Name
Title | ||
|---|---|---|
A simplified adaptive neural network prescribed performance controller for uncertain MIMO feedback linearizable systems. |
Abstract | ||
|---|---|---|
In this paper, the problem of deriving a continuous, state-feedback controller for a class of multiinput multioutput feedback linearizable systems is considered with special emphasis on controller simplification and reduction of the overall design complexity with respect to the current state of the art. The proposed scheme achieves prescribed bounds on the transient and steady-state performance of the output tracking errors despite the uncertainty in system nonlinearities. Contrary to the current state of the art, however, only a single neural network is utilized to approximate a scalar function that partly incorporates the system nonlinearities. Furthermore, the loss of model controllability problem, typically introduced owing to approximation model singularities, is avoided without attaching additional complexity to the control or adaptive law. Simulations are performed to verify and clarify the theoretical findings. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TNNLS.2014.2320305 | IEEE transactions on neural networks and learning systems |
Keywords | Field | DocType |
controllability,uncertain nonlinear systems,design complexity reduction,continuous state-feedback controller,model controllability problem,robust adaptive control,uncertain systems,linearisation techniques,neurocontrollers,mimo systems,control system synthesis,multiple input multiple output systems,uncertain nonlinear systems.,performance controller,feedback,scalar function,approximation model singularities,adaptive control,uncertain mimo feedback linearizable systems,system nonlinearities,control nonlinearities,controller simplification,neuro-adaptive control,continuous systems,simplified adaptive neural network,neural networks,steady state | Control theory,Nonlinear system,Controllability,Computer science,Control theory,MIMO,Gravitational singularity,Artificial neural network,Scalar field | Journal |
Volume | Issue | ISSN |
26 | 3 | 2162-2388 |
Citations | PageRank | References |
19 | 0.82 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Achilles Theodorakopoulos | 1 | 60 | 3.49 |
| George A. Rovithakis | 2 | 581 | 52.21 |