Discrete-time Indirect Adaptive Control for Systems with State-Dependent Disturbances via Directional Forgetting: Concurrent Learning Approach
<|reference_start|>Discrete-time Indirect Adaptive Control for Systems with State-Dependent Disturbances via Directional Forgetting: Concurrent Learning Approach: An adaptive controller design for cases with disturbances is critical in practical applications for preventing unexpected control performance degradation and instability. Recently, adaptive control systems with relaxed persistent excitation (PE) conditions have been proposed to solve this problem; however, most discussions have focused on continuous-time systems. In this study, we propose a novel adaptive control method for discrete-time systems with disturbances that combines directional forgetting and concurrent learning. The proposed method does not require the PE condition, information on disturbances, unknown parameters, or matching conditions, and it guarantees exponential uniform ultimate unbounded (UUB). It was also theoretically demonstrated that the upper bound of the UUB can be designed based on the forgetting factor, which is a design parameter. Numerical simulation results illustrate the effectiveness of the proposed method.<|reference_end|>
@article{tsuruhara2024discrete-time,
    title={Discrete-time Indirect Adaptive Control for Systems with State-Dependent
  Disturbances via Directional Forgetting: Concurrent Learning Approach},
    author={Satoshi Tsuruhara and Kazuhisa Ito},
    journal={arXiv preprint arXiv:2409.09316},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09316},
    primaryClass={eess.SY cs.SY}
}