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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sanner, R.M. Slotine, J.-J.E. |
| Copyright Year | 1991 |
| Description | Author affiliation: Nonlinear Syst. Lab., MIT, Cambridge, MA, USA (Sanner, R.M.; Slotine, J.-J.E.) |
| Abstract | Previous work has provided the theoretical foundations of a constructive design procedure for uniform approximation of smooth functions to a chosen degree of accuracy using networks of Gaussian radial basis functions. This construction and the guaranteed uniform bounds were shown to provide the basis for stable adaptive neurocontrol algorithms for a class of nonlinear plants. The authors detail and extend these ideas in three directions. First, some practical details of the construction are provided, explicitly illustrating the relation between the free parameters in the network design and the degree of approximation error on a particular set. Next, the original adaptive control algorithm is modified to permit incorporation of additional prior knowledge of the system dynamics, allowing the neurocontroller to operate in parallel with conventional fixed or adaptive controllers. Finally, it is shown how the Gaussian network construction may also be utilized in recursive identification algorithms with similar guarantees of stability and convergence.< |
| Starting Page | 2116 |
| Ending Page | 2123 |
| File Size | 875569 |
| Page Count | 8 |
| File Format | |
| ISBN | 0780304500 |
| DOI | 10.1109/CDC.1991.261511 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1991-12-11 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptive control Nonlinear systems Programmable control Neural networks Laboratories Error correction Neurocontrollers Stability Convergence Approximation error |
| Content Type | Text |
| Resource Type | Article |
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