Master of Science, The Ohio State University, 1985, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2020, Mechanical Engineering

The objective of this work is to benchmark Advanced Driver Assistance Systems (ADAS) using a limited motion Driver-in-the-Loop (DiL) Simulator and Model Predictive Control (MPC). These ADAS features include Adaptive Cruise Control (ACC), Lane Keeping Assist (LKA), Forward Collision Warning (FCW), and Automatic Emergency (AEB). The handoff of these features is at the discretion of the driver but maintains the operational design domain. Simplified internal models for the ACC MPC and LKA MPC are presented, so the MPC toolbox CasADi could be used as Software-in-the-Loop (SiL). Both MPC's leverage adaptive linear techniques alleviating the inherent nonlinearities. SiL ensures robust, real-time execution of the features integrating with the simulator. Regulators and automotive manufacturers are tasked with eliminating automotive deaths and injuries. Of active safety tools at their disposal, ADAS features provide a promising ability to aiding this cause. Driving simulators are becoming an important development tool for active safety systems, automated driving features, and vehicle dynamics development. This driving simulator couples SCANeR Studio®, CarSim®, and MATLAB/Simulink®. Refined and custom cues give the driver a sense of the virtual world providing the immersion. Offline verification using the testbed and sample results using the driving simulator shows the efficacy of prototyping and evaluating ADAS features using the simulator. Combining these elements allows for both quantitative and qualitative assessment of the systems' functionality, performance, and safety assurance.

Committee: Giorgio Rizzoni (Advisor); Qadeer Ahmed (Committee Member); Jeffrey Chrstos (Committee Member) Subjects: Mechanical Engineering

Master of Science, University of Toledo, 2017, Electrical Engineering

Roll force control is a commonly practiced type of control used in cold mills to help give the overall end product a desired surface quality. It is becoming more advantageous for manufactures to utilize hot band material over cold band due to processing cost. By utilizing roll force control in a hot strip mill, the desired surface quality can be obtained to compete against those of a cold mill product, specifically for material that will be painted. This work concerns the derivation and implementation of a practical force control model in the last stand of a hot strip mill. Integration of the force control model coupled with gauge control models in previous stands is investigated in regards to gauge tolerances. Discussion of tuning parameters and procedures is presented. Stability of the force control model is interrogated independently and then repeated after integration. Lastly, simulation results are compared with that of the installed controller.

Committee: Richard Molyet (Committee Chair); Alam Mansoor (Committee Member); Kim Junghwan (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 1966, Graduate School

Committee: Not Provided (Other) Subjects: Engineering

Master of Science, The Ohio State University, 2014, Mechanical Engineering

In this thesis, a mathematical model of an automotive powertrain featuring a wet dual clutch transmission is developed. The overall model is comprised of models that describe the dynamic behavior of the engine, the transmission mechanical components, the hydraulic actuation components, and the vehicle and driveline. A lumped-parameter model, that incorporates fluid film dynamics and a simplified thermal model, is used to describe wet clutch friction. The model of the hydraulic actuation system includes detailed models of the clutch and synchronizer actuation subsystems. A simulation of the dynamic powertrain model is built using AMEsim and MATLAB/Simulink. The powertrain simulator is used to demonstrate how changes in transmission parameters affect the quality of clutch-to-clutch shifts and the overall dynamic response of the powertrain. Based on this model, measurements of clutch pressure and the rotational speeds and estimated accelerations at the input and output sides of the clutch are used in the design of a friction parameter estimation scheme that can be implemented offline using past simulation data or online using current simulation signals. For both offline and online cases, simulation results demonstrate that friction parameters are estimated with reasonable accuracy. An integrated powertrain controller is developed with a model-based feedforward controller and multiple feedback loops. The feedforward controller, which generates a pressure command to either clutch, is developed by inverting a simplified model of the powertrain, and using a static friction model to relate clutch pressure to friction torque. The inputs to the feedforward controller are speeds and estimated accelerations of the engine and clutches. The feedforward controller adapts to changes in friction characteristics by updating the friction parameters used in the static friction model using the values generated by the estimation scheme. The feedback controller contains loops tha (open full item for complete abstract)

Committee: Krishnaswamy Srinivasan (Advisor); Shawn Midlam-Mohler (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 1982, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1987, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1983, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1971, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1971, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1960, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1969, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1966, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1967, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2008, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1971, Graduate School

Committee: Not Provided (Other) Subjects:

Doctor of Philosophy, The Ohio State University, 1985, Graduate School

Committee: Not Provided (Other) Subjects: Engineering

Doctor of Philosophy, The Ohio State University, 1984, Graduate School

Committee: Not Provided (Other) Subjects: Physics

Doctor of Philosophy, The Ohio State University, 1984, Graduate School

Committee: Not Provided (Other) Subjects: Engineering

Doctor of Philosophy, The Ohio State University, 1972, Graduate School

Committee: Not Provided (Other) Subjects: Engineering