CasADi: a software framework for nonlinear optimization and optimal control

Andersson, Joel; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz

doi:10.1007/s12532-018-0139-4

Cited by 2,172 publications

(997 citation statements)

References 111 publications

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“…Within this work, the MBPE algorithm is implemented using CasADi in MATLAB environment. The system dynamics taken into account are the nonlinear longitudinal motions expressed in with differential states

boldx false(t false) = {([], V_{normalT} false(t false) α false(t false) θ false(t false) q false(t false))}^{⊤},

assuming steady wing‐level flight condition, ie, β = ϕ = p = r = 0.…”

Section: Parameter Estimation Resultsmentioning

confidence: 99%

Aerodynamic model identification of an autonomous aircraft for airborne wind energy

Licitra

Bürger

Williams³

et al. 2019

Optim Control Appl Methods

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Summary Airborne wind energy (AWE) refers to a novel technology capable of harvesting energy from wind by flying crosswind patterns with tethered autonomous aircraft. Successful design of flight controllers for AWE systems relies on the availability of accurate mathematical models. Due to an expected nonconventional structure of the airborne component, the system identification procedure must be ultimately addressed via an intensive flight test campaign to gain additional insight about the aerodynamic properties. In this paper, the longitudinal dynamics of a rigid‐wing, high lift, autonomous aircraft for AWE are identified from experimental data obtained within flight tests. The aerodynamic characteristics are estimated via an efficient time‐domain multiple experiments model‐based parameter estimation algorithm.

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boldx false(t false) = {([], V_{normalT} false(t false) α false(t false) θ false(t false) q false(t false))}^{⊤},

assuming steady wing‐level flight condition, ie, β = ϕ = p = r = 0.…”

Section: Parameter Estimation Resultsmentioning

confidence: 99%

Aerodynamic model identification of an autonomous aircraft for airborne wind energy

Licitra

Bürger

Williams³

et al. 2019

Optim Control Appl Methods

Self Cite

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“…The simulator generates all possible converter's subsystems first . For every subsystem, the system matrices are determined, both in numpy and CasADi format. For the list of possible subsystems are formed possible switching sequences between subsystems.…”

Section: Model Predictive Control Of Dc‐dc Convertersmentioning

confidence: 99%

“…The MPC controller relies on the PANOC algorithm developed in Stella et al, tailored for NMPC problems. PANOC is implemented in the C programming language using the CasADi package for automatic differentiation. NMPC files containing the C code are automatically generated, and they can be used for programming the microcontroller or DSP.…”

Section: Introductionmentioning

confidence: 99%

Microsecond nonlinear model predictive control for DC‐DC converters

Lekić

Hermans

Jovičić

et al. 2020

Circuit Theory & Apps

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Summary In this paper, we present a novel nonlinear model predictive control (NMPC) formulation for the transient control of a DC‐DC converter. We demonstrate that a real‐time implementation of the proposed NMPC scheme using the PANOC solver can be efficiently applied to control DC‐DC converters in the microsecond range. Moreover, an embedded, code‐generated version of PANOC can be implemented using microcontrollers or digital signal processors. The algorithm is incorporated in the transient simulator of PWM DC‐DC converters, and the operation of the simulator on the boost converter's example is presented, comparing the performance of our NMPC‐controller with that of a classical PID controller. The operation of the boost converter controlled using the proposed NMPC algorithm is validated experimentally.

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“…Currently, there are two possible ways to specify these functions. In the first the user has to utilize the CasADi framework to define Function objects for the system ODE and a path. The optimal path following problem will be automatically formulated and solved using CasADi interface to the IPOPT solver.…”

Section: Overview Of the Librarymentioning

confidence: 99%

PolyMPC: An efficient and extensible tool for real‐time nonlinear model predictive tracking and path following for fast mechatronic systems

Listov

Jones

2020

Optim Control Appl Methods

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Summary This paper presents PolyMPC, an open‐source C++ library for pseudospectral‐based real‐time predictive control of nonlinear systems. It provides a necessary background on the computational aspects of the pseudospectral approximation of optimal control problems and explains how various model predictive control and parameter estimation algorithms can be implemented using the software. We discuss the key algorithmic modules and architectural features of the PolyMPC library. The workflow of a path following controller design for a highly nonlinear mechatronic system is demonstrated in a tutorial example. Another example illustrates how the core functionality might be used to approximate and solve a custom optimal control problem.

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CasADi: a software framework for nonlinear optimization and optimal control

Cited by 2,172 publications

References 111 publications

Aerodynamic model identification of an autonomous aircraft for airborne wind energy

Aerodynamic model identification of an autonomous aircraft for airborne wind energy

Microsecond nonlinear model predictive control for DC‐DC converters

PolyMPC: An efficient and extensible tool for real‐time nonlinear model predictive tracking and path following for fast mechatronic systems

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