Moving horizon estimation and nonlinear model predictive control for autonomous agricultural vehicles

Kraus, Tom; Ferreau, Hans Joachim; Kayacan, Erdal; Ramón, Herman; Baerdemaeker, Josse De; Diehl, Moritz; Saeys, Wouter

doi:10.1016/j.compag.2013.06.009

Cited by 77 publications

(76 citation statements)

References 23 publications

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“…A classical reference dealing with skid-steering mobile platforms is [5], that in the RobREx project has inspired a research direction explored in [6], whose methodology may be traced back to [7]. The approach presented here, motivated by nonholonomic robotics [8], the theory of automotive systems [9,10], and the application of predictive control reported in [11], is based on more general assumptions, including lateral and longitudinal slips and an under actuated model of the platform dynamics. The dynamics equations of the Rex platform can be derived classically [12].…”

Section: Introductionmentioning

confidence: 99%

Modeling and control of a skid-steering mobile platform with coupled side wheels

Tchoń¹,

Zadarnowska²,

Juszkiewicz³

et al. 2015

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. This study is devoted to the modeling and control of a 4-wheel, skid-steering mobile platform with coupled side wheels, subject to lateral and longitudinal slips. The dynamics equations of the platform are derived, and 16 variants of motion distinguished. For the variant of motion allowing for all possible slips of the wheels two control problems are addressed: the motion planning problem and the trajectory tracking problem. The former problem is solved by means of a Jacobian motion planning algorithm based on the Endogenous Configuration Space Approach and, complementarily, using the Optimal Control Approach. The Nonlinear Model Predictive Control is applied to the latter problem. Performance of these control algorithms is illustrated by a sort of the parking problem. Significant robustness of the predictive control algorithm against the model uncertainty is revealed.

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Section: Introductionmentioning

confidence: 99%

Modeling and control of a skid-steering mobile platform with coupled side wheels

Tchoń¹,

Zadarnowska²,

Juszkiewicz³

et al. 2015

Bulletin of the Polish Academy of Sciences Technical Sciences

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“…One can easily imagine applying such a strategy to vehicles and robots with more complex kinematics or environments, an example pointing in that direction is presented in [5].…”

Section: Discussionmentioning

confidence: 99%

“…The equations for Θ were presented in [9], and methods for online parameter identification and an adaptive controller has been presented [10]. The parameter equations are reproduced here for reference: (1) (5) The disturbances in the δ have been left at zero for the simulations shown here, while it does provide a possible input for perturbing the system in simulation.…”

Section: Modelling and Simulationmentioning

confidence: 99%

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Non-linear model predictive control for constrained robot navigation in row crops

Utstumo

Berge

Gravdahl

2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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This work addresses the implementation issue of constrained Model Predictive Control (MPC) for the autonomous trajectory-tracking problem. The chosen process to control is a Wheeled Mobile Robot (WMR) described by a discrete, Multiple Input Multiple Output (MIMO), state-space and linear parameter varying kinematic model. The main motivation of the constrained MPC usage in this case relies on its ability in considering, in a straightforward way, control and states constraints that naturally arise in trajectory tracking practical problems. The efficiency of the presented control scheme is validated through experimental results on a two wheeled mobile robot using both STM32F429II and STM32F407ZG microcontrollers. The controller implementation is facilitated by the usage of the automatic C code generation and interesting optimization before real-time execution. Based on the experimental results obtained, the good performance and robustness of the proposed control scheme are established.

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“…More importantly, for our study, DDEV is able to provide us in autonomous driving using MHE. Kraus [31] proposed an MHE-nonlinear model predictive control (NMPC) framework to control field vehicles using an adaptive nonlinear kinematic model.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Two Dynamics-Model-Based Estimation Algorithms for Distributed Drive Electric Vehicles

Zhang

Göhlich

2017

Applied Sciences

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Featured Application: The proposed estimators can be applied in many vehicle active safety systems. They are capable of estimating vehicle states that are normally not measurable or that are too complicated to measure directly using additional expensive sensors. Abstract:The effect of vehicle active safety systems is subject to the accurate knowledge of vehicle states. Therefore, it is of great importance to develop a precise and robust estimation approach so as to deal with nonlinear vehicle dynamics systems. In this paper, a planar vehicle model with a simplified tire model is established first. Two advanced model-based estimation algorithms, an unscented Kalman filter and a moving horizon estimation, are developed for distributed drive electric vehicles. Using the proposed algorithms, vehicle longitudinal velocity, lateral velocity, yaw rate as well as lateral tire forces are estimated based on information fusion of standard sensors in today's typical vehicle and feedback signals from electric motors. Computer simulations are implemented in the environment of CarSim combined with Matlab/Simulink. The performance of both estimators regarding convergence, accuracy, and robustness against an incorrect initial estimate of longitudinal velocity is compared in detail. The comparison results demonstrate that both estimation approaches have favourable coincidence with the corresponding reference values, while the moving horizon estimation is more accurate and robust, and owns faster convergence.

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Moving horizon estimation and nonlinear model predictive control for autonomous agricultural vehicles

Cited by 77 publications

References 23 publications

Modeling and control of a skid-steering mobile platform with coupled side wheels

Modeling and control of a skid-steering mobile platform with coupled side wheels

Non-linear model predictive control for constrained robot navigation in row crops

Comparative Study of Two Dynamics-Model-Based Estimation Algorithms for Distributed Drive Electric Vehicles

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