Real-time Nonlinear MPC Strategy with Full Vehicle Validation for Autonomous Driving

Allamaa, Jean Pierre; Listov, Petr; Auweraer, Herman Van der; Jones, Colin N.; Son, Tong Duy

doi:10.23919/acc53348.2022.9867514

Cited by 15 publications

(12 citation statements)

References 5 publications

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“…Additionally, the method can accommodate the lack of state measurements by coupling a nonlinear state estimator. The most significant advantage of their approach is its real-time applicability for embedded implementation since the computation time is in the order of milliseconds on the simulation reported, following Allamaa et al [18], which presented recently an NMPC strategy formulated and validated for a real-time application that suggests its implementation on an embedded system. The results showed a low-level controller working at 25 Hz in a constrained and disturbance application.…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

“…The cost-contraction constraint (18) is imposed for all models to guarantee robustly stabilizing properties of the controller. The predictive model must be on the canonical state-space based on the analytical form of the step response of the system outlined in [23], in which x s n (k|k) is artificial integrating states vector and x st n (k|k) is the stable states vector.…”

Section: Robust Infinite-horizon Mpcmentioning

confidence: 99%

“…However, it is important to point out that the RIHMPC has robust stability, with the introduction of the cost-contraction constraint (18), which naturally makes the optimization problem more complex. Still, warm starting the primal variables, the SQP solver turned out to be efficient for real-time operation, handling nonlinearities and converging to the optimal solution.…”

Section: Control Performance Under Setpoint Tracking and Disturbance ...mentioning

confidence: 99%

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Embedded MPC Strategies for ESP-Lifted Oil Wells: Hardware-in-the-Loop Performance Analysis of Nonlinear and Robust Techniques

et al. 2023

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This paper proposes embedded model predictive control strategies for oil-production processes equipped with electric submersible pump (ESP) installations. The novelty of this paper is the robustness and computational performance analysis of the Robust Infinite-Horizon Model Predictive Controller (RIHMPC) and Nonlinear Model Predictive Controller (NMPC) strategies, which have not yet been documented by the oil and gas exploration and production literature. The proposed method to embed the control laws is flexible with different hardware and is based on automatic code generation, which facilitates the project workflow. Hardware-in-the-loop simulation cases were used to compare the performance of both control strategies embedded in the Teensy 4.1 microcontroller, using key indices for real applications. The results showed that the RIHMPC strategy is a very promising alternative for real-time operation in ESP-lifted oil wells, with overall performance similar to the NMPC controller, even in noisy and plant–model mismatch scenarios, and using only linear models in its formulation.

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Section: State Of the Art And Related Workmentioning

confidence: 99%

Section: Robust Infinite-horizon Mpcmentioning

confidence: 99%

Section: Control Performance Under Setpoint Tracking and Disturbance ...mentioning

confidence: 99%

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Embedded MPC Strategies for ESP-Lifted Oil Wells: Hardware-in-the-Loop Performance Analysis of Nonlinear and Robust Techniques

et al. 2023

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“…Prescan has been used to validate automotive XiL development cycle: MiL with high-fidelity vehicle dynamics, HiL with vehicle actuation and embedded platform, and vehicle testing (ViL). The autonomous driving environment contains both simulation and physical proving ground environments, see [6], [7], [8].…”

Section: A Adas Simulation Pipelinementioning

confidence: 99%

Virtual Reality Assisted Human Perception in ADAS Development: a Munich 3D Model Study

Bognar¹,

Markus²,

Auweraer³

et al. 2022

Preprint

Self Cite

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As the development of autonomous driving (AD) and advanced driver assistance systems (ADAS) progresses, the relevance of the comfort of users is gaining increasing interest. It becomes significant to test and validate perceived comfort performance from the early phase of system development before driving on roads. Most of the present ADAS test procedures are not efficient in performing such comfort evaluation. One of the main challenges is to integrate high-quality, realistic and predictable virtual traffic scenarios into an ADAS testing framework that has physics-based sensors capable of sensing the virtual environment.In this paper, we present our development of a virtual reality based ADAS testing framework that enhances human perception evaluation. The main contribution relies on three aspects. First, we introduce our development of a large and high-quality (realism, structure, texture) 3D traffic model of the Munich city in Germany. Second, we optimize the 3D model for virtual reality purpose, and real-time capable for human-in-the-loop ADAS testing. Finally, the model is then integrated into an ADAS framework for testing and validating ADAS functionalities and perceived comfort performance. The developed framework components are presented with illustrative examples.

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“…The NMPC is designed to perform path following based on a dynamic single-track vehicle model, as described in [15], in a Frenet coordinate system with respect to the road centerline, as defined in [6]. The control actions u are the steering wheel angle rate δ and the normalized throttle t r .…”

Section: B Nmpc and Cnn Designmentioning

confidence: 99%

Safe and Computational Efficient Imitation Learning for Autonomous Vehicle Driving

Acerbo

Auweraer

Son

2020

2020 American Control Conference (ACC)

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Autonomous vehicle driving systems face the challenge of providing safe, feasible and human-like driving policy quickly and efficiently. The traditional approach usually involves a search or optimization-based planning followed by a model-based controller. This may prove to be inadequate in some driving scenarios due to disturbance, uncertainties and limited computation time. The more recent end-to-end approaches aim at overcoming these issues by learning a policy to map from sensor data to controls using machine learning techniques. Although being attractive for its simplicity, they also show some drawbacks such as sample inefficiency and difficulties in validation and interpretability. This work presents an approach that attempts to exploit both worlds, combining machine learning-based and model-based control into an imitation learning framework that mimic expert driving behavior while obtaining safe and smooth driving. The dataset is generated from high-fidelity simulations of vehicle dynamics and model predictive control (MPC). A smooth spline-based motion planning represents the policy provided by a constrained neural network exploiting the convex hull property of B-splines. The policy network is trained with few dataset aggregations coming from its induced distribution of states. The learned policy is used as guidance for model-based feedback control and tested on a 15DOF high fidelity vehicle model.

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Real-time Nonlinear MPC Strategy with Full Vehicle Validation for Autonomous Driving

Cited by 15 publications

References 5 publications

Embedded MPC Strategies for ESP-Lifted Oil Wells: Hardware-in-the-Loop Performance Analysis of Nonlinear and Robust Techniques

Embedded MPC Strategies for ESP-Lifted Oil Wells: Hardware-in-the-Loop Performance Analysis of Nonlinear and Robust Techniques

Virtual Reality Assisted Human Perception in ADAS Development: a Munich 3D Model Study

Safe and Computational Efficient Imitation Learning for Autonomous Vehicle Driving

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