I.D.S'90, PRAGUE,CZECHOSLOVAKIA

ABSTRACT

Fluidized bed dryers are utilised in almost every area of drying applications and therefore improved control strategies are always of great interest. The nonlinear character of the process, exhibited in the mathematical model and the open loop analysis, implies that a fuzzy logic controller is appropriate because, in contrast with conventional control schemes, fuzzy control inherently compensates for-process nonlinearities and exhibits more robust behaviour. In this study, a fuzzy logic controller is proposed; its design is based on a heuristic approach and its performance is compared against a conventional PI controller for a variety of responses. It is shown that the fuzzy controller exhibits a remarkable dynamic behaviour, equivalent if not better than the PI controller, for a wide range of disturbances. In addition, the proposed fuzzy controller seems to be less sensitive to the nonlinearities of the process, achieves energy savings and enables MIMO control.     

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.     

ADVANCED CONTROL STRATEGIES FOR FLUIDIZED BED DRYERS

Generating the best possible control strategy comprises a necessity for industrial processes, by virtue of product quality, cost reduction and design simplicity. Three different control approaches, namely an Input-Output linearizing, a fuzzy logic and a PID controller, are evaluated for the control of a fluidized bed dryer, a typical non-linear drying process of wide applicability. Based on several closed loop characteristics such as settling times, maximum overshoots and dynamic performance criteria such as IAE, ISE and ITAE, it is shown that the Input-Ouput linearizing and the fuzzy logic controller exhibit a better performance compared to the PID controller tuned optimally with respect to the IAE, for a wide range of disturbances; yet, the relevant advantage of the fuzzy logic over the conventional nonlinear controller issues upon its design simplicity. Typical load rejection and set-point tracking examples are given to illustrate the effectiveness of the proposed approach.     

ADVANCED CONTROL STRATEGIES FOR FLUIDIZED BED DRYERS

ABSTRACT

Generating the best possible control strategy comprises a necessity for industrial processes, by virtue of product quality, cost reduction and design simplicity. Three different control approaches, namely an Input-Output linearizing, a fuzzy logic and a PID controller, are evaluated for the control of a fluidized bed dryer, a typical non-linear drying process of wide applicability. Based on several closed loop characteristics such as settling times, maximum overshoots and dynamic performance criteria such as IAE, ISE and ITAE, it is shown that the Input-Ouput linearizing and the fuzzy logic controller exhibit a better performance compared to the PID controller tuned optimally with respect to the IAE, for a wide range of disturbances; yet, the relevant advantage of the fuzzy logic over the conventional nonlinear controller issues upon its design simplicity. Typical load rejection and set-point tracking examples are given to illustrate the effectiveness of the proposed approach.     

DYNAMIC FUZZY ADAPTIVE CONTROLLER FOR pH

Control of pH processes is very difficult due to nonlinear dynamics, high sensitivity at the neutral point, and changes in the concentrations of known or unknown chemical species. In this study, a dynamic fuzzy adaptive controller (DFAC) with a new inference mechanism is proposed and applied for the control of pH processes. The DFAC consists of a low-level basic control phase with a minimum rule base and a high-level dynamic learining phase with an updating mechanism to interact and modify the control rule base. The DFAC can self-adjust its fuzzy control rules using information from the process during on-line control and create new fuzzy control rules or modify the present control rules using its learning capability from past control trends. The controller is evaluated by applying it to a weak acid-strong base pH process with input disturbances and to another pH process that involve that has changes in acidic/buffering streams. The results of the DFAC with the new inference mechanism are compared with the known inference mechanisms, the fuzzy controller, the conventional PI controller, and also with an adaptive PID controller. The proposed DFAC provides better performance for set point tracking of the pH and rejection of load disturbances and buffering affects.     

Fuzzy logic control of an unstable biological reactor

In the present work, we employ a fuzzy logic controller (FLC) to control the unstable state of a nonlinear biological reaction. The state variable vectors consist of cell density and substrate concentration. The dilution rate is used as a manipulated variable to control the reaction dynamics. An analytic form of FLC employing Zadeh AND logic along with Center of Mass defuzzification method is considered. Simulations reveal that for servo response test, the FLC shows satisfactory performance for natural unsteady states for which a conventional PI controller is known to fail. Further simulations also show that the FLC gives satisfactory regulatory response and is relatively insensitive to the deviations in model parameters.     

