混合驱动水下机器人浮游与爬行双模式轨迹跟踪控制

doi:10.12382/bgxb.2023.0575

摘要/Abstract

摘要：

针对混合驱动水下机器人在进行水下浮游和爬行双模式作业时的轨迹跟踪控制问题,结合混合驱动水下机器人在浮游模式和爬行模式下的不同运动学特性,分别设计了一种基于改进灰狼优化模型预测控制算法的控制器,以实现机器人在浮游模式下的轨迹跟踪,以及一种基于模糊滑模控制算法的控制器,以实现机器人在爬行模式下的轨迹跟踪。仿真结果表明:所设计的改进灰狼优化模型预测控制器通过引入改进灰狼优化算法求解模型预测控制器滚动优化阶段中的目标函数,使得浮游模式的水下机器人能够快速、精确和稳定地跟踪预设轨迹;所设计的模糊滑模控制器采用模糊控制对滑模控制器中趋近律参数进行在线调整,使得爬行模式下的水下机器人能够实现有限时间收敛的同时并抑制抖振问题。

关键词: 水下机器人, 混合驱动, 轨迹跟踪控制, 模型预测控制, 灰狼优化, 滑模控制, 模糊控制

Abstract:

The trajectory tracking control of hybrid-driven unmanned underwater vehicles in underwater dual-mode operation is studied. In accordance with the different kinematics characteristics of hybrid-driven unmanned underwater vehicle in free-flying and crawling modes, a controller based on improved grey wolf optimization model predictive control algorithm is designed to realize the trajectory tracking of the vehicles in free-flying mode, and a controller based on fuzzy sliding mode control algorithm is designed to realize the trajectory tracking of the vehicles in crawling mode. The simulated results show that the designed improved grey wolf optimization model predictive controller solves the objective function in the rolling optimization stage of the model predictive controller by introducing the improved grey wolf optimization algorithm, enabling the unmanned underwater vehicles in free-flying mode to quickly, accurately and stably track the preset trajectory. The designed fuzzy sliding mode controller adopts fuzzy control to adjust the convergence law parameters online in the sliding mode controller, enabling the unmanned underwater vehicles in crawling mode to achieve finite time convergence while suppressing chattering.

Key words: unmanned underwater vehicle, hybrid-driven, trajectory tracking control, model predictive control, grey wolf optimization, sliding mode control, fuzzy control

中图分类号:

TP242
TP273

陈琦, 覃国样. 混合驱动水下机器人浮游与爬行双模式轨迹跟踪控制[J]. 兵工学报, 2024, 45(9): 3216-3229.

CHEN Qi, QIN Guoyang. Trajectory Tracking Control for Hybrid-driven Unmanned Underwater Vehicles with Free-flying and Crawling Dual-mode[J]. Acta Armamentarii, 2024, 45(9): 3216-3229.

图/表 14

图1 混合驱动水下机器人结构示意图

Fig.1 Structure diagram of hybrid-driven unmanned underwater vehicle

图2 浮游模式坐标系

Fig.2 Coordinate system of free-flying mode

图3 爬行模式坐标系

Fig.3 Coordinate system of crawling mode

图4 基于IGWO-MPC算法的控制器结构图

Fig.4 Structure diagram of controller based on IGWO-MPC algorithm

表1 模糊控制规则

Table 1 Fuzzy control rule

S	NB	NM	NS	ZE	PS	PM	PB
K	PB	PM	PS	ZE	NS	NM	NB
L	PB	PB	PS	PM	PS	PS	PS

图5 模糊控制器隶属度函数

Fig.5 Membership functions of fuzzy logic controller

图6 基于FSMC算法的控制器框图

Fig.6 Block diagram of controller based on FSMC algorithm

表2 标准测试函数

Table 2 Standard test function

函数	表达式	维度	取值范围
F₁	F(x)= $∑ i = 1 D x i 2$	30	[-100,100]
F₂	F(x)= $m a x i$ {\|x_i\|,1≤i≤D}	30	[-100,100]
F₃	F(x)= $∑ i = 1 D$ [ $x i 2$ -10cos (2πx_i)+10]	30	[-5.12,5.12]
F₄	$F(x)=-20 \exp \left(-0.2 \sqrt{\frac{1}{D} \sum_{i=1}^{D} x_{i}^{2}}\right)-\exp \left(\frac{1}{D} \sum_{i=1}^{D} \cos \left(2 \pi x_{i}\right)\right)+20+e$	30	[-32,32]

