Evolutionary design of fuzzy logic controllers for medium access control in WBAN

Fuzzy logic techniques have been successfully employed in wireless body area networks (WBANs) for media access control (MAC). However, most of the existing research work considered only single-level fuzzy logic controls (FLCs) for either contention-based or contention-free MAC, neglecting the importance of utilizing both contention-based and contention-free MAC to improve the reliability and performance of the network. This paper introduces a two-level FLC control scheme at both the coordinator level and the sensor level to further enhance communication quality in WBANs. The sensor-level FLC controls contention-based channel access and the coordinator-level FLC controls contention-free channel access. This two-level FLC architecture can effectively improve the cooperation between sensors and the coordinator such that both the performance and reliability of the network can be significantly improved. We also proposed a cooperative co-evolutionary approach to design of our proposed two-level control scheme automatically. With the goal of effectively designing useful FLCs, we have particularly developed two new collaborator selection methods for our cooperative co-evolutionary approach, which enable us to effectively select collaborators while evaluating the candidate FLC design in each subpopulation. Specifically, we demonstrate that, by employing network knowledge, our collaborator selection methods can judge the suitability of potential collaborator designs and notably improve the effectiveness of our evolutionary design approach. Empirically the FLCs designed by our approach clearly outperformed several state-of-the-art MAC schemes from recent literature and the IEEE 802.15.4 standard.

Genetic programming (GP) is applied to the design of fuzzy logic controllers (FLCs) for mobile robot path tracking. GP is applied to automatic discovery of full knowledge bases for use in fuzzy logic control applications. An extension to a rule learning GP system is presented that achieves this objective. In addition, GP is employed to handle selection of fuzzy set intersection operators (t-norms). The new GP system is applied to design a mobile robot path tracking controller and performance is shown to be comparable to that of a manually designed controller. GP was successfully applied to discover FLCs capable of steering a mobile robot to track straight-line paths in the plane. Instances of simultaneous evolution of membership functions and rules showed that GP was capable of evolving a FLC that demonstrated satisfactory responsiveness to various initial conditions while utilizing minimal human interface.

Power efficient ultra-wideband based wireless communication for wireless body area network applications

This paper presents fuzzy logic based velocity feedback control for active vibration control of beams. The controller is first developed for a single degree of freedom spring mass system. Rule base consisting of three simple rules based on velocity is used. It is found theoretically as well as experimentally, that for the same settling time maximum applied force required by fuzzy logic controller is much less than that required by direct negative velocity feedback control. The fuzzy controller so developed is then applied for active vibration control of beams. The controller is implemented experimentally on a test beam and the results are found satisfactory. The test system consists of a cantilevered beam with piezoelectric sensor and actuator patches mounted in collocated fashion. The fuzzy logic controller is based on modal velocity of the beam. Modal velocity of the beam acts as an input to the fuzzy controller and actuation force is output from the inference engine. The issues related to design of fuzzy logic controller based on velocity are discussed.

This paper presents the design of a fuzzy logic controller for a process air heater system which produced an overdamped temperature response. The process air heater system is modelled mathematically and simulated using MATLAB/Simulink. Five types of membership functions (Gaussian, triangular, trapezoidal, combined trapezoid and triangular and generalized Bell) with four different sizes of rule base (3×3, 5×5, 7×7 and 9×9) were designed and tested. The performances of the fuzzy logic controllers were compared based on maximum overshoot, settling time and integral of absolute error. The results showed that fuzzy logic controller with combined trapezoid and triangular membership function with 3×3 rule base can achieve the desired response.

Design of fuzzy logic controller for smart base isolation system using genetic algorithm

Power system stabilizers (PSS) has been widely used to enhance damping due to the electromechanical low frequency oscillations occurrence in power systems. In this paper, a new method is used for the online tuning of parameters of conventional power system stabilizers (CPSS) using fuzzy logic. Fuzzy logic enables mathematical modeling and computation of some nonlinear parameters of the system, which are usually derived empirically by utilization of expert knowledge rules. Various literatures has shown that fuzzy logic controller is one of the most useful methods for expert knowledge utilization. This type of controller is adaptive in nature and can be used successfully as a power system stabilizer. The design of fuzzy logic controllers is mainly based on fuzzy rules and input/output membership functions. Simple and efficient clustering algorithms allow data classification in distinct groups using distance and/or similarity functions. In the present paper, the optimum generation of fuzzy rules base using Fuzzy C-means (FCM) clustering technique is used. In fact, data are classified and the number of fuzzy rules which depends on convergence radius is determined. Finally, the performance of proposed FCM controller is compared with that of conventional controller. The active power, reactive power and bus voltages used as inputs to the fuzzy logic network based power system stabilizer and the parameters of the optimum stabilizer , i.e. gain factor as well as time constants of the lead/lag compensator, are the outputs of the proposed system. The design method has been successfully implemented on a single machine power system connected to an infinite bus over various operating conditions.

Fuzzy logic controllers suffer from rule explosion problem as the number of rules increases exponentially with the number of input variables. Although several methods have been proposed for eliminating the combinatorial rule explosion problem, none of them is fully satisfactory. In this paper, we describe a new adaptive method for the design of cascaded layer based hierarchical fuzzy system with high input dimensions. This new adaptive hierarchical architecture could be applied to dimensionality reduction in fuzzy modeling. An evolutionary algorithm based off-line leaning algorithm is employed to generate the fuzzy rules and their corresponding membership functions. The evolutionary learning paradigm is a powerful tool to tune the fuzzy logic controllers since it requires no prior knowledge about the system's behavior in order to formulate a set of functional control rules through adaptive learning. The resulting hierarchical fuzzy system has not only an equivalent approximation capability, but less number of fuzzy rules compared with the conventional fuzzy logic system. Simulation studies exhibit competing results with high accuracy that illustrating the effectiveness of the approach.