模糊自适应PI控制在空调系统中的研究

以空气焓差法试验台空调系统的温度控制系统为具体仿真对象建立了数学模型,该空调系统可以看作是一阶惯性加纯滞后的环节,而且对象的过程参数和时延时间是时变的,传统的PID控制无法获得理想的控制效果。提出了一种无需辨识环节的具有智能的模糊自适应PI的控制算法并将其应用在该空调系统中,该算法对模糊控制和PI控制进行有机结合,根据实际控制经验,通过模糊控制规则对控制回路中PI控制器的参数进行实时整定,并将该控制算法和经过良好整定的PI控制器在空调系统中的控制性能进行比较。仿真结果表明,模糊自适应PI控制提高了系统的鲁棒性、减小了超调量、提高了抗干扰能力、缩短了调整时间。     

FUZZY GAIN SCHEDULED CSTR WITH GA-BASED PID

In this article, a new control scheme, the gain scheduled genetic algorithm (GA)-based PID is proposed for a continuous stirred tank reactor (CSTR). A CSTR is a highly nonlinear process that exhibits stability in certain regions and instability in other regions. The proposed control scheme implements the characteristics of the genetic algorithm's (GA) global optimization to optimize the PID's three control parameters, k_p, k_i, k_d, to obtain the best control effect by minimizing the integral square error online. The PID controller parameters tuned by the GA for each region are gain scheduled by a fuzzy logic scheduler. Fuzzy gain scheduling is a special form of fuzzy control that uses linguistic rules and fuzzy reasoning to determine the controller parameter transition policy for the dynamic plant subject to large changes in its operating state. Simulation results show the feasibility of using the proposed controller for the control of the dynamical nonlinear CSTR.     

基于DSP的pH过程FNNC-PI控制器研究

提出一种模糊神经网络控制与传统PI相结合的控制方法,即pH过程的FNNC PI控制方案,将模糊控制具有的较强逻辑推理功能、神经网络的自学习能力以及传统PI控制的优点融为一体,能很好地处理pH过程的非线性和滞后性,具有较强的鲁棒性和抗干扰能力。为了满足控制运算实时性的要求,采用TMS320VC33高速数字信号处理器(DSP)作为控制与运算单元,成功地完成了模糊神经网络控制器的DSP实现。     

Advantage of Low‐Cost Predictive Control: Study Case on a Train of Distillation Columns

The process of enriching the ¹³C isotope, performed in trains of cryogenic distillation columns, exhibits large settling times, nonlinearities, large dead‐times, and are difficult to model precisely. Such equipment has been developed in Romania, with concentration increasing up to 70 %. A control analysis for a single unit has already been done including a decentralized multivariable PI controller and two decoupling control algorithms based on the internal model control (IMC) approach. Here, a multivariable predictive controller, the extended prediction self‐adaptive controller is proposed. The simulation results, considering significant modeling errors, demonstrate that this represents a more suitable choice than the previously designed strategies. Comparisons are included to support this idea.     

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.     

Fuzzy logic controller implementation on a microbial electrolysis cell for biohydrogen production and storage

This work presents the implementation of fuzzy logic control (FLC) on a microbial electrolysis cell (MEC). Hydrogen has been touted as a potential alternative source of energy to the depleting fossil fuels. MEC is one of the most extensively studied method of hydrogen production. The utilization of biowaste as its substrate by MEC promotes the waste to energy initiative. The hydrogen production within the MEC system, which involves microbial interaction contributes to the system’s nonlinearity. Taking into account of the high complexity of MEC system, a precise process control system is required to ensure a well-controlled biohydrogen production flow rate and storage application inside a tank. Proportional-derivative-integral (PID) controller has been one of the pioneer control loop mechanism. However, it lacks the capability to adapt properly in the presence of disturbance. An advanced process control mechanism such as the FLC has proven to be a better solution to be implemented on a nonlinear system due to its similarity in human-natured thinking. The performance of the FLC has been evaluated based on its implementation on the MEC system through various control schemes progressively. Similar evaluations include the performance of Proportional-Integral (PI) and PID controller for comparison purposes. The tracking capability of FLC is also accessed against another advanced controller that is the model predictive controller (MPC). One of the key findings in this work is that the FLC resulted in a desirable hydrogen output via MEC over the PI and PID controller in terms of shorter settling time and lesser overshoot.     

Controller adjustment for improved nominal performance and robustness—II. Robust geometric control of a distillation column

In Part I of this paper a general frequency domain method to adjust multivariable controllers with respect to both nominal performance and robustness are presented. In Part II this method is used to examine and improve the control of a binary distillation column. The selected control strategies are conventional PI control and geometric control. The PI controller is adjusted in order to obtain satisfactory robustness properties. The basic geometric controller is extended with feedback from state variables which do not alter the nominal disturbance rejection. Both constant gain feedback and integration are examined. The included control parameters are adjusted for improved nominal performance and for robustness. The major result is that the adjusted geometric controller has a robustness which equals that of the adjusted conventional PI controller. However, the nominal performance of the geometric controller is superior to that of conventional PI control. Thus we expect the adjusted geometric controller to have improved performance on a real column compared to that of conventional PI control.     

