基于观测器的四旋翼控制-抗扰-避障一体化

doi:10.13229/j.cnki.jdxbgxb20220592

摘要/Abstract

摘要：

针对四旋翼飞行器“规划-跟踪”避障方法中存在执行器跟踪误差的问题，采用控制-抗扰-避障一体化的设计方案，提出了一种带障碍约束的避障位置控制器。将Barrier李亚普诺夫函数用于约束障碍物边界，通过引入飞行器与目标位置的距离信息使目标点成为平衡点，从而解决传统势场方法中目标不可到达的问题。对于四旋翼控制系统状态强耦合、模型建立不精确的问题，提出了一种基于观测器的模型补偿控制策略并应用于姿态控制。采用补偿函数观测器估计模型偏差及外界扰动，并实时将估计值反馈补偿给控制器以达到自适应抗扰的控制跟踪效果。最后，对上述算法仿真验证，结果表明，基于观测器的模型补偿控制相较于其他控制算法在暂态性能、跟踪期望响应和抗干扰方面有更优的控制效果；避障位置控制器无需考虑跟踪误差问题，在仿真时间角度上，一体化避障方法较传统的“规划-跟踪”避障方法大幅度缩短，通过给定起始、目标位置后可以实现对静态障碍的躲避。

关键词: 控制理论与控制工程, 四旋翼飞行器, 控制-抗扰-避障一体化, Barrier李亚普诺夫势函数, 模型补偿控制, 补偿函数观测器, 抗干扰

Abstract:

Aiming at the problem of actuator tracking error in the commonly used “planning-tracking”obstacle avoidance method for quadrotors， an obstacle avoidance position controller with obstacle constraints was proposed by adopting an integrated design scheme of control-anti-disturbance-obstacle avoidance. The Barrier Lyapunov function was used to constrain obstacle boundary. The target point becomes the balance point by introducing distance information between the aircraft and the target position， through which the problem of the unreachable target in traditional potential field methods was solved. For the problems of strong state coupling and inaccurate model building in the quadrotor control system， an observer-based model compensation control strategy is proposed and applied to the attitude control. The compensation function observer is used to estimate the model-bias and external disturbance， and then the estimated value is fed back compensated to the controller in real time to achieve the adaptive anti-disturbance control effect. Finally， the above algorithm is simulated and verified， and the results show that the observer-based model compensation control has better control effects than other control algorithms in transient performance， tracking expected response and anti-disturbance. The obstacle avoidance position controller does not need to consider the problem of tracking error. In terms of time in simulation， the integrated obstacle avoidance method is greatly shortened compared with traditional "planning-tracking" obstacle avoidance method， and static obstacle avoidance can be achieved by giving the starting and target positions.

Key words: control theory and control engineering, quadrotor unmanned aerial vehicle(UAV), control-anti-disturbance-obstacle avoidance integration, Barrier Lyapunov potential function, model compensation control （MCC）, compensation function observer （CFO）, anti-disturbance

中图分类号:

V249.12

齐国元,陈浩. 基于观测器的四旋翼控制-抗扰-避障一体化[J]. 吉林大学学报(工学版), 2023, 53(3): 810-822.

Guo-yuan QI,Hao CHEN. Observer⁃based control⁃anti⁃disturbance⁃obstacle avoidance of quadrotor unmanned aerial vehicle[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 810-822.

图/表 18

图1

图2

图3

图4

图5

图6

图7

图8

表1

姿态控制仿真3种控制算法参数"

控制器参数		滚转角	俯仰角	偏航角
PID	$k P$	0.40	0.40	0.44
	$k I$	0.001	0.001	0.001
	$k D$	0.07	0.07	0.1
ESO-MCC	$h$	15	15	15
	$b$	46	46	27
	$K$	［ $168$ ］	［ $168$ ］	［ $128$ ］
	$a e$	12	12	12
CFO-MCC	$h$	15	15	15
	$b$	46	46	27
	$K$	［ $168$ ］	［ $168$ ］	［ $128$ ］
	$a c$	5	5	5

表1

图9

图10

图11

图12

表2

位置通道控制参数"

参数	$z$	$x$	$y$
$a$	5	4	4
$b$	-0.714	-10	-10
$h$	15	—	—
$K$	［ $96$ ］	—	—

表2

表3

障碍物信息"

参数	障碍物1	障碍物2	障碍物3
位置	（1.5，1）	（2.5，2.5）	（4.1，3.3）
半径/m	0.4	0.5	0.5
膨胀距离/m	0.1	0.1	0.1
$k w$	0.1	0.2	0.2
$σ$	0.5	0.5	0.5

表3

表4

位置通道控制其他参数"

参数		参数
$P x$	0.6	$P 3$	0.1
$P y$	0.6	$P 4$	0.1
$P 1$	0.4	$k x$	6
$P 2$	0.4	$k y$	6

表4

表5

图13

参考文献 26

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到达时间	传统方法（APF）		一体化方案
到达时间	规划	跟踪	ESO-MCC	CFO-MCC
测试时间	19.06	5.38	10.75	10.90
	18.98	5.41	11.08	11.01
	19.15	5.39	11.00	11.05
	19.11	5.32	10.75	11.00
	18.92	5.25	10.83	10.88
综合时间	24.394		10.882	10.968

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