Trajectory Tracking Control of Dual Independent Electric Drive Unmanned Tracked Vehicle Based on MPC-MFAC

doi:10.12382/bgxb.2022.0886

Abstract

Abstract:

The model mismatch caused by the simplified model and uncertainty of external environment are the main reasons for the trajectory tracking error. Especially for the unmanned tracked vehicle, its complex physical characteristics and working environment magnify the adverse effects of these two factors. To solve this problem, this paper combines the model-based and data-based control methods, and proposes a trajectory tracking control method for the dual independent electric drive unmanned tracked vehicle based on a model predictive control algorithm (MPC) combined with a model-free adaptive control algorithm (MFAC) as compensation. Firstly, based on balancing modeling accuracy and solution time, the MPC is used for feedforward solution. Then, for the inevitable differences between the simplified model in the MPC and the actual vehicle model and environmental uncertainty, the MFAC algorithm is constructed based on the dynamic tracking effect for compensation. That is, the error between the actual trajectory of the vehicle and the trajectory predicted by the model is used to correct the speed control quantities of the dual tracks solved by the MPC in real time. The simulation results show that this method can suppress the influence of internal and external uncertainties of the system to a certain extent, and improve the trajectory tracking control accuracy of the dual independent electric drive unmanned tracked vehicle in a dynamic environment.

Key words: unmanned tracked vehicle, model predictive control, model-free adaptive control, improved particle swarm optimization algorithm, trajectory tracking control

CLC Number:

TP242.6

TANG Zeyue, LIU Haiou, XUE Mingxuan, CHEN Huiyan, GONG Xiaojie, TAO Junfeng. Trajectory Tracking Control of Dual Independent Electric Drive Unmanned Tracked Vehicle Based on MPC-MFAC[J]. Acta Armamentarii, 2023, 44(1): 129-139.

Figures/Tables 12

Fig.1 Kinematic model of the tracked vehicle

Fig.2 Dynamic model of the tracked vehicle

Fig.3 Schematic diagram of vehicle trajectory

Fig.4 Overall structure diagram of trajectory tracking control algorithm for dual independent electric drive unmanned tracked vehicle based on MPC-MFAC

Fig.5 Schematic diagram of vehicle motion description in the Frenet coordinate system

Table 1 Parameters of the simulated vehicle

车辆参数	数值
整车质量m/kg	2960
车长L/mm	2616
车宽W/mm	1598
车高H/mm	1060
履带中心距B/mm	1232
质心离地高度H_g/mm	200

Table 2 MPC controller parameters

MPC参数	数值
预测时域	12
控制时域	12
时间步长/s	0.1
横向偏差权重	100
航向偏差权重	120
速度偏差权重	30
左侧控制量变化率权重	50
右侧控制量变化率权重	50

Table 3 MFAC controller parameters

MFAC参数	数值
[ρ₁,ρ₂,ρ₃,ρ₄,ρ₅,ρ₆]	[0.1,0.5,0.9,0.5,0.8,1.0]
η	1.1
λ	18.5
μ	1.2
φ₁(1)=φ₂(1)=φ₃(1)	$1001$
φ₄(1)=φ₅(1)=φ₆(1)	$0000$

Table 3 MFAC controller parameters

MFAC参数	数值
[ρ₁,ρ₂,ρ₃,ρ₄,ρ₅,ρ₆]	[0.1,0.5,0.9,0.5,0.8,1.0]
η	1.1
λ	18.5
μ	1.2
φ₁(1)=φ₂(1)=φ₃(1)	$1001$
φ₄(1)=φ₅(1)=φ₆(1)	$0000$

Fig.6 Simulation model in VREP

Fig.7 External characteristics of motor

Fig.8 Trajectory tracking performances of simulation

Table 4 Statistical results of tracking performance of simulation

控制方法	参数	均值	标准差	最大值
	横向绝对偏差/m	0.2090	0.1636	1.0029
纯MPC	航向绝对偏差/(°)	1.6142	1.3643	9.4634
	求解用时/ms	29.2679	6.7551	44.4548
	横向绝对偏差/m	0.1525	0.1235	1.0029
MPC+MFAC	航向绝对偏差/(°)	1.2391	0.7161	5.7133
	MFAC求解用时/ms	6.2606	2.1833	27.2119
	总求解用时/ms	35.5285	7.2582	65.4892

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