This paper addresses the event-triggered based adaptive asymptotic tracking control problem for switched nonlinear systems with unknown control directions based on neural network technique. A novel asymptotic tracking controller, in which Nussbaum functions are introduced to address the issue of unknown control directions, is designed by combining neural network control technology and event-triggered strategy. Different from the existing tracking control schemes, the proposed controller in this paper can guarantee that the tracking error ς 1 asymptotically converges to the origin and reduce the communication burden from the controller to the actuator. Finally, the effectiveness of the presented control design is proved by numerical examples.
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Posted 03 Mar, 2021
Invitations sent on 26 Feb, 2021
Received 26 Feb, 2021
On 21 Feb, 2021
On 19 Feb, 2021
Posted 03 Mar, 2021
Invitations sent on 26 Feb, 2021
Received 26 Feb, 2021
On 21 Feb, 2021
On 19 Feb, 2021
This paper addresses the event-triggered based adaptive asymptotic tracking control problem for switched nonlinear systems with unknown control directions based on neural network technique. A novel asymptotic tracking controller, in which Nussbaum functions are introduced to address the issue of unknown control directions, is designed by combining neural network control technology and event-triggered strategy. Different from the existing tracking control schemes, the proposed controller in this paper can guarantee that the tracking error ς 1 asymptotically converges to the origin and reduce the communication burden from the controller to the actuator. Finally, the effectiveness of the presented control design is proved by numerical examples.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
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