Football is a much loved sport in the United States. Unfortunately, it is also hard on the players and puts them at very high risk of concussion. To combat this an inventor in Santa Barbara brought a new design to Cal Poly to be tested. The design was tested in small scale first in order to make some preliminary conclusions about the design. In order to fully test the helmet design; however, full scale testing was required. In order to carry out this testing a drop tower was built based on National Operating Committee on Standards for Athletic Equipment, NOCSAE, specification. The drop tower designed for Cal Poly is a lower cost and highly portable version of the standard NOCSAE design. Using this drop tower and a 3D printed prototype the new design was tested in full scale.

In this paper, a roll stability controller (RSC) is presented based on an eight degree of freedom dynamic vehicle model. The controller is designed for and tested on a scaled vehicle performing obstacle avoidance maneuvers on a populated test track. A rapidly-exploring random tree (RRT) algorithm is used for the vehicle to execute a trajectory around an obstacle, and examines the geographic, non-homonymic, and dynamic constraints to maneuver around the obstacle. A model predictive controller (MPC) uses information about the vehicle state and, based on a weighted performance measure, generates an optimal trajectory around the obstacle. The RSC uses the standard vehicle state sensors: four wheel mounted encoders, a steering angle sensor, and a six degree of freedom inertial measurement unit (IMU). An emphasis is placed on the mitigation of rollover and spin-out, however if a safe maneuver is not found and a collision is inevitable, the program will run a brake command to reduce the vehicle speed before impact. The trajectory is updated at a rate of 20 Hz, providing improved stability and maneuverability for speeds up to 10 ft/s and turn angles of up to 20°.

A non-contact angular measurement system for Pitch Static Attitude (PSA) and Roll Static Attitude (RSA) of hard disk drive sliders is designed and built. Real-time sampling at over 15 KHz is achieved with accuracy of +/- 0.05 degrees over a range of approximately 2-3 degrees. Measuring the PSA and RSA is critical for hard drive manufacturers to control and improve the quality and reliability of hard drives. Although the hard drive industry is able to measure the PSA and RSA at the subassembly level at this time, there is no system available that is able to measure PSA/RSA at the final assembly level. This project has successfully demonstrated a methodology that the PSA/RSA can be reliably measured in-situ using a laser and position sensitive detector (PSD) technology. A prototype of the measurement system has been built using simple and inexpensive equipment. This device will allow a continuous measurement between the parked position on the ramp and the loading position just off of the disk surface. The measured data can be used to verify manufacturing processes and reliability data.

The focus of this document is on the design, modeling, and control of a self-balancing two wheel robot, hereafter referred to as the balance bot, driven by independent brushless DC (BLDC) motors. The balance bot frame is composed of stacked layers allowing a lightweight, modular, and rigid mechanical design. The robot is actuated by a pair of brushless DC motors equipped with Hall effect sensors and encoders allowing determination of the angle and angular velocity of each wheel. Absolute orientation measurement is accomplished using a full 9-axis IMU consisting of a 3-axis gyroscope, a 3-axis accelerometer, and a 3-axis magnetometer. The control algorithm is designed to minimize deviations from a set point specified by an external radio remote control, which allows the remote operator to steer and drive the bot wirelessly while it remains balanced. Multiple dynamic models are proposed in this analysis, and the selected model is used to develop a linear-quadratic regulator based state-feedback controller to perform reference tracking. Controller tracking performance is improved by incorporating a prefilter stage between the setpoint command from the remote control and the state-feedback controller. Modeling of the actuator dynamics is considered brie y and is discussed in relation to the control algorithm used to balance the robot. Electrical and software design implementations are also presented with a focus on effective implementation of the proposed control algorithms. Simulated and physical testing results show that the proposed balance bot and controller design are not only feasible but effective as a means of achieving robust performance under dynamic tracking profiles provided by the remote control.

