水下机器人参数辨识的量子粒子群算法

Parameter Identification of Underwater Vehicles Based on the Quantum-behaved Particle Swarm Optimization Algorithm

水下机器人动力学模型参数辨识是水下机器人运动状态控制、路径跟踪、状态监测、故障诊断及容错系统开发的基础,是水下机器人研究的核心内容之一。针对Falcon开架缆控水下机器人的动力学模型,将量子粒子群优化算法引入到水下机器人动力学模型参数辨识之中,提出基于量子粒子群优化算法(Quantum-behaved PSO,QPSO)的水下机器人动力学模型参数辨识,并将其辨识结果与粒子群优化算法(Particle Swarm Optimization,PSO)及遗传算法(GA)的辨识结果进行比较。仿真结果表明应用QPSO算法的参数辨识结果明显优于其它对比方法,说明了算法的有效性与合理性。

Dynamic model parameter identification of underwater vehicles is the basis of motion control, path tracking, condition monitoring, fault diagnosis and fault tolerant system development of underwater vehicles, and one of the core contents of underwater vehicles. The dynamic model of the underwater vehicle is established and simplified appropriately based on the open-frame remotely operated vehicle. The quantum-behaved particle swarm optimization （QPSO） is applied to the dynamic parameter identification of the simplified Falcon underwater vehicle model and the identification result is compared with the identification result based on the particle swarm optimization （PSO） algorithm and genetic algorithm （GA）. The simulation results show that the QPSO algorithm is superior to other methods and illustrate the effectiveness and rationality of the proposed method.