Modelling of skid steering and fuzzy logic vehicle ground interaction

Economou, J.T.; Colyer, R.E.

doi:10.1109/acc.2000.878781

Cited by 23 publications

(6 citation statements)

References 7 publications

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“…According to the requirement of real SBW system, when the vehicle speed is high, the transmission ratio should be increased to reduce system gain [7]. When the vehicle speed is low, the transmission ratio should be reduced to increase steering sensitiveness and handiness [8]. By consultation relative experts and drivers, the designed fuzzy control rules of SBW system are list in table IV.…”

Section: B Fuzzy Scalesmentioning

confidence: 99%

Simulation and optimization of angle characteristic model for steer by wire system

Yuan

Guo

et al. 2010

2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery

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The steer by wire (SBW) system use electric wires to transmit angle of steering wheel between steering wheel and front wheel. The influence factors of ideal transmission ratio in SBW system were studied in this paper. Ideal transmission ratio model of SBW system was established by applying fuzzy control theory and this model has relation with vehicle speed and steering angle of steering wheel in the case of turning, therefore this model can reflect the relation between transmissions ratio of SBW system and each affect factor more reasonable. The front angle distinguish controller resolved the destabilization problem of SBW system in the condition of large steering angle in high speed. The system error of front angle distinguish controller is less than 2×10 -3 and it has better approach learning capability.

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Section: B Fuzzy Scalesmentioning

confidence: 99%

Simulation and optimization of angle characteristic model for steer by wire system

Yuan

Guo

et al. 2010

2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery

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“…Some advanced methods were also used to describe vehicle-terrain interaction. For example, Economou and Colyer [16] utilized the Fuzzy Logic for modeling of the vehicle-ground interactions based on the experimental results obtained for an electric wheeled skid steer vehicle under steady-state conditions and a variety of motions and surfaces. A neural network was employed to model the steering dynamics of an autonomous vehicle in Reference [17].…”

Section: Introductionmentioning

confidence: 99%

On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics

Strawa¹,

Ignatyev²,

Zolotas³

et al. 2021

Electronics

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Increasing levels of autonomy impose more pronounced performance requirements for unmanned ground vehicles (UGV). Presence of model uncertainties significantly reduces a ground vehicle performance when the vehicle is traversing an unknown terrain or the vehicle inertial parameters vary due to a mission schedule or external disturbances. A comprehensive mathematical model of a skid steering tracked vehicle is presented in this paper and used to design a control law. Analysis of the controller under model uncertainties in inertial parameters and in the vehicle-terrain interaction revealed undesirable behavior, such as controller divergence and offset from the desired trajectory. A compound identification scheme utilizing an exponential forgetting recursive least square, generalized Newton–Raphson (NR), and Unscented Kalman Filter methods is proposed to estimate the model parameters, such as the vehicle mass and inertia, as well as parameters of the vehicle-terrain interaction, such as slip, resistance coefficients, cohesion, and shear deformation modulus on-line. The proposed identification scheme facilitates adaptive capability for the control system, improves tracking performance and contributes to an adaptive path and trajectory planning framework, which is essential for future autonomous ground vehicle missions.

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“…The steering controller consists of a proportional-integralderivative (PID) controller with two filters, a prediction filter and a safety filter [3]. Economou and Colyer proposed fuzzy logic control of wheeled skid-steer electric vehicles [4]. Dixon et al investigated nonlinear control of wheeled mobile robots [5].…”

Section: Introductionmentioning

confidence: 99%

Design, implementation, and test of skid steering-based autonomous driving controller for a robotic vehicle with articulated suspension

et al. 2010

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This paper describes an autonomous driving control algorithm based on skid steering for a Robotic Vehicle with Articulated Suspension (RVAS). The driving control algorithm consisted of four parts: speed controller for following the desired speed, trajectory tracking controller to track the desired trajectory, longitudinal tire force distribution algorithm which determines the optimal desired longitudinal tire force and wheel torque controller which determines the wheel torque command at each wheel to keep the slip ratio below the limit value as well as to track the desired tire force. The longitudinal and vertical tire force estimators were designed for optimal tire force distribution and wheel slip control. The dynamic model of the RVAS is validated using vehicle test data. Simulation and vehicle tests were conducted in order to evaluate the proposed driving control algorithm. Based on the simulation and test results, the proposed driving controller was shown to produces satisfactory trajectory tracking performance.

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Modelling of skid steering and fuzzy logic vehicle ground interaction

Cited by 23 publications

References 7 publications

Simulation and optimization of angle characteristic model for steer by wire system

Simulation and optimization of angle characteristic model for steer by wire system

On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics

Design, implementation, and test of skid steering-based autonomous driving controller for a robotic vehicle with articulated suspension

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