Hardware/software codesign methodology for fuzzy controller implementation

This paper describes a HW/SW codesign methodology for the implementation of fuzzy controllers on a platform composed by a general-purpose microcontroller and specific processing elements implemented on FPGAs or ASICs. The different phases of the methodology, as well as the CAD tools used in each design stage, are presented, with emphasis on the fuzzy system development environment Xfuzzy. Also included is a practical application of the described methodology for the development of a fuzzy controller for a dosage system

A pan-tilt camera Fuzzy vision controller on an unmanned aerial vehicle

is paper presents an implementation of two Fuzzy Logic controllers working in parallel for a pan-tilt camera platform on an UAV. This implementation uses a basic Lucas-Kanade tracker algorithm, which sends information about the error between the center of the object to track and the center of the image, to the Fuzzy controller. This information is enough for the controller, to follow the object moving a two axis servo-platform, besides the UAV vibrations and movements. The two Fuzzy controllers of each axis, work with a rules-base of 49 rules, two inputs and one output with a more significant sector defined to improve the behavior of those

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

An artificial immune systems based predictive modelling approach for the multi-objective elicitation of Mamdani fuzzy rules: a special application to modelling alloys

In this paper, a systematic multi-objective Mamdani fuzzy modeling approach is proposed, which can be viewed as an extended version of the previously proposed Singleton fuzzy modeling paradigm. A set of new back-error propagation (BEP) updating formulas are derived so that they can replace the old set developed in the singleton version. With the substitution, the extension to the multi-objective Mamdani Fuzzy Rule-Based Systems (FRBS) is almost endemic. Due to the carefully chosen output membership functions, the inference and the defuzzification methods, a closed form integral can be deducted for the defuzzification method, which ensures the efficiency of the developed Mamdani FRBS. Some important factors, such as the variable length coding scheme and the rule alignment, are also discussed. Experimental results for a real data set from the steel industry suggest that the proposed approach is capable of eliciting not only accurate but also transparent FRBS with good generalization ability

A survey of fuzzy control for stabilized platforms

This paper focusses on the application of fuzzy control techniques (fuzzy type-1 and type-2) and their hybrid forms (Hybrid adaptive fuzzy controller and fuzzy-PID controller) in the area of stabilized platforms. It represents an attempt to cover the basic principles and concepts of fuzzy control in stabilization and position control, with an outline of a number of recent applications used in advanced control of stabilized platform. Overall, in this survey we will make some comparisons with the classical control techniques such us PID control to demonstrate the advantages and disadvantages of the application of fuzzy control techniques

Validation and Verification of Aircraft Control Software for Control Improvement

Validation and Verification are important processes used to ensure software safety and reliability. The Cooper-Harper Aircraft Handling Qualities Rating is one of the techniques developed and used by NASA researchers to verify and validate control systems for aircrafts. Using the Validation and Verification result of controller software to improve controller\u27s performance will be one of the main objectives of this process. Real user feedback will be used to tune PI controller in order for it to perform better. The Cooper-Harper Aircraft Handling Qualities Rating can be used to justify the performance of the improved system

Using a fuzzy inference system to control a pumped storage hydro plant

The paper discusses the development of a fuzzy inference system (FIS) based governor control for a pumped storage hydroelectric plant. The First Hydro Company's plant at Dinorwig in North Wales is the largest of its kind in Europe and is mainly used for frequency control of the UK electrical grid. In previous investigations, a detailed model of the plant was developed using MATLAB(R)/SIMULINK(R) and this is now being used to compare FIS governor operation with the proportional-integral-derivative (PID) controller currently used. The paper describes the development of an FIS governor, and shows that its response to a step increase in load is superior to the PID under certain conditions of load. The paper proceeds to discuss the implications of these results in view of the possible practical application of an FIS governor at the Dinorwig plant