Abstract
To deal with the effects of the input saturation and time-varying input delay, this article presents a serial-parallel identifier-based composite neural network learning control for uncertain nonlinear systems subject to external disturbances. Based on the backstep** technique, a radial basis function network is adopted to identify the unknown term, where the neural network learning accuracy is studied by considering a modeling error. In addition, a compensation system is designed to cope with input delay and input saturation, simultaneously. Besides, the explosion of complexity is mitigated by employing the command-filtered control approach. To enhance the robust performance of the overall system, the proposed control structure is enriched by a disturbance observer. Therefore, new adaptive rules are constructed. The stability of the closed-loop system is ensured by the Lyapunov theorem. Simulation results clarify the efficiency of the proposed control algorithm.
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Javad Keighobadi:Problem formulation; Control design; Writing-original draft; Writing-revised manuscript Ali Mehrjouyan: Data analysis; Software; Updating Software; Validation Alireza Alfi: Conceptualization; Writing-original draft; Writing- revised manuscript; Validation
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Keighobadi, J., Mehrjouyan, A. & Alfi, A. Efficient learning control of uncertain nonlinear systems with input constraints: a disturbance observer-based neural network approach. Int. J. Dynam. Control (2024). https://doi.org/10.1007/s40435-024-01416-5
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DOI: https://doi.org/10.1007/s40435-024-01416-5