Observer-Based Adaptive Neural Control for Non-Triangular Form Systems With Input Saturation and Full State Constraints
This paper addresses the problem of adaptive output feedback control for a class of non-triangular time-varying delay system with input constraints and full-state constraints. A variable separation approach is adopted to overcome the design difficulty from the non-triangular structure. A novel Lyapu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8586785/ |
| Summary: | This paper addresses the problem of adaptive output feedback control for a class of non-triangular time-varying delay system with input constraints and full-state constraints. A variable separation approach is adopted to overcome the design difficulty from the non-triangular structure. A novel Lyapunov function is introduced to compensate the time-delay terms. Unknown functions are approximated by the radial basis function neural networks. Only one parameter needs to be adjusted online, and a dynamic surface control technique is employed to reduce the computation burden. Combining the barrier Lyapunov function with a backstepping technique in the controller design procedure, the proposed controller guarantees that all the signals in the closed-loop system are uniformly ultimately bounded and the full-state constraints are met. The simulation results demonstrate the effectiveness of the proposed approach. |
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| ISSN: | 2169-3536 |