TY - CHAP
T1 - High order sliding mode neurocontrol for uncertain nonlinear SISO systems
T2 - Theory and applications
AU - Chairez, Isaac
AU - Poznyak, Alexander
AU - Poznyak, Tatyana
PY - 2008
Y1 - 2008
N2 - Uncertainties in dynamic systems are common in real applications, provoking substantial troubles in any control realization and being a source of instability or poor performance for tracking or regulation problems. Considerable research efforts had been undertaken on control designing for uncertain nonlinear dynamic systems over the last thirty years. There are several approaches to design and construct a control in this situation. Among them, the more effective are the Artificial Neural Networks (ANN) and the Sliding Mode (SM) technique with all possible variants within (Integral Sliding Mode, Higher Order Sliding Mode, etc.). Such combination seems to be very promising [21], [28] because it provides a new instrument for identification, state estimation and control of many classes of uncertain systems affected by external perturbations. This chapter deals with the realization of this idea and suggests an adaptive control designing based on both Differential Neural Network Observation and High Order Sliding Mode Technique. Below this approach is referred to as High Order Sliding Mode Neural Control (HOSMNC).
AB - Uncertainties in dynamic systems are common in real applications, provoking substantial troubles in any control realization and being a source of instability or poor performance for tracking or regulation problems. Considerable research efforts had been undertaken on control designing for uncertain nonlinear dynamic systems over the last thirty years. There are several approaches to design and construct a control in this situation. Among them, the more effective are the Artificial Neural Networks (ANN) and the Sliding Mode (SM) technique with all possible variants within (Integral Sliding Mode, Higher Order Sliding Mode, etc.). Such combination seems to be very promising [21], [28] because it provides a new instrument for identification, state estimation and control of many classes of uncertain systems affected by external perturbations. This chapter deals with the realization of this idea and suggests an adaptive control designing based on both Differential Neural Network Observation and High Order Sliding Mode Technique. Below this approach is referred to as High Order Sliding Mode Neural Control (HOSMNC).
UR - http://www.scopus.com/inward/record.url?scp=41849089752&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-79016-7_9
DO - 10.1007/978-3-540-79016-7_9
M3 - Capítulo
AN - SCOPUS:41849089752
SN - 9783540790150
T3 - Lecture Notes in Control and Information Sciences
SP - 179
EP - 200
BT - Modern Sliding Mode Control Theory
A2 - Bartolini, Giorgio
A2 - Pisano, Alessandro
A2 - Usai, Elio
A2 - Fridman, Leonid
ER -