Fuzzy Control of Chaos

We introduce the idea of the fuzzy control of chaos: we show how fuzzy logic can be applied to the control of chaos, and provide an example of fuzzy control used to control chaos in Chua's circuit

Design and implementation of fuzzy logic controllers

The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real system. We first present the fundamental concepts of fuzzy sets and fuzzy logic. Fuzzy sets and basic fuzzy operations are defined. In addition, for control systems, it is important to understand the concepts of linguistic values, term sets, fuzzy rule base, inference methods, and defuzzification methods. Second, we introduce a four-step fuzzy logic control system design procedure. The design procedure is illustrated via four examples, showing the capabilities and robustness of fuzzy logic control systems. This is followed by a tuning procedure that we developed from our design experience. Third, we present two Lyapunov based techniques for stability analysis. Finally, we present our design and implementation of a fuzzy logic controller for a linear actuator to be used to control the direction of the Free Flight Rotorcraft Research Vehicle at LaRC

Fuzzy logic particle tracking velocimetry

Fuzzy logic has proven to be a simple and robust method for process control. Instead of requiring a complex model of the system, a user defined rule base is used to control the process. In this paper the principles of fuzzy logic control are applied to Particle Tracking Velocimetry (PTV). Two frames of digitally recorded, single exposure particle imagery are used as input. The fuzzy processor uses the local particle displacement information to determine the correct particle tracks. Fuzzy PTV is an improvement over traditional PTV techniques which typically require a sequence (greater than 2) of image frames for accurately tracking particles. The fuzzy processor executes in software on a PC without the use of specialized array or fuzzy logic processors. A pair of sample input images with roughly 300 particle images each, results in more than 200 velocity vectors in under 8 seconds of processing time

Applications of fuzzy logic to control and decision making

Long range space missions will require high operational efficiency as well as autonomy to enhance the effectivity of performance. Fuzzy logic technology has been shown to be powerful and robust in interpreting imprecise measurements and generating appropriate control decisions for many space operations. Several applications are underway, studying the fuzzy logic approach to solving control and decision making problems. Fuzzy logic algorithms for relative motion and attitude control have been developed and demonstrated for proximity operations. Based on this experience, motion control algorithms that include obstacle avoidance were developed for a Mars Rover prototype for maneuvering during the sample collection process. A concept of an intelligent sensor system that can identify objects and track them continuously and learn from its environment is under development to support traffic management and proximity operations around the Space Station Freedom. For safe and reliable operation of Lunar/Mars based crew quarters, high speed controllers with ability to combine imprecise measurements from several sensors is required. A fuzzy logic approach that uses high speed fuzzy hardware chips is being studied

Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Overspeed correction scheme for dc motor using artifical intelligent approach

The conventional PI, PD and PID controllers were used as a control strategy for various industrial processes from many years due to their simplicity in operation. They used mathematical models to control the plant for different process control applications. A fuzzy controller for DC speed motor fed by DC Chopper, H-Bridge converter is developed and presented in this paper. Fuzzy logic based control systems were introduced by Lotfi Zadeh to optimize the speed and process control parameters in better way. During implement this project, we have an experienced in modeling the physical quantities such as dc motor, and modeling a mathematical equations for dc motor, develop simulink block for PI controller and then develop fuzzy logic speed controller using MATLAB Simulink blocks

Fuzzy Logic Implementation to Control Temperature and Humidity in a Bread Proofing Machine

Factors that need to be considered of producing good quality bread are raw materials, balance formulas (recipes) and production processes. The bread dough that cannot proof perfectly has become a problem in the process of bread production. Therefore, the temperature and humidity of the room must be controlled at a certain temperature range. The solution of this problem is proposing a controller that uses Fuzzy logic to control temperature and humidity in the bread examination room. A bread proofing machine is added a controller such as evaporator that it is can controlled the temperatur and humidity automatically. The heat and steam produced are regulated using a Fuzzy logic algorithm embedded in the microcontroller with a predetermined set point of temperature and humidity is 35 oC and 80%. The test is done by determining the percentage error from the temperature and humidity test results, that is when the machine is free of load obtained the percentage error to set points is 0,429 % and 0,937 %. While the engine is loaded. It gives the results are 0,024 % and 0,015%. The results of this test prove that controlling temperature and humidity in a bread proofing machine using Fuzzy logic can provide good results compared to conventional controllers. as a result, the bread mixture can expand uniformly

Summary report: A preliminary investigation into the use of fuzzy logic for the control of redundant manipulators

The Rice University Department of Mechanical Engineering and Materials Sciences' Robotics Group designed and built an eight degree of freedom redundant manipulator. Fuzzy logic was proposed as a control scheme for tasks not directly controlled by a human operator. In preliminary work, fuzzy logic control was implemented for a camera tracking system and a six degree of freedom manipulator. Both preliminary systems use real time vision data as input to fuzzy controllers. Related projects include integration of tactile sensing and fuzzy control of a redundant snake-like arm that is under construction