Flexible-step Model Predictive Control based on Generalized Lyapunov Functions
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866911833391104000 |
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| author | Fürnsinn, Annika Ebenbauer, Christian Gharesifard, Bahman |
| author_facet | Fürnsinn, Annika Ebenbauer, Christian Gharesifard, Bahman |
| contents | We present a novel nonlinear model predictive control (MPC) scheme with relaxed stability criteria, based on the idea of generalized discrete-time control Lyapunov functions. These functions need to satisfy an average descent over a finite window of time, rather than a descent at every time step. One feature of this scheme is that it allows for implementing a flexible number of control inputs in each iteration, in a computationally attractive manner, while guaranteeing recursive feasibility and stability. The benefits of our flexible-step implementation are also demonstrated in an application to nonholonomic systems, where the one-step standard implementation may suffer from lack of asymptotic convergence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_02780 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Flexible-step Model Predictive Control based on Generalized Lyapunov Functions Fürnsinn, Annika Ebenbauer, Christian Gharesifard, Bahman Optimization and Control We present a novel nonlinear model predictive control (MPC) scheme with relaxed stability criteria, based on the idea of generalized discrete-time control Lyapunov functions. These functions need to satisfy an average descent over a finite window of time, rather than a descent at every time step. One feature of this scheme is that it allows for implementing a flexible number of control inputs in each iteration, in a computationally attractive manner, while guaranteeing recursive feasibility and stability. The benefits of our flexible-step implementation are also demonstrated in an application to nonholonomic systems, where the one-step standard implementation may suffer from lack of asymptotic convergence. |
| title | Flexible-step Model Predictive Control based on Generalized Lyapunov Functions |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2211.02780 |