A robust discrete-time adaptive control approach for systems with almost periodic time-varying parameters

2012 ◽  
Vol 24 (1) ◽  
pp. 166-178 ◽  
Author(s):  
Khalid Abidi
Keyword(s):  
Adaptive Control ◽  
Discrete Time ◽  
Time Varying ◽  
Almost Periodic ◽  
Varying Parameters ◽  
Control Approach ◽  
Time Varying Parameters

scholarly journals Data-Driven Model-Free Adaptive Control Based on Error Minimized Regularized Online Sequential Extreme Learning Machine

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3241 ◽  
Author(s):  
Xiaofei Zhang ◽  
Hongbin Ma
Keyword(s):  
Adaptive Control ◽  
Extreme Learning Machine ◽  
Discrete Time ◽  
Control Method ◽  
Time Varying ◽  
Varying Parameters ◽  
Model Free ◽  
Time Varying Parameters ◽  
Learning Machine ◽  
Linearized Model

Model-free adaptive control (MFAC) builds a virtual equivalent dynamic linearized model by using a dynamic linearization technique. The virtual equivalent dynamic linearized model contains some time-varying parameters, time-varying parameters usually include high nonlinearity implicitly, and the performance will degrade if the nonlinearity of these time-varying parameters is high. In this paper, first, a novel learning algorithm named error minimized regularized online sequential extreme learning machine (EMREOS-ELM) is investigated. Second, EMREOS-ELM is used to estimate those time-varying parameters, a model-free adaptive control method based on EMREOS-ELM is introduced for single-input single-output unknown discrete-time nonlinear systems, and the stability of the proposed algorithm is guaranteed by theoretical analysis. Finally, the proposed algorithm is compared with five other control algorithms for an unknown discrete-time nonlinear system, and simulation results show that the proposed algorithm can improve the performance of control systems.

Sign in / Sign up

Export Citation Format

Share Document