Towards a Framework for Nonlinear Predictive Control using Derivative-Free Optimization
| Autor: | Eric C. Kerrigan, Alberto Oliveri, Lucian Nita, Ian McInerney, Yuanbo Nie |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Mathematical optimization Computational complexity theory Computer science Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control optimal control Automation & Control Systems Derivative-free optimization FOS: Mathematics FOS: Electrical engineering electronic engineering information engineering Mathematics - Optimization and Control derivative-free optimization eess.SY Science & Technology mesh adaptive direct search math.OC Function (mathematics) Optimal control cs.SY Term (time) System dynamics Model predictive control Control and Systems Engineering Optimization and Control (math.OC) Path (graph theory) |
| Zdroj: | Mcinerney, I, Nita, L, Nie, Y, Oliveri, A & Kerrigan, E C 2021, ' Towards a Framework for Nonlinear Predictive Control using Derivative-Free Optimization ', IFAC-PapersOnLine, vol. 54, no. 6, pp. 284-289 . https://doi.org/10.1016/j.ifacol.2021.08.558 7th IFAC Conference on Nonlinear Model Predictive Control |
| DOI: | 10.1016/j.ifacol.2021.08.558 |
| Popis: | The use of derivative-based solvers to compute solutions to optimal control problems with non-differentiable cost or dynamics often requires reformulations or relaxations that complicate the implementation or increase computational complexity. We present an initial framework for using the derivative-free Mesh Adaptive Direct Search (MADS) algorithm to solve Nonlinear Model Predictive Control problems with non-differentiable features without the need for reformulation. The MADS algorithm performs a structured search of the input space by simulating selected system trajectories and computing the subsequent cost value. We propose handling the path constraints and the Lagrange cost term by augmenting the system dynamics with additional states to compute the violation and cost value alongside the state trajectories, eliminating the need for reconstructing the state trajectories in a separate phase. We demonstrate the practicality of this framework by solving a robust rocket control problem, where the objective is to reach a target altitude as close as possible, given a system with uncertain parameters. This example uses a non-differentiable cost function and simulates two different system trajectories simultaneously, with each system having its own free final time. Comment: Accepted for presentation at the 7th IFAC Conference on Nonlinear Model Predictive Control |
| Databáze: | OpenAIRE |
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