AI Article Synopsis
- The article introduces an event-triggered adaptive command-filtered control approach tailored for MIMO nonlinear systems that experience unknown rate-dependent hysteresis in actuators and state constraints.
- It utilizes a command filter method to manage the challenges of non-differentiable control signals and to prevent complexity issues, while employing barrier Lyapunov functions to maintain system state stability.
- Stability is analyzed, and the method's effectiveness is demonstrated through simulation results, showing how adaptive neural networks approximate unknown non-linear elements and update hysteresis parameters.
Article Abstract
This article presents an event-triggered adaptive acrlong NN command-filtered control for a class of multi-input and multi-output (MIMO) nonlinear systems with unknown rate-dependent hysteresis in the actuator and the constraints on full states. The acrlong ETM is used to reduce the communication frequency between controller and actuator. The command filter technique is first employed to solve the dilemma between the nondifferentiable control signal at triggering instants and rate-dependent hysteresis input premise while avoiding the "explosion of complexity" problem. During the backstepping design, the barrier Lyapunov functions are utilized to guarantee that system states will stay in certain regions and the unknown nonlinear items are approximated by adaptive neural networks. The compensating signals are constructed to eliminate filtering errors. The estimates of unknown hysteresis parameters are updated by adaptive laws. The stability analysis is given and the effectiveness of the proposed method is verified by simulation.
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| http://dx.doi.org/10.1109/TCYB.2023.3312047 | DOI Listing |
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