Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control

This paper presents the simulation and calculation-based aspect of constructing a dynamically stable, self-balancing electric monowheel from first principles. It further goes on to formulate a reference model-based adaptive control structure in order to maintain balance as well as the desired output...

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Bibliographic Details
Published in:Applied sciences 2021-10, Vol.11 (20), p.9766
Main Authors: Sengupta, Ipsita, Gupta, Sagar, Deb, Dipankar, Ozana, Stepan
Format: Article
Language:English
Subjects:
Automobiles
Balancing
Control methods
Controllers
Design
Disturbances
Dynamic stability
First principles
Friction
inverted pendulum cart system
Kalman filter estimation
Kalman filters
Linear quadratic Gaussian control
LQG control
LQR control
Mathematical models
Matlab
monowheel system
Motor task performance
Parameter estimation
Pole placement
reference model-assisted control
Robots
Robust control
State space models
Vehicles
Wheels
White noise
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Summary:This paper presents the simulation and calculation-based aspect of constructing a dynamically stable, self-balancing electric monowheel from first principles. It further goes on to formulate a reference model-based adaptive control structure in order to maintain balance as well as the desired output. First, a mathematical model of the nonlinear system analyzes the vehicle dynamics, followed by an appropriate linearization technique. Suitable parameters for real-time vehicle design are calculated based on specific constraints followed by a proper motor selection. Various control methods are tested and implemented on the state-space model of this system. Initially, classical pole placement control is carried out in MATLAB to observe the responses. The LQR control method is also implemented in MATLAB and Simulink, demonstrating the dynamic stability and self-balancing system property. Subsequently, the system considers an extensive range of rider masses and external disturbances by introducing white noise. The parameter estimation of rider position has been implemented using Kalman Filter estimation, followed by developing an LQG controller for the system, in order to mitigate the disturbances caused by factors such as wind. A comparison between LQR and LQG controllers has been conducted. Finally, a reference model-assisted adaptive control structure has been established for the system to account for sudden parameter changes such as rider mass. A reference model stabilizer has been established for the same purpose, and all results have been obtained by running simulations on MATLAB Simulink.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11209766