Name
Abstract | ||
|---|---|---|
Combination of neural networks and sliding mode control (SMC) can reduce chattering, because the upper bound of uncertainties becomes smaller when neural networks are used to model unknwn nolinear systems. The tracking error of normal neural sliding mode control is asymptotically stable, while neural control and SMC are applied at same time. In this paper, neural control and SMC are connected serially: first a dead-zone neural control assures that the tracking error is bounded, then super-twisting second-order sliding-mode is used to guarantee finite time convergence of the contoller. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IJCNN.2009.5178652 | IJCNN |
Keywords | Field | DocType |
tracking error,mode control,asymptotically stable,finite time convergence,neural network,second-order sliding-mode,normal neural,unknwn nolinear system,neural control,dead-zone neural control,artificial neural networks,feedback control,nonlinear system,asymptotic stability,switches,upper bound,error correction,second order,sliding mode control,uncertainty,robust control,convergence,tracking,neural networks,data mining,nonlinear systems | Convergence (routing),Nonlinear system,Control theory,Computer science,Upper and lower bounds,Exponential stability,Artificial neural network,Robust control,Tracking error,Sliding mode control | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
14 | 2 | |