摘要
提出了一种基于免疫粒子群优化的CMAC反演超机动控制律设计方法。推导并建立了具有大迎角特性的飞机六自由度非线性模型并与发动机模型进行综合。对飞机非线性模型进行重新构建,利用反演方法,通过逐步迭代设计Lypaunov函数并采用CMAC神经网络逼近系统的不确定性形成虚拟的控制输入量,在实现超机动非线性飞行控制律设计的同时对模型不确定性进行自适应补偿。为了提高控制性能与设计效率,将免疫调节机制、粒子群算法与反演控制结合,对控制器参数进行优化设计,形成了基于免疫粒子群优化的反演超机动飞行控制律设计方法。超机动数字仿真结果表明:所设计的超机动飞行控制律能够实现大迎角控制,并具有优异的大迎角机动控制性能。
A discussion is devoted to designing the super maneuver flight control law using backstepping method with immune particle swarm optimization. For backstepping control law design, nonlinear airplane and thrust vector engine model are integrated and reconfigurated into a new form. The cerebellar model articulation controller （ CMAC） neural network is used to approximate the model uncertainty and disturbance. In order to improve the control performance, the immune particle swarm optimization algorithm is used to select automatically the control parameters. This method is verified with the super maneuver flight simulation;the control performance and efficiency are much improved.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2015年第3期500-505,共6页
Journal of Northwestern Polytechnical University
基金
国家自然科学基金(61374032)
航空科学基金(20130753005)资助
关键词
免疫粒子群
CMAC网络
反演控制
超机动
immune particle swarm optimization
. cerebellar model articulation controller （CMAC）
backsteppingcontrol
super maneuver
angle of attack
angular velocity
backstepping
computer simulation
con-trol
controllers
degrees of freedom
mechanics
design
efficiency
iterative methods, low pass ill-ters
Lyapunov functions
ticle swarm optimization （mathematical models
matrix method
neutral networks
optimization
par-PSO）
probability
vectors
