Data-driven optimal stabilization for discrete-time nonlinear systems by approximate value iteration

2014 ◽  
Author(s):  
Yongqiang Li ◽  
Zhengsheng Hou ◽  
Yuanjing Feng
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Data Driven ◽  
Value Iteration ◽  
Optimal Stabilization ◽  
Approximate Value Iteration

scholarly journals A Nearer Optimal and Faster Trained Value Iteration ADP for Discrete-Time Nonlinear Systems

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 14933-14944
Author(s):  
Junping Hu ◽  
Gen Yang ◽  
Zhicheng Hou ◽  
Gong Zhang ◽  
Wenlin Yang ◽  
...  
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Value Iteration

scholarly journals A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shangtai Jin ◽  
Zhongsheng Hou ◽  
Ronghu Chi
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Prediction Algorithm ◽  
Control Law ◽  
Control Input

A data-driven predictive terminal iterative learning control (DDPTILC) approach is proposed for discrete-time nonlinear systems with terminal tracking tasks, where only the terminal output tracking error instead of entire output trajectory tracking error is available. The proposed DDPTILC scheme consists of an iterative learning control law, an iterative parameter estimation law, and an iterative parameter prediction law. If the partial derivative of the controlled system with respect to control input is bounded, then the proposed control approach guarantees the terminal tracking error convergence. Furthermore, the control performance is improved by using more information of predictive terminal outputs, which are predicted along the iteration axis and used to update the control law and estimation law. Rigorous analysis shows the monotonic convergence and bounded input and bounded output (BIBO) stability of the DDPTILC. In addition, extensive simulations are provided to show the applicability and effectiveness of the proposed approach.

Data-driven model-free sliding mode learning control for a class of discrete-time nonlinear systems

2020 ◽  
Vol 42 (13) ◽  
pp. 2533-2547
Author(s):  
Lei Cao ◽  
Shouli Gao ◽  
Dongya Zhao
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Discrete Time ◽  
Sliding Mode ◽  
Learning Control ◽  
Linearization Method ◽  
Data Driven ◽  
Controlled System ◽  
Model Free ◽  
The Mathematical Model

This paper proposes a data-driven model-free sliding mode learning control (MFSMLC) for a class of discrete-time nonlinear systems. In this scheme, the control design does not depend on the mathematical model of the controlled system. The nonlinear system can be transformed into a dynamic linear data system by a novel dynamic linearization method. A recursive learning control algorithm is designed for the nonlinear system that can drive the sliding variable reach and remain on the sliding surface only by using output and input data. Moreover, the chattering is reduced because there is no non-smooth term in MFSMLC. After the strict stability analysis, the effectiveness of MFSMLC is validated by MATLAB simulations.

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