Fuzzy Logic Based Temperature Control

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.

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.

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.

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.

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

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.

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.

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 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.

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.

Oyster mushroom is one type of mushroom that is commonly consumed by Indonesian people which is relatively easy to cultivate. It can produce the optimum fruit body at temperatures below 30°C, the range of humidity is 60–90% with altitudes between 500–1000 meters above sea level. This paper describes on the development of the prototype of temperature and humidity control system in oyster mushroom cultivation using a fuzzy logic controller. The prototype comprises of DHT11 sensor for detecting temperature and humidity, Arduino microcontroller, mistmaker, and cooling fan. Fuzzy logic controller is installed in the Arduino microcontroller to generate an actuating signal for the mistmaker and cooling fan based on fuzzy inputs. The whole fuzzy system is designed using two inputs namely temperature and humidity with each input consist of three fuzzy sets with the triangular membership function. Meanwhile, the output variable is the speed of cooling fan with three fuzzy sets and triangular membership functions. A Mamdani inference system is adopted to achieve the control objective in the whole system. Based on the appropriate rule bases, fuzzy logic controllers are designed to produce the temperature and humidity setpoint around 27°C and 60–90 % respectively. Experimental results show the time response to achieve the setpoint span from 1 minute 27 seconds until 8 minutes 10 seconds.

Multivariable fuzzy controller under Gödel's implication

Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulation of a PI controller. The performance of the controllers is studied for a variety of operating conditions, such as step change in speed command and step change in load torque. The parameters selected for the comparison are the steady-state error and the rise time of the response. It is shown that ANN speed controllers provide a superior speed response in terms of rise time and the steady-state error compared to PI and FL controllers. This advantage arises from the fact that the neural network has the property of generalization and the control surface of the neural controller is smooth. The designed neural network controller is simple, with three neurons only, and so it is best suited for embedded system implementation. It is also found that the ANN controller trained with the training data from a PI controller has a better response compared to the ANN controller trained with data from a fuzzy controller.