Model predictive control for comfort optimization in assisted and driverless vehicles

Luciani, Sara; Bonfitto, Angelo; Amati, Nicola; Tonoli, Andrea

doi:10.1177/1687814020974532

Cited by 14 publications

(15 citation statements)

References 25 publications

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“…[33][34][35] From the state-of-the-art literature, MPC results to be the most applied control strategy, used for both cooperative purposes and for longitudinal/lateral vehicle control. In particular, most of the recent published methods concern predictive control strategies for ADAS and Automated Vehicles (AVs), mainly adaptive [36][37][38][39] and nonlinear. 40,41 The Adaptive MPC is considered one of the most suitable control strategies for CAVs, since it allows the vehicle to cope with time-varying parameters also during the prediction horizon.…”

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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“…Lee and Kang [159] used a non-linear MPC model to obtain optimal control datasets to train a Deep Neural Network (DNN), obtaining similar performance with reduced computation time and achieving the real-time capability. Luciani et al [160] proposed a MPC strategy for tracking longitudinal and lateral dynamics of an assisted and driverless vehicle with the objective of optimizing the comfort.…”

Section: Towards the Future: Higher Levels Of Automationmentioning

confidence: 99%

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

et al. 2021

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Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments.

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“…Passenger comfort is particularly measured here in terms of the root-mean-square (RMS) of the longitudinal vehicle acceleration throughout the given driving mission. The value of RMS for vehicle acceleration indeed represents a common index for evaluating the quality of passengers' ride perception [55,56].…”

Section: =mentioning

confidence: 99%

Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort

Anselma

2021

Energies

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Assessing the potential of advanced driver assistance systems requires developing dedicated control algorithms for controlling the longitudinal speed of automated vehicles over time. In this paper, a multiobjective off-line optimal control approach for planning the speed of the following vehicle in adaptive cruise control (ACC) driving is proposed. The implemented method relies on the principle of global optimality fostered by dynamic programming (DP) and aims to minimize propelling energy consumption and enhance passenger comfort. The powertrain model and onboard control system are integrated within the proposed car-following optimization framework. The retained ACC approach ensures that the distance between the following vehicle and the preceding vehicle is always maintained within allowed limits. The flexibility of the proposed method is demonstrated here through ease of implementation on a wide range of powertrain categories, including a conventional vehicle propelled by an internal combustion engine solely, a pure electric vehicle, a parallel P2 hybrid electric vehicle (HEV) and a power-split HEV. Moreover, different driving conditions are considered to prove the effectiveness of the proposed optimization-driven ACC approach. Obtained simulation results suggest that up to 22% energy-saving and 48% passenger comfort improvement might be achieved for the ACC-enabled vehicle compared with the preceding vehicle by implementing the proposed optimization-driven ACC approach. Engineers may adopt the proposed workflow to evaluate corresponding real-time ACC approaches and assess optimal powertrain design solutions for ACC driving.

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Model predictive control for comfort optimization in assisted and driverless vehicles

Cited by 14 publications

References 25 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

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort

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