Identification with non-parametric uncertainty

Author(s):  
R.C. Younce ◽  
C.E. Rohrs
Robotica ◽  
2018 ◽  
Vol 37 (3) ◽  
pp. 405-427 ◽  
Author(s):  
Seyed Mohammad Ahmadi ◽  
Mohammad Mehdi Fateh

SUMMARYAchieving the asymptotic tracking control of electrically driven robot manipulators is a challenging problem due to approximation/modelling error arising from parametric and non-parametric uncertainty. Thanks to the specific property of Taylor series systems as they are universal approximators, this research outlines two robust control schemes using an adaptive Taylor series system for robot manipulators, including actuators' dynamics. First, an indirect adaptive controller is designed such as to approximate an uncertain continuous function by using a Taylor series system in the proposed control law. Second, a direct adaptive scheme is established to employ the Taylor series system as a controller. In both controllers, not only a robustifying term is constructed using the estimation of the upper bound of approximation/modelling error, but the closed-loop stability, as well as the asymptotic convergence of joint-space tracking error and its time derivative, is ensured. Due to the design of the Taylor series system in the tracking error space, our technique clearly has an advantage over fuzzy and neural network-based control methods in terms of the small number of tuning parameters and inputs. The proposed methods are simple, model free in decentralized forms, no need for uncertainty bounding functions and perfectly capable of dealing with parametric and non-parametric uncertainty and measurement noise. Finally, simulation results are introduced to confirm the efficiency of the proposed control methods.


2021 ◽  
Author(s):  
Unni Krishnan R Nair ◽  
Anish Gupta ◽  
D. A. Sasi Kiran ◽  
Ajay Shrihari ◽  
Vanshil Shah ◽  
...  

2020 ◽  
Vol 62 (10) ◽  
pp. 685-693
Author(s):  
I.A. Egoshin ◽  
A. A. Kolchev ◽  
A. E. Nedopekin

1994 ◽  
Vol 27 (2) ◽  
pp. 315-320 ◽  
Author(s):  
E. Gazi ◽  
W.D. Seider ◽  
L.H. Ungar

Author(s):  
H.R. Ghazisaeedi ◽  
M.S. Tavazoei

This paper deals with event-triggered adaptive tracking control of a class of nonlinear systems with non-parametric uncertainty and unknown control input direction, in the presence of actuator faults. The proposed event-triggered control method takes advantage of the radial basis function neural networks to approximate the non-parametric uncertainties. Moreover, this control method benefits from the Nussbaum-type function-based adaptation laws for simultaneously dealing with unknown input direction and actuator faults. Numerical simulation results confirm the efficiency of the proposed control method to confront the above mentioned limitations.


2020 ◽  
Vol 5 (2) ◽  
pp. 2690-2697
Author(s):  
SriSai Naga Jyotish Poonganam ◽  
Bharath Gopalakrishnan ◽  
Venkata Seetharama Sai Bhargav Kumar Avula ◽  
Arun Kumar Singh ◽  
K. Madhava Krishna ◽  
...  

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