Tracking control optimization scheme for a class of partially unknown fuzzy systems by using integral reinforcement learning architecture

2019 ◽  
Vol 359 ◽  
pp. 344-356 ◽  
Author(s):  
Kun Zhang ◽  
Huaguang Zhang ◽  
Yunfei Mu ◽  
Shaoxin Sun
Keyword(s):  
Reinforcement Learning ◽  
Tracking Control ◽  
Fuzzy Systems ◽  
Optimization Scheme ◽  
Control Optimization

scholarly journals A Hybrid Tracking Control Strategy for Nonholonomic Wheeled Mobile Robot Incorporating Deep Reinforcement Learning Approach

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 15592-15602
Author(s):  
Xueshan Gao ◽  
Rui Gao ◽  
Peng Liang ◽  
Qingfang Zhang ◽  
Rui Deng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robot ◽  
Control Strategy ◽  
Tracking Control ◽  
Wheeled Mobile Robot ◽  
Learning Approach

Robust Tracking Control for Takagi–Sugeno Fuzzy Systems With Unmeasurable Premise Variables: Application to Tank System

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
H. Ghorbel ◽  
A. El Hajjaji ◽  
M. Souissi ◽  
M. Chaabane
Keyword(s):  
Tracking Control ◽  
Fuzzy Systems ◽  
Design Procedure ◽  
Control Design ◽  
Linear Matrix ◽  
Robust Tracking Control ◽  
Fuzzy Observer ◽  
Tank System ◽  
System States ◽  
Takagi Sugeno

In this paper, a robust fuzzy observer-based tracking controller for continuous-time nonlinear systems presented by Takagi–Sugeno (TS) models with unmeasurable premise variables, is synthesized. Using the H∞ norm and Lyapunov approach, the control design for TS fuzzy systems with both unmeasurable premises and system states is developed to guarantee tracking performance of closed loop systems. Sufficient relaxed conditions for synthesis of the fuzzy observer and the fuzzy control are driven in terms of linear matrix inequalities (LMIs) constraints. The proposed method allows simplifying the design procedure and gives the observer and controller gains in only one step. Numerical simulation on a two tank system is provided to illustrate the tracking control design procedure and to confirm the efficiency of the proposed method.

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