Output Feedback Terminal Sliding Mode Control for a Class of Second Order Nonlinear Systems

Abstract

In this study, a novel output feedback terminal sliding mode control (TSMC) approach is proposed for a class of second order nonlinear systems in light of the equivalent output injection sliding mode observer (SMO) method and TSMC principle. The SMO method is applied to reconstruct full states in finite time and the non‐singular TSMC algorithm is designed to stabilize system states to equilibrium points in finite time. The corresponding stability analysis is presented. An indispensable illustrative example is bench tested to validate the effectiveness of the proposed approach.

Model development based on evolutionary framework for condition monitoring of a lathe machine

“…The higher generalization ability of the proposed model obtained is beneficial for accurately predicting the number of days before the maintenance is needed for the lathe machines when it is subjected to the three operations (idle run, turning and facing) for the three conditions (overall acoustic, overall vibration and headstock vibration). Future work for authors is to apply more sophisticated variants of neural networks [39][40][41][42][43][44] and evaluate any economic and environmental differences [45,46] based on previous works. …”

A Novel Sliding Mode Control for a Class of Second‐order Mechanical Systems

“…Here, q 1 is the angular position of link 1 form the reference axis x, q 2 is the angular position between the link 1 and link 2, g is the acceleration of gravity, l 1 …”

Adaptive output feedback finite time control for a class of second order nonlinear systems

“…This method requires computation of the equivalent injection in order to provide an estimate of the state estimation error. This yields problems when the system contains disturbances and/or uncertainty in the dynamics of the measured state and existing contributions applying this method for second order nonlinear systems assume the corresponding disturbance signals and/or uncertainty are known or measurable [22,23]. This is clearly a limiting assumption.…”