arXiv:2605.24852v1 Announce Type: new Abstract: Recent advances in learning-based model predictive control (MPC) have leveraged neural networks for online model learning, achieving strong performance when nonstationary system dynamics deviate from nominal models. However, existing approaches primarily address specific or relatively structured forms of dynamical variation, leaving more general, unknown, and unpredictable time-varying dynamics insufficiently handled. To tackle this challenge, we propose T2S-MPC, a framework that adaptively learns a residual dynamics model online and integrates i
The continuous evolution of AI in real-world applications drives the need for models that can robustly adapt to unpredictable, time-varying dynamics, pushing research in online adaptive control.
This development enhances the reliability and safety of AI-driven control systems, enabling their deployment in more complex and dynamic environments, which is crucial for autonomous systems and industrial automation.
Control systems can now more effectively manage and adapt to unknown and unpredictable real-time changes, moving beyond limitations of existing learning-based MPC approaches.
- · AI developers
- · Robotics companies
- · Autonomous vehicle manufacturers
- · Industrial automation sector
- · Systems reliant on static models
- · Companies with brittle control algorithms
Improved performance and safety of autonomous systems in dynamic environments.
Accelerated adoption of AI in critical infrastructure and manufacturing due to enhanced reliability.
Reduced need for human intervention in complex operational settings, potentially leading to fully autonomous facilities.
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Read at arXiv cs.LG