Dynamics and control of rider-bicycle systems

How can an autonomous bicycle robot system keep balance and track a path? How does a human rider ride a bicycle? And how can we enhance human riding safety and efficiency? Answers of these questions can provide guidance for autonomous single-track vehicle control system design, understanding human riding skills and vehicle assistive design. Furthermore, riding a bicycle is an unstable physical human-machine interaction (upHMI). Riding skills analysis is a good example about understanding human control mechanism, including human body movement control and human neuro-control. The bicycle assisted balancing system also provides the inspiration for designing other human-robot cooperation system. This dissertation has three objectives: the first one is to design control system for autonomous bicycle for balancing and tracking; the second one is to model and analyze the human riding skills of balancing and tracking; and the last one is to design tuning method for human riding balancing skills. The first part of this dissertation focuses on the autonomous bicycle control system design for balancing and path following. The bikebot, an autonomous bicycle system, is designed for these control mechanism implementation. The gyro-balancer control law and steering motion control law are designed for balancing the bikebot system in the stationary and moving stages, respectively. Using these two control laws, a switching control strategy is proposed for a stationary-moving transition process. The control performances are demonstrated by the experimental results for a complete maneuver. For the trajectory tracking tasks, the external/internal convertible (EIC) structure-based control strategies are proposed and implemented. The EIC-based control takes the advantages of the non-minimum phase underactuated dynamics structure. We first analyze and demonstrate the EIC-based motion tracking controller. An auxiliary gyro subsystem control law is then designed to enhance the tracking performance of the EIC-based controller. The errors dynamics and control properties are discussed and analyzed. Finally, the control strategies are implemented on the bikebot system. The experiments results confirm and demonstrate the controllers effectiveness. The second part of the dissertation focuses on the analysis of human riding skills, including the balance control and the tracking skills. For the motion tracking with balancing motor skills, using the EIC structure, a balance equilibrium manifold (BEM) concept is proposed for analyzing the human trajectory tracking behaviors and balancing properties. The contributions of steering and upper-body motion are analyzed quantitatively. Finally, performance metrics are introduced to quantify the balance motor skills using the BEM concept. These analysis and discussions are demonstrated and validated by extensive human riding experiments. Comparison between the EIC-based control and human control is also presented and demonstrated. For the balance skill studies, we first present the control models of human steering angle and upper-body leaning torque. These models are inspired by the human stance balance studies and built on several groups of human riding experiments. The parameters sensitivity analyses are also discussed with experiment validation. Using the time-delayed system stability analysis, the quantitative influences of the model parameters on closed-loop stability are also demonstrated and experimentally verified. Based on aforementioned results, actively tuning the rider-bikebot interaction is the aim of the last part of the dissertation. First, from the rider-bikebot interaction dynamics, the stiffness and damping effect for balancing are analyzed. The control of the rider-bikebot interactions is designed to tune the stiffness and damping effects by reshaping the rider steering motion. From experiments observation, the rider balancing performances are significantly improved under the tuned interaction dynamics. Furthermore, under a special tuned stiffness and damping effect, the rider-bikebot system can be balanced autonomously without considering the rider steering input. This property is also theoretically proven and also verified by the experiments. The outcomes of this dissertation not only advances the understanding the human rider balance motor skills but also provides the guidance for the autonomous bicycle control design, and the human balancing performance tuning method through rider-bikebot interactions. At the end of this dissertation, future work directions are also discussed and presented.Ph.D.Includes bibliographical referencesby Pengcheng Wan