The United States trucking industry is immense. Employing over three million drivers and traveling to every city in the country. Semi-Trucks travel millions of miles each week and encompass roads that civilians travel on. These vehicles should be safe and allow efficient travel for all. Autonomous vehicles have been discussed in controls for many decades. Now fleets of autonomous vehicles are beginning their integration into society. The ability to create an autonomous system requires domain and system specific knowledge. Approaches to implement a fully autonomous vehicle have been developed using different techniques in control systems such as Kalman Filters, Neural Networks, Model Predictive Control, and Adaptive Control. However some of these control techniques require superb models, immense computing power, and terabytes of storage. One way to circumvent these issues is by the use of an adaptive control scheme. Adaptive control systems allow for an existing control system to self-tune its performance for unknown variables i.e. when an environment changes. In this thesis a LQR error state control system is derived and shown to maintain a magnitude of 15 cm of steady state error from the center-line of the road. In addition a proposed LQR-MRAC controller is used to test the robustness of a lane-keeping control system. The LQR-MRAC controller was able to improve its transient response peak error from the center-line of the road of the tractor and the trailer by 9.47 [cm] and 7.27 [cm]. The LQR-MRAC controller increased tractor steady state error by 0.4 [cm] and decreased trailer steady state error by 1 [cm]. The LQR-MRAC controller was able to outperform modern control techniques and can be used to improve the response of the tractor-trailer system to handle mass changes in its environment.

No / A vehicle that deviates laterally from its intended path of travel when the brakes are applied is considered to demonstrate ‘instability’ in the form of an unexpected and undesirable response to the driver input. Even where the magnitude of lateral displacement of the vehicle is small (i.e. ‘drift’ rather than ‘pull’) such a condition would be considered unacceptable by manufacturers and customers. Steering ‘drift’ during braking can be caused by several factors, some of which relate to vehicle design and others to external influences such as road conditions. The study presented here examines the causes and effects of steering drift during straight-line braking. A comparative analysis is made between two types of vehicle model: one built with rigid suspension components and the other with flexible components. In both the cases, the vehicle behaviour is simulated during braking in a straight line, and responses including lateral acceleration, yaw rate, and lateral displacement of the vehicle are predicted and analysed under fixed steering control. Suspension/steering geometry characteristics, namely toe steer and caster angle, have been studied to understand how the effect of variations in these parameters differs in models with rigid or flexible components drift during straight-line braking. Results from both vehicle models show that differences between rigid and flexible components can affect the predicted steering drift propensity. The differences between the two models have emphasized the importance of using flexible (compliant) components in vehicle handling simulations to achieve better correlation between prediction and experiment.

<p>Hydraulic systems are commonly used for actuation and manipulation of heavy loads. They are found in a variety of different industries, such as in automotive, manufacturing, robotics, construction, and aerospace. Conventional hydraulic systems use a centralized constant pressure supply system. Pressurized fluid is then channeled to actuators using servo-valves. The advantages of these systems are their high torque to mass ratio, and the ability to control speed and direction with relative precision. However, there are also disadvantages such as the requirement of a bulky centralized supply, leakage, noise, and reduced energy efficiency due to orifice flow and the requirement for maintaining a constant supply pressure.</p> <p>Electro-Hydrostatic Actuation systems (EHA) alleviate many of the above mentioned shortcomings of servo-valve controlled hydraulic systems. In the EHA position control is achieved by regulating the pumping action. Here, a fixed or a variable displacement pump can be used to move oil from one chamber of the actuator to the other. In these actuators, the presence of nonlinearities associated with pump/motor static friction and backlash, pressure drop in the piping system, and nonlinear friction at the load have a significant effect on the performance and positional precision of the system.</p> <p>This research will focus on developing a multiple inner-loop control strategy by implementing multiple inner-loops that utilize the differential pump/load position and velocity. The main goal will be to decrease the effect of the pump backlash as well as the nonlinear friction at the load; both of which negatively impact positional precision. Therefore, the main benefit of this method is an improvement in trajectory tracking precision, which is particularly important for high precision hydrostatic systems. Furthermore, a sliding mode control strategy will be incorporated into the design to suppress load oscillations reported in precision trajectory tracking applications. The research hypothesis states that sliding mode control in conjunction with multiple inner-loops, will improve the trajectory tracking performance of a hydrostatic actuation system by partially compensating the effects of static friction at the load. Theoretical analysis, simulation supported by experimental results are presented to demonstrate the effectiveness of the newly developed methods in suppressing the effects of nonlinearities on the EHA performance, with the downside of an increased complexity due to the increased number of controller parameters.</p> / Doctor of Philosophy (PhD)

