Adaptive PSO-LS-wavelet H control for two-wheeled self-balancing scooter

  • Askar Azizi
  • Hamid Nourisola
  • Amin Sadeghi-Emamgholi
  • Fahime Naderisafa
Regular Papers Control Theory and Applications


The current study is concerned with adaptive Particle Swarm Optimization Least Square Wavelet H for a two-wheel self-balancing scooter that provides a platform in order to balance itself and transport the driver in accordance to its natural lean. In order to keep the rider close to the upright position over smooth and non-smooth surfaces, providing a stable control system is the main challenge for the aforementioned vehicle. For this purpose, H is combined with adaptive algorithm, Least Square Support Vector Machine (LS-SVM) and Particle Swarm Optimization (PSO) to construct the adaptive control. The most important feature of the proposed control strategy is its inherent robustness and ability to handle the nonlinear behavior of the system. Simulations results indicated that the introduced motion control architecture is capable of providing appropriate control actions to achieve both position control and trajectory tracking satisfactorily.


Adaptive H least square wavelet particle swarm optimization two-wheeled self-balancing scooter 


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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Askar Azizi
    • 1
  • Hamid Nourisola
    • 1
  • Amin Sadeghi-Emamgholi
    • 2
  • Fahime Naderisafa
    • 3
  1. 1.Electrical Control Engineering Group, Faculty of Electrical and Computer EngineeringUniversity of TabrizTabrizIran
  2. 2.Malek Ashatr University of technologyTehranIran
  3. 3.Faculty of Electrical and Computer EngineeringIslamic Azad University, Kashan BranchKashanIran

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