Helicopter attitude active disturbance rejection control based on ant colony algorithm
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摘要:
为减弱外界环境扰动对直升机飞行姿态影响，设计了直升机姿态自抗扰控制器。建立直升机线性动力学模型；构造虚拟控制量实现直升机通道间的操纵解耦, 进一步构建了基于误差后处理的状态观测器，以消除控制量的抖振，避免自抗扰控制器存在的小信号易发生抖振的局限性；在解耦通道上蚁群算法优化内外回路自抗扰控制器参数整定，实现直升机飞行姿态角及角速率的稳定控制。仿真结果表明：引入虚拟控制量实现了高阶耦合系统解耦，便于实现单通道控制；构建基于误差后处理观测器有效消除因带宽过大引起控制量的抖振，提高了控制器跟踪性能和抗扰性能；蚁群算法用于整定自抗扰控制器参数，简化了控制器参数选取难度，提高了系统的响应速率和稳定性。
Abstract:An active disturbance rejection control for helicopter attitude was created to lessen the impact of external environment disturbance on helicopter flight attitude. The helicopter linear dynamic model was established. The active disturbance rejection control’s small signal was prone to buffeting, so the state observer based on error post-processing was built to further eliminate buffeting of the control quantity, and the virtual control quantity was established to realize the control decoupling between the helicopter channels. To achieve stable management of the helicopter’s flight attitude and angular rate on the decoupling channel, the ant colony algorithm was used for optimizing the parameter setting of the active disturbance rejection control in the inner and outer loops. Simulation results demonstrated that the introduction of virtual control variables enabled decoupling of high-order coupled systems, facilitating the implementation of single-channel control. Constructing an error post-processing observer effectively mitigated the control variable buffeting caused by excessive bandwidth, thereby enhancing controller tracking performance and disturbance rejection capability. The ant colony algorithm was employed for tuning the parameters of the anti-disturbance controller, simplifying the selection difficulty of controller parameters and improving both the system’s response rate and stability.
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Key words:
- flight control /
- helicopter /
- active disturbance rejection control /
- ant colony algorithm /
- virtual control /
- buffeting
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表 1 直升机的主要参数
Table 1. Main parameters of helicopter
参数 数值 总质量/kg 7258 旋翼直径/m 16.4 旋翼转速/（rad/s） 27 尾桨直径/m 3.35 尾桨转速/（rad/s） 124.62 惯性矩$ {I_x} $/（kg·m2） 50150 惯性矩$ {I_y} $/（kg·m2） 414553 惯性矩$ {I_{\textit{z}}} $/（kg·m2） 396081 惯性积$ {I_{x{\textit{z}}}} $/（kg·m2） 20258 
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表 2 蚁群算法整定参数及迭代结果
Table 2. ACO tuning parameters and iterative results
蚁群参数 滚转通道 俯仰通道 偏航通道 蚁群数量 10 10 10 信息素挥发系数 0.2 0.2 0.2 迭代次数 28 30 35 目标函数值 0.3452 0.3458 0.3527
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表 3 蚁群算法整定控制器参数结果
Table 3. ACO tuning controller parameter results
控制器参数 $ p （\phi ） $通道 $ q （\theta ） $通道 $ r （\psi ） $通道 $ r $ 20（6） 20（6） 20（6） $ h $ 1（1） 1（1） 1（1） $ {b_0} $ 1.5（1.5） 1.8（1.3） 2（1） $ {\omega _0} $ 121（101） 132（122） 107（96） $ {\omega _{\text{c}}} $ 119（111） 142（113） 163（113） $ \lambda $ 690（320） 860（570） 610（960）
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表 4 直升机姿态PID控制器参数
Table 4. Helicopter attitude PID controller parameters
控制器参数 $ p （\phi ） $通道 $ q （\theta ） $通道 $ r （\psi ） $通道 比例系数P 8（2） 10（2.2） 2.5（2） 积分系数I 5（0.1） 2（0.2） 1（0） 微分系数D 0（0.05） 0（0.02） 0（0.05）
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表 5 直升机姿态LADRC控制器参数
Table 5. Helicopter attitude LADRC controller parameters
控制器参数 $ p （\phi ） $通道 $ q （\theta ） $通道 $ r （\psi ） $通道 $ r $ 8（3） 8（3） 8（3） $ h $ 1（1） 1（1） 1（1） $ {b_0} $ 1（10） 1（20） 1（20） $ {\omega _0} $ 80（20） 58（20） 58（20） $ {\omega _c} $ 60（12） 20（5） 30（5）
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表 6 滚转通道角速率回路控制器参数
Table 6. Roll channel angle rate loop controller parameters
控制器参数 数值 控制器参数 数值 $ r $ 20 $ {\omega _0} $ 100 $ h $ 1 $ {\omega _{\text{c}}} $ 100 $ {b_0} $ 5 $ \lambda $ 500
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