模糊PID控制器的发展

将模糊控制技术和PID控制相结合，可克服常规PID控制器的不足，使PID控制器具有参数自适应能力。介绍了模糊控制器和PID控制器间的关系，从一维、二维及三维模糊控制器的输入－输出形式上，以及解析分析角度出发，可以说模糊控制器人本质上讲是一种非线性PID控制器。还总结了模糊PID控制器的结构。模糊PID控制器可分成两大类，一类由参数可变的PID控制器及一个模糊控制器共同构成，模糊控制器用于完成PID参数的在线整定，控制信号仍由PID控制器生成；另一类直接用模糊控制器来构造、实现PID控制功能。分别归纳了这两类控制器的多种结构形式。最后还对模糊PID控制器的发展进行了展望，提出了模糊PID 控制器结构的选择、控制器参数调整及控制器评价标准的确定这几方面是今后工作的重点。     

NONLINEAR PI CONTROLLER FOR pH PROCESS

The control of pH is one of the most difficult challenges in the process industry because of the severe nonlinearities and high precision required in manipulating the flow rate. The Wiener model, which consists of a linear dynamic element followed by a nonlinear static element, is used for representing such nonlinear processes. Piecewise continuous polynomials are used for mapping the nonlinear static gain accurately. A nonlinear PI controller was designed based on the Wiener model. Simulation results on the nonlinear mathematical model are presented to highlight the superior performance of the Wiener model based nonlinear PI controller in comparison to that of the local linear PI controller. The performance of the nonlinear PI controller was further improved upon by using the method of inequalities to obtain a single set of PI controller settings that takes into account the parametric variations in the linear dynamic element at different operating points. Simulation and experimental results are presented to support the work.     

LabVIEW中模糊控制器的设计及应用

通过火电厂给水加氨模糊控制实例 ,详细介绍利用LabVIEW提供的模糊逻辑工具箱 (FuzzyLogicforGToolkit)设计开发模糊控制器的方法。     

NONLINEAR PI CONTROLLER FOR pH PROCESS

The control of pH is one of the most difficult challenges in the process industry because of the severe nonlinearities and high precision required in manipulating the flow rate. The Wiener model, which consists of a linear dynamic element followed by a nonlinear static element, is used for representing such nonlinear processes. Piecewise continuous polynomials are used for mapping the nonlinear static gain accurately. A nonlinear PI controller was designed based on the Wiener model. Simulation results on the nonlinear mathematical model are presented to highlight the superior performance of the Wiener model based nonlinear PI controller in comparison to that of the local linear PI controller. The performance of the nonlinear PI controller was further improved upon by using the method of inequalities to obtain a single set of PI controller settings that takes into account the parametric variations in the linear dynamic element at different operating points. Simulation and experimental results are presented to support the work.     

Control of injection velocity using a fuzzy logic rule-based controller for thermoplastics injection molding

Injection velocity to a large degree determines the melt injection rate during the injection phase, and it has critical impact on the molded part quality, such as shrinkage, warpage, and impact strength. An injection molding machine operates with different injection velocity profiles, barrel temperatures, molds, and materials. These strongly different molding conditions cause the injection velocity dynamics to very significantly and to make the control performance of the injection velocity poor with a typical PID controller. A real-time, closed-loop feedback and feedforward control system based on fuzzy logic has been designed, developed, and implemented to control the injection velocity. The fuzzy logic rules of the controller are optimized by analyzing phase plane characteristics. The controller output membership functions are optimized based on a 2^k factorial design technique. The experimental results reveal that the fuzzy logic-based controller works well with different molds, materials, barrel temperatures, and injection velocity profiles, indicating that the fuzzy logic controller has superior performance over the conventional PID controller in response speed, set-point tracking ability, noise rejection, and robustness.     

Intelligent decoupling PID control for the forced-circulation evaporation system

The control objective of the forced-circulation evaporation process of alumina production is not only to avoid large fluctuations of the level, but also to ensure the product density to track its setpoint quickly. Due to the existence of strong coupling between the level loop and the product density loop, and high nonlinearities in the process, the conventional control strategy cannot achieve satisfactory control performance, and thus the production demand cannot bemet. In this paper, an intelligent decoupling PID controller including conventional PID controllers, a decoupling compensator and a neural feedforward compensator is proposed. The parameters of such controller are determined by generalized predictive control law. Real-time experiment results show that the proposed method can decouple the loops effectively and thus improve the evaporation efficiency.     

纯碱装置碳化塔温度的模糊控制仿真研究

针对纯碱装置碳化塔温度的控制过程,利用参数自整定PID模糊控制方案进行了该控制过程的仿真研究。结果表明过程参数发生变化时,该类模糊控制器能有效地进行PID参数的修正,较好满足了工程控制要求。其控制效果优于常规PID控制器。