Fuzzy Logic Control Rules Research Articles

A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System

This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC.

Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller

In this paper, the fuzzy logic controller (FLC) based power system stabilizer (PSS) with compressed / reduced rule is presented. The FLC rule base is generally based on empirical control rules. In this method, the fuzzy system with a large number of fuzzy rules is compressed to a fuzzy system with a reduced number of rules by removing the redundant and inconsistent rules from the rule base which doesn’t affect the performance of the fuzzy logic controller. The FLC based PSS has two input signals as speed deviation and derivative of speed deviation with an appropriate number of linguistic variables. The number of compressed rules in the rule base through the proposed dominant rule algorithm is reduced to a number as low in the number of selected linguistic variables to represent input and output signals. The application of the FLC with compressed rules as a power system stabilizer (CR-FPSS) is investigated by simulation studies on a single-machine infinite-bus system (SMIB). The superior performance of this compressed rule based fuzzy PSS (CR-FPSS) as compared to conventional PSS and proves the better efficiency of this new CR-FPSS. The reduced CPU computational time and storage space as compared to the fuzzy power system stabilizer (FPSS), proves its applicability in control.

Optimized Reactive Power Compensation Using Fuzzy Logic Controller

Reactive power flow in a long transmission line plays a vital role in power transfer capability and voltage stability in power system. Traditionally, shunt connected compensators are used to control reactive power in long transmission line. Thyristor controlled reactor is used to control reactive power under lightly loaded condition. By controlling firing angle of thyristor, it is possible to control reactive power in the transmission lines. However, thyristor controlled reactor will inject harmonic current into the system. An attempt to reduce reactive power injection will increase harmonic distortion in the line current and vice versa. Thus, there is a trade-off between reactive power injection and harmonics in current. By optimally controlling the reactive power injection, harmonics in current can be brought within the specified limit. In this paper, a Fuzzy Logic Controller is implemented to obtain optimal control of reactive power of the compensator to maintain voltage and harmonic in current within the limits. An algorithm which optimizes the firing angle in each fuzzy subset by calculating the rank of feasible firing angles is proposed for the construction of rules in Fuzzy Logic Controller. The novelty of the algorithm is that it uses a simple error formula for the calculation of the rank of the feasible firing angles in each fuzzy subset.

Control of Traffic Light in Isolated Intersections Using Fuzzy Neural Network and Genetic Algorithm

 Abstract— In this paper a fuzzy neural network is applied for real time traffic signal control at an isolated intersection. The FNN has advantages of both fuzzy expert system (fuzzy reasoning) and artificial neural network (self-study). A traffic light controller based on fuzzy neural network can be used for optimum control of fluctuating traffic volumes such as oversaturated or unusual load condition. The objective is to improve the vehicular throughput and minimize delays. The rules of fuzzy logic controller are formulated by following the same protocols that a human operator would use to control the time intervals of the traffic light. For adjusting the parameters of FNN, genetic algorithm was used. Compared with traditional control methods for traffic signal, the proposed FNN algorithm shows better performance and adaptability.

Intelligent Control for Torque Ripple Minimization in Combined Vector and Direct Controls for High Performance of IM Drive

In Conventional Combined Vector and Direct Controls (VC-DTC) of induction motor, stator current is very rich in harmonic components. It leads to high torque ripple of induction motor in high and low speed region. To solve this problem, a control method based on the concept of fuzzy logic approach is used. The control scheme proposed uses stator current error as variable. Through the fuzzy logic controller rules, the choice of voltage space vector is optimized and then torque and speed are controlled successfully with a less ripple level in torque response, which improve the system's performance. Simulation results trough MATLAB/SIMULINK® software gave results that justify the claims.

