基于MPC-MFAC的双侧独立电驱动无人履带车辆轨迹跟踪控制

doi:10.12382/bgxb.2022.0886

摘要/Abstract

摘要：

简化模型带来的模型失配以及外部环境不确定性是导致轨迹跟踪误差的主要原因,尤其对于无人履带车辆,其复杂的物理特性和工作环境更放大了两大因素的不利影响。针对该问题,将基于模型和基于数据的控制方法结合起来,提出一种基于模型预测控制结合无模型自适应控制补偿的双侧独立电驱动无人履带车辆轨迹跟踪控制方法。在平衡建模准确度和求解耗时的基础上,利用模型预测控制进行前馈求解。针对模型预测控制中简化模型与车辆实际模型之间必然存在的差异以及环境不确定性,基于动态跟踪效果构建无模型自适应控制算法进行补偿,即利用车辆实际轨迹与模型预测所得轨迹之间的误差,对模型预测控制求解的两侧履带速度控制量进行实时修正。仿真实验结果表明,该方法能够在一定程度上抑制系统内外部不确定因素的影响,提高动态环境下双侧独立电驱动无人履带车辆轨迹跟踪控制的精度。

关键词: 无人履带车辆, 模型预测控制, 无模型自适应控制, 改进粒子群优化算法, 轨迹跟踪控制

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

中图分类号:

TP242.6

唐泽月, 刘海鸥, 薛明轩, 陈慧岩, 龚小杰, 陶俊峰. 基于MPC-MFAC的双侧独立电驱动无人履带车辆轨迹跟踪控制[J]. 兵工学报, 2023, 44(1): 129-139.

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.

图/表 12

图1 履带车辆运动学模型

Fig.1 Kinematic model of the tracked vehicle

图2 履带车辆动力学模型

Fig.2 Dynamic model of the tracked vehicle

图3 车辆轨迹示意图

Fig.3 Schematic diagram of vehicle trajectory

图4 基于MPC-MFAC双侧独立电驱动无人履带车辆轨迹跟踪控制算法整体结构图

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

图5 Frenet坐标系下车辆运动描述示意图

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

表1 仿真模型参数

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

表2 MPC控制器参数

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

表3 MFAC控制器参数

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)	$0000$

表3 MFAC控制器参数

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)	$0000$

图6 VREP中的仿真模型

Fig.6 Simulation model in VREP

图7 电机外特性图

Fig.7 External characteristics of motor

图8 仿真试验跟踪效果图

Fig.8 Trajectory tracking performances of simulation

表4 仿真实验轨迹跟踪效果统计

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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