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

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.