Synthesized design of a fuzzy logic controller for an underactuated unicycle

In this paper, we propose synthesized design of a fuzzy logic controller (FLC) for control of an underactuated unicycle system. The FLC objective is velocity control of the wheel while keeping the pendulum upright, which is an unstable equilibrium. The synthesized design consists of three phases. First, the FLC structures including the number of rules, membership functions, inference and parametric relations are chosen based on heuristic knowledge about the unicycle. Second, on the basis of a linearized model and linear feedback, the FLC output parameters are determined quantitatively for stabilization of the unicycle. Third, the FLC output parameters are tuned using an iterative learning tuning (ILT) algorithm, which minimizes an objective function that specifies the desired unicycle performance. The rationale for the synthesized FLC design is full utilization of the available information, which is achieved by combining model-based and model-free designs, and hence improves the FLC performance. We minimize the number of FLC rules and fuzzy labels. Six rules are used for regulation or setpoint tasks, whereas 10 rules are used with extra integral control to eliminate steady-state errors induced by system uncertainties and disturbances. Only two fuzzy labels are adopted for each fuzzy variable. The ILT process consists of two phases, exploration for stabilization and exploitation for better performance. The effectiveness of the proposed FLC is validated using intensive simulations and comparisons.

Cooperative Control for Urban Vehicle Traffic

The paper presents a cooperative control approach applied to an urban vehicle traffic system that is implemented through a set of communicating agents. The distributed controllers use fuzzy logic rules to get the control decisions. To improve the local control performances the agents send each other their contexts. Improving the control involves more terms in the fuzzy logic control rules. This has the effect of strongly increasing the number of rules. Hierarchical architectures are chosen to diminish the number of logic rules for different methods. To obtain the unknown fuzzy logic control rules the generic genetic approach is used. The control rule sets, the simulations results and their comparison are given for some cooperative methods.

Efficacy of Different Rule Based Fuzzy Logic Controllers for Induction Motor Drive

Performance of an electric drive is paramount for crucial motion applications and greatly influenced by the controller capabilities. Vector control technique is normally applied with the induction motor drive for high performance applications. For such applications fuzzy logic controller (FLC) has been widely used instead of conventional PID controller. However, size of rule-base of FLC is directly influencing the real time computational burden, which subsequently restricts its application with the processors of limited speed & memory. The number of rule base and performance of drive are inversely related with each other as it is evident that all the rules don't participate equally in the response and can be reduced for simplicity which utilizing less computational resources. In this paper the performance of vector controlled induction motor drive is presented for three different FLC rule bases namely 49, 25 and 9 rules. The drive performance has been investigated for these cases for speed control, disturbance rejection control ability. Moreover, sensitivity of the drive is evaluated for stator resistance control. The performance of drive system using larger FLC rule base is found superior as compared to the performance with lesser rules at the cost of large computational resources and speed.

Soft computing technique for power control of Triga Mark-II reactor

In this study, a trajectory tracking fuzzy genetic controller for Istanbul Technical University Triga Mark-II nuclear research reactor design approach is given. Power output of reactor is controlled along the predefined trajectory by fuzzy logic controller. Designed zero order Sugeno type fuzzy logic controller membership boundary value and rule weights are found by genetic algorithm. Non-chattering control with smooth control surface is also achieved using constrained fitness functions. Simulation results shows that reactor power successfully tracks the given trajectory under various working conditions and reaches the desired power level within the determined period within small tracking error.

Design and Implementation of Fuzzy Logic system for DC motor Speed Control

In this paper an integrated electronic system has been designed, constructed and tested. The system utilizes an interface card through the parallel port in addition to some auxiliary circuits to perform fuzzy control operations for DC motor speed control with load and no load. Software is written using (C++ language Ver. 3.1) to display the image as control panel for different types of both conventional and fuzzy control. The main task of the software is to simulate: first, how to find out the correct parameters for fuzzy logic controller (membership's function, rules and scaling factor). Second, how to evaluate the gain factors (K P , K I and K D ) by Ziegler-Nichols method. When executing any type of control process the efficiency is estimated by drawing the relative speed response for this control.

Intelligent Control and Simulation of Piezoelectric Smart Plate

Active vibration control based on fuzzy algorithm for piezoelectric smart plate is studied. After the characteristic of piezoelectric ceramic (PZT) is analyzed, modal and transient analysis are developed in this paper with finite element model of the piezoelectric smart plate constructed in ANSYS. Fuzzy logic control rules are established by the analysis of the deformation of the plate. With mode shape and displacement response curve obtained, active vibration suppression for the smart plate system is achieved by using commercial finite element programs. The simulation results illustrate that analysis of piezoelectric smart plate using ANSYS is practicable and the intelligent control method is effective.

