Fuzzy Logic Controllers may be considered as knowledge-based sys- tems, incorporating ... Fuzzy logic controllers, genetic algorithms, machine learning, rule-.
This paper presents a reactive controller for Sony legged robots to play soc- cer. ... Therefore, the input state vector is S = [s1,s2,s3]. T. = [θ, h, α]. T.
used as well as two IFAC benchmark control problems (ship autopilot and passenger bus) to investigate the .... B Documentation for software design tools. 158.
Scott Lancaster. Fuzzy Flight. 1. Fuzzy Logic Controllers. •Description of Fuzzy
Logic. •What Fuzzy Logic Controllers Are Used for. •How Fuzzy Controllers Work.
Mar 28, 2012 - Genetic algorithms are global search techniques modeled following ... PD-Like fuzzy-logic controller and optimization ..... engine. Satisfactory handling behavior is characterized by the fact .... maneuverability is guaranteed.
focus of this artificial life experiment is to firstly evolve embodied locomotion ... was found that under all extraterrestrial conditions the artificial creature was still.
May 14, 2016 - Maximum Power Point Tracking of Photovoltaic Modules. Jemaa AYMEN1, Zarrad ... MPPT controller in order to improve energy conversion efficiency. ..... 3232 (Paper) ISSN 2225-0573 (Online), Vol.2, No.6, 2012. [15] M. M. ...
Mar 28, 2012 - Roll-angle-tracking controller for motorcycle (Sharp et al., 2005) .... vehicle's braking system to support the driver in critical driving situations.
Mar 28, 2012 - steering angle using non-linear control based on the sliding patch and stuck phenomena ..... measured from the yaw-rate sensor.
Mamdani as well as a new hybrid adaptation structure, called gradient-incremental adaptive fuzzy controller connecting gradient-descent methods with the first ...
In the sequel, a detailed presentation of those advantages ... the advantages of the fuzzy logic controllers (FLC), that is ... calculus or first-order predicate logic.
cial structure of the problem and test it in some typical numerical examples. ... so that precise and yet significant statements can be made on the behavior of a complex system. Successful applications of fuzzy logic control include automatic train o
by carrying PID tuning rules over to the fuzzy domain. .... V\VWHP FI )LJ 1 WKHUHIRUH WKH FRQWUROOHU PXVW FRQWDLQ DQ LQWHJUDWRU 7KH.
16.2 Fuzzy Logic and Fuzzy Expert System - A Primer. 16.3 Fuzzy ... Design of
Stable Feedback Fuzzy Expert Systems and Stable closed Loop. Systems with ...
Evolving Spiking Neural Network Controllers for. Autonomous Robots. Hani Hagras, Anthony Pounds-Cornish, Martin Colley, Victor Callaghan and Graham ...
Among these models, both the Average Landmark Vector model1 and the snapshot ..... random and directly applies a weight update (eq. (3)). From the equation ...
produces a con guration based on the performance of a software simulation of the recon gurable ..... obtained from an implementation of the FSM in software.
Nicoud, editors, From Perception to Action Conference, pages 146{157. IEEE. Computer Society Press, 1994. 24. David P.M. Northmore and John G. Elias.
Jun 29, 2005 - using EH methods to learn to control a hardware analog ... analog computer built to model TA instability and feedback phenomena.
PhD Thesis Dissertation: New Methodologies for the Design of. Evolving Fuzzy
Systems for. Online Intelligent Control by. Ana Belén Cara Carmona. Advisors:.
Apr 17, 2012 - struct System const* sys, char const* name,. 3 void* var, long ...... S. Shukla, A. Hu, J. Abrahams, P. Ashar, H. Foster, A. Landver, and C. Pixley.
Apr 18, 2017 - F-91192 Gif-sur-Yvette ... based on a timed game formulation whose solution provides ...... Conference & Exhibition (DATE), pages 636â641.
the place of fuzzy technology in modern science and industry. .... P. Holmblad (Denmark) â the first permanent industrial application .... and fire control missions.
Institute for Information and Communication Systems, Neural and Fuzzy Systems
... tion of the model and an application example under the MATLAB/SIMULINK ...
76.1 Overview ............................................. 1452 76.2 Type-1 and Type-2 Fuzzy Controllers ..... 1454 76.3 Host Technology .................................. 1457 76.4 Hardware Implementation Approaches . 1458 76.4.1 Multiprocessor Systems .............. 1458 76.4.2 Implementations into FPGAs ...... 1459 76.5 Development of a Standalone IT2FC ...... 1461 76.5.1 Development of the IT2 FT2KM Design Entity............................. 1462 76.6 Developing of IT2FC Coprocessors .......... 1466 76.6.1 Integrating the IT2FC Through Internal Ports............................ 1466 76.6.2 Development of IP Cores ............ 1466 76.7 Implementing a GA in an FPGA ............. 1468 76.7.1 GA Software Based Implementations ...................... 1469 76.7.2 GA Hardware Implementations ... 1469 76.8 Evolving Fuzzy Controllers .................... 76.8.1 EAPR Flow for Changing the Controller Structure .............. 76.8.2 Flexible Coprocessor Prototype of an IT2FC ................................ 76.8.3 Conclusion and Further Reading .
1470 1471 1472 1474
References................................................... 1475 guage, using a multiprocessor system and a highlevel language, and combining both methods. We explain how to use the IT2FC developed in VHDL as a standalone system, and as a coprocessor for the FPGA Fusion of Actel, Spartan 6, and Virtex 5. We present the methodology and two new proposals to achieve evolution of the IT2FC for FPGA, one for the static region of the FPGA, and the other one for the reconfigurable region using the dynamic partial reconfiguration methodology.
Part G | 76
The interest in research and implementations of type-2 fuzzy controllers (T2FCs) is increasing. It has been demonstrated that these controllers provide more advantages in handling uncertainties than type-1 FCs (T1FCs). This characteristic is very appealing because real-world problems are full of inaccurate information from diverse sources. Nowadays, it is no problem to implement an intelligent controller (IC) for microcomputers since they offer powerful operating systems, high-level languages, microprocessors with several cores, and co-processing capacities on graphic processing units (GPUs), which are interesting characteristics for the implementation of fast type-2 ICs (T2ICs). However, the above benefits are not directly available for the design of embedded ICs for consumer electronics that need to be implemented in devices such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGAs), etc. Fortunately, for T1FCs there are platforms that generate code in VHSIC hardware description language (VHDL; VHSIC: very high speed integrated circuit), C++, and Java. This is not true for the design of T2ICs, since there are no specialized tools to develop the inference system as well as to optimize it. The aim of this chapter is to present different ways of achieving high-performance computing for evolving T1 and T2 ICs embedded into FPGAs. Therefore, we provide a compiled introduction to T1 and T2 FCs, with emphasis on the wellknown bottle neck of the interval T2FC (IT2FC), and software and hardware proposals to minimize its effect regarding computational cost. An overview of learning systems and hosting technology for their implementation is given. We explain different ways to achieve such implementations: at the circuit level using a hardware description lan-
