Name
Abstract | ||
|---|---|---|
Modeling and control of a nonlinear system is a challenging problem, particularly when the system has uncertainties. Usually, the problem is partitioned into a parameter identification component and a nonlinear control component. Each of these tasks require an efficient computational approach, particularly when the system must be modeled and controlled on-line in real-time. A new method for nonlinear control using a self-organizing fuzzy sliding mode architecture was proposed in an earlier document. The control effort is divided into two parts: a sliding mode part that compensates for the uncertainties and provides exponential convergence and a fuzzy rule base part that approximates the control function through a self-organizing procedure. Since the new approach combines these two different methods, convergence of the resulting algorithm is a major issue, particularly when applied to a nonlinear system. This paper develops the general convergence proof. Parameter identification is performed on-line using a recursive least-squares approach. In order to analyze the method, the parameter-identifier/self-organizing fuzzy sliding mode controller is applied to some nonlinear systems that contains several of the nonlinear effects (complex dynamics and uncertainties) that may be faced in real world applications. Results of the control architecture illustrate the feasibility of this approach. |
Year | Venue | Keywords |
|---|---|---|
2001 | INTELLIGENT SYSTEMS | self organization |
Field | DocType | Citations |
Convergence (routing),Control theory,Computer science,Control theory,Fuzzy logic,Open-loop controller | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| W. Huang | 1 | 267 | 33.97 |
| Warren Jasper | 2 | 0 | 1.01 |
| Edward Grant | 3 | 0 | 0.34 |
| Gordon K. Lee | 4 | 96 | 29.59 |