Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Automatica (Journal of IFAC)
Two-stage structural damage detection using fuzzy neural networks and data fusion techniques
Expert Systems with Applications: An International Journal
A functional neural fuzzy network for classification applications
Expert Systems with Applications: An International Journal
Fuzzy control of an electrodynamic shaker for automotive and aerospace vibration testing
Expert Systems with Applications: An International Journal
A GA-based method for constructing fuzzy systems directly from numerical data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The WM method completed: a flexible fuzzy system approach to data mining
IEEE Transactions on Fuzzy Systems
Stable adaptive fuzzy control of nonlinear systems
IEEE Transactions on Fuzzy Systems
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Design of fuzzy systems using neurofuzzy networks
IEEE Transactions on Neural Networks
Neural-network approximation of piecewise continuous functions: application to friction compensation
IEEE Transactions on Neural Networks
Efficient learning algorithms for three-layer regular feedforward fuzzy neural networks
IEEE Transactions on Neural Networks
A high speed railway control system based on the fuzzy control method
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Vibration caused by friction, termed as friction-induced self-excited vibration (FSV), is harmful to engineering systems. Understanding this physical phenomenon and developing some strategies to effectively control the vibration have both theoretical and practical significance. This paper proposes a self-tuning active control scheme for controlling FSV in a class of mechanical systems. Our main technical contributions include: setup of a data mining based neuro-fuzzy system for modeling friction; learning algorithm for tuning the neuro-fuzzy system friction model using Lyapunov stability theory, which is associated with a compensation control scheme and guaranteed closed-loop system performance. A typical mechanical system with friction is employed in simulation studies. Results show that our proposed modeling and control techniques are effective to eliminate both the limit cycle and the steady-state error.