Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning
AutoMPC is a Python package that automates and optimizes data-driven model predictive control. However, it can be computationally expensive and unstable when exploring large search spaces using pure Bayesian Optimization (BO). To address these issues, this paper proposes to employ a meta-learning approach called Portfolio that improves AutoMPC's efficiency and stability by warmstarting BO. Portfolio optimizes initial designs for BO using a diverse set of configurations from previous tasks and stabilizes the tuning process by fixing initial configurations instead of selecting them randomly. Experimental results demonstrate that Portfolio outperforms the pure BO in finding desirable solutions for AutoMPC within limited computational resources on 11 nonlinear control simulation benchmarks and 1 physical underwater soft robot dataset.
Code (1)
Tasks
Bayesian OptimizationMeta-LearningModel Predictive ControlMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
AI Control Scientist: LLM-driven Agentic System for Automated Control Design
Control system design is critical for modern industry, such as chemical process temperature regulation and aero-engine control. However,traditional control design workflows rely heavily on expert knowledge and extensive …
Harnessing Uncertainty for a Separation Principle in Direct Data-Driven Predictive Control
Model Predictive Control (MPC) is a powerful method for complex system regulation, but its reliance on an accurate model poses many limitations in real-world applications. Data-driven predictive control (DDPC) aims at ov…
Model Predictive ControlvalidData-Driven Adaptive PID Control Based on Physics-Informed Neural Networks
This article proposes a data-driven PID controller design based on the principle of adaptive gain optimization, leveraging Physics-Informed Neural Networks (PINNs) generated for predictive modeling purposes. The proposed…
On the impact of regularization in data-driven predictive control
Model predictive control (MPC) is a control strategy widely used in industrial applications. However, its implementation typically requires a mathematical model of the system being controlled, which can be a time-consumi…
Model Predictive ControlData-driven predictive control in a stochastic setting: a unified framework
Data-driven predictive control (DDPC) has been recently proposed as an effective alternative to traditional model-predictive control (MPC) for its unique features of being time-efficient and unbiased with respect to the …
Model Predictive Control