This work proposes an optimization scheme for smooth, saturated integral sliding mode control in various types of network communication, including 5G-SA, 5G-NSA, WiFi-6, and experimental WiFi. First, the ball and plate model system is analyzed. Then, an integral sliding mode control with a smooth saturation function is designed, and the stability of the controller is analyzed. To optimize the parameter, the Bald Eagle Search (BES) algorithm is implemented by applying the Integral of Absolute Error (IAE), Integral of Squared Error (ISE), and Integral of Time multiplied by Absolute Error (ITAE) objective functions to constant and stochastic time delays from network communications. The BES algorithm is compared with the Genetic Algorithm (GA) and Particle Swarm Optimizer (PSO) algorithms. The comparison results reveal that BES algorithm outperforms the other algorithms in terms of the convergence rate and final objective function. To speed up the convergence rate and execution time, an accelerated-BES is proposed. The accelerated-BES is also compared with PSO and GA. Finally, the optimized controller is validated by simulations and experiments.

This work proposes an optimization scheme for smooth, saturated integral sliding mode control in various types of network communication, including 5G-SA, 5G-NSA, WiFi-6, and experimental WiFi. First, the ball and plate model system is analyzed. Then, an integral sliding mode control with a smooth saturation function is designed, and the stability of the controller is analyzed. To optimize the parameter, the Bald Eagle Search (BES) algorithm is implemented by applying the Integral of Absolute Error (IAE), Integral of Squared Error (ISE), and Integral of Time multiplied by Absolute Error (ITAE) objective functions to constant and stochastic time delays from network communications. The BES algorithm is compared with the Genetic Algorithm (GA) and Particle Swarm Optimizer (PSO) algorithms. The comparison results reveal that BES algorithm outperforms the other algorithms in terms of the convergence rate and final objective function. To speed up the convergence rate and execution time, an accelerated-BES is proposed. The accelerated-BES is also compared with PSO and GA. Finally, the optimized controller is validated by simulations and experiments.

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