A Data-driven Hierarchical Control Structure for Systems with Uncertainty
- Resource Type
- Authors
- Hanzhe Teng; Xinyue Kan; Konstantinos Karydis; Lu Shi
- Source
- CCTA
- Subject
- 0209 industrial biotechnology
System deployment
Computer science
System identification
02 engineering and technology
Systems and Control (eess.SY)
01 natural sciences
Electrical Engineering and Systems Science - Systems and Control
010305 fluids & plasmas
Data-driven
020901 industrial engineering & automation
Control theory
Component (UML)
0103 physical sciences
FOS: Electrical engineering, electronic engineering, information engineering
Robot
Sensitivity (control systems)
Linear approximation
- Language
The paper introduces a Data-driven Hierarchical Control (DHC) structure to improve performance of systems operating under the effect of system and/or environment uncertainty. The proposed hierarchical approach consists of two parts: 1) A data-driven model identification component to learn a linear approximation between reference signals given to an existing lower-level controller and uncertain time-varying plant outputs. 2) A higher-level controller component that utilizes the identified approximation and wraps around the existing controller for the system to handle modeling errors and environment uncertainties during system deployment. We derive loose and tight bounds for the identified approximation's sensitivity to noisy data. Further, we show that adding the higher-level controller maintains the original system's stability. A benefit of the proposed approach is that it requires only a small amount of observations on states and inputs, and it thus works online; that feature makes our approach appealing to robotics applications where real-time operation is critical. The efficacy of the DHC structure is demonstrated in simulation and is validated experimentally using aerial robots with approximately-known mass and moment of inertia parameters and that operate under the influence of ground effect.
Comment: Accepted to IEEE Conference on Control Technology and Applications (CCTA) 2020