基于双参数自适应优化的无人履带车辆轨迹跟踪控制

doi:10.12382/bgxb.2022.0009

摘要/Abstract

摘要：

为解决定参数轨迹跟踪控制器工况适应性差的问题,基于改进粒子群优化(IPSO)、多层感知机(MLP)算法,设计一种双参数自适应优化的无人履带车辆轨迹跟踪控制算法。离线状态下,基于采集的实车数据,以轨迹跟踪的高精度、高稳定性、低时间成本为目标,利用IPSO算法构建了不同运动基元下的最优参数组合数据集,并以运动基元类型和车速等为特征向量,控制时域长度、时间步长为标签,采用学习率自适应优化算法完成MLP神经网络模型的训练。在线状态下,根据规划层下发的轨迹信息和车辆状态反馈信息,由MLP神经网络输出预测的最优控制时域长度和控制时间步长,作为双参数输入到模型预测控制算法中,完成自适应轨迹跟踪控制。基于ROS-VREP的联合仿真和基于双侧独立电驱动履带平台进行实车试验。研究结果表明,在包含大曲率转向的综合工况下,与相同计算时间成本的定参数轨迹跟踪控制算法相比,所设计的轨迹跟踪控制器横向偏差均值、航向偏差均值以及转角变化率均值分别降低了30.5%、17.2%、7.8%,证明了算法的可行性和有效性。

关键词: 履带车辆, 轨迹跟踪控制, 改进粒子群优化算法

Abstract:

To improve the poor adaptability of trajectory tracking controllers with fixed parameters, an optimized adaptive dual-parameter trajectory tracking algorithm for unmanned tracked vehicles based on the improved Particle Swarm Optimization (IPSO) and Multi-Layer Perceptron (MLP) algorithms is proposed. In the offline state, based on the collected actual vehicle data, the IPSO algorithm is used to construct the optimal parameter data set under different motion primitives, aiming for high accuracy, high stability, and low time cost of trajectory tracking. With the motion primitive type and vehicle speed as feature vectors, control time domain length and control time step length as labels, adaptive learning rate optimization algorithm is used to complete the training of the MLP neural network model. In the online state, according to the trajectory information and vehicle state feedback information provided by the planning layer, the MLP neural network outputs the predicted optimal control time domain length and control time step. These parameters are then input to the model predictive controller as dual parameters, enabling the adaptive trajectory tracking control. ROS-VREP co-simulation test and actual vehicle test based on a bilateral electric drive platform are carried out. Vehicle test results show that under various working conditions including large curvature steering, the proposed controller achieves a 30.5% reduction in average lateral error, a 17.2% decrease in average heading error, and a 7.8% reduction in average change rate of rotation angle, compared with the fixed-parameter trajectory tracking control method with the same calculation time cost. The results verify the feasibility and effectiveness of the new algorithm.

Key words: tracked vehicle, trajectory tracking control, improved PSO algorithm

卢佳兴, 刘海鸥, 关海杰, 李德润, 陈慧岩, 刘龙龙. 基于双参数自适应优化的无人履带车辆轨迹跟踪控制[J]. 兵工学报, 2023, 44(4): 960-971.

LU Jiaxing, LIU Haiou, GUAN Haijie, LI Derun, CHEN Huiyan, LIU Longlong. Trajectory Tracking Control of Unmanned Tracked Vehicles Based on Adaptive Dual-Parameter Optimization[J]. Acta Armamentarii, 2023, 44(4): 960-971.

图/表 16

图1 总体框架

Fig.1 Overall framework

图2 履带车辆运动学模型

Fig.2 Kinematics model of a tracked vehicle

图3 履带车辆动力学模型

Fig.3 Dynamic model of a tracked vehicle

图4 电机外特性

Fig.4 External characteristics of the motor

图5 改进PSO-MLP算法伪代码

Fig.5 Pseudo codes of the improved PSO-MLP algorithm

图6 参考路径图

Fig.6 Diagram of the reference path

图7 MLP算法原理图

Fig.7 Schematic diagram of the MLP algorithm

图8 学习率自适应优化算法流程伪代码表

Fig.8 Adaptive learning rate optimization algorithm flow pseudo codes

图9 MLP神经网络预测效果

Fig.9 Prediction of the MLP neural network results

表1 各平台试验参数表

Table 1 Parameters of each platform

验证方式	参数	数值
	质量/kg	2000
仿真	车长×车宽×车高/mm	2616×1500×1260
	履带中心距/mm	1298
	主动轮半径/mm	165
	质量/kg	9360
	车长×车宽×车高/mm	5476×2978×1800
	履带中心距/mm	2464
	履带接地段长度/mm	3095
实车	主动轮半径/mm	265.4
	侧减速器传动比	5.5
	1挡/2挡传动比	2.708/1.0
	重心高度/mm	850
	质量增加系数	1.446
	原地转向阻力系数	0.574

图10 ROS-VREP联合仿真环境

Fig.10 ROS-VREP co-simulation environment

图11 仿真实验轨迹跟踪效果

Fig.11 Simulated trajectory tracking performance

表2 仿真实验轨迹跟踪效果统计表

Table 2 Statistics of simulated tracking performance

试验组别	$l ¯ e$ /m	$h ¯ e$ /(°)	\|Δ $θ ¯$ \|/((°)·s^-1)	$T ¯ c o s t$ /ms
对照组1	0.147	1.202	0.138	26.490
对照组2	0.112	1.008	0.257	21.940
对照组3	0.072	0.694	0.602	17.111
本文算法	0.075	0.824	0.262	20.297

表2 仿真实验轨迹跟踪效果统计表

Table 2 Statistics of simulated tracking performance

试验组别	$l ¯ e$ /m	$h ¯ e$ /(°)	\|Δ $θ ¯$ \|/((°)·s^-1)	$T ¯ c o s t$ /ms
对照组1	0.147	1.202	0.138	26.490
对照组2	0.112	1.008	0.257	21.940
对照组3	0.072	0.694	0.602	17.111
本文算法	0.075	0.824	0.262	20.297

图12 无人驾驶履带车辆试验平台

Fig.12 Experimental platform of the unmanned tracked vehicle

图13 履带车辆试验轨迹跟踪效果

Fig.13 Track performance for the unmanned tracked vehicle

表3 实车试验轨迹跟踪效果统计表

Table 3 Statistics of the tracking performance in real vehicle experiments

试验组别	$l ¯ e$ /m	$h ¯ e$ /(°)	\|Δ $θ ¯$ \|/((°)·s^-1)	$T ¯ c o s t$ /ms
对照组1	0.419	3.746	0.801	19.302
对照组2	0.394	3.828	0.991	19.113
对照组3	0.188	2.321	1.075	18.203
本文算法	0.274	3.171	0.914	18.789

表3 实车试验轨迹跟踪效果统计表

Table 3 Statistics of the tracking performance in real vehicle experiments

试验组别	$l ¯ e$ /m	$h ¯ e$ /(°)	\|Δ $θ ¯$ \|/((°)·s^-1)	$T ¯ c o s t$ /ms
对照组1	0.419	3.746	0.801	19.302
对照组2	0.394	3.828	0.991	19.113
对照组3	0.188	2.321	1.075	18.203
本文算法	0.274	3.171	0.914	18.789

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