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State and output feedback local control schemes for nonlinear discrete-time 2-D Roesser systems under saturation, quantization and slope restricted input

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Listed:
  • Malik, Saddam Hussain
  • Tufail, Muhammad
  • Rehan, Muhammad
  • Ahmed, Shakeel

Abstract

Control theory of one dimensional (1-D) systems is not directly applicable to two dimensional (2-D) systems due to involvement of complex dynamics in space and time. This paper deals with the control of nonlinear Roesser systems by employing feedback strategies with multiple constraints on the input signal. The input constraints are slope restriction, quantization, and generalized nonlinearities related to actuator overflow. Modeling deficiencies are also handled with the generalized actuator nonlinearities in a robust fashion. Novel results for state and output feedback topologies are furnished. Local stability criteria are developed for both feedback topologies by considering intrinsic nonlinearity as one-sided Lipschitz and generalized nonlinearity in a bounded sector. To the best of our knowledge, the outcomes of present paper are novel for nonlinear Roesser discrete-time systems while considering such nested restrictions on input signal. The results are verified by applying the proposed method on different 2-D practical systems.

Suggested Citation

  • Malik, Saddam Hussain & Tufail, Muhammad & Rehan, Muhammad & Ahmed, Shakeel, 2022. "State and output feedback local control schemes for nonlinear discrete-time 2-D Roesser systems under saturation, quantization and slope restricted input," Applied Mathematics and Computation, Elsevier, vol. 423(C).
    • Handle: RePEc:eee:apmaco:v:423:y:2022:i:c:s0096300322000510
    DOI: 10.1016/j.amc.2022.126965
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    References listed on IDEAS

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    1. Lee, Tae H. & Park, Ju H. & Jung, Hoyoul, 2018. "Network-based H∞ state estimation for neural networks using imperfect measurement," Applied Mathematics and Computation, Elsevier, vol. 316(C), pages 205-214.
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