Trajectory Tracking Control of Unmanned Tracked Vehicles Based on Adaptive Dual-Parameter Optimization

doi:10.12382/bgxb.2022.0009

Abstract

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

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.

Figures/Tables 16

Fig.1 Overall framework

Fig.2 Kinematics model of a tracked vehicle

Fig.3 Dynamic model of a tracked vehicle

Fig.4 External characteristics of the motor

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

Fig.6 Diagram of the reference path

Fig.7 Schematic diagram of the MLP algorithm

Fig.8 Adaptive learning rate optimization algorithm flow pseudo codes

Fig.9 Prediction of the MLP neural network results

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

Fig.10 ROS-VREP co-simulation environment

Fig.11 Simulated trajectory tracking performance

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

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

Fig.12 Experimental platform of the unmanned tracked vehicle

Fig.13 Track performance for the unmanned tracked vehicle

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

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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