Research Papers

Position Control of Servomotors Using Neural Dynamic Sliding Mode

[+] Author and Article Information
A. Karami-Mollaee1

Student Electrical Engineering Department, Faculty of Engineering,  Ferdowsi University of Mashhad, Iran e-mail: akarami@wali.um.ac.ir

N. Pariz

Electrical Engineering Department,  Ferdowsi University of Mashhad, Iran e-mail: n-pariz@um.ac.ir

H. M. Shanechi

Electrical and Computer Engineering Department,  Illinois Institute of Technology, Chicago, IL e-mail: shanechi@iit.edu


Corresponding author.

J. Dyn. Sys., Meas., Control 133(6), 061014 (Nov 11, 2011) (10 pages) doi:10.1115/1.4004782 History: Received December 09, 2009; Revised March 13, 2011; Published November 11, 2011; Online November 11, 2011

In this paper, position control of servomotors is addressed. A radial basis function neural network is employed to identify the unknown nonlinear function of the plant model, and then a robust adaptive law is developed to train the parameters of the neural network, which does not require any preliminary off-line weight learning. Moreover, base on the identified model, we propose a new dynamic sliding mode control (DSMC) for a general class of nonaffine nonlinear systems by defining a new adaptive proportional-integral sliding surface and employing a linear state feedback. The main property of proposed controller is that it does not need an upper bound for the uncertainty and identified model; moreover, the switching gain increases and decreases according to the system circumstance by employing an adaptive procedure. Then, chattering is removed completely by using the DSMC with a small switching gain.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 4

(a) Sliding surface; (b) switching gain; (c) input control signal of state feedback; (d) input control signal of system

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Figure 3

Reference signal tracking of augmented system: (a) first state; (b) second state; (c) third state; (d) input control signal of reference system

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Figure 2

(a) f and its estimation f∧ (output of RBFNN); (b) error between the f∧ and its actual value f; (c) the norm of the weight vector; (d) the outputs of the each neuron in RBFNN

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Figure 1

Functions ξi(t)=exp⁡(−(t2−(5−i))/5):i=1,2,…,9



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