Applications of fuzzy sets to rule-based expert system development

Problems of implementing rule-based expert systems using fuzzy sets are considered. A fuzzy logic software development shell is used that allows inclusion of both crisp and fuzzy rules in decision making and process control problems. Results are given that compare this type of expert system to a human expert in some specific applications. Advantages and disadvantages of such systems are discussed

Fuzzy logic applications to expert systems and control

A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings

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

Mendocino power plant site ecological study, Quarterly Report No. 1; July 1 - September 30, 1971

This report is the first quarterly report submitted in partial fulfillment of Research Contract No. S-1902 between the Department of Fish and Game and the Pacific Gas and Electric Company. Through this contract the Department of Fish and Game is to conduct a pre-operational ecological study to establish a base line inventory of the marine biota with special reference to fish and to abalone, including food chains. Quarterly reports will be followed by annual reports. The first annual report will cover all work from September 1971 through December 1972. Full tables and species lists will be included in each annual report

How to combine probabilistic and fuzzy uncertainties in fuzzy control

Fuzzy control is a methodology that translates natural-language rules, formulated by expert controllers, into the actual control strategy that can be implemented in an automated controller. In many cases, in addition to the experts' rules, additional statistical information about the system is known. It is explained how to use this additional information in fuzzy control methodology

Autonomous control procedures for shuttle rendezvous proximity operations

The results are presented of a study which uses fuzzy sets to model a Space Shuttle pilot's reasoning and actions while performing rendezvous proximity operation maneuvers. In this model fuzzy sets are used to simulate smooth and continuous actions as would be expected from an experienced pilot and to simulate common sense reasoning in the decision process. The present model assumes visual information available to the Shuttle pilot from the Shuttle Crew Optical Alignment Sighting (COAS) device and the overhead window and rendezvous radar sensor information available to him from an onboard display. This model will be used in a flight analysis simulator to perform studies requiring a large number of runs, each of which currently needs an engineer in the loop to supply the piloting decisions. This work has much broader implications in control of robots such as the Flight Telerobotic Servicer, in automated pilot control and attitude control, and in advisory and evaluation functions that could be used for flight data monitoring or for testing of various rule sets in flight preparation

Mendocino Power Plant site ecological study annual report; July 1, 1971 to December 31, 1972

This report is the first annual report submitted in partial fulfillment of Research Contract No. S-1902 between the Department of Fish and Game and the Pacific Gas and Electric Company. Through this contract the Department of Fish and Game is to conduct a preoperational ecological study to establish a base line inventory of the marine biota with special reference to fish and to abalone, including food chains. This first annual report covers all work from September 1971 through December 1972. This annual report covers the first 18 month period of the project from July 1, 1971 through December 31, 1972. The report includes results (discussion and tables) of our subtidal, intertidal, sportfishery, fish collection, fish food habit, and plankton studies. The quarterly report for October 1 to December 31, 1972 is included herein and these data are incorporated in this annual report. (86pp.