This dissertation presents new steering and parameter estimation algorithms for Automated Ground Vehicles (AGVs) to perform robust and graceful motion. While much current work focuses on sensor-based navigation and path planning, this dissertation addresses challenges of dynamic system response and control in automated driving. It is formatted as two journal articles focused on output feedback control and parameter estimation.
The first paper focuses on novel steering algorithms that provide path-following accuracy and graceful error convergence. This algorithm considers both vehicle kinematics and dynamics via a multi-tiered overarching structure based on sideslip and yaw rate models. This configuration allows steering commands that stabilize path-following error using observer-based sideslip estimates to create graceful lateral motion. A time-varying variable structure kinematic controller provides yaw rate commands that aim to provide path-following accuracy, robustness, and graceful motion. Tuned hierarchical path manifolds are a key part of this algorithm since they adapt error convergence based upon vehicle speed, passenger comfort and safety, and actuator limits. A backstepping dynamic controller transforms these yaw rate commands to produce steering rate commands that consider actuator capability and vehicle dynamics. A model-based high-gain observer then estimates sideslip and yaw rate to provide sensor-based output feedback control.
Parameter estimation, the focus of the second paper, is critical for both sensor-based navigation and motion control of automated ground vehicles. Multistage estimation is proposed to ultimately allow estimation of key parameters during regular driving along curved paths. Sensitivity analysis first identifies a minimal set of parameters that are critical for achieving graceful dynamic system response. A lumped parameter model is then derived to combine these parameters into a reduced model allowing parameter estimation with limited sensing and steering inputs. Stage 1 then estimates these lumped parameters using varying steering inputs. Analysis highlights how input variations affect estimation response while considering vehicle limitations, passenger comfort, and road features (width, length, and path shape). Stage 2 then uses constant steering inputs to extract critical dynamic parameters from the lumped parameters. Simulation and experimental results verify and validate the algorithms.
|Advisor:||Minor, Mark Andrew|
|Commitee:||Henderson, Thomas, Bodson, Marc, Abbott, Jake, Meek, Sanford G.|
|School:||The University of Utah|
|School Location:||United States -- Utah|
|Source:||DAI-A 82/8(E), Dissertation Abstracts International|
|Subjects:||Robotics, Automotive engineering, Technical Communication, Mechanics, Artificial intelligence, Mechanical engineering, Transportation|
|Keywords:||Adaptive and robust control, Cognitive robotics, Design and control, Kinematics, Online parameter estimation, Steering input and sensitivity analysis, Automated Ground Vehicles (AGVs)|
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