Multivariable fuzzy controller under Gödel's implication

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. >

This paper includes the design and implementation of a fuzzy logic controller for level control system to overcome the limitations of the conventional controllers. Mostly preferred controller is a Proportional Integral Derivative (PID) controller which has fixed control parameters. Selection of wrong parameters in a PID design results into uncontrollable process. But fuzzy logic controller is a rule based. These rules are designed on the basis of heuristic information provided by an operator. Process model is identified and used for the design of Proportional Integral (PI) and fuzzy controllers by using MATLAB. To evaluate the performance of both the controllers, it is tested on a real time system with the help of Real Time Workspace (RTW) module of a MATLAB. From real time results it is understood that, fuzzy controller provides more stable and improved output for level control. Even fuzzy controller has ability to remove overshoot which is present in PI controller. Also it requires less settling time than PI controller. Hence fuzzy control system is more relevant control strategy to control the level.

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.

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.

This paper proposes the design of fuzzy logic controllers to revolve a wheeled mobile robot (WMR) through digital images, and the design of WMR anti-collision system. After the image processing, the authors utilize the center of object image to design the fuzzy logic controllers that incorporate expert knowledge in terms of 9 linguistic rules to drive the WMR to revolve. By the way, the WMR can execute auto moving and anti-collision via infrared sensors. The program codes of fuzzy logic controllers are implemented on a PC which could send all the motion commands to the WMR by wireless communication.

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

Soft computing techniques including fuzzy logic have been successfully applied to Wireless Body Area Networks (WBANs). However, most of the existing research works rely on manual design of the Fuzzy Logic Controller (FLC). To address this issue, in this paper, we propose to use Evolutionary Computation (EC) techniques to automate the design of FLCs for cross layer medium access control in WBANs. With the goal of improving network reliability while keeping the communication delay at a low level, we have particularly experimented on three different coding schemes. The influence of fitness functions has also been examined carefully in order to achieve a good balance between reliability and performance. We have also evaluated the effectiveness of two widely used evolutionary algorithms. Particularly, Particle Swarm Optimisation (PSO) is shown to be more effective than Differential Evolution (DE) for our design problem. Moreover, the FLC designed by our approach is also shown to outperform some related algorithms as well as the IEEE 802.15.4 standard.

Fuzzy logic control based on the Takagi–Sugeno inference method has been applied for the yeast alcoholic fermentation running in a continuous-time biochemical reactor in this paper. A type-1 fuzzy PID controller was designed to temperature control in a biochemical reactor. The fuzzy PID controller was also designed using type-2 fuzzy sets. The advantage of the fuzzy control is that it can be used very successfully for control of strongly non-linear processes and processes that are difficult to model because of complicated reaction kinetics. Obtained simulation results confirm this fact. The disadvantage of the fuzzy control design lies in the time-consuming tuning of controllers. The subtractive clustering method was used to identify the rule base. This approach was chosen to minimize the number of rules of the designed fuzzy logic controllers and to simplify the fuzzy controller design. Simulation results confirm that fuzzy PID controllers can assure better performance than conventional PID controllers. Yeast alcoholic fermentation is an exothermic process and the cooling is necessary to maintain the optimal temperature in the reactor. Fuzzy PID control reduces coolant consumption in comparison with conventional PID control and so it assures energy-efficient control of continuous-time alcoholic fermentation. Using type-2 fuzzy sets in the fuzzy PID controller design is very promising as the type-2 fuzzy controller offers even better results than the type-1 fuzzy controller.

The single-input-rule-modules (SIRMs) connected fuzzy reasoning method is an efficient way to reduce the number of fuzzy rules in the rule-based fuzzy controllers. However, there still exist fuzzy rule generation problems in the design process of the SIRMs connected fuzzy logic controllers (SIRMFLCs). To tackle this problem, this paper presents a prior information driven method for constructing the SIRMFLCs, including both type-2 and type-1 cases. Here, prior information means the requirements that the controllers must meet, e.g. continuity, symmetry and monotonicity of the controllers. To encode such prior information into the SIRMFLCs, the parameter conditions or constraints are provided. To show the effectiveness of the proposed method, applications to the stabilization control of the overhead crane system is given. Through utilizing the prior information to constrain the structures and parameters of fuzzy rules, the proposed strategy can reduce the blindness in the initialization of fuzzy rules and can generate the single input rule modules systematically.

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.

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

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.

Dual mode linguistic hedge fuzzy logic controller for an isolated wind–diesel hybrid power system with superconducting magnetic energy storage unit

This paper presents a comparative evaluation of proportional-integral and fuzzy logic controller to a constant frequency zero voltage switching quasi resonant buck-boost converter. Sensitivity of these controllers to load disturbances are studied and results are presented. The simulation study indicates, the superiority of fuzzy control over the conventional control methods. Fuzzy logic seems to have a lot of promise in the application of power electronics.