Summary: A Neural Network Based Adaptive Sliding Mode
Controller: Application to a Power System Stabilizer
Hussain N. Al-Duwaisha*
and Zakariya M. Al-Hamouza,1
Department of Electrical Engineering, King Fahd University of Petroleum & Minerals,
Dhahran 31261, Saudi Arabia
Corresponding Author- e-mail: email@example.com, Tel: +966 3 860 2747, Fax: +966 3 860 3535
e-mail: firstname.lastname@example.org, Tel:+966 3 860 2782, Fax: +966 3 860 3535
Abstract-- In this paper, a neural networks (NN) based adaptive sliding mode controller (SMC) is introduced. The
selection of SMC feedback gains is normally based on one operating point and thus the performance of the
controller away from the design operating point is, of necessity, a compromise. The Adaptive SMC is proposed to
overcome the limitations imposed on the effectiveness of the SMC under different operating conditions. Neural
networks are used for on-line prediction of the optimal SMC gains when the operating point changes. The
proposed method has been applied to a power system stabilizer (PSS) of a single machine power system.
Simulation results are included to demonstrate the performance of the proposed control scheme.
Key Words: Neural networks, sliding mode control, power system stabilizers, Genetic algorithms.