A direct adaptive control scheme for robot trajectory tracking using fuzzy neural networks

2007 ◽  
Vol 3 (1) ◽  
pp. 1-10
Author(s):  
M. Al dossari ◽  
T.Y. Abdalla
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Trajectory Tracking ◽  
Fuzzy Neural Networks ◽  
Direct Adaptive Control ◽  
Robot Trajectory ◽  
Control Scheme ◽  
Fuzzy Neural ◽  
Adaptive Control Scheme

scholarly journals Adaptive control scheme based on the least squares support vector machine network

2011 ◽  
Vol 21 (4) ◽  
pp. 685-696 ◽  
Author(s):  
Tarek Mahmoud
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Adaptive Control ◽  
Least Squares ◽  
Nonlinear System Identification ◽  
Support Vector ◽  
Reference Trajectory ◽  
Control Scheme ◽  
Support Vector Machine Network ◽  
Adaptive Control Scheme

Adaptive control scheme based on the least squares support vector machine networkRecently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.

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.

scholarly journals Improved Adaptive Sliding Mode Control for a Class of Uncertain Nonlinear Systems Subjected to Input Nonlinearity via Fuzzy Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Tat-Bao-Thien Nguyen ◽  
Teh-Lu Liao ◽  
Jun-Juh Yan
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Fuzzy Neural Networks ◽  
Adaptive Sliding Mode Control ◽  
Input Nonlinearity ◽  
Mode Control ◽  
Model Dynamics ◽  
Fuzzy Neural

The paper presents an improved adaptive sliding mode control method based on fuzzy neural networks for a class of nonlinear systems subjected to input nonlinearity with unknown model dynamics. The control scheme consists of the modified adaptive and the compensation controllers. The modified adaptive controller online approximates the unknown model dynamics and input nonlinearity and then constructs the sliding mode control law, while the compensation controller takes into account the approximation errors and keeps the system robust. Based on Lyapunov stability theorem, the proposed method can guarantee the asymptotic convergence to zero of the tracking error and provide the robust stability for the closed-loop system. In addition, due to the modification in controller design, the singularity problem that usually appears in indirect adaptive control techniques based on fuzzy/neural approximations is completely eliminated. Finally, the simulation results performed on an inverted pendulum system demonstrate the advanced functions and feasibility of the proposed adaptive control approach.

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