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System Design, Modeling and Control of Self-Balancing Human Transportation Vehicles
digital signal processors
Lyapunov stability theory
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This dissertation develops techniques for system design, modeling and control of two personal human transportation vehicles, including one-wheeled and two-wheeled vehicles, which are constructed using common-tech commercial components including gyro scope, tilt sensor, motor driver, and digital signal processors (DSPs). The mechatronic structures for the vehicles are briefly described and their nonlinear and linearized mathematical models incorporating the fictions between the wheels and motion surface are derived. With decomposition of the two-wheeled transporter into two subsystems: the yaw motion subsystem and mobile inverted pendulum subsystem, classical control methods, adaptive control laws and nonlinear control strategies are proposed to maintain the inverted pendulum self-balancing and achieve the yaw motion control, where radial-basis-function neural networks (RBFNNs) are employed to approximate the friction forces and uncertainties. The close-loop stabilities of the proposed control laws are established utilizing the Lyapunov stability theory. The nonlinear mathematical modeling adaptive controls and nonlinear controls extend to an electric unicycle driven by one DC motor. The proposed methods are expected to be useful and powerful in keeping self-balancing and achieving consistent motion control performance for different riders. Numerical simulations and experimental results show that the proposed controllers are capable of giving satisfactory control actions to steer the transporters.
|Appears in Collections:||電機工程學系所|
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