Quantum genetic sliding mode controller design for depth control of an underwater vehicle

Zahra Tavanaei-Sereshki,Mohammad Reza Ramezani-Al

doi:10.1177/0020294018789199

Abstract

Quantum genetic algorithm (QGA) is an optimization algorithm based on the probability that combines the idea of quantum computing and traditional genetic algorithm. In this paper, a new type of control law is developed for an underwater vehicle along with the desired path. The proposed controller is based on sliding mode control (SMC) in which the reaching law is modified to overcome two challenging problems, chattering, and sensitivity against noise. The disturbance is considered as a set of sinus waves with different frequencies which its parameters are estimated by Particle Swarm Optimization (PSO). Since QGA has some advantages such as fast convergence speed, small population size, and strong global search capabilities, we use QGA to determine the gain of the proposed controller. Finally, the Lyapunov theorem is used to prove that trajectory-tracking error converges to zero. Simulation results show that QGA can converge to the optimal response with a population consist of one chromosome.

Highlights

A lot of control methods are used in the autonomous underwater vehicles (AUVs) such as fuzzy and neural network methods, nonlinear control, adaptive control
Fuzzy control method in AUVs is studied in Smith et al.,1 Raeisy et al.,2 Shi et al.,3 Huajun et al.,4 and intelligent methods are surveyed in Venugopal et al.,5 Kodogiannis et al.,6 and Van De Ven et al
A quantum genetic sliding mode control (SMC) was developed for trajectory tracking of an AUV

Summary

Introduction

A lot of control methods are used in the autonomous underwater vehicles (AUVs) such as fuzzy and neural network methods, nonlinear control, adaptive control. Keywords Sliding mode control, underwater vehicle, particle swarm optimization, quantum genetic algorithm This paper is based on our previous work21 in which we applied a sliding mode control (SMC) with adaptive gains such that some of its parameters should be specified by the designer with trial and error.

Results

Conclusion