Central Non-Linear Model-Based Predictive Vehicle Dynamics Control

Sieberg, Philipp Maximilian; Schramm, Dieter

doi:10.3390/app11104687

Cited by 6 publications

(6 citation statements)

References 30 publications

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“…Then as a next step, the influence of an active roll torque distribution system can be taken into account. As soon as the number of control systems increases, the classic control scheme as presented here must then also be compared with approaches from central control and with approaches from optimal control, which are especially advantageous for quadratic multiple-inputmultiple-output systems [30] or even for overactuated systems [31,32].…”

Section: Discussionmentioning

confidence: 99%

A Concept for Using Road Wetness Information in an All-Wheel-Drive Control

Warth¹,

Sieberg

Unterreiner³

et al. 2022

Energies

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This paper presents a concept for using road wetness information in an all-wheel-drive (AWD) control that distributes drive torques in the longitudinal direction. Driving on wet roads requires special attention. Not only does the road surface friction coefficient decrease, but driving dynamics targets must be adjusted to prevent vehicle instability under wet conditions. As an exemplary application, the otherwise generic control concept is implemented on an AWD vehicle with a torque-on-demand transfer case. Therefore, the AWD topology of a drive train with a torque-on-demand transfer case is analysed in advance in terms of occurring torques and rotational speeds. In the following, the vehicle dynamics goals for driving in wet road conditions are described—divided into primary and secondary goals. Starting from a state-of-the art AWD control, an adaptive control strategy is derived by superimposing a wetness coordination unit. With the knowledge of occurring road wetness, this unit adapts newly introduced parameters in order to meet the target driving behaviour under wet conditions. Lastly, the derived AWD control is implemented into a 14-DOF, non-linear vehicle model in Matlab/Simulink, which is used as a virtual plant. The performance of the developed concept is assessed by the driving maneuver “Power On Cornering“ (PON), which means an acceleration out of steady-state circular motion. As its essential benefit, the AWD control enables a maximum spread between driving stability, agility and traction under combined dynamics when using wetness information. The newly introduced wetness coordination unit uses only a few additional and physically interpretable key parameters for this purpose, without significantly increasing the controller complexity.

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Section: Discussionmentioning

confidence: 99%

A Concept for Using Road Wetness Information in an All-Wheel-Drive Control

Warth¹,

Sieberg

Unterreiner³

et al. 2022

Energies

Self Cite

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“…In our framework, SUMO is responsible for the simulation of the investigated scenario. Due to SUMO’s inaccurate vehicle dynamics modelled by a simple accelerated point-mass approach, the ego vehicle’s dynamics are modelled in Simulink 31 , 32 . For this purpose, the linearized single-track model is used.…”

Section: Methodsmentioning

confidence: 99%

Self-evaluation of automated vehicles based on physics, state-of-the-art motion prediction and user experience

Novo

Hürten

Baumann

et al. 2023

Sci Rep

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Legal restrictions allow to give full control to automated vehicles for longer time periods either in restricted areas or when moving with reduced speed. Although being technically feasible for a wide range of driving scenarios, the restrictions are still in place due to the lack of a clear safety strategy. An essential step towards safety is the introduction of a self-monitoring component. In this study, a self-evaluation concept is presented which assesses a system based on a physics-defined minimum prediction horizon for state-of-the-art Deep Learning-based trajectory prediction models. Since User Experience is a key metric for car manufacturers, a further manoeuvre constraint is added to the model. We emphasize the generalizability of the presented assessment concept, however, in order to demonstrate feasibility in practical use, three specific scenarios are discussed. The results are gained with real data from publicly available driving campaigns as well as synthetically generated simulation data. Two exemplary models, a simple LSTM-based model and VectorNet, a prominent motion prediction model, are evaluated. A quantitative assessment shows a lack of training data in the public datasets for vehicle speeds > 25 m/s in order to offer safe driving above such vehicle speeds.

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“…19 Artificial Neural Networks are used to estimate the side-slip angle better to control vehicle dynamics. 20 The quadratic programming problem with constraints derived from MPC is solved formulaically by the primal-dual neural network. 21 Deep neural network-based supervised learning meets the requirement of the controller, but it calls for a large number of sample data to support it.…”

Section: Introductionmentioning

confidence: 99%

Lateral semi-trailer truck control using a parameter self-learning MPC method in urban environment

Chen

Jiang

Zhou

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper analyzes the lateral control technology of autonomous semi-trailer trucks. Existing researches on the lateral control algorithm of semi-trailer trucks focus on making the head-truck or trailer follow a track well while ignoring the motion characteristics during the turning process, leading to specific security issues. Meanwhile, it is difficult to cope with the complex and uncertain factors influencing lateral control effect, such as the curvature of the desired trajectory, the load, and the velocity of the semi-trailer truck. This paper proposes a parametric self-learning model predictive control (MPC) based on the Proximal Policy Optimization of One Step (OSPPO) method to solve these problems. After modeling the kinematics of the semi-trailer truck, a lateral motion controller for the relationship between the head-truck and trailer based on the MPC method is established. The traditional MPC method has difficulty in adapting to the changeable influencing factors. Thus, a deep reinforcement learning algorithm named OSPPO is introduced to improve the flexibility of the MPC method. OSPPO establishes the nonlinear mapping relationship between the critical parameter of the MPC method and the factors influencing control effect by self-learning, avoiding a large amount of labeled data for training. In simulations, Trucksim and Matlab were used to conduct co-simulation to verify the usefulness of the control method. The method was implemented on an autonomous semi-trailer truck for many outdoor scenes. The actual experimental results showed the validity and advantages of the proposed method.

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Central Non-Linear Model-Based Predictive Vehicle Dynamics Control

Cited by 6 publications

References 30 publications

A Concept for Using Road Wetness Information in an All-Wheel-Drive Control

A Concept for Using Road Wetness Information in an All-Wheel-Drive Control

Self-evaluation of automated vehicles based on physics, state-of-the-art motion prediction and user experience

Lateral semi-trailer truck control using a parameter self-learning MPC method in urban environment

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