Application Specific System Identification for Model-Based Control in Self-Driving Cars

Ducaju, Julian M. Salt; Tang, Chaojing; Tomizuka, Masayoshi; Chan, Ching-Yao

doi:10.1109/iv47402.2020.9304586

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…With the aim of extending the kinematic model, it is required an equation to relate the derivative of the yaw rate in body frame coordinates of the vehicle, ṙ, with the lateral tire forces at the front and rear wheels, F yf and F yr , respectively. Different alternatives can be used (see, e.g., [45]). In this work, the Stanford model [46] is chosen:…”

Section: B Dynamic Modelmentioning

confidence: 99%

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Resource-Efficient Path-Following Control for a Self-Driving Car in a Networked Control System

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Cuenca,

Salt

et al. 2023

IEEE Access

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The main aim of this work is to reduce the use of resources (bandwidth, batteries) in a networked control system (NCS) while maintaining an accurate path following for a self-driving car. Some typical network-induced drawbacks such as time-varying delays, packet dropouts and packet disorder will also be coped with. In order to reach the goals, a systematic integration of periodic event-triggered sampling techniques, packet-based control strategies, and state estimation methods is proposed. A novel non-uniform dual-rate extended Kalman filter (NUDREKF) is formulated to estimate the system state at fast, control rate from scarce slow-rate measurements. Due to its mathematical simplicity and low computational cost, the dynamic control law is designed from an inverse kinematic bicycle model and a proportional feedforward controller. Interestingly, optimal parameters for the event-triggered conditions are reached, leading to a satisfactory trade-off between resource savings and control performance. Simulation results for a real trajectory considering actual limitations for the actuators reveal the benefits of the control proposal compared to a conventional control approach.INDEX TERMS Event-triggered communication, Kalman filter, networked control system, resource efficiency, self-driving car.

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Section: B Dynamic Modelmentioning

confidence: 99%

“…The model used to represent the plant in simulations is similar to the previous one (see, e.g., in [45], [47]), but replacing (15) by ṙ = l f F yf cos(δ) − l r F yr I z (24) and then, (23) gives place to…”

Section: B Dynamic Modelmentioning

confidence: 99%