Predictive Control
Model Predictive Control (MPC) is an advanced control technique that optimizes a system's trajectory over a future time horizon by iteratively solving optimization problems. Current research emphasizes improving MPC's efficiency and robustness, particularly through integrating machine learning methods like reinforcement learning and neural networks (e.g., using transformers for faster computation or neural networks to approximate MPC solutions), and addressing challenges posed by model uncertainties and safety constraints using techniques such as control barrier functions and Bayesian optimization. These advancements are significantly impacting various fields, including robotics, autonomous vehicles, and energy systems, by enabling more efficient, safe, and adaptable control of complex systems.
Papers
Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning
Kartik Krishna, Steven L. Brunton, Zhuoyuan Song
Computationally-efficient Motion Cueing Algorithm via Model Predictive Control
Akhil Chadha, Vishrut Jain, Andrea Michelle Rios Lazcano, Barys Shyrokau
Data-driven HVAC Control Using Symbolic Regression: Design and Implementation
Yuki Ozawa, Dafang Zhao, Daichi Watari, Ittetsu Taniguchi, Toshihiro Suzuki, Yoshiyuki Shimoda, Takao Onoye
Differentiable Compliant Contact Primitives for Estimation and Model Predictive Control
Kevin Haninger, Kangwagye Samuel, Filippo Rozzi, Sehoon Oh, Loris Roveda
Switching Pushing Skill Combined MPC and Deep Reinforcement Learning for Planar Non-prehensile Manipulation
Bo Zhang, Cong Huang, Haixu Zhang, Xiaoshan Bai