A Novel Neural-Network-Based Adaptive Control Scheme for Output-Constrained Stochastic Switched Nonlinear Systems

2019 ◽  
Vol 49 (2) ◽  
pp. 418-432 ◽  
Author(s):  
Ben Niu ◽  
Ding Wang ◽  
Huan Li ◽  
Xuejun Xie ◽  
Naif D. Alotaibi ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Switched Nonlinear Systems ◽  
Control Scheme ◽  
Stochastic Switched Nonlinear Systems ◽  
Adaptive Control Scheme

NEURAL ADAPTIVE CONTROL FOR VIBRATION SUPPRESSION IN COMPOSITE FIN-TIP OF AIRCRAFT

2008 ◽  
Vol 18 (03) ◽  
pp. 219-231 ◽  
Author(s):  
S. SURESH ◽  
N. KANNAN ◽  
N. SUNDARARAJAN ◽  
P. SARATCHANDRAN
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Adaptive Control ◽  
Inverse Dynamics ◽  
Vibration Suppression ◽  
Mode Shapes ◽  
Neural Adaptive Control ◽  
Control Scheme ◽  
Adaptive Neural Network Control ◽  
Adaptive Control Scheme

In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H∞ control scheme.

Adaptive Feedback Linearization Control of SISO Nonlinear Processes Using a Self-Generating Neural Network-Based Approach

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Karim Salahshoor ◽  
Amin Sabet Kamalabady
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Control ◽  
Feedback Linearization ◽  
Learning Algorithm ◽  
Rbf Neural Networks ◽  
Adaptive Feedback ◽  
Control Scheme ◽  
Feedback Linearization Control ◽  
Adaptive Control Scheme

This paper presents a new adaptive control scheme based on feedback linearization technique for single-input, single-output (SISO) processes with nonlinear time-varying dynamic characteristics. The proposed scheme utilizes a modified growing and pruning radial basis function (MGAP-RBF) neural network (NN) to adaptively identify two self-generating RBF neural networks for online realization of a well-known affine model structure. An extended Kalman filter (EKF) learning algorithm is developed for parameter adaptation of the MGAP-RBF neural networks. The MGAP-RBF growing and pruning criteria have been endeavored to enhance its performance for online dynamic model identification purposes. A stability analysis has been provided to ensure the asymptotic convergence of the proposed adaptive control scheme using Lyapunov criterion. Capabilities of the adaptive feedback linearization control scheme is evaluated on two nonlinear CSTR benchmark processes, demonstrating good performances for both set-point tracking and disturbance rejection objectives.

A new adaptive control scheme for uncertain nonlinear systems with quantized input signal

2015 ◽  
Vol 352 (12) ◽  
pp. 5599-5610 ◽  
Author(s):  
Lantao Xing ◽  
Changyun Wen ◽  
Hongye Su ◽  
Jianping Cai ◽  
Lei Wang
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Input Signal ◽  
Uncertain Nonlinear Systems ◽  
Control Scheme ◽  
Adaptive Control Scheme
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