Model-free H∞ control design for unknown linear discrete-time systems via Q-learning with LMI

Automatica ◽  
2010 ◽  
Vol 46 (8) ◽  
pp. 1320-1326 ◽  
Author(s):  
J.-H. Kim ◽  
F.L. Lewis
Keyword(s):  
Discrete Time ◽  
Control Design ◽  
Q Learning ◽  
Model Free ◽  
Discrete Time Systems ◽  
Time Systems

H∞ Tracking Control for Linear Discrete-Time Systems: Model-Free Q-Learning Designs

2021 ◽  
Vol 5 (1) ◽  
pp. 175-180
Author(s):  
Yunjie Yang ◽  
Yan Wan ◽  
Jihong Zhu ◽  
Frank L. Lewis
Keyword(s):  
Discrete Time ◽  
Tracking Control ◽  
Q Learning ◽  
Model Free ◽  
Systems Model ◽  
Discrete Time Systems ◽  
Time Systems

Model-free adaptive PID control for nonlinear discrete-time systems

2020 ◽  
Vol 42 (10) ◽  
pp. 1797-1807 ◽  
Author(s):  
Shuhua Zhang ◽  
Ronghu Chi
Keyword(s):  
Discrete Time ◽  
Mathematical Analysis ◽  
Pid Control ◽  
Contraction Mapping Principle ◽  
Unknown Parameters ◽  
Model Free ◽  
Discrete Time Systems ◽  
Gradient Parameter ◽  
Adaptive Pid Control ◽  
Time Systems

This work explores a model-free adaptive PID (MFA-PID) control for nonlinear discrete-time systems with rigorous mathematical analysis under a data-driven framework. An improved compact form dynamic linearization (iCFDL) is proposed to transfer the original nonlinear system into an affined linear data model including a nonlinear residual term. Both a time-difference estimator and a gradient parameter estimator are designed to estimate the nonlinear residual uncertainties and the unknown parameters in the iCFDL model. Subsequently, a novel improved CFDL based MFA-PID (iCFDL-MFA-PID) control is proposed by incorporating these two estimators. The results are extended by the use of improved partial format dynamic linearization (iPFDL) and full format dynamic linearization (iFFDL). The theoretical results are shown using contraction mapping principle-based mathematical analysis, as well as simulations.

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