MPC-based approach to active steering for autonomous vehicle systems

Borrelli, Francesco; Falcone, Paolo; Keviczky, Tamás; Asgari, Jahan; Hrovat, Davor

doi:10.1504/ijvas.2005.008237

Cited by 361 publications

(177 citation statements)

References 32 publications

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“…It should also be mentioned that model predictive control designs, incorporating dynamics vehicles models and actuators models, are effective methods for solving the combined motion control and control allocation problem for vehicle dynamics control, e.g. (Borrelli, Falcone, Keviczky, Asgari & Hrovat 2005), although the computational complexity is much more of a challenge than with a control allocation design.…”

Section: Yaw Stability Controlmentioning

confidence: 99%

Control allocation—A survey

2013

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The control algorithm hierarchy of motion control for over-actuated mechanical systems with a redundant set of effectors and actuators commonly includes three levels. First, a high-level motion control algorithm commands a vector of virtual control efforts (i.e. forces and moments) in order to meet the overall motion control objectives. Second, a control allocation algorithm coordinates the different effectors such that they together produce the desired virtual control efforts, if possible. Third, low-level control algorithms may be used to control each individual effector via its actuators. Control allocation offers the advantage of a modular design where the high-level motion control algorithm can be designed without detailed knowledge about the effectors and actuators. Important issues such as input saturation and rate constraints, actuator and effector fault tolerance, and meeting secondary objectives such as power efficiency and tear-and-wear minimization are handled within the control allocation algorithm. The objective of the present paper is to survey control allocation algorithms, motivated by the rapidly growing range of applications that have expanded from the aerospace and maritime industries, where control allocation has its roots, to automotive, mechatronics, and other industries. The survey classifies the different algorithms according to two main classes based on the use of linear or nonlinear models, respectively. The presence of physical constraints (e.g input saturation and rate constraints), operational constraints and secondary objectives makes optimization-based design a powerful approach. The simplest formulations allow explicit solutions to be computed using numerical linear algebra in combination with some logic and engineering solutions, while the more challenging formulations with nonlinear models or complex constraints and objectives call for iterative numerical optimization procedures. Experiences using the different methods in aerospace, maritime, automotive and other application areas are discussed. The paper ends with some perspectives on new applications and theoretical challenges.

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Section: Yaw Stability Controlmentioning

confidence: 99%

Control allocation—A survey

2013

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“…A reduced design Copyright notice: 978-1-5386-5346-6/18/$31.00 2018 IEEE model with this technique permits to obtain acceptable results, having the versatility to specify constraints as acceleration and jerk responses [5]. The present work aims to use a MPC for longitudinal maneuvers, in order to take advance of its prediction capacity without complex speed planner calculation.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal Model Predictive Control with comfortable speed planner

Matute

Marcano

Pérez

2018

2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

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Abstract-Guaranteeing simplicity and safety is a real challenge of Advanced Driver Assistance Systems (ADAS), being these aspects necessary for the development of decision and control stages in highly automated vehicles. Considering that a human-centered design is generally pursued, exploring comfort boundaries in passenger vehicles has a significant importance. This work aims to implement a simple Model Predictive Control (MPC) for longitudinal maneuvers, considering a bare speed planner based on the curvature of a predefined geometrical path. The speed profiles are constrained with a maximum value at any time, in such way that total accelerations are lower than specified constraint limits. A double proportional with curvature bias control was employed as a simple algorithm for lateral maneuvers. The tests were performed within a realistic simulation environment with a virtual vehicle model based on a multi-body formulation. The results of this investigation permits to determine the capabilities of simplified control algorithms in real scenarios, and comprehend how to improve them to be more efficient.

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“…Autonomous trucks is a significant research area, since mining and construction is a big market segment, specific for trucks rather than for personal cars. MPC has been widely used to address the autonomous vehicle guidance problem [8]- [11]. In [8], an NMPC controller is presented in order to stabilize a vehicle along a desired path, while fulfilling its physical constraints.…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of economic model predictive control for an autonomous truck

Lima

Trincavelli

Nilsson

et al. 2016

2016 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-In this paper, we propose a controller for smooth autonomous path following. The controller is formulated as an economic model predictive controller. The economic cost introduced in the objective function leads to a smooth driving, since we minimize the first and second derivatives of the curvature function (i.e., we encourage linear curvature profiles). Since the curvature in clothoids varies linearly with the path arc-length, we use the smoothness and comfort characteristics of clothoid-driving to obtain a compact and intuitive controller formulation. We enforce convergence of the controller to the reference path with soft constraints that avoid deviations from the reference path. Finally, we present real life experiments where the controller is deployed on a Scania construction truck that show that the proposed controller outperforms a purepursuit controller. Moreover, we detail how the few tuning parameters can affect the obtained solution in practice.

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MPC-based approach to active steering for autonomous vehicle systems

Cited by 361 publications

References 32 publications

Control allocation—A survey

Control allocation—A survey

Longitudinal Model Predictive Control with comfortable speed planner

Experimental evaluation of economic model predictive control for an autonomous truck

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