fuel utilization factor limiting stage. SOFC model and the power conditioning unit (PCU), which consists of a DC‐DC boost converter, a DC‐AC inverter, their controller, transformer and filter, are developed on Matlab/Simulink environment. The simulation results show that the real and reactive power management of the inverter is performed successfully in an AC power system with the proposed thermal based SOFC model under three‐phase load conditions such as ohmic loads, switched ohmic−inductive loads and a three‐phase induction motor. Finally, the three‐phase induction motor is performed both no load and load conditions. The simulation results show that the modified thermal based fuel cell model provides an accurate representation of the dynamic and steady state behavior of the fuel cell under different three‐phase load conditions.     

大功率PEMFC空气供给系统建模与实验验证

近年来,质子交换膜燃料电池(PEMFC)作为车载燃料电池的主要动力源受到广泛关注。空气压缩机为电堆提供系统所需的氧气和阴极压力,是质子交换膜燃料电池系统中必不可少的一部分,其工作性能对燃料电池稳态和动态工作性能有很大的影响。基于实验室已有150 kW质子交换膜燃料电池系统,对离心式空压机的工作特性进行了研究,建立了包含离心式空气压缩机的空气供给系统应用模型。通过实验验证,仿真模型能够准确地反映离心式空压机与空气系统的特性,同时能真实反映包含离心式空压机的大功率质子交换膜燃料电池空气系统的稳态控制效果,以及不同控制策略下的动态响应效果。该模型对研究大功率质子交换膜燃料电池空气供给系统以及相应的控制策略提供理论支持,仿真模型与实验结果为下一步控制策略优化提供基础与参考。     

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.     

水冷PEMFC热管理系统流量跟随控制策略

针对传统温度控制策略在水冷型质子交换膜燃料电池（proton exchange membrane fuel cell,PEMFC）工作中水泵和散热器风扇存在的强耦合性,并为了提高电堆的工作性能和寿命,提出一种流量跟随电流的温度控制策略,根据电堆电流变化调节冷却水流量来控制电堆冷却水进出口温差,通过PID控制器调节散热风扇控制电堆入口温度。在水冷PEMFC热管理平台上对传统控制策略、流量跟随控制策略做了实验对比。结果表明,流量跟随电流控制策略使冷却水出口温度最大超调量减少64.3%,冷却水出入口温差最大偏差减少46.7%,调整时间平均缩短73 s,达到了较高的控制精度和响应速度,削弱了水泵和散热风扇的强耦合作用,流量跟随电流控制策略能够满足PEMFC系统对温度控制的要求。     

Proton Exchange Membrane Fuel Cell Operation and Degradation in Short‐Circuit

Hybridization of proton exchange membrane fuel cells (PEMFC) and ultra capacitors (UC) are considered as an alternative way to implement high autonomy, high dynamic, and reversible energy sources. PEMFC allow high efficiency and high autonomy, however their dynamic response is limited and this source does not allow recovering energy. UC appears to be a complementary source to fuel cell systems (FCS) due to their high power density, fast dynamics, and reversibility. A direct hybridization of these sources could allow reducing the number of power converters and then the total cost of the hybridized system. Simulations show the behavior of the hybrid source when the fuel cell and ultra capacitors are interconnected and the natural energy management when a charge is connected. The results show that the magnitude of the transient current supplied by the fuel cell to charge the UC can be much higher than its nominal value. An experimental setup is implemented to study the effects of these high currents in a PEMFC. This is done by imposing a controlled short‐circuit between the electrodes. The PEMFC degradation is quantified by using electrochemical impedance spectroscopy.     

FUZZY MODELING AND CONTROL OF BATCH BEER FERMENTATION+

Fuzzy reasoning based modeling of heuristic control rules are employed for control of batch beer fermentation. The effect of different types of membership functions, viz., line, triangular and phi membership functions is evaluated for the fuzzy subset. Various fuzzy model based controllers are presented using two approaches, namely simple fuzzy controller of few rules (FCFR) and rigorous fuzzy controller of many rules (FCM R), and also applied for the temperature control of fermenter. Zadeh's logic and Lukasiewicz's logic are adopted for computing the compositional rule of fuzzy logic inference. The results demonstrate that the proposed fuzzy controllers show better performance than the conventional controllers. FCFR approach provides better control performance, but needs optimum tuning or selection of gains for the fuzzy input and output variables, whereas FCMR approach is preferred due to flexibility in the operation of many control rules. Further, FCMR approach is free from optimum tuning or selection of gains for the fuzzy input and output variables.