The upper bounds for a multivariable fuzzy logic controller under Godel's implication are investigated, and a generalized multivariable structure for a fuzzy controller is proposed. Contribution operators, such as the max( nu ) operation, ensure the greatest upper bound as well as the least upper bound. Conditions that determine an appropriate upper bound are also determined. The theory has applications in the design of fuzzy logic controllers. >

With the increasing size of modern large wind turbine (WT), the effects of dynamic loading on the structures become an important influence factor. There are mitigation measures for WT control systems to reduce these imbalance structural loads and regulate power. It has motivated the development of blade individual pitch control (IPC) methodologies. This study focuses on the design of fuzzy logic controller (FLC) for IPC. The controllers are designed in order to optimise a trade‐off among several control objectives such as blade root moment and generator torque. Three different FLC had been used in the controllers, the first one related to blade pitch angle and electromagnetic torque control variables to meet specified objectives for operation region, the second control model and the third model related to d‐q axis blade moment in non‐rotating frame of reference. Likewise, the optimisation criteria of FLC consider for each controller objective to mitigate fatigue loads and regulate output power. Finally, the effectiveness of proposed method is verified by simulation results for three‐bladed NREL 2 MW WT. The results proved that the fatigue loads in the turbine are reduced obviously.

A fuzzy logic controller (FLC), intended to plan and control the motion of a car-like robot during its navigation among several moving obstacles, is designed automatically using a genetic algorithm (GA). The FLC is expressed by utilizing the structure of a neural network (NN), and a GA is used to o ptimize both its data base as well as rule base. An importance factor is introduced to determine the strength of a rule and thus justify its redundancy, if any. Results of the proposed approach are compared to those of a neuro-fuzzy approach, genetic-neuro-fuzzy approach and a potential field method, through computer simulations, for solving the navigation problems of a car-like robot. The present approach is found to perform better than the other three approaches, for most randomly-generated test scenarios.

One of the advantages of using fuzzy logic controllers is that it does not require a mathematical model. It performs control process with verbal expression. In this study, temperature control of a treatment chamber using PIC16f877A microcontroller is presented and fuzzy logic controller design is made according to expert knowledge. Brief information about fuzzy logic and fuzzy logic controller is given. Triangular membership functions are used for input and output variables for ease of use. With the realized design, the temperature of the treatment chamber can be kept between 24 °C and 45 °C at the desired temperature.

임의의 비선형 시스템을 제어하기 위한 새로운 방법의 제어 알고리즘이 널리 보고되고 있으며, 그 유용성을 입증하기 위한 제어 대상 시스템으로 역진자 시스템이 널리 활용되고 있다. 본 논문에서는 스프링으로 연결된 2개의 차량에 장착된 역진자를 제어하는 알고리즘을 제시한다. 여기서 두 개의 차량 중 하나는 구동용, 다른 하나는 역진자를 장착한 무구동용이다. 이를 위한 시스템 모델링을 제시하고, 퍼지논리제어 시스템 기반의 양질의 제어기 설계를 제안한다. 본 논문에서는 퍼지논리제어기의 입력변수로 사용될 6개의 변수를 2개로 축소하기 위하여 LQR(Linar Quadratic Regulator) 기법을 도입하며, 이를 통하여 퍼지논리제어기 설계의 복잡성을 줄일 수 있음을 보인다. 더욱이 개선된 2-입력 퍼지논리제어기의 제어 규칙표가 skew-symmetric의 특성을 가지는 성질로부터 다시 단일입력 퍼지논리제어기 설계를 제안한다. 제안한 방법의 유용성을 입증하기 위하여 시뮬레이션을 수행하며, 이를 통하여 제안한 방법의 우수성을 입증한다. Any new method for controlling a nonlinear system has widely been reported. An inverted pendulum system has typically been used as a target system for demonstrating its usefulness. In this paper, we propose an algorithm to control a flexible joint cart based inverted pendulum system. Two carts are connected with a spring and one is a driving cart and the other is no driving cart with a pole. We here present a system modeling and a good fuzzy logic based control algorithm. We also introduce LQR (Linar Quadratic Regulator) technique for reducing the number of control variables. By using this technique, the number of input variables for a fuzzy logic controller is become only two not six. So the computational complexity is largely reduced. Moreover, a two-input fuzzy logic controller has a control rule table with a skew-symmetric property. And it will lead the design of a single-input fuzzy logic controller. In order to demonstrate the usefulness of the proposed method and prove the superiority of the proposed method, some computer simulations are presented.

Design of a fuzzy logic approach for optimization reinforced zirconia depositions using plasma sprayings

In this paper, design and simulation of a current-mode CMOS Fuzzy Logic Controller (FLC) is presented. The proposed controller is composed of Membership Function Circuit (MFC), fuzzy inference engine with min-product method and current-mode defuzzification circuit. MFC has a new architecture that generates basic four membership functions (trapezoidal, triangle, Z-shape and S-shape). These membership functions are simply tuneable by adjusted two voltages via setting some analogue switches in a shape/label select circuit block which is called programming unit. Also, a novelty designed current-mode defuzzification circuit is based on current-mode four-quadrant multipliers that have been constructed simple current squarer and substractor circuits. The proposed current-mode CMOS FLC has been simulated and verified for two inputs, one output and nine tuneable fuzzy rules by using PSPICE program with MIETEC 1.2 µm design parameters. The proposed architecture has an operation speed of 6.25 MFLIPS (6.25 × 106 Fuzzy Logic Inference per Second).