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

Adaptive Resonance Theory

SyNAPSE program of the Defense Advanced Projects Research Agency (Hewlett-Packard Company, subcontract under DARPA prime contract HR0011-09-3-0001, and HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, an NSF Science of Learning Center (SBE-0354378

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

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

Automatic train control using neuro-fuzzy modeling and optimal control techniques

In rapid transit applications, it is often necessary to optimize the ride of the train for certain parameters based upon time of day, occupant density, and system-wide scheduling. Trade-offs have to be made between energy conservation, time minimization, and ride comfort. Typically the dynamics of the train are not well known (or not initially known at all), change over time, and are non-linear. In the past, a transit control engineer would typically use P-I control but could spend days or weeks on-site adjusting the P-I constants to obtain a ride that felt good and met the design constraints. This process was both time consuming and expensive. This paper presents a control scheme for a rapid transit train that uses optimal concepts coupled with fuzzy control and neuro-fuzzy modeling techniques. The optimal controller allows users to define different ride types by adjusting weights on the cost equation. The controller design is done almost automatically, with minimal control engineer effort needed, by post-processing data collected from the train. The post-processing process uses neuro-fuzzy modeling techniques to create a dynamic model for the train, which can be used with optimal techniques to obtain fuzzy control rules for controlling the train. Once the initial design is in place, the controller becomes adaptive and fine-tunes itself to match the dynamics of the particular train that it is on

Target Tracking in Mobile Robot under Uncertain Environment using Fuzzy Logic Controller

This paper discusses a design of fuzzy logic algorithm in a robot.   This algorithm is useful for the robot in seeking and reaching the target.  The robot is also accomplished with an ability to avoid obstacles. Although the fuzzy rule that is embedded to the robot is very simple, it gives a good result in target seeking and obstacles avoiding task.   The originality of this research is an approach to the rules that can simplify the task by creating faster track for the robot in uncertain environment.

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