Combining the advantages from electric motor and the pneumatic actuator, the hybrid pneumatic-electric actuator is expected to be safe, low-cost, clean, high power to weight ratio, and to provide precise position control. In this thesis, the modeling and control of an improved hybrid pneumatic-electric actuator prototype is presented. The actuator’s main components consist of a low-friction pneumatic cylinder, two on/off solenoid valves, and a small DC motor. The cylinder and motor are connected to a common output shaft using gears. The shaft rotates a single-link robot arm. Its position is measured by an incremental encoder. The prototype was improved by incorporating faster switching valves, flow controls, a faster valve drive circuit, a high resolution encoder rather than the existing linear potentiometer, more accurate pressure sensors and stronger gears. A system dynamic model without the valve dynamic was developed identified and validated using open-loop experiments. The valve models for a discrete input and PWM input were then developed and validated separately. The use of bipolynomial function and artificial neural network fitting methods for modeling the valve mass flow rates were compared. The combined system model with valve dynamics was validated experimentally. Two model-based nonlinear position controllers, using the backstepping and discrete-valued model predictive control (DVMPC) methods, were designed, simulated and extensively tested. Testing was done with the actuator operating using the cylinder alone, the motor alone and in hybrid mode using the cylinder and motor together. Operating in the hybrid mode reduced the root-mean-square error (RMSE) up to 80%. A stability analysis for the backstepping control including the valve modeling error, friction model error, and electric motor torque modeling error was performed. Compensation terms were designed to improve the performance for the two controllers. Additional stability analyses were performed for backstepping controller with a feedback term and the DVMPC with motor control. A payload estimation algorithm was proposed and shown to enhance the robustness of the DVMPC operating in vertical configuration. Simulations and experiments demonstrated that the model-based controllers performed well for both vertical and horizontal configurations. Regarding robustness to payload mismatch, if the payload was within the load capacity of the hybrid actuator, the model-based controllers were both insensitive to the payload variations in horizontal configuration. The backstepping controller was also robust to the payload variations in the vertical configuration. In experiments, the backstepping control in hybrid actuation mode produced a RMSE of 0.0066 radian for a 2 Hz sine wave desired position trajectory with a 0.3 radian amplitude. With DVMPC, this value decreased to 0.0045 radian. These tracking errors were shown to be 30 to 50% less than those produced by a modified linear position plus velocity plus acceleration controller. / Master of Applied Science (MASc)

Electrical impedance tomography (EIT) is a non-destructive, non-invasive, and non-radioactive imaging technique used for reconstructing the internal conductivity distribution of a sensing domain. Performing EIT often requires large, stationary benchtop equipment that can be expensive and impractical. Other researchers have attempted to make portable EIT systems, but they all rely on external computation for image reconstruction/data analysis. This study outlines the development of a low-cost, portable, and wireless EIT data acquisition (DAQ) system that is capable of independently performing image reconstructions on-board. With the proposed system, EIT can be performed on carbon fiber reinforced polymers to spatially locate damages. Since EIT reconstruction algorithms can be extremely computationally intensive, this study has also developed an alternative deep-learning algorithm that leverages the compressed-sensing technique to strategically train a neural network. The proposed neural network has not only achieved comparable results to traditional iterative algorithms, but it can do so in a fraction of the time.

This thesis presents an impedance control strategy for a novel powered hip exoskeleton designed to provide partial assistance and leverage the dynamics of human gait. The control strategy is based on impedance control and provides the user assistance as needed which is determined by the user’s interaction with the exoskeleton. A series elastic element is used to drive the exoskeleton and measures the interaction torque between the user and the device. The device operates in two modes. Free mode is a low impedance state that attempts to provide no assistance. Assist mode increases the gains of the controller to provide assistance as needed. The device was tested on five healthy subjects, and the resulting assistive hip torque was evaluated to determine the ability of the controller to provide gait assistance. The device was evaluated at different speeds to assess the gait speed adaptation performance of the controller. Results show that hip torque assistance range was between 0.3 to 0.5 Nm/kg across the subjects, corresponding to 24% to 40% of the maximum hip torque requirements of healthy adults during walking. The peak power provided by the system is 35 W on average and a peak power of up to 45 W.