Non-linear dual-mode receding horizon control for multiple unmanned air vehicles formation flight based on chaotic particle swarm optimisation
Non-linear dual-mode receding horizon control for multiple unmanned air vehicles formation flight based on chaotic particle swarm optimisation
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DOI：
10.1049/iet-cta.2009.0256
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发表时间：
2010-11-01
影响因子：
2.6
通讯作者：
Liu, S. Q.
中科院分区：
文献类型：
--
作者：
Duan, H. B.;Liu, S. Q.
提出了一种非线性双模滚动时域控制（RHC）方法来研究复杂环境下多无人机的编队飞行问题。针对约束非线性系统，提出了一种基于混沌粒子群优化（PSO）的非线性双模RHC方法。提出的混沌粒子群算法既能推导编队模型，又能确定编队模型的参数值，并预测控制序列，同时又能保证全局收敛速度。为了提高多无人机编队飞行控制器的稳定性和可行性，采用了基于不变集理论的双模型控制策略，并采用了状态反馈控制。系列实验结果表明了该控制算法的可行性和有效性。该方法也是一种很有前途的控制策略，在解决其他复杂的现实世界的问题。
This study presents a non-linear dual-mode receding horizon control (RHC) approach to investigate the formation flight problem for multiple unmanned air vehicles (UAVs) under complicated environments. A chaotic particle swarm optimisation (PSO)-based non-linear dual-mode RHC method is proposed for solving the constrained non-linear systems. The presented chaotic PSO derives both formation model and its parameter values, and the control sequence is predicted in this way, which can also guarantee the global convergence speed. A dual-model control strategy is used to improve the stability and feasibility for multiple UAVs formation flight controller, and the state-feedback control is also adopted, where the model is based on the invariant set theory. Series experimental results show the feasibility and validity of the proposed control algorithm over other algorithms. The proposed approach is also a promising control strategy in solving other complicated real-world problems.