Model Analysis of Unmanned Bicycle and Variable Gain LQR Control

Zhang, Yongli; Liu, Yu; Yi, Guorong

doi:10.1109/itaic49862.2020.9338891

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…When no stabilizer is added to an unmanned bicycle, the most typical method used (Cui et al, 2020;Yongli et al, 2020; is to control the vehicle's turning angle. This is done by combining the rear wheel speed of the bicycle to generate centrifugal force to maintain its balance.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Bicycle Balance Control Based on Tunicate Swarm Algorithm Optimized BP Neural Network PID

Zhang

et al. 2023

International Journal of Information Technologies and Systems Approach

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In this study, the authors introduce a novel approach that leverages the tunicate swarm algorithm (TSA) to optimize proportional-integral-derivative (PID) controller based on a back propagation (BP) neural network. The core objective of the approach is to manage and counteract uncertainties and disturbance that may jeopardize the balance and stability of self-driving bicycles in operation. By using the self-learning capabilities of BP neural networks, the controller can dynamically adjust PID parameters in real time. This enables an enhanced robustness and reliability during operation. Further bolstering the efficiency of our controller, the authors use the TSA to optimize the initial weights of a neural network. This effectively mitigates the commonly associated with slow convergence and being entrapped in local minima. Through simulation and experimentation, the findings reveal that the TSA-optimized BP neural network PID controller dramatically improves dynamic performance and robustness. It also proficiently manages changes in the environment such as wind and ground bumps. Therefore, the proposed controller design offers an effective solution to the balancing problem of self-driving bicycles and paves the way for a promising future in designing versatile controllers with broad application potential.

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Section: Introductionmentioning

confidence: 99%