Quantifying Controllability for Nonlinear State-Dependent Riccati Equation Control
- Resource Type
- Conference
- Authors
- Hu, Yuhui; Neusypin, Konstantin Avenirovich; Shen, Kai
- Source
- 2022 International Russian Automation Conference (RusAutoCon) Automation Conference (RusAutoCon), 2022 International Russian. :80-85 Sep, 2022
- Subject
- Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Satellites
Automation
Simulation
Computational modeling
Riccati equations
Control system synthesis
Controllability
controllability Gramian
degree of controllability
state-dependent coefficient
state-dependent Riccati equation
- Language
Degree of controllability (DOC) characterizes how controllable a given system is and thus quantifying controllability can facilitate the control system synthesis and optimization. In this paper, the controllability of nonlinear input-affine systems and the state-dependent-coefficient (SDC) factorization are first reviewed. A computational procedure based on the scalarization of the controllability Gramian is proposed to quantify the controllability of both system and state variables of the SDC factored system for state-dependent Riccati equation (SDRE) control. The simulation of coordinate satellite control is carried out to validate the effectiveness of the proposed DOC criterion. It is shown that SDC-parameterized models with higher DOC can promote the performance of the SDRE control algorithm.