Real-time nonlinear MPC and MHE for a large-scale mechatronic application

Vukov, Milan; Gros, Sébastien; Horn, Greg; Frison, Gianluca; Geebelen, Kurt; Jørgensen, John Bagterp; Swevers, Jan; Diehl, Moritz

doi:10.1016/j.conengprac.2015.08.012

Cited by 65 publications

(41 citation statements)

References 34 publications

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“…Like Vukov et al. (), the Gauss‐Newton iteration is divided into two parts: preparation and feedback parts. As illustrated in Figure , the preparation part is executed prior to the feedback part, and the feedback part is executed after measurements for RHE and estimates for RHC are available.…”

Section: Implementationsmentioning

confidence: 99%

High‐precision control of tracked field robots in the presence of unknown traction coefficients

Kayacan

Young

Peschel

et al. 2018

Journal of Field Robotics

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Accurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off‐track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this paper presents the development, application, and experimental results of a real‐time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steering accuracy. Recent advances in cheap and fast microprocessors, as well as advances in solution methods for nonlinear optimization problems, have made nonlinear receding horizon control (RHC) and receding horizon estimation (RHE) methods suitable for field robots that require high‐frequency (milliseconds) updates. A real‐time RHEC framework is developed and applied to a fully autonomous mobile robotic platform designed by the authors for in‐field phenotyping applications in sorghum fields. Nonlinear RHE is used to estimate constrained states and parameters, and nonlinear RHC is designed based on an adaptive system model that contains time‐varying parameters. The capabilities of the real‐time RHEC framework are verified experimentally, and the results show an accurate tracking performance on a bumpy and wet soil field. The mean values of the Euclidean error and required computation time of the RHEC framework are equal to 0.0423 m and 0.88 ms, respectively.

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Section: Implementationsmentioning

confidence: 99%

High‐precision control of tracked field robots in the presence of unknown traction coefficients

Kayacan

Young

Peschel

et al. 2018

Journal of Field Robotics

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“…Unlike MPC, this estimation technique started receiving wider attention only in the recent years [9]. Indeed, the combination of MPC and MHE has been applied in diverse fields such as autonomous agricultural vehicles [10], unmanned aerial vehicles [11], preventive sensor maintenance [12], airborne wind energy systems [13] and blood glucose regulation [14]. Concerning water systems, the combination of these techniques is not so common, although it has been used for flood prevention in rivers [15] and pollution mitigation for combined sewer networks [16].…”

Section: Introductionmentioning

confidence: 99%

Model predictive control and moving horizon estimation for water level regulation in inland waterways

Segovia

Rajaoarisoa

Nejjari

et al. 2019

Journal of Process Control

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This work regards the design of optimization techniques for the purposes of state estimation and control in the framework of inland waterways, often characterized by negligible bottom slopes and large time delays. The derived control-oriented model allows these issues to be handled in a suitable manner. Then, the analogous moving horizon estimation and model predictive control techniques are applied in a centralized manner to estimate the unmeasurable states and fulfill the operational goals, respectively. Finally, the performance of the methodology is tested in simulation by means of a realistic case study based on part of the inland waterways in the north of France. The resultsshow that the proposed methodology is able to guarantee the navigability condition, as well as the other operational goals.

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“…10 Model predictive control (MPC) finds its roots in the control of industrial process control systems, including chemical and petroleum industries. [11][12][13] The reason for its use in these industries is that these systems have slow dynamics which give the controller sufficient time for solving the optimization problem. Nevertheless, the technological advancements in modern computation power have opened the doors for MPC applications, even in aerospace systems with very small time constants, i.e.…”

Section: Introductionmentioning

confidence: 99%

Receding horizon control of a 3 DOF helicopter using online estimation of aerodynamic parameters

Mehndiratta

Kayacan

2017

Proceedings of the Institution of Mechanical Engineers, Part G:

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This study presents a numerical implementation of fast nonlinear model predictive control (NMPC) and nonlinear moving horizon estimation (NMHE) for the trajectory tracking problem of a 3 degree of freedom (DOF) helicopter. The motivation behind using the NMPC instead of its linear counterpart is that the helicopter is operated over nonlinear regions. Moreover, this system has cross-couplings that make the control of the system even more complicated. What is more, according to our simulation scenario, the system has a time-varying dynamical model because it has time-varying parameters which are estimated online using NMHE and the extended Kalman filter (EKF) throughout the control. Although NMHE is computationally more demanding, its capability of incorporating the constraints encourages us to utilize NMHE rather than EKF. Two reference trajectories, namely, sinusoidal and square-like, are tracked, and owing to the better learning capability of NMHE over EKF, the NMPC-NMHE closed-loop control framework is able to track both reference signals with more accuracy than the NMPC-EKF control framework, even under parameter uncertainties. Thanks to the ACADO toolkit, the combined average execution time is 4 milliseconds, demonstrating the potential of the proposed framework for real-time aerospace applications using relatively cheaper processors.

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Real-time nonlinear MPC and MHE for a large-scale mechatronic application

Cited by 65 publications

References 34 publications

High‐precision control of tracked field robots in the presence of unknown traction coefficients

High‐precision control of tracked field robots in the presence of unknown traction coefficients

Model predictive control and moving horizon estimation for water level regulation in inland waterways

Receding horizon control of a 3 DOF helicopter using online estimation of aerodynamic parameters

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