Python code generation for explicit MPC in MPT

Takács, Bálint; Stevek, Juraj; Valo, Richard; Kvasnica, Michal

doi:10.1109/ecc.2016.7810473

Cited by 7 publications

(7 citation statements)

References 12 publications

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“…Implementing code generation techniques that allow the multiparametric solver to export solutions to different code environments for example C code for microcontrollers, Javascript code for scripting interfaces, or Python for software interfacing. The MPT3 solver implements C and Python generation (Takács et al, 2016). This is an important feature as it allows for the deployment of multiparametric solutions to multiple different hardware and software platforms.…”

Section: Softwarementioning

confidence: 99%

Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward

et al. 2021

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The inevitable presence of uncertain parameters in critical applications of process optimization can lead to undesirable or infeasible solutions. For this reason, optimization under parametric uncertainty was, and continues to be a core area of research within Process Systems Engineering. Multiparametric programming is a strategy that offers a holistic perspective for the solution of this class of mathematical programming problems. Specifically, multiparametric programming theory enables the derivation of the optimal solution as a function of the uncertain parameters, explicitly revealing the impact of uncertainty in optimal decision-making. By taking advantage of such a relationship, new breakthroughs in the solution of challenging formulations with uncertainty have been created. Apart from that, researchers have utilized multiparametric programming techniques to solve deterministic classes of problems, by treating specific elements of the optimization program as uncertain parameters. In the past years, there has been a significant number of publications in the literature involving multiparametric programming. The present review article covers recent theoretical, algorithmic, and application developments in multiparametric programming. Additionally, several areas for potential contributions in this field are discussed, highlighting the benefits of multiparametric programming in future research efforts.

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Section: Softwarementioning

confidence: 99%

Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward

et al. 2021

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“…The optimization process is repeated at every iteration, for selecting the best input sequence that will drive the system to the desired reference, while the complexity of this calculation depends on various characteristics, such as the complexity of the model, the prediction and control horizon, the number of optimization variables, constraints, etc. This repeated task is considered to be computational intensive [29] and quite often there is a need for expensive computation units to handle this type of problems. A strategy to overcome the challenge of extended computation power of the MPC is to design the controller in an off-line manner.…”

Section: Background and Motivationmentioning

confidence: 99%

On the Optimal Adhesion Control of a Vortex Climbing Robot

Papadimitriou

Ανδρικόπουλος

Nikolakopoulos

2021

J Intell Robot Syst

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This article tackles the challenge of negative pressure adhesion control of a Vortex Robotic (VR) platform, which utilizes a modified Electric Ducted Fan (EDF)-based design for successfully adhering to surfaces of variable curvature. The resulting Vortex Actuation (VA) system is estimated through a switching Autoregressive-Moving-Average with eXternal input (ARMAX) identification, for accurately capturing the throttle to adhesion force relationship throughout its operating range. For safe attachment of the robot on a surface, the critical adhesion is modeled based on the geometrical properties of the robotic platform for providing the required reference forces. Within this work, an explicit controller via the use of a Constraint Finite Time Optimal Control (CFTOC) approach is designed in an offline manner, which results in a lookup table realization that ensures overall system stability in all state transitions. In an effort to further improve the tracking response for arbitrary setup orientations, the adhesion control scheme is extended through a switching EMPC framework. The resulting frameworks are tested through both dynamic simulation and experimental sequences involving: a) adhesion control on a rotating test curved surface and, b) adhesion and locomotion sequences on a water pipe.

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“…where the parameter F i and G i are obtained from the MPT. R i = {x|H i x ≤ h i } are M polyhedral critical regions obtained as well from the MPT [16]. The explicit solution of the optimization problem provides a look-up table, which converts the on-line optimization problem to a on-line sequential search algorithm.…”

Section: Control Formulationmentioning

confidence: 99%

“…The optimization process is repeated at every iteration and the complexity of this calculation depends on various characteristics, such as the complexity of the model, the prediction and control horizon, the number of optimization variables, constraints etc. This repeated task is considered to be computational intensive [16] and quite often there is a need for expensive computation units to handle these types of problems. A solution to this computational burden is the use of an EMPC.…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of an Explicit Model Predictive Controller for an Adhesion Vortex Actuated Climbing Robot

Papadimitriou¹,

Ανδρικόπουλος²,

Nikolakopoulos³

2020

2020 American Control Conference (ACC)

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This article establishes an Explicit Model Predictive Control (EMPC) scheme for controlling the adhesion of a climbing Vortex Robot (VR). The VR utilizes an Electric Ducted Fan (EDF) as the Vortex Actuator (VA), where the dynamics have been identified via an Autoregressive-Moving-Average with eXternal input (ARMAX) identification scheme. An explicit controller via the use of a Constraint Finite Time Optimal Control (CFTOC) approach is designed in an offline manner and implemented for the case of the VR, where the adhesion reference is provided by a static force model. The presented approach results in a lookup table realization that ensures overall system stability in all state transitions, while being able to accurately control the adhesion force for arbitrary setup orientations. The efficacy of the proposed control scheme is demonstrated through experimental results involving a moving test surface under random inclinations and robot orientations.

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Python code generation for explicit MPC in MPT

Cited by 7 publications

References 12 publications

Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward

Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward

On the Optimal Adhesion Control of a Vortex Climbing Robot

Experimental Evaluation of an Explicit Model Predictive Controller for an Adhesion Vortex Actuated Climbing Robot

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