表2 标准测试函数

Table 2 Standard test function

函数	表达式	维度	取值范围
F₁	F(x)= $∑ i = 1 D x i 2$	30	[-100,100]
F₂	F(x)= $m a x i$ {\|x_i\|,1≤i≤D}	30	[-100,100]
F₃	F(x)= $∑ i = 1 D$ [ $x i 2$ -10cos (2πx_i)+10]	30	[-5.12,5.12]
F₄	$F(x)=-20 \exp \left(-0.2 \sqrt{\frac{1}{D} \sum_{i=1}^{D} x_{i}^{2}}\right)-\exp \left(\frac{1}{D} \sum_{i=1}^{D} \cos \left(2 \pi x_{i}\right)\right)+20+e$	30	[-32,32]

表3 算法优化对比结果

Table 3 Comparison of optimizated results of different algorithms

函数	GWO		EGWO1		EGWO2		IGWO
函数	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差
F₁	9.8207×10^-19	8.7469×10^-19	1.5871×10^-23	8.1979×10^-24	1.0664×10^-29	2.1005×10^-29	8.1052×10^-36	2.5631×10^-35
F₂	4.1946×10^-05	2.0654×10^-05	8.2416×10^-07	3.9997×10^-07	1.0228×10^-08	7.8674×10^-09	1.1079×10^-11	3.4305×10^-11
F₃	6.5767	5.9961	1.3252	2.8940	1.1637	2.6976	0	0
F₄	1.5696×10^-10	6.4676×10^-11	1.0671×10^-12	8.2545×10^-13	3.0376×10^-14	2.3979×10^-15	8.8800×10^-16	0

图7 测试函数寻优结果(左为参数空间,右为适应度空间)

Fig.7 Optimized results of test function (left: the parameter space, right: the fitness space)

图8 不同控制器螺旋线轨迹跟踪结果

Fig.8 Spiral trajectory tracking results for different controllers

表4 不同控制器轨迹跟踪结果

Table 4 Trajectory tracking results of different controllers

控制器	x平均误差/m	y平均误差/m	z平均误差/m	平均值/m	方差/ m
MPC	0.5497	0.6211	0.2169	0.4626	0.0466
GWO-MPC	0.3175	0.3960	0.1781	0.2972	0.0122
EGWO1-MPC	0.3306	0.4003	0.1810	0.2988	0.0122
EGWO2-MPC	0.3177	0.4240	0.1982	0.3133	0.0128
IGWO-MPC	0.3007	0.3280	0.1463	0.2583	0.0096

图9 圆轨迹跟踪结果

Fig.9 Results of circular trajectory tracking

表5 不同控制器轨迹跟踪效果

Table 5 Trajectory tracking results of different controllers

控制器	x_m稳态误差/m	y_m稳态误差/m	平均稳态误差/m	方差/m	x_m收敛时间/s	y_m收敛时间/s	θ_m收敛时间/s	平均收敛时间/s
SMC	0.0361	0.0042	0.0403	5.0881×10^-4	4.9	4.8	0	3.2
TSMC	2.3196×10^-15	1.5625×10^-16	1.9411×10^-16	2.3400×10^-30	1.7	1.5	1.8	1.7
BSMC	6.3116×10^-04	2.2597×10^-04	4.2857×10^-04	8.2089×10^-08	4.8	2.3	4.7	3.9
FSMC	4.1125×10^-19	4.2774×10^-19	4.1950×10^-19	1.3596×10^-40	0.8	0.9	1.1	0.9

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