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Nonlinear Optimal Control
One of the basic problem classes which can be solved with ACADO toolkit
are standard optimal control problems [1]. These problems typically consist
of a dynamic system with differential states and possibly also
algebraic states, the objective can usually be written as a sum of a
Lagrange and a Mayer term. Moreover, ACADO toolkit tackles several
types of constraints, such as control and state bounds, terminal
constraints, general nonlinear path constraints, periodic boundary
conditions, etc.
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Multi-Objective Optimal Control
As an extension, ACADO toolkit offers systematic and
advanced tools for solving general optimal control problems with
multiple and conflicting objectives [2]. Pareto frontiers (or trade-off
surfaces) can automatically and efficiently be generated by
several scalarization approaches, which convert the
original multi-objective optimal
control problem into a series of parametric single objective optimal
control problems. The available scalarization approaches involve the
classic convex
Weighted Sum as well as recent techniques as Normal Boundary Intersection
and Normalized Normal Boundary Intersection. Typical algorithmic
features include smart re-initialization strategies for computational
speed-ups and post-processing tools as Pareto filter algorithms.
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State and Parameter Estimation
An important class of optimal control problems, which obtains a special
attention within ACADO toolkit, are state and parameter estimation
problems. This subclass of optimal control problems can theoretically
also be transformed into a standard nonlinear optimal control problem.
However, state and parameter estimation problems have often a certain
structure, which can be used by the algorithms. In ACADO Toolkit
Gauss-Newton algorithms are implemented to deal with the least-squares
terms, which typically occur within this class of optimization
problems. Moreover, a-posteriori analysis tools are available such as a
variance-covariance compuation for the estimated states and parameters.
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Model Predictive Control
Another highlight of ACADO toolkit are its model based feedback control
algorithms. The corresponding problems can be divided into two kinds of
online dynamic optimization problems: the Model Predictive Control
(MPC) problem of finding (approximately) optimal control actions to be fed back
to the controlled process [3], and the Moving Horizon Estimation (MHE) problem of
estimating the current process states using measurements of its
outputs.
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