Adaptive approximation control of nonlinear system based on fuzzy logic with approximation error compensation

2009 ◽  
Author(s):  
Sun Lim
Keyword(s):  
Fuzzy Logic ◽  
Nonlinear System ◽  
Error Compensation ◽  
Approximation Error ◽  
Adaptive Approximation ◽  
Adaptive Approximation Control

scholarly journals New Tuning Approach of Fuzzy Logic System Using Proportional Integral Observer for Tracking a Nonlinear System

2021 ◽  
Author(s):  
Mustefa Jibril
Keyword(s):  
Fuzzy Logic ◽  
Nonlinear System ◽  
The State ◽  
State Variables ◽  
Hydraulic Actuator ◽  
Nonlinear Controller ◽  
Nonlinear Method ◽  
Proportional Integral ◽  
Road Disturbance ◽  
System Output

Proportional integral observer (PIO) for tracking a nonlinear method has a lower sentiency to cipher the state and output variables. So a more nonlinear controller has to be else to control to activity. In this paper, a fuzzy logic (FLC) controller has been added to the PIO to meliorate the calculation transmute. A fuzzy proportional integral observer (FPIO) for following a nonlinear system has been premeditated to decimate the susceptibleness to cipher the tell and turnout variables with the existent posit and product variables. The FPIO controller has been tested for improving the estimation control using a nonlinear quarter vehicle active suspension system with a nonlinear hydraulic actuator. A comparison simulation of the proposed nonlinear system for estimating the state variables and tracking the output (suspension deflection) with a set point bump road disturbance using FPIO and PIO. The comparison simulation result shows that the estimated state variables and system output match the actual ones perfectly using a fuzzy PIO controller.

Towards Fine-Grained Online Adaptive Approximation Control for Dense SLAM on Embedded GPUs

2022 ◽  
Vol 27 (2) ◽  
pp. 1-19
Author(s):  
Tiancong Bu ◽  
Kaige Yan ◽  
Jingweijia Tan
Keyword(s):  
Energy Saving ◽  
Real Time ◽  
High Accuracy ◽  
Important Application ◽  
The Other ◽  
Approximation Techniques ◽  
Fine Grained ◽  
Critical Data ◽  
Adaptive Approximation ◽  
Adaptive Approximation Control

Dense SLAM is an important application on an embedded environment. However, embedded platforms usually fail to provide enough computation resources for high-accuracy real-time dense SLAM, even with high-parallelism architecture such as GPUs. To tackle this problem, one solution is to design proper approximation techniques for dense SLAM on embedded GPUs. In this work, we propose two novel approximation techniques, critical data identification and redundant branch elimination. We also analyze the error characteristics of the other two techniques—loop skipping and thread approximation. Then, we propose SLaPP, an online adaptive approximation controller, which aims to control the error to be under an acceptable threshold. The evaluation shows SLaPP can achieve 2.0× performance speedup and 30% energy saving on average compared to the case without approximation.

Fuzzy Logic and Cell to Cell Mapping

1996 ◽  
Author(s):  
D. Edwards ◽  
H. T. Choi ◽  
J. Canning
Keyword(s):  
Fuzzy Logic ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Chaotic System ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Cell Mapping ◽  
Generalized Cell Mapping ◽  
Simple Cell Mapping

Abstract Nonlinear systems are important in many fields of science, mathematics, and engineering. In recent years, simple cell mapping (SCM) and generalized cell mapping (GCM) methods have been proposed and successfully used to analyze nonlinear systems. The GCM method requires the determination of a transition probability matrix. In a manner similar to GCM, we use fuzzy logic to calculate a transition possibility matrix for a nonlinear system. This matrix can then be used to establish the statistical properties of strange attractors associated with a chaotic system. We analyze a chaotic system using fuzzy logic to demonstrate this approach and then compare our result with the GCM method.

Comparison of various fuzzy sliding mode based controller on single link inverted pendulum

2021 ◽  
pp. 1-10 ◽  
Author(s):  
Ajit Kumar Sharma ◽  
Bharat Bhushan
Keyword(s):  
Fuzzy Logic ◽  
Nonlinear System ◽  
Sliding Mode Control ◽  
Inverted Pendulum ◽  
Sliding Mode ◽  
Control Technique ◽  
Mode Control ◽  
Linguistic Rules ◽  
Single Link ◽  
Fuzzy Sliding Mode

The present work represents the implementation of the various fuzzy controller with robust sliding mode control (SMC) technique on a nonlinear system considering various external disturbances and model uncertainties. The nonlinear system considered here is a single link inverted pendulum. The proposed work combines the advantages of the sliding mode controlling technique and fuzzy logic controller. A set of linguistic rules are designed in fuzzy logic control, which causes the system to be chattering free. Parameters of the nonlinear system are adjusted according to fuzzy adaptive laws, while the uncertainties of the nonlinear system have been approximated using a fuzzy system. Various types of controller based on fuzzy sliding mode, like approximation based sliding mode control technique; equivalent control based fuzzy sliding mode technique, and switch-gain regulation based sliding mode control methods have been implemented here. A comparative analysis of various methods is also have been discussed.

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