Adaptive continuous barrier function-based super-twisting global sliding mode stabilizer for chaotic supply chain systems

Mohammadreza Askari Sepestanaki, Hamidreza Rezaee, Mohammad Soofi, Hossein Fayazi, Seyed Hossein Rouhani and Saleh Mobayen

Chaos, Solitons & Fractals, 2024, vol. 182, issue C

Abstract: Supply chains face various uncertainties due to the dynamic and unstable nature of today's worldwide market. Introducing these uncertainties to both the upstream and downstream elements of supply chains adds complexity and nonlinearity to them. This paper investigates the dynamic behavior of a chaotic three-echelon supply chain system and designs a finite-time global nonlinear super-twisting sliding mode controller based on an adaptive continuous barrier function technique for stabilizing the defined system under external disturbances and model uncertainties. This method dynamically adapts the controller parameters according to the system's current state using parameter adaptation algorithms. The adaptive barrier function is used to ensure the system's stability, while the super-twisting algorithm is used to speed up the convergence rate and robustness of the system. Moreover, the genetic optimization algorithm has been employed to attain the optimal values of parameters, thereby enhancing the performance of the designed controller. Ultimately, the effectiveness of this research's proposed approach has been evaluated through simulation in the MATLAB-Simulink environment and experimental testing using the Baseline Real-Time Speedgoat test platform. These analyses are paramount and helpful for strategic decision-makers because they allow visualization and control of the states/dynamics of the entire supply chain. The internal parameters of the supply chain are used as control parameters, and different significant states are discovered to achieve synchronization.

Keywords: Chaotic supply chains; Sliding mode control; Nonlinear control; Adaptive control; Model uncertainties (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:182:y:2024:i:c:s0960077924003801

DOI: 10.1016/j.chaos.2024.114828

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