Journal of South China University of Technology Natural Science Edition. ADAMS-Matlab /Simulink . 1. f θ = v fsin γ + l rγ

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1 Journal of South China University of Technology Natural Science Edition Vol 44 No 12 December X * ADAMS ADAMS-Matlab /Simulink 10% 90% U461 1 doi /j issn X f θ = v fsin γ + l rγ 1 l f cos γ + l r v f θ f γ l f l γ P f = x f y f * AA Foundation itemssupported by the National High-Tech R & D Program of China863 Program2011AA wmzhang@ ustb edu cn yangjue@ ustb edu cn

2 Fig 1 1 Trajectory analysis of articulated vehicle θ f γ x f cosθ f 0 y sinθ f 0 f = sinγ v f + l r γ θ f l f cosγ + l r l f cosγ + l r γ ADAMS 2 35 t 1 Table t Fig 2 Steady-state steering experiment based on ADAMS model Basic design parameters of 35 ton mine articulated dump truck /t 28 8 /t 63 8 /t 35 /km h /mm 2278 /mm 5120 / 45 mm/ / E m /s 2 80 s Fig 4 Comparison of lateral acceleration of steady state steering Fig ADAMS Articulated angle input of steady state steering

3 76 44 Δw j k= ηe c k yk uk h j 6 w j k= w j k - 1+ Δw j k+ αδw j k 7 η η 0 1α α 2 0 1w j j h j j Sigmoid Ps Fs Bs δ ft f sw e t yt - y p t 7 fs e δ T p T d T h T c C 0 δ * sw δ sw Vs 5 Fig 5 Driver preview tracking model Fig 7 6 Fig 6 Neural network-based driver preview model y m k 15 km /h ek= y m k- yk 3 8 Ek= 1 b =10m a 1 - a 0 =24m s 2 =30m s 3 =25m s 4 =30m 2 ek2 4 9 uk= h 1 w 1 + h 2 w h m w m Driver-vehicle closed loop system model ADAMS

12 77 10 Fig 10 mm Simulation roadway & planunitmm 8 Fig 8 Ideal experience path 761 0 ~ 135 11 CAN Fig 9 9 Simulation results of

4 Fig 10 mm Simulation roadway & planunitmm 8 Fig 8 Ideal experience path ~ CAN Fig 9 9 Simulation results of neural network-based driver model lane change Fig 11 Flow chart of the control of unmanned driving m

78 44 12 12 761 Fig 12 Real road and data display 13

x min 280 mm 10 x max 8 11-13 761 5 14 P xi = e - d2

5 Fig 12 Real road and data display 13 x mm k = x k - x min 8 x max - x min x k x min 280 mm 10 x max P xi = e - d2 i /10 31 P xi d i 13 Fig 13 Driver's driving data 761 9

6 Fig 15 Neural network-based driver driving data mm Fig 14 Head back data mm mm - 54 mm % 200 mm 90% 3 s 100 mm 1 J LIU Jin-xia ZHANG Wen-ming Dong Cui-yan Articulated dump truck and rigid self unloading J Mining Ma- chinery JOHN Chadwick Advanced mine automation J Mining

7 80 44 Magazine TASCILLO AMILLER R An in-vehicle virtual driving assistant using neural networks C Proceedings of the International Joint Conference on S l IEEE DEMIRLI K KHOSHNEJAD M Autonomous parallel parking of a car-like mobile robot by a neuro-fuzzy sensorbased controller J Fuzzy Sets and Systems view optimization neural network driver model J Chi- GUO Kong-hui PAN Feng MA Feng-jun et al A pre nese Journal of mechanical engineering SEBASTIAN Thrum MAREN Bennewitz WOLFRAM Bur- 11 ADAMS gard et al A second-generation museumtour-guide robot D 2008 J IEEE International Conference on Robotics & Auto- 12 Adams Matlab mation D J D 2014 ZHAO Xuan YANG Jue ZHANG Wen-ming et al Agricultural wheeled articulated vehicle sliding mode trajectory tracking control algorithm J The Chinese Society of Agricultural Engineering D POMERLEAU D Efficient training of artificial neural network for autonomous navigation J Neural Computation J J WANG Wu-hong Automobile driver behavior patterns and their psychological factors on the reliability of the 7 impact of J Automotive Technology D D 2013 A Neural Network-Based Autonomous Articulated Vehicle System Considering Driver Behavior ZHANG Wen-ming HAN Hong-bing YANG Jue YI Xiao School of Mechanical EngineeringUniversity of Science and Technology BeijingBeijing China AbstractIn view of the steering characteristics of articulated dump trucksan autonomous articulated vehicle system is proposed based on neural networks and by considering driver behaviors Firsta sensor collecting system based on the laser radar and the angular transducer is establishedand an articulated vehicle kinematics model and a dynamics model of articulated dump trucks are constructed by analyzing the steering characteristics of articulated dump trucks Thenby using the ADAMS softwarea dynamic model of the trucks is constructed to perform a steady state test Moreovera driver model of the artificial neural network control algorithm is constructed based on the optimal preview controland it is verified by an Adams-Matlab /Simulink co-simulation Finallythis control model is also verified by establishing a simulation ground tunnel to perform the straight-road-return and curve-roadfollowing tests The results show thatwhen the constructed control model is applied to the variable curvature road the lateral position error is less than 10% of the passable distanceand 90% of the course angle deviation is optimizedwhich indicates that the constructed control model has a high convergence speeda good steady state and an excellent unmanned driving performance Key wordsdriver modelneural networksdynamic modelco-simulation

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