Engin Yesil

Self tuning fuzzy PID type load and frequency controller

In this paper, a self tuning fuzzy PID type controller is proposed for solving the load frequency control (LFC) problem. The fuzzy PID type controller is constructed as a set of control rules, and the control signal is directly deduced from the knowledge base and the fuzzy inference. Moreover, there exists a self tuning mechanism that adjusts the input scaling factor corresponding to the derivative coefficient and the output scaling factor corresponding to the integral coefficient of the PID type fuzzy logic controller in an on-line manner. The self tuning mechanism depends on the peak observer idea, and this idea is modified and adapted to the LFC problem. A two area interconnected system is assumed for demonstrations. The proposed self tuning fuzzy PID type controller has been compared with the fuzzy PID type controller without a self tuning mechanism and the conventional integral controller through some performance indices.

Self-tuning of PID-type fuzzy logic controller coefficients via relative rate observer

Abstract In this study, a new method is proposed for tuning the coefficients of PID-type fuzzy logic controllers (FLCs). The new method adjusts the input scaling factor corresponding to the derivative coefficient and the output scaling factor corresponding to the integral coefficient of the PID-type FLC using a fuzzy inference mechanism in an on-line manner. The fuzzy inference mechanism that adjusts the related coefficients has two inputs, one of which is called “normalized acceleration” and the other one is the classical “error”. The “normalized acceleration” gives the “relative rate” information about the fastness or slowness of the system response. An appropriate rule-base is generated for the adaptation of the derivative coefficient of the PID-type FLC using these two input variables. The integral coefficient is then updated as the reciprocal of the derivative coefficient. The robustness and effectiveness of the new self-tuning algorithm have been compared with the other related tuning methods proposed in the literature through simulations. The simulations are done on a second-order system with varying parameters and time delay.

Interval type-2 fuzzy PID load frequency controller using Big Bang-Big Crunch optimization

This paper proposes an optimization based design methodology of interval type-2 fuzzy PID (IT2FPID) controllers for the load frequency control (LFC) problem. Hitherto, numerous fuzzy logic control structures are proposed as a solution of LFC. However, almost all of these solutions use type-1 fuzzy sets that have a crisp grade of membership. Power systems are large scale complex systems with many different uncertainties. In order to handle these uncertainties, in this study, type-2 fuzzy sets, which have a grade of membership that is fuzzy, have been used. Interval type-2 fuzzy sets are used in the design of a load frequency controller for a four area interconnected power system, which represents a large power system. The Big Bang-Big Crunch (BB-BC) algorithm is applied to tune the scaling factors and the footprint of uncertainty (FOU) membership functions of interval type-2 fuzzy PID (IT2FPID) controllers to minimize frequency deviations of the system against load disturbances. BB-BC is a global optimization algorithm and has a low computational cost, a high convergence speed, and is therefore very efficient when the number of optimization parameters is high as presented in this study. In order to show the benefits of IT2FPID controllers, a comparison to conventional type-1 fuzzy PID (T1FPID) controllers and conventional PID controllers is given for the four-area interconnected power system. The gains of conventional PID and T1FPID controllers are also optimized using the BB-BC algorithm. Simulation results explicitly show that the performance of the proposed optimum IT2FPID load frequency controller is superior compared to the conventional T1FPID and PID controller in terms of overshoot, settling time and robustness against different load disturbances.

Type-2 fuzzy model based controller design for neutralization processes.

In this study, an inverse controller based on a type-2 fuzzy model control design strategy is introduced and this main controller is embedded within an internal model control structure. Then, the overall proposed control structure is implemented in a pH neutralization experimental setup. The inverse fuzzy control signal generation is handled as an optimization problem and solved at each sampling time in an online manner. Although, inverse fuzzy model controllers may produce perfect control in perfect model match case and/or non-existence of disturbances, this open loop control would not be sufficient in the case of modeling mismatches or disturbances. Therefore, an internal model control structure is proposed to compensate these errors in order to overcome this deficiency where the basic controller is an inverse type-2 fuzzy model. This feature improves the closed-loop performance to disturbance rejection as shown through the real-time control of the pH neutralization process. Experimental results demonstrate the superiority of the inverse type-2 fuzzy model controller structure compared to the inverse type-1 fuzzy model controller and conventional control structures.

An Intelligent Hybrid Fuzzy Pid Controller

In this study, a design methodology is introduced that blends the classical PID and the fuzzy controllers in an intelligent way and thus a new intelligent hybrid controller has been achieved. Basically, in this design methodology, the classical PID and fuzzy controller have been combined by a blending mechanism that depends on a certain function of actuating error. Moreover, an intelligent switching scheme is induced on the blending mechanism that makes a decision upon the priority of the two controller parts; namely, the classical PID and the fuzzy constituents. The simulations done on various processes using the new hybrid fuzzy PID controller provides ‘better’ system responses in terms of transient and steady-state performances when compared to the pure classical PID or the pure fuzzy controller applications. The controller parameters are all tuned by the aid of genetic search algorithm.

