$π$-MPPI: A Projection-based Model Predictive Path Integral Scheme for Smooth Optimal Control of Fixed-Wing Aerial Vehicles
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908372675067904 |
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| author | Andrejev, Edvin Martin Manoharan, Amith Unt, Karl-Eerik Singh, Arun Kumar |
| author_facet | Andrejev, Edvin Martin Manoharan, Amith Unt, Karl-Eerik Singh, Arun Kumar |
| contents | Model Predictive Path Integral (MPPI) is a popular sampling-based Model Predictive Control (MPC) algorithm for nonlinear systems. It optimizes trajectories by sampling control sequences and averaging them. However, a key issue with MPPI is the non-smoothness of the optimal control sequence, leading to oscillations in systems like fixed-wing aerial vehicles (FWVs). Existing solutions use post-hoc smoothing, which fails to bound control derivatives. This paper introduces a new approach: we add a projection filter $π$ to minimally correct control samples, ensuring bounds on control magnitude and higher-order derivatives. The filtered samples are then averaged using MPPI, leading to our $π$-MPPI approach. We minimize computational overhead by using a neural accelerated custom optimizer for the projection filter. $π$-MPPI offers a simple way to achieve arbitrary smoothness in control sequences. While we focus on FWVs, this projection filter can be integrated into any MPPI pipeline. Applied to FWVs, $π$-MPPI is easier to tune than the baseline, resulting in smoother, more robust performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10962 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | $π$-MPPI: A Projection-based Model Predictive Path Integral Scheme for Smooth Optimal Control of Fixed-Wing Aerial Vehicles Andrejev, Edvin Martin Manoharan, Amith Unt, Karl-Eerik Singh, Arun Kumar Robotics Systems and Control Model Predictive Path Integral (MPPI) is a popular sampling-based Model Predictive Control (MPC) algorithm for nonlinear systems. It optimizes trajectories by sampling control sequences and averaging them. However, a key issue with MPPI is the non-smoothness of the optimal control sequence, leading to oscillations in systems like fixed-wing aerial vehicles (FWVs). Existing solutions use post-hoc smoothing, which fails to bound control derivatives. This paper introduces a new approach: we add a projection filter $π$ to minimally correct control samples, ensuring bounds on control magnitude and higher-order derivatives. The filtered samples are then averaged using MPPI, leading to our $π$-MPPI approach. We minimize computational overhead by using a neural accelerated custom optimizer for the projection filter. $π$-MPPI offers a simple way to achieve arbitrary smoothness in control sequences. While we focus on FWVs, this projection filter can be integrated into any MPPI pipeline. Applied to FWVs, $π$-MPPI is easier to tune than the baseline, resulting in smoother, more robust performance. |
| title | $π$-MPPI: A Projection-based Model Predictive Path Integral Scheme for Smooth Optimal Control of Fixed-Wing Aerial Vehicles |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2504.10962 |