The Application of PSO‐AFSA Method in Parameter Optimization for Underactuated Autonomous Underwater Vehicle Control

Abstract

In consideration of the difficulty in determining the parameters of underactuated autonomous underwater vehicles in multidegree-of-freedom motion control, a hybrid method that combines particle swarm optimization (PSO) with artificial fish school algorithm (AFSA) is proposed in this paper. The optimization process of the PSO-AFSA method is firstly introduced. With the control simulation models in the horizontal plane and vertical plane, the PSO-AFSA method is elaborated when applied in control parameter optimization for an underactuated autonomous underwater vehicle. Both simulation tests and field trials were carried out to prove the efficiency of the PSO-AFSA method in underactuated autonomous underwater vehicle control parameter optimization. The optimized control parameters showed admirable control quality by enabling the underactuated autonomous underwater vehicle to reach the desired states with fast convergence.

Greedy Mechanism Based Particle Swarm Optimization for Path Planning Problem of an Unmanned Surface Vehicle

“…The complete route was finally rebuilt by the max-min merging algorithm [27]. Admirable results were also achieved by the trials of combining PSO with the genetic algorithm and artificial fish swarm method, respectively [28,29].…”

Two-Stage Switching Hybrid Control Method Based on Improved PSO for Planar Three-Link Under-Actuated Manipulator

“…In this section, the particle swarm optimization algorithm is selected to solve the inverse kinematics. In order to reduce the probability of falling into local optimum, we introduce the Metropolis criterion of simulated annealing algorithm [24] and crowding factor of artificial fish swarm algorithm [25] into PSO algorithm to calculate the target angles of two active joints. The proposed algorithm reduces the errors caused by the inaccurate joint angles and provides the optimal target angles for the position control.…”

“…In this section, we analyze the stability of the designed control laws (17) and (25). In first stage, under the premise that the control law (12) is designed to maintain the angle of the third joint unchanged, the PAA under-actuated manipulator is equivalent to a PA under-actuated manipulator.…”

Multi-Subdomain Grouping-Based Particle Swarm Optimization for the Traveling Salesman Problem

“…This method attained a success rate of approximately 100% for large TSPs in a crisp environment [1]. To optimize the parameters for underactuated autonomous underwater vehicle control, the PSO and artificial fish school algorithm were executed simultaneously, providing an admirable control effect in both simulation tests and field trials [20]. Cabrera et al combined the SA with PSO, using the SA to enhance the solution diversity [21].…”