A Highly Accurate Model-Free Motion Control System with a Mamdani Fuzzy Feedback Controller combined with a TSK Fuzzy Feed-forward Controller

Article

Abstract

In this paper, a new intelligent robot motion control architecture – a highly accurate model-free fuzzy motion control- is proposed in order to achieve improved robot motion accuracy and dynamic performance. Its architecture combines a Mamdani fuzzy proportional (P) and a conventional integral (I) plus derivative (D) controller for the feedback part of the system, and a Takagi-Sugeno-Kang fuzzy controller for the feed-forward, nonlinear part. The fuzzy P + ID controller improves the performance of the nonlinear system, and the TSK fuzzy controller uses a TSK fuzzy inference system based on extended subtractive- clustering method which integrates information on joint angular displacement, velocity and acceleration for torque identification. The advantage of this kind of model-free control is that it uses the information directly from the input/output of the nonlinear system, without any complex robot model computation, in order to decrease the control system’s sensitivity to any dynamical uncertainty. Furthermore, parametric search for clustering parameters in extended subtractive clustering secures the high accuracy of the system identification. Consequently, this proposed model-free fuzzy motion control benefits from the advantages of two kinds of fuzzy system. It not only incorporates flexible design, good performance and simple conception but also ensures precise motion control and great robustness. Comparisons with other intelligent models and results from numerical studies on a 4-bar planar parallel mechanism show the effectiveness and competitiveness of the proposed control.

Keywords

Fuzzy systems Nonlinear dynamics Motion control Fuzzy logic control Model-free control Intelligent control 

Nomenclature

AI

artificial intelligence

AMFFMC

accurate model-free fuzzy motion control

ANFIS

adaptive network-based fuzzy inference system

FIS

fuzzy inference system

FL

fuzzy logic

FLC

fuzzy logic control

FLS

fuzzy logic system

fuzzy P + ID

fuzzy logic proportional plus conventional integral and derivative

MF

membership function

MF-PID-FLC

model-free PID fuzzy feed forward control

MISO

multi-input–single-output

NN

neural network

PID

proportional, integral, derivative

RMSE

root-mean-square-error

TSK

Takagi-Sugeno-Kang

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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Department of Automatic Manufacturing Engineering, École de Technologie SupérieureUniversity of QuebecMontrealCanada

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