Nonlinear Adaptive Control Design for Affine Systems

2007 ◽  
Author(s):  
Xuejun Zong ◽  
Minghao Tan ◽  
Decheng Yuan
Keyword(s):  
Adaptive Control ◽  
Control Design ◽  
Affine Systems ◽  
Nonlinear Adaptive Control

Nonlinear Adaptive Control Design for Power System with STATCOM device

2018 ◽  
Author(s):  
Moath H. Alqatamin ◽  
M. L. McIntyre ◽  
Joseph Latham ◽  
Pablo Rivera ◽  
Nicholas Hawkins
Keyword(s):  
Adaptive Control ◽  
Power System ◽  
Control Design ◽  
Nonlinear Adaptive Control

An Accelerated Algorithm for Improvement of Adaptation in Nonlinear Adaptive Control

2017 ◽  
Vol 18 (7-8) ◽  
Author(s):  
M. Davanipour ◽  
H. R. Javanmardi ◽  
N. Goodarzi
Keyword(s):  
Adaptive Control ◽  
Control Design ◽  
Adaptive Controller ◽  
Asymptotic Performance ◽  
Transient Performance ◽  
Polynomial Roots ◽  
Nonlinear Adaptive Control ◽  
Satisfactory Performance ◽  
Optimal Values ◽  
The Times

AbstractAdaptive control is capable of handling systems with uncertain parameters in terms of asymptotic performance; however, it is not so well in the transient performance. Even though adaptation gain is part of adaptive controller having much effects on the transient response, it has been considered as a constant gain most of the times. In this paper, a new Laypunov-based mechanism is proposed to find optimal values of adaptation gains in nonlinear adaptive control design. The algorithm inspired of the halving method for finding polynomial roots tries to find optimum values of the adaptation gains in a direction of minimizing a cost function. The simulation results show satisfactory performance of the proposed controller especially in terms transient performance.

Model Following Robust Neuro-Adaptive Control Design for Non-Square, Non-Affine Systems

2004 ◽  
Author(s):  
Radhakant Padhi ◽  
Nishant Unnikrishnan ◽  
S. N. Balakrishnan
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Control Design ◽  
Lyapunov Theory ◽  
Unmodeled Dynamics ◽  
Design Technique ◽  
Affine Systems ◽  
Nominal Model ◽  
Model Following ◽  
Adaptation Procedure

Using neural networks, this paper proposes a new model-following adaptive control design technique for nonlinear systems. The nonlinear system for which the method is applicable is assumed to be of known order. Furthermore, it is assumed that using a nominal model an appropriate nominal controller has been designed for the system. However, it is well-known that because of unmodeled dynamics and/or parameter uncertainties, a nominal controller seldom works the way it is intended to; and sometimes it even leads to instability. Hence there is a need to modify this nominal controller online, in a stable manner, to suppress these unwanted behaviors. An online control adaptation procedure proposed in this paper to achieve this objective. The control design is carried out in two steps: (i) synthesis of a set of neural networks which collectively capture the algebraic function that arises either because of the unmodeled dynamics or uncertainties in parameters and (ii) computation of a controller that drives the state of the actual plant to that of a desired nominal model. The neural network weight update rule is derived using Lyapunov theory, which guarantees both stability of the error dynamics as well as boundedness of the weights of the neural networks. Unlike existing methods, a distinct characteristic of the adaptation procedure presented in this paper is that it is independent of the technique used to design the nominal controller; and hence can be used in conjunction with any known control design technique. Moreover, this technique is applicable to non-square and non-affine systems as well. Numerical results for a fairly-challenging problem are presented in this paper, which demonstrate these features and clearly bring out the potential of the proposed approach.

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