Controle preditivo iterativo não linear multivariável sob restrições com complexidade temporal reduzida
This thesis deals with the numerical resolution of optimal control problems using an iterative Model Predictive Control (MPC) method for non-linear multivariable systems under constraints. This iterative method was recently presented in the literature and avoids the need to solve a nonconvex opti...
Wedi'i Gadw mewn:
| Prif Awdur: | |
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| Awduron Eraill: | |
| Fformat: | doctoralThesis |
| Iaith: | por |
| Cyhoeddwyd: |
Brasil
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| Pynciau: | |
| Mynediad Ar-lein: | https://repositorio.ufrn.br/jspui/handle/123456789/25898 |
| Tagiau: |
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| Crynodeb: | This thesis deals with the numerical resolution of optimal control problems using an
iterative Model Predictive Control (MPC) method for non-linear multivariable systems
under constraints. This iterative method was recently presented in the literature and
avoids the need to solve a nonconvex optimization problem using a time-variant linearization
of the nonlinear model of the system, which is iteratively adjusted by solving at
each sampling time an iterative optimization problem using quadratic programming. The
main advantage is the faster resolution of the optimal control problem using quadratic
programming rather than non-convex programming, while maintaining an appropriate
description of the nonlinear dynamics of the process being controlled. The approach presented
is an evolution of the original iterative algorithm, based on the convergence analysis
of the method, and a tightening strategy of the domain of admissible states for constraint
observance, which is based on reachable sets obtained using the interval arithmetic.
Firstly, MPC as an optimal control technique is presented. Next, we analyze some MPC
approaches available in the literature that deal with the reduction of the time complexity
of the method, and then the proposed approach is introduced, being systematically discussed
the convergence of the method and its uncertainty, a new and concise mathematical
description of the algorithm, the technique for observing the constraints, as well as the
aspects related to its implementation. In sequence, applications of the proposed algorithm
are presented to demonstrate the feasibility of the approach used and to emphasize the
form of its application. |
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