知识与财富的链接
舰载无人机着舰会受到舰尾流、航母甲板运动的干扰。为加快无人机在着舰时横侧向响应以及提高舰载机着舰对干扰的鲁棒性,本文提出了一种基于扩张鸽群优化算法的显式模型预测控制方法,并将其应用于舰载机姿态控制器设计,用于解决所设计控制器的参数优化问题。与基本鸽群优化算法、粒子群算法的仿真对比实验表明,相比传统智能优化算法,本文所提出的扩张鸽群优化算法收敛更快,普适性和稳定性也更强,采用显式模型预测控制的舰载无人机着舰系统相比比例-积分-微分控制下的系统响应更快,鲁棒性更强。
Carrier landing of unmanned aerial vehicles (UAV) can be disturbed by carrier air wake and deck motion. To improve the response speed and disturbance rejection ability of UAV carrier landing, this paper proposes an explicit model predictive control method based on expanded pigeon inspired optimization (EPIO), and apply it in the design of carrier attitude controler to solve parameter optimization problem of the designed controler. Simulations and comparative experiments are conducted on the proposed basic pigeon inspired optimization algorithm and the particle swarm optimization algorithm, which show quicker rate of convergence, stronger universality and stability of EPIO. The designed control method is verified to be faster and more robust after compared with the proportional-integral-derivative control method.
