Data-driven control for SISO feedback linearizable systems with unknown control gain

2019 ◽  
Author(s):  
Paulo Tabuada ◽  
Lucas Fraile
Keyword(s):  
Data Driven ◽  
Control Gain ◽  
Unknown Control ◽  
Feedback Linearizable Systems

scholarly journals Fuzzy Adaptive Prescribed Performance Control for Uncertain Horizontal Platform System with Unknown Control Gain

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Wei Xiang ◽  
Yeguo Sun ◽  
Chunzhi Yang
Keyword(s):  
Control Method ◽  
Adaptive Controller ◽  
Performance Bounds ◽  
Constrained System ◽  
Prescribed Performance ◽  
Performance Technique ◽  
Control Gain ◽  
Unknown Control ◽  
Platform System ◽  
Fuzzy Adaptive

This paper proposes a fuzzy adaptive control method for uncertain horizontal platform system with unknown control gain, which is capable of guaranteeing the prescribed performance bounds. An error transformation is introduced to transform the original constrained system into an equivalent unconstrained one. Then, based on the error transformation technique and the predefined performance technique, a fuzzy adaptive controller is designed for the unconstrained system. It is shown that all the variables of the resulting closed-loop system are bounded. Finally, an illustrative example is given to demonstrate the effectiveness and usefulness of the proposed method.

Adaptive Iterative Learning Control for Nonlinear Systems With Unknown Control Gain1

2004 ◽  
Vol 126 (4) ◽  
pp. 916-920 ◽  
Author(s):  
Huadong Chen ◽  
Ping Jiang
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Interval ◽  
Learning Gain ◽  
Adaptive Iterative Learning Control ◽  
Control Gain ◽  
Unknown Control

An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.

scholarly journals Prescribed Performance Synchronization Control of Chaotic Systems with Unknown Control Gain Signs

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xin Ma ◽  
Fang Zhu
Keyword(s):  
Chaotic Systems ◽  
Control Method ◽  
Lyapunov Theory ◽  
Synchronization Control ◽  
Fuzzy Logic Systems ◽  
Prescribed Performance ◽  
Synchronization Errors ◽  
Nussbaum Function ◽  
Control Gain ◽  
Unknown Control

For a class of uncertain nonlinear chaotic systems with unknown control gain signs and saturated input, by means of Nussbaum function, a scheme of finite-time prescribed performance synchronization control is proposed. Here, Nussbaum function is used to eliminate the influence of unknown control gain signs, and fuzzy logic systems are used to estimate unknown functions. Lyapunov theory is used to prove that all synchronization errors converge to a predefined small performance range under the designed control method. Finally, simulation results are provided to illustrate the feasibility of the proposed method.

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