Linear Matrix Inequality Conditions of Nonlinear Systems by Genetic Algorithm-based H ∞ Adaptive Fuzzy Sliding Mode Controller

In this paper, the stability analysis of a genetic algorithm-based (GA-based) H∞ adaptive fuzzy sliding model controller (AFSMC) for a nonlinear system is discussed. First, a nonlinear plant is well-approximated and described with a reference model and a fuzzy model, both involving fuzzy logic control (FLC) rules. Then, FLC rules and the consequent parameter are decided on via a genetic algorithm. After this, we guarantee a new H∞ tracking performance inequality for the control system. The H∞ tracking problem is characterized to solve an eigenvalue problem. Next, an AFSMC is proposed to stabilize the system so as to achieve good H∞ control performance. Lyapunov’s direct method can be used to ensure the stability of the nonlinear system. It is shown that the stability analysis can reduce nonlinear systems into a linear matrix inequality problem. Finally, a numerical simulation is provided to demonstrate the control methodology.

Speed control of permanent magnet excitation transverse flux linear motor by using adaptive neuro-fuzzy controller

This paper presents a novel adaptive neuro-fuzzy controller applies on transverse flux linear motor for controlling its speed. The proposed controller presents fuzzy logic controller with self tuning scaling factors based on artificial neural network structure. It has two input variables and one control output variable. Firstly the fuzzy logic control rules are described then NN architecture is represented to self tune the output scaling factors of the controller. The application of this control technique represents the novelty of work, where this algorithm has so far not been stated before for this type of drives. This methodology solves the problem of nonlinearities and load changes of TFLM drives. The dynamic response of the motor is studied under the rated load condition as well as load disturbances. The proposed controller ensures fast and accurate dynamic response with an excellent steady state performance. The dynamic response of the motor with the proposed controller is compared with PI and adaptive NN controllers. It is found that the proposed controller gives better and faster response from the viewpoint of overshoot and settling time. Matlab/Simulink tool is used for this dynamic simulation study.

A NOVEL STABILITY CONDITION AND ITS APPLICATION TO GA-BASED FUZZY CONTROL FOR NONLINEAR SYSTEMS WITH UNCERTAINTY

In this study, we strive to combine the advantages of fuzzy logic control (FLC), genetic algorithms (GA), H(subscript ∞) tracking control schemes, smooth control and adaptive laws to design an adaptive fuzzy sliding model controller for the rapid and efficient stabilization of complex and nonlinear systems. First, we utilize a reference model and a fuzzy model (both involving FLC rules) to describe and well-approximate an uncertain, nonlinear plant. The FLC rules and the consequent parameter are decided on via GA. A boundary-layer function is introduced into these updated laws to cover modeling errors and to guarantee that the state errors converge into a specified error bound. After this, a H(subscript ∞) tracking problem is characterized. We solve an eigenvalue problem (EVP), and derive a modified adaptive neural network controller (MANNC) to simultaneously stabilize and control the system and achieve H(subscript ∞) control performance. Furthermore, a stability criterion is derived utilizing Lyapunov's direct method to ensure the stability of the nonlinear system. Finally, the control methodology is demonstrated via a numerical simulation.

GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems

In this paper, the stability analysis of the GA-based adaptive fuzzy sliding model controller for a nonlinear system is presented. First, an uncertain and nonlinear plant for the tracking of a reference trajectory is well approximated and described via the reference model and the fuzzy model involving fuzzy logic control rules. Next, the difficulty in designing a fuzzy sliding mode controller (FSMC) capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector. The initial values of the consequent parameter vector are decided via the genetic algorithm. After this, a modified adaptive law can be adopted to find the best high-performance parameters for the fuzzy sliding model controller. The adaptive fuzzy sliding model controller is derived to simultaneously stabilize and control the system. The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov’s direct method. Finally, a numerical simulation is provided as an example to demonstrate the control methodology.

