Maximum Hands-Off Control: A Paradigm of Control Effort Minimization
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Abstract (arXiv)
In this paper, we propose a new paradigm of control, called a maximum hands-off control. A hands-off control is defined as a control that has a short support per unit time. The maximum hands-off control is the minimum support (or sparsest) per unit time among all controls that achieve control objectives. For finite horizon control, we show the equivalence between the maximum hands-off control and L1-optimal control under a uniqueness assumption called normality. This result rationalizes the use of L1 optimality in computing a maximum hands-off control. We also propose an L1/L2-optimal control to obtain a smooth hands-off control. Furthermore, we give a self-triggered feedback control algorithm for linear time-invariant systems, which achieves a given sparsity rate and practical stability in the case of plant disturbances. An example is included to illustrate the effectiveness of the proposed control.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Aug 2014.
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Citing papers per year: 2022: 3; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
6 papers.
- 2022Adaptive Composite Online Optimization: Predictions in Static and Dynamic Environmentscited by 6
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- 2026Let it Cook: Learning to Wait in Sequential Decision Makingcited by 1
- 2022Fast Algorithm for Constrained Linear Inverse Problemscited by 0
- 2024Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spacescited by 0
- 2025Tractable downfall of basis pursuit in structured sparse optimizationcited by 0
- 2026Let it Cook: Learning to Wait in Sequential Decision Making11 Aug 2026
- 2025Tractable downfall of basis pursuit in structured sparse optimization24 Mar 2025
- 2024Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces24 May 2024
- 2022Fast Algorithm for Constrained Linear Inverse Problems2 Dec 2022
- 2022Adaptive Composite Online Optimization: Predictions in Static and Dynamic Environments1 May 2022
- 2022Sharper Bounds for Proximal Gradient Algorithms with Errors4 Mar 2022