Một phương pháp điều khiển cho hệ phi tuyến sử dụng bộ điều khiển Sliding Mode kết hợp với mạng Neural RBF

Lê Thị Huyền Linh và Đtg Tạp chí KHOA HỌC & CÔNG NGHỆ 172(12/1): 49 - 53

49

A CONTROL METHOD FOR NONLINEAR SYSTEMS USING SLIDING MODE

CONTROL CONBINED WITH RBF NEURAL NETWORK

Le Thi Huyen Linh*

, Tran Thi Thanh Hai

University of Technology – TNU

SUMMARY

In industrial systems, the SISO system in particular and the systems in general are uncertain

nonlinear systems with the effects of external disturbance factors. The uncertainty of the system

and external disturbances are always changeable, which can not be measured, they would be a

major obstacle for linear control method. This paper proposes a method to evaluate the uncertainty

and disturbance in the system by using Radial Basis Function (RBF) neural network and builds

Sliding mode control algorithm for nonlinear systems to ensure sustainable stability against

disturbances. Obtained Sliding Mode Control algorithms and weights update rules for the

Network, ensuring exist and stability Sliding Mode system. Through illustrative examples Matlab

Simulink, simulation confirmed efficiency and ability of the proposed algorithm.

Key word: SISO system, sliding mode control, robust adaptive control, estimative algorithm for

disturbanc, RBF neural network.

INTRODUCTION *

Nowadays, most of industrial systems are the

uncertain nonlinear systems affected by the

external disturbances. The utilization of the

conventional controllers such as PID to

control this mentioned complex objects

normally does not guarantee the stability of

system, in fact, the quality requirements of

control keeps increasing dramatically.

Therefore, the construction of intelligent

control that ensures the high precision,

robustness with the real disturbances is

urgently needed. One of the most effective

approaching of control algorithm is the

sliding mode control (SMC) based on the

selection of sliding modes according to the

sliding functions S [1].

The sliding mode controller applied to the

current nonlinear systems is usually

associated with Neural network [2, 4]. The

selection of function of the sliding surface S,

the assurance of the sliding modes as well as

the reduction of shake phenomenon

“chattering” during the manipulation process

is always complex and difficult problem that

*

Tel: 0918 127781, Email: [email protected]

requires the careful consideration of the

designers [3]. The Neural network can be

used for the estimation of the effects of external

disturbances to the system and approximation of

uncertain components of the object thereby

compensating those impacts on the system by

compensating the control signals.

The following proposes the new control

method for nonlinear SISO system in which

applies the Neural RBF network to

approximate the uncertain components, then

updating the system control law with respect

to the adjustment of the uncertain parts based

on the sliding mode in order to ensure the

robust stability of the system.

THE SYNTHETIC OF SLIDING MODE

CONTROL BASED ON UNCERTAIN

COMPONENTS ESTIMATION BY THE

NEURAL RBF NETWORK FOR THE

NONLINEAR SISO SYSTEM

Constructing the sliding mode controller

for the nonlinear SISO system

Considering the second order nonlinear

system as following form:

        ( , ). ( , ) ( )

  

g u f d t (1)

where:

g f ( ), ( )  

: the uncertain function of the system