A Scalable Procedure for $\mathcal{H}_{\infty}-$Control Design
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915289852018688 |
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| author | Kumar, Amit Chanekar, Prasad Vilas |
| author_facet | Kumar, Amit Chanekar, Prasad Vilas |
| contents | This paper proposes a novel gradient based scalable procedure for $\mathcal{H}_{\infty}-$control design. We compute the gradient using algebraic Riccati equation and then couple it with a novel Armijo rule inspired step-size selection procedure. We perform numerical experiments of the proposed solution procedure on an exhaustive list of benchmark engineering systems to show its convergence properties. Finally we compare our proposed solution procedure with available semi-definite programming based gradient-descent algorithm to demonstrate its scalability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10979 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Scalable Procedure for $\mathcal{H}_{\infty}-$Control Design Kumar, Amit Chanekar, Prasad Vilas Optimization and Control Systems and Control This paper proposes a novel gradient based scalable procedure for $\mathcal{H}_{\infty}-$control design. We compute the gradient using algebraic Riccati equation and then couple it with a novel Armijo rule inspired step-size selection procedure. We perform numerical experiments of the proposed solution procedure on an exhaustive list of benchmark engineering systems to show its convergence properties. Finally we compare our proposed solution procedure with available semi-definite programming based gradient-descent algorithm to demonstrate its scalability. |
| title | A Scalable Procedure for $\mathcal{H}_{\infty}-$Control Design |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2505.10979 |