Adaptive neural sliding mode compensator for a class of nonlinear systems with unmodeled uncertainties
This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adapt...
| Authors: | , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2013 |
| Country: | Argentina |
| Institution: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repository: | CONICET Digital (CONICET) |
| Language: | English |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/25357 |
| Online Access: | http://hdl.handle.net/11336/25357 |
| Access Level: | Open access |
| Keyword: | Nonlinear Systems Neural Networks Mimo Systems Sliding Mode Control Radial Basis Functions https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
| Summary: | This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input multi-output nonlinear system. The control strategy is an inverse nonlinear controller combined with an adaptive neural network with sliding mode control using an on-line learning algorithm. The adaptive neural network with sliding mode control acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations in its entire structure (kinematics and dynamics). The controllers are obtained by using Lyapunov's stability theory. Experimental results of a case study show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties. |
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