Active control of friction self-excited vibration using neuro-fuzzy and data mining techniques

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.

► Friction-induced self-excited vibration is a complex and nonlinear physical phenomenon with some uncertainties.
► An improved data mining algorithm is employed to extract a complete and robust fuzzy rulebase, which forms a basis of a data-driven neuro-fuzzy friction model.
► Based on the well-known Lyapunov stability theory, the parameters of the neuro-fuzzy friction model are on-line adjusted to ensure the desired performances of the closed-loop system.