Observer-based adaptive neural networks control for Markovian jump nonlinear systems with partial mode information and input saturation. (English) Zbl 1527.93470
Summary: The observer-based adaptive neural networks control problem is studied in this article for nonlinear Markovian jump systems (MJSs) with partial mode information and input saturation. The existing adaptive control scheme for MJSs works only when the Markov mode of system is completely known. For this reason, the concept of partial mode information of Markov chain is embedded into the framework of adaptive backstepping method and then a mode detector is set to emit the mode. To achieve the control objective, only the emitted mode is used to design the state observer and adaptive control scheme. Besides, an auxiliary signal is introduced in MJSs to compensate the effect of input saturation and this signal appears in controller as the input of neural networks instead of a separate term, which makes the form of proposed controller simpler than that in the literature. Finally, a numerical example and a practical example are provided to demonstrate the feasibility of our control scheme.
{© 2021 John Wiley & Sons Ltd.}
{© 2021 John Wiley & Sons Ltd.}
MSC:
| 93E35 | Stochastic learning and adaptive control |
| 93B53 | Observers |
| 93E03 | Stochastic systems in control theory (general) |
| 93C10 | Nonlinear systems in control theory |
Keywords:
adaptive neural networks control; input saturation; Markovian jump nonlinear system; partial mode information; state observerReferences:
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