A new control approach for automated drifting in consideration of the driving characteristics of an expert human driver

Joa, Eunhyek; Cha, Hyunsoo; Hyun, Y.; Koh, Youngil; Yi, Kyongsu; Park, Jae Yong

doi:10.1016/j.conengprac.2019.104293

Cited by 31 publications

(17 citation statements)

References 8 publications

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“…In this paper, a three-state, two-wheel bicycle model with a nonlinear tire model was chosen for this purpose. It was shown in [10,11,20] that a two-wheel bicycle model can be sufficient for optimal control methods to achieve steady-state self-drifting.…”

Section: Vehicle Modelingmentioning

confidence: 99%

Model Predictive Controller Design for Vehicle Motion Control at Handling Limits in Multiple Equilibria on Varying Road Surfaces

et al. 2021

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Electronic vehicle dynamics systems are expected to evolve in the future as more and more automobile manufacturers mark fully automated vehicles as their main path of development. State-of-the-art electronic stability control programs aim to limit the vehicle motion within the stable region of the vehicle dynamics, thereby preventing drifting. On the contrary, in this paper, the authors suggest its use as an optimal cornering technique in emergency situations and on certain road conditions. Achieving the automated initiation and stabilization of vehicle drift motion (also known as powerslide) on varying road surfaces means a high level of controllability over the vehicle. This article proposes a novel approach to realize automated vehicle drifting in multiple operation points on different road surfaces. A three-state nonlinear vehicle and tire model was selected for control-oriented purposes. Model predictive control (MPC) was chosen with an online updating strategy to initiate and maintain the drift even in changing conditions. Parameter identification was conducted on a test vehicle. Equilibrium analysis was a key tool to identify steady-state drift states, and successive linearization was used as an updating strategy. The authors show that the proposed controller is capable of initiating and maintaining steady-state drifting. In the first test scenario, the reaching of a single drifting equilibrium point with −27.5° sideslip angle and 10 m/s longitudinal speed is presented, which resulted in −20° roadwheel angle. In the second demonstration, the setpoints were altered across three different operating points with sideslip angles ranging from −27.5° to −35°. In the third test case, a wet to dry road transition is presented with 0.8 and 0.95 road grip values, respectively.

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Section: Vehicle Modelingmentioning

confidence: 99%

Model Predictive Controller Design for Vehicle Motion Control at Handling Limits in Multiple Equilibria on Varying Road Surfaces

et al. 2021

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“…These cornering stiffnesses are estimated based on the empirical magic formula tire model, 17 which will be specifically explained in section 3.4. The cornering stiffness of the front and rear can be estimated using recursive least square with exponential forgetting factor based on the following equations 26,41 :…”

Section: Integrated Control Of In-wheel Motor and Electronic Limited ...mentioning

confidence: 99%

An integrated control of front in-wheel motors and rear electronic limited slip differential for high-speed cornering performance

Cha

Hyun

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper presents an integrated control of in-wheel motor (IWM) and electronic limited slip differential (eLSD) for high-speed cornering performance. The proposed algorithm is designed to improve the handling performance near the limits of handling. The proposed controller consists of a supervisor, upper-level controller, and lower-level controller. First, the supervisor determines a target motion based on the yaw rate reference with a target understeer gradient. The target understeer gradient is devised to improve the lateral stability with in-wheel motor control based on a nonlinear static map. The yaw rate reference is designed based on the target understeer gradient to track the yaw reference with eLSD control. Second, the upper-level controller calculates the desired yaw moments for IWM and eLSD to generate the target motion. Third, the lower-level controller converts the desired yaw moment to the actuator torque commands for IWMs and eLSD. The tire friction limits are estimated based on the tire model and friction circle model to prevent tire saturation by limiting the torque inputs. The proposed algorithm has been investigated via both simulations and vehicle tests. The performance of the integrated control was compared with those of individual control and uncontrolled case in the simulation study. The vehicle tests have been performed using a rear wheel drive vehicle equipped with two front IWMs and eLSD in the rear axle. The vehicle test has been conducted at a racing track to show that the proposed algorithm can improve the lateral stability near the limits of handling.

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“…In addition, expert control can also accumulate knowledge by adding and modifying rules, aiming to improve the control performance of the system. In Joa et al, 16 a drift control algorithm is proposed, which makes use of the driving characteristics of expert drivers in order to realize automatic drift. This controller can realize steady drift without knowing the balance of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Gao

Xiong

2022

Advances in Mechanical Engineering

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Considering the contradiction among the response speed, overshoot and stability of system when the motor load system adopts PID control, a control strategy combining RBF (Radial Basis Function) neural network supervisory control and expert PID control is designed to effectively improve this problem in this paper. First of all, the related algorithms of RBF neural network supervisory control composed of RBF neural network and PID control (RSC-PID) is introduced. This method can make the motor load system reach a steady state faster than simple PID control. But RSC-PID is also unsatisfactory in terms of overshoot. Based on the RSC-PID control method, a hybrid controller combining RBF neural network supervisory control and expert PID control (RSC-EPID) is proposed. This method combines RSC-PID control theory with expert PID control ideas, further improves the stability and rapidity of the system, reduces the overshoot. Moreover, when the input signal is a time-varying signal with interference, the motor load system shows better anti-interference performance after using RSC-EPID. The simulation results show that RSC-EPID control improves the tracking effect of the output signal of the motor load system, ensures the stability of the system, and improves the performance of the system.

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A new control approach for automated drifting in consideration of the driving characteristics of an expert human driver

Cited by 31 publications

References 8 publications

Model Predictive Controller Design for Vehicle Motion Control at Handling Limits in Multiple Equilibria on Varying Road Surfaces

Model Predictive Controller Design for Vehicle Motion Control at Handling Limits in Multiple Equilibria on Varying Road Surfaces

An integrated control of front in-wheel motors and rear electronic limited slip differential for high-speed cornering performance

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

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