Inverse fuzzy Model Control with online adaptation via Big Bang-Big Crunch optimization

Fuzzy logic modeling is a powerful tool in representing nonlinear systems. Moreover, inverse fuzzy model can be used as a controller in an open loop fashion to produce perfect control. However, in the case of modeling mismatches and disturbances that might occur on the system, open loop control would not be sufficient. In that case, the modeling errors and disturbances could be compensated by internal model control (IMC) with an on-line model adaptation scheme. The on-line adaptation is usually accomplished via recursive least square algorithm. In this study, big bang-big crunch (BB-BC) optimization method, which has a low computational time and high convergence speed, has been used as an on-line adaptation scheme. The inverse fuzzy model based IMC and the BB-BC optimization method based adaptation have been implemented and tested on a real time heating process system.

Fuzzy cognitive maps learning using Artificial Bee Colony optimization

Most of the dynamic systems are hard to express in mathematical models due to their complex, nonlinear and uncertain characteristics. Thus, advanced methodologies are needed, using human experience, present expert knowledge and historical data. Hence fuzzy cognitive maps are quite convenient, simple, powerful and practical tools for simulation and analysis of these kinds of dynamic systems. Yet, human experts are subjective and cannot handle relatively complex fuzzy cognitive maps (FCMs); hence, new approaches are required to develop for an automatic building of fuzzy cognitive maps. In this study, Artificial Bee Colony (ABC) global optimization algorithm is proposed for the first time in literature for an automated generation of fuzzy cognitive maps from historical data. An ERP management model is used as the illustrative example to obtain the data for training and validation. The obtained results show the success of the ABC learning for FCMs.

Big Bang Big Crunch Optimization Method Based Fuzzy Model Inversion

The inverse fuzzy model can be used as a controller in an open loop fashion to produce perfect control if there does not exist any disturbance or parameter variation in the system. In this paper, a new fuzzy model inversion technique that is based on an evolutionary search algorithm called Big Bang Big Crunch (BB-BC) optimization is introduced. Even though various fuzzy inversion methods can be found in literature, these methods are only applicable under certain conditions or limitations. On the other hand, there does not exist any limitation or condition for the new methodology presented here. In this new technique, the inverse fuzzy model control signal is generated iteratively as a consequence of an optimization operation. Since the BB-BC optimization algorithm has a high convergence speed and low computational time, the optimal inverse fuzzy model control signal is generated within each sampling time. The beneficial sides of the open loop control approach based on the proposed fuzzy model inversion technique are illustrated through two simulation studies.

Analysis of the performances of type-1, self-tuning type-1 and interval type-2 fuzzy PID controllers on the Magnetic Levitation system

In this paper, we will compare the closed loop control performance of interval type-2 fuzzy PID controller with the type-1 fuzzy PID and conventional PID controllers counterparts for the Magnetic Levitation Plant. We will also compare the control performance of the interval type-2 fuzzy PID controller with the self-tuning type-1 fuzzy PID controllers. The internal structures of implemented controllers are firstly examined and then the design parameters of each controller are optimized for a given reference trajectory. The paper also show the effect of the extra degree of freedom provided by antecedent membership functions of interval type-2 fuzzy logic controller on the closed loop system performance. The real-time experiments are accomplished on an unstable nonlinear system, QUANSER Magnetic Levitation Plant, in order to show the superiority of the optimized interval type-2 fuzzy PID controller compared to optimized PID and type-1 counterparts.

Goal-oriented decision support using Big Bang-Big Crunch learning based Fuzzy Cognitive Map: An ERP management case study

In this study, a new learning method called Big Bang-Big Crunch (BB-BC) is proposed for Fuzzy Cognitive Map (FCM), which is an approach to knowledge representation and inference. FCMs are basically fuzzy signed directed graphs with feedbacks, and they model the world as a collection of concepts and causal relations between concepts. Till now, little research has been done on the goal-oriented analysis with FCM. Therefore a methodology based on the use of Fuzzy cognitive map and BBBC algorithm is proposed to find the initial state of the model from among a large number of possible states for goal-oriented decision support. This optimization method is preferred for learning purpose since it has a low computational time and a high convergence speed. An ERP management model is used as the illustrative example, its results for different 8 scenarios show that the method is capable of goal-oriented decision support. Since, the proposed method is not limited with the number of concept or causal relations between these concepts; it can easily be used for the goal-oriented decision analysis of complex systems.