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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2201.06180 |
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| _version_ | 1866914730489151488 |
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| author | Khan, Hafiz Zeeshan Iqbal Mobeen, Surrayya Rajput, Jahanzeb Riaz, Jamshed |
| author_facet | Khan, Hafiz Zeeshan Iqbal Mobeen, Surrayya Rajput, Jahanzeb Riaz, Jamshed |
| contents | Modern aircraft are designed with redundant control effectors to cater for fault tolerance and maneuverability requirements. This leads to aircraft being over-actuated and requires control allocation schemes to distribute the control commands among control effectors. Traditionally, optimization-based control allocation schemes are used; however, for nonlinear allocation problems, these methods require large computational resources. In this work, an artificial neural network (ANN) based nonlinear control allocation scheme is proposed. The proposed scheme is composed of learning the inverse of the control effectiveness map through ANN, and then implementing it as an allocator instead of solving an online optimization problem. Stability conditions are presented for closed-loop systems incorporating the allocator, and computational challenges are explored with piece-wise linear effectiveness functions and ANN-based allocators. To demonstrate the efficacy of the proposed scheme, it is compared with a standard quadratic programming-based method for control allocation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_06180 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Nonlinear Control Allocation: A Learning Based Approach Khan, Hafiz Zeeshan Iqbal Mobeen, Surrayya Rajput, Jahanzeb Riaz, Jamshed Systems and Control Artificial Intelligence Optimization and Control Modern aircraft are designed with redundant control effectors to cater for fault tolerance and maneuverability requirements. This leads to aircraft being over-actuated and requires control allocation schemes to distribute the control commands among control effectors. Traditionally, optimization-based control allocation schemes are used; however, for nonlinear allocation problems, these methods require large computational resources. In this work, an artificial neural network (ANN) based nonlinear control allocation scheme is proposed. The proposed scheme is composed of learning the inverse of the control effectiveness map through ANN, and then implementing it as an allocator instead of solving an online optimization problem. Stability conditions are presented for closed-loop systems incorporating the allocator, and computational challenges are explored with piece-wise linear effectiveness functions and ANN-based allocators. To demonstrate the efficacy of the proposed scheme, it is compared with a standard quadratic programming-based method for control allocation. |
| title | Nonlinear Control Allocation: A Learning Based Approach |
| topic | Systems and Control Artificial Intelligence Optimization and Control |
| url | https://arxiv.org/abs/2201.06180 |