# Model-based controller assisted domain randomization for transient vibration suppression of nonlinear powertrain system with parametric uncertainty

arXiv: https://arxiv.org/abs/2504.19715 · HTML: https://mlsanity.com/paper/2504.19715/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 58 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 17 of 17)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Learning Agile Robotic Locomotion Skills by Imitating Animals](https://mlsanity.com/paper/2004.00784/) (2020)
- [Sim-to-Real Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/1806.07851/) (2018)
- [Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots](https://mlsanity.com/paper/2103.14295/) (2021)
- [BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators](https://mlsanity.com/paper/1906.01728/) (2019)
- [Understanding Domain Randomization for Sim-to-real Transfer](https://mlsanity.com/paper/2110.03239/) (2021)
- [Deep Drone Racing: From Simulation to Reality with Domain Randomization](https://mlsanity.com/paper/1905.09727/) (2019)
- [RL for Latent MDPs: Regret Guarantees and a Lower Bound](https://mlsanity.com/paper/2102.04939/) (2021)
- [Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning](https://mlsanity.com/paper/2408.14472/) (2024)
- [Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real](https://mlsanity.com/paper/1908.05224/) (2019)
- [System Identification and H_∞-based Control of Quadrotor Attitude](https://mlsanity.com/paper/1812.10931/) (2018)
- [Improving the Robustness of Reinforcement Learning Policies with L1 Adaptive Control](https://mlsanity.com/paper/2112.01953/) (2021)

## Built on by (most cited first, 1 of 1)

- [Continual Uncertainty Learning for Robust Control of Nonlinear Systems with Multiple Heterogeneous Uncertainties](https://mlsanity.com/paper/2602.17174/) (2026)

## Abstract (arXiv)

Complex mechanical systems such as vehicle powertrains are inherently subject to multiple nonlinearities and uncertainties arising from parametric variations. Modeling errors are therefore unavoidable, making the transfer of control systems from simulation to real-world systems a critical challenge. Traditional robust controls have limitations in handling certain types of nonlinearities and uncertainties, requiring a more practical approach capable of comprehensively compensating for these various constraints. This study proposes a new robust control approach using the framework of deep reinforcement learning (DRL). The key strategy lies in the synergy among domain randomization-based DRL, long short-term memory (LSTM)-based actor and critic networks, and model-based control (MBC). The problem setup is modeled via the latent Markov decision process (LMDP), a set of vanilla MDPs, for a controlled system subject to uncertainties and nonlinearities. In LMDP, the dynamics of an environment simulator is randomized during training to improve the robustness of the control system to real testing environments. The randomization increases training difficulties as well as conservativeness of the resultant control system; therefore, progress is assisted by concurrent use of a model-based controller based on a physics-based system model. Compared to traditional DRL-based controls, the proposed approach is smarter in that we can achieve a high level of generalization ability with a more compact neural network architecture and a smaller amount of training data. The controller is verified via practical application to active damping for a complex powertrain system with nonlinearities and parametric variations. Comparative tests demonstrate the high robustness of the proposed approach.