Adaptive Dynamic Surface Control for Uncertain Nonlinear Systems With Interval Type-2 Fuzzy Neural Networks

2014 ◽  
Vol 44 (2) ◽  
pp. 293-304 ◽  
Author(s):  
Yeong-Hwa Chang ◽  
Wei-Shou Chan
2018 ◽  
Vol 41 (2) ◽  
pp. 516-531 ◽  
Author(s):  
Maryam Shahriari-kahkeshi ◽  
Sanaz Rahmani

In this study, an adaptive dynamic surface control (DSC) scheme based on the interval type-2 fuzzy systems is proposed for uncertain nonlinear systems with unknown asymmetric dead-zone input and unknown control gains. The dead-zone nonlinearity is represented as a time-varying system with a bounded disturbance. The proposed approach invokes the interval type-2 fuzzy system to approximate the unknown nonlinear dynamics that appears in the virtual and actual control inputs. Also, it proposes adaptive terms to compensate the effect of the disturbance-like term in the dead-zone constraint. Then, the DSC scheme is designed based on the interval type-2 fuzzy system and the dead-zone model. Adaptive laws are derived to tune the consequent parameters of the interval type-2 fuzzy system and the dead-zone model. Stability analysis of the proposed scheme shows that all the signals of the closed-loop system are uniformly ultimately bounded and the tacking error can be made arbitrary small by proper selection of the design parameters. The proposed scheme avoids the “explosion of complexity” and “singularity” problems, simultaneously. Furthermore, it can compensate the effect of the dead-zone constraint without any need to its parameters. The simulation and comparison results are presented to demonstrate the effectiveness of the proposed control scheme.


2018 ◽  
Vol 41 (7) ◽  
pp. 1861-1879 ◽  
Author(s):  
Teh-Lu Liao ◽  
Wei-Shou Chan ◽  
Jun-Juh Yan

This paper presents a distributed adaptive formation control method for uncertain multiple quadrotor systems under a directed graph that characterizes the interaction among the leader and followers. The proposed approach is based on an adaptive dynamic surface control, consensus algorithm and graph theory, where the system uncertainties are approximately modelled by interval type-2 fuzzy neural networks. The adaptive laws of interval type-2 fuzzy neural network parameters are derived from the stability analysis. In this study, the robust stability of the closed-loop system is guaranteed by the Lyapunov theorem, and the leader-follower formation goal can be asymptotically achieved. The developed control scheme is applied to the followers of quadrotor systems for performance evaluations. Simulation results are also provided to compare with the existing methods and reveal the superiority of the proposed adaptive formation controller.


2012 ◽  
Vol 3 (3) ◽  
pp. 179-188 ◽  
Author(s):  
Sevil Ahmed ◽  
Nikola Shakev ◽  
Andon Topalov ◽  
Kostadin Shiev ◽  
Okyay Kaynak

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