In recent years, smartphones have become an integral part of our daily lives and advancements in mobile technology have redefined the capabilities of these devices. The sensing, storage, computation and communication (SSCC) power of smartphones has reached an all-time high, creating a unique opportunity for the integration of smartphone as a platform in engineering laboratory education. Specifically, the advanced sensors embedded in smartphones enable a wide range of sensing applications that can be exploited in the closed-loop feedback control of laboratory test-beds. Furthermore, the touchscreen facilitates an intuitive interface that can improve students’ learning experiences while interacting with the test-beds. In this paper, we will present three examples of wirelessly controlling a DC motor test-bed using different modes of sensing from a mounted smartphone, with all sensing and computation being performed in the background by the mobile application. To make this new class of educational systems more accessible to researchers and educators, an open-source library is developed and made available.
To control the position of the DC motor, both the angular position and angular velocity of its arm must be known at each time step. The smartphone has been used to sense these quantities with two different sensing schemes: inertial measurements and vision-based measurements. To measure the position and velocity of the motor arm, the smartphone is rigidly mounted to it. In the first approach, the embedded inertial measurement unit (IMU) of the phone is used to measure both the angular position and angular velocity of the smartphone, and in turn, of the motor arm. The gyroscope provides raw measurements of the angular velocity, while sensor fusion from gyroscope and accelerometer measurements yields the angular position estimate. In the second approach, vision-based measurements are collected using the front-facing camera of the mounted smartphone. A platform is fitted with colored markers in the view of the camera and a color segmentation approach is used to determine the location of each marker in the image. Changes in the orientation of the phone are determined from changes in the location of each marker in the image. Finally, in the third approach, a multi-modal sensing technique is used wherein inertial and vision-based measurements are fused to produce reliable estimates of the arm’s motion. The variance in each measurement is considered in the data fusion technique implemented. Process and measurement noise are handled by implementing a Kalman filter, which yields estimates of angular position and angular velocity. Both the Kalman filter and feedback controller algorithms are implemented on the mobile application.
Smartphone-mounted experimental test-beds facilitate readily accessible, inquiry-based learning experiences, where standard control techniques such as system identification and pole placement controller design are performed on the device and their effects on the system’s response are investigated in real-time. System identification is used to obtain models of dynamic systems using empirical data. In the context of the DC motor test-bed, students make use of data extracted from the smartphone to generate a dynamic model of the system which in turn is used to design different controllers and to investigate the system’s response. A fundamental approach to full-state feedback controller design is the pole placement technique, where the location of the poles in the s-plane determines the characteristics of the system’s response. In this case, the touchscreen on the smartphone is used to create an interactive s-plane, where students choose the desired poles and a new controller is designed on the fly. Students then investigate phenomena such as overshoot, oscillations, and steady-state errors.
The use of mounted-smartphone test-beds to teach students closed-loop feedback control concepts creates the opportunity to model systems, design controllers, and observe system behavior using the students’ personal devices. The full version of this paper will further elaborate on each of the different sensing approaches, include a full lesson design, discuss the open-source library, and provide results of assessment by a cohort of undergraduate students.
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