Adaptive Motion Estimation and Control of Intelligent Walkers
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One of the most critical factors in the quality of elderly lives is their ability to move. As the size of the ageing society grows, more elderly people suffer from walking impairments. Most of them prefer to stay at home due to the shortage of the nursing care staff, since they deal with the daily challenges alone. Robotics researchers have developed various intelligent walking support systems to meet the needs of elderly and handicapped people. A particular problem in path tracking for such systems is maintaining the tracking performance, which is affected by the center of gravity (CG) shifts and load changes due to human-walker interactions. This thesis focuses on design of feedback controllers for safe motion of intelligent walker (i-walker) systems robust to CG shifts and load changes. Our design follows a two level approach, one for kinematics, the other for dynamics. The high level kinematic controller is designed based on integrator backstepping to produce desired velocities required for trajectory tracking. The low level dynamic controller is composed of a feedback linearization unit and a linear feedback controller to apply the control torque for tracking the desired velocity produced by the high level kinematic controller. As dynamic controllers, proportional-derivative (PD) and sliding mode controllers (SMCs) are designed. In our initial design, we assume that all system states are available. However, in the actual case, even if the wheel velocities can be measured with some sensor devices like tachometers, the measurements carry noise, which poses important problems in control algorithms. To obtain the estimates of the wheel velocities, avoiding the noise problems, the design of sliding mode observers and high gain observers is studied. The state feedback PD and SMC schemes are later integrated with these observers to form implementable output feedback controllers. In practice, the human mass and the distance due to the CG shift depend on the user. To address this issue, the output feedback control designs are further made adaptive, integrating with a parameter identifier to estimate these variables. The parameter identifier design involves a linear parametric model of the i-walker system dynamics and a least-square adaptive law based on this parametric model. Adaptive versions of the above observers and control designs are done utilizing estimated parameters and states. The effectiveness and applicability of the proposed controllers are verified via various simulations in MATLAB/Simulink environment.
Cite this version of the work
Nursefa Zengin (2015). Adaptive Motion Estimation and Control of Intelligent Walkers. UWSpace. http://hdl.handle.net/10012/9462