Optimized adaptive MPC for lateral control of autonomous vehicles

Kebbati, Yassine; Puig, Vicenç; Ait-Oufroukh, Naïma; Vigneron, Vincent; Ichalal, Dalil

doi:10.1109/iccma54375.2021.9646218

Cited by 21 publications

(9 citation statements)

References 24 publications

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“…42 Furthermore, the Adaptive MPC results into a good trade-off between tracking performances and computational load, compared to more elaborate algorithms based on nonlinear MPC. 43 Different applications have been discussed in literature, concerning lateral and longitudinal dynamics in autonomous driving, 36 comfort optimization, 38 lane keeping and lateral control, 13,44 path planning, 45 and trajectory tracking. 46 Thus, in view of the numerous applications in terms of ADAS and CAVs and given its characteristics in managing constrained optimal problems based on MIMO prediction models and time-varying parameters, the adaptive MPC is the most suitable choice for an industrial application to be executed in real-time on an embedded hardware device.…”

Section: Advanced Control Strategies For Adas and Cavsmentioning

confidence: 99%

Hardware‐in‐the‐loop validation of an adaptive model predictive control on a connected and automated vehicle

Landolfi

Salvi²,

Troiano³

et al. 2023

Adaptive Control & Signal

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Summary Connected and automated vehicles will characterize the future of the mobility, featured by Smart Roads and Internet of Things technologies. Vehicles will behave as mobile nodes of a network enabling communication between them and with respect to the road infrastructure. In order to provide advanced driver assistance and automated driving along a Smart Road, control strategies need to take into account both parametric variations due to environmental and/or weather conditions (e.g., wind speed, presence of ice, and/or rain), and constraints related to road safety and vehicular traffic. For this reason, advanced adaptive control strategies result particularly suitable to provide a complete control of the vehicle. This article proposes an extension to a previous conference paper, concerning a Processor in the Loop testing of an Adaptive Model Predictive Control for coupled longitudinal/lateral vehicle dynamics, where the ego vehicle was simulated through a bicycle model. In the present work, a low‐cost real‐time Hardware In the Loop test bench, featured by Raspberry boards, is used to verify the control strategy in a way closer to a real application. A four‐wheel vehicle dynamic model is considered to simulate the ego vehicle, then robustness and sensitivity tests with respect to parametric variations related to road friction, wind speed and vehicle mass, and a comparison with an existing adaptive MPC are performed. The achieved results show good robustness properties, safety performances and confirm the real‐time execution of the control strategy, paving the way for future developments, where the goal will be the testing in a real road scenario by using an IoT gateway purposefully designed by the NetCom company.

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Section: Advanced Control Strategies For Adas and Cavsmentioning

confidence: 99%

Hardware‐in‐the‐loop validation of an adaptive model predictive control on a connected and automated vehicle

Landolfi

Salvi²,

Troiano³

et al. 2023

Adaptive Control & Signal

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“…By applying Euler-Newton's formalism to the model in (Fig. 1), using small angle approximations and linearized tire model, the full model can be summarized in the following equations [9]:…”

Section: B Lateral Dynamics Modelingmentioning

confidence: 99%

“…Nevertheless, most of the previously mentioned papers only consider constant longitudinal speeds, while variable speeds greatly affect lateral control performance. Study [9] developed an adaptive MPC for the path tracking task; they proposed an improved PSO algorithm to optimize the MPC parameters and used a lookup table approach to adapt these parameters online. Despite the optimal results for time-varying longitudinal velocities, this strategy cannot cover all possible situations and the lookup table method requires certain approximations which reduce the overall control precision.…”

Section: Introductionmentioning

confidence: 99%

Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehicles

Kebbati

Oufroukh

Vigneron

et al. 2022

2022 European Control Conference (ECC)

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Autonomous driving is achieved by controlling the coupled nonlinear longitudinal and lateral vehicle dynamics. Longitudinal control greatly affects lateral dynamics and must preserve lateral stability conditions, while lateral controllers must take into account actuator limits and ride comfort. This work deals with the coordinated longitudinal and lateral control for autonomous driving. An improved particle swarm optimized PID (PSO-PID) is proposed to handle the task of speed tracking based on nonlinear longitudinal dynamics. An enhanced linear parameter varying model predictive controller (LPV-MPC) is also designed to control lateral dynamics, the latter is formulated with an adaptive LPV model in which the tire cornering stiffness coefficients are estimated by a recursive estimator. The proposed LPV-MPC is enhanced with an improved cost function to provide better performance and stability. Matlab/Carsim cosimulations are carried out to validate the proposed controllers.

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“…Secondly, when this solver has difficulty in searching for the optimal solution, it will provide a near-optimal solution, and will not consume a lot of time and space or even interrupt the program for exact convergence to a certain solution, which has good stability. Finally, because of the simplicity of the algebraic description of the Hildreth real-time solution method, it is more friendly for the development of MATLAB-based embedded automatic code generation [ 32 , 33 , 34 ].…”

Section: Model Predictive Control Algorithmmentioning

confidence: 99%

Weight Adaptive Path Tracking Control for Autonomous Vehicles Based on PSO-BP Neural Network

Tang

Shi

Wang

et al. 2022

Sensors

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In order to improve the tracking adaptability of autonomous vehicles under different vehicle speeds and road curvature, this paper develops a weight adaptive model prediction control system (AMPC) based on PSO-BP neural network, which consists of a dynamics-based model prediction controller (MPC) and an optimal weight adaptive regulator. Based on the application of MPC to achieve high-precision tracking control, the optimal weight under different operating conditions obtained by automated simulation is used to train the PSO-BP neural network offline to achieve online adjustment of MPC weight. The validation results of the Prescan-Carsim-Simulink joint simulation platform show that the adaptive control system has better tracking adaptation capability compared with the original classical MPC control. The control strategy was also verified on an autonomous vehicle test platform, and the test results showed that the adaptive control strategy improved tracking accuracy while meeting the vehicle’s requirements for real-time control and lateral stability.

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Optimized adaptive MPC for lateral control of autonomous vehicles

Cited by 21 publications

References 24 publications

Hardware‐in‐the‐loop validation of an adaptive model predictive control on a connected and automated vehicle

Hardware‐in‐the‐loop validation of an adaptive model predictive control on a connected and automated vehicle

Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehicles

Weight Adaptive Path Tracking Control for Autonomous Vehicles Based on PSO-BP Neural Network

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