Incorporating ObjectStab library and fuzzy logic toolbox for design of power system damping controller

AbstractObjectStab is a general purpose simulation tool for power system stability studies developed by Modelica which is an object‐oriented modeling language. It provides enough modeling flexibility to allow addition or modification of new power system components. This article describes an incorporated use of ObjectStab library and fuzzy logic toolbox in Matlab/Simulink to enhance the application of this library into fuzzy control design environment. The example provided here is the modeling of the static synchronous series compensator (SSSC) which is the new device developed in the ObjectStab. In addition, the interface of ObjectStab with Matlab/Simulink for an SSSC damping controller design by fuzzy logic toolbox is explained step by step. In fuzzy control design, a simple approach to extract membership functions and fuzzy logic control rules based on observed signals is presented. Simulation studies in a two‐area four‐generator power system confirm the effectiveness of the fuzzy controller of SSSC designed by the incorporated use of ObjectStab and fuzzy logic toolbox. © 2008 Wiley Periodicals, Inc. Comput Appl Eng Educ 16: 243–255, 2008; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae20126

GA‐Based Fuzzy Sliding Mode Controller for Nonlinear Systems

Generally, the greatest difficulty encountered when designing a fuzzy sliding mode controller (FSMC) or an adaptive fuzzy sliding mode controller (AFSMC) capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector. In this paper, we describe a method of stability analysis for a GA‐based reference adaptive fuzzy sliding model controller capable of handling these types of problems for a nonlinear system. First, we approximate and describe an uncertain and nonlinear plant for the tracking of a reference trajectory via a fuzzy model incorporating fuzzy logic control rules. Next, the initial values of the consequent parameter vector are decided via a genetic algorithm. After this, an adaptive fuzzy sliding model controller, designed to simultaneously stabilize and control the system, is derived. The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov′s direct method. Finally, an example, a numerical simulation, is provided to demonstrate the control methodology.

Design of fuzzy controller for magneto-rheological suspension using a hybrid Taguchi genetic algorithm to improve ride quality

Since magneto-rheological (MR) suspension has nonlinearity and uncertainty, a new adaptive fuzzy control strategy using a hybrid Taguchi genetic algorithm (HTGA) is proposed to improve ride quality. The controller consists of two control loops. The inner open loop controls a nonlinear MR damper to achieve tracking of a desired force. The outer loop implements a fuzzy logic controller (FLC) using HTGA. The HTGA is used to tune the membership functions and fuzzy control rules of FLC with initial skyhook control rules. To verify the control performance, FLC based on HTGA for semi-active suspension system is simulated. The simulation results show that FLC based on HTGA can achieve smaller acceleration root mean square (RMS) than simple FLC and better ride quality compared with passive suspension under random input.

Fuzzy Logic Controller Based on Observed Signals arid a Genetic Algorithm Application with STATCOM for Power System Stabilization

Fuzzy logic control has been applied to various applications in power systems. Its control rules and membership functions are typically obtained by trial and error methods or experience knowledge. Proposed here is the application of a micro-genetic algorithm (micro-GA) to simultaneously design optimal membership functions and control rules for STATCOM. First, we propose a simple approach to extract membership functions and fuzzy logic control rules based on observed signals. Then a proposed GA will be applied to optimize membership functions and its control rules. To validate the effectiveness of the proposed approach, several simulation studies have been performed on a multimachine power system. Simulation results show that the proposed fuzzy logic controller with STATCOM can effectively and robustly enhance the damping of oscillations.

A modified fuzzy PI controller for a flexible-joint robot arm with uncertainties

Fuzzy control strategy offers an alternative approach for many conventional control systems, which has certain advantages over the other techniques. For example, the improved PI fuzzy controller we designed in this paper, which is based on an existing fuzzy PI controller, can control an uncertain flexible-joint robot arm to produce satisfactory tracking results. The improved fuzzy PI controller not only can control (stable and unstable) conventional linear systems, performing as well as the conventional PI controller, but also is capable of controlling many nonlinear systems such as the flexible-joint robot arm under investigation which contains uncertainties within 10% tolerance of all nominal system-parameter values. In this improved fuzzy PI controller, we used only three simple membership functions plus six simple fuzzy logic control rules. In this paper, we describe the design principle of this controller and demonstrate its tracking performance in handling nonlinearity, flexibility, and uncertainty within the flexible-joint robot arm system.