The multi actuator drive technology was unveiled by Seagate in December 2017, a breakthrough that can almost double the data performance of the future generation hard disk drives. This technology will equip drives with dual actuators operating on the same pivot point. Each actuator will control half of the drive’s arms. Since two actuators operate independently on the same pivot timber, the control forces and torques generated by one actuator can affect the operation of the other actuator. We will have a scenario when one actuator is track seeking and the other actuator is in the track following mode. The track seeking actuator will impart vibration disturbances to the track following actuator. Previously, we presented a single-input single-output (SISO) data-driven feedforward control design method (Shah and Horowitz, 2019. “Active Vibration Rejection in Multi Actuator Drives: Data Driven Approach,” Dynamic Systems and Control Conference, Vol. 3) to obtain feedforward controllers for the voice coil motor (VCM) and the micro actuator sequentially. The design was based on multiple frequency response measurements of the actuators. In this paper, first, we present a single-input multi-output (SIMO) data-driven feedforward control design technique to simultaneously obtain feedforward controllers for the VCM and the micro actuator. This methodology will obtain a common controller for multiple drives. We will compare the performance of this algorithm with the sequential SISO design technique (Shah and Horowitz, 2019. “Active Vibration Rejection in Multi Actuator Drives: Data Driven Approach,” Dynamic Systems and Control Conference, Vol. 3). Second, we present an add-on input shaping technique to suppress the residual vibration.

Response amplitude operator (RAO) curves are commonly employed to assess the dynamic behavior of floating offshore structures in the frequency domain. There are multiple methods used to obtain RAOs for numerical models, scaled physical models, and full-scale tests. While for numerical modeling many studies detail the precise methods used, the literature around experimental RAO curves often do not detail them or leave methodological information incomplete. There exists inadequate experimental evidence in assessing the differences in results obtained by following different RAO generation methods from scaled physical testing. This paper addresses this gap by comparing two most popular RAO generation methods: the energy spectra (ES) and the cross spectral auto spectra (CSAS) method. These are experimentally compared on scaled semisubmersible and spar-buoy platforms in an ocean wave basin. Differences of heave and pitch RAOs generated by different methods are investigated. A method for reasonably collating multiple tests to create a representative RAO is also presented. RAO amplitudes vary significantly and how they decay off beyond certain frequencies is dependent on the method adopted to create them. This variation can be a source of significant uncertainty for floating structures for further analysis, design, control, or repair. Some RAOs (e.g., pitch) are sensitive to scaling and should be considered when converting scaled tests to full-scale equivalent. Detailing methods of RAO generation and comparing approaches of developing them can be important for crucial decisions from scaled physical testing of floating structures at design/development stages.

Active and passive control techniques have been devised over the years to mitigate the effect of vibrations on drill-string life with varying degrees of success. Here, it is proposed to design a robust trajectory tracking controller, which ultimately forces the rotary table and the drill-bit to move with the same speed (speed synchronization), hence reducing/eliminating torsional vibrations from the drill pipes. A model of the rotary drilling system, which includes torsional stick-slip, is first developed; then, an integral sliding mode control with time-varying exponent (ISMC-TVE) scheme is developed such that the bit motion tracks that of the rotary table to mitigate the effects of the induced vibrations. The ISMC-TVE is able to control the transient stage of the drill-string system’s response, maintain the system in the sliding state even under abrupt or existing external disturbances, and guarantee asymptotic stability of the rotary drilling system. The Lyapunov stability theorem is used here to analyze the performance of the closed-loop system, and the simulation results showed that the ISMC-TVE law is capable of accurately synchronizing the bit and rotary table speeds.

Behavior of animals living in the wild is often studied using visual observations made by trained experts. However, these observations tend to be used to classify behavior during discrete time periods and become more difficult when used to monitor multiple individuals for days or weeks. In this work, we present automatic tools to enable efficient behavior and dynamic state estimation/classification from data collected with animal borne bio-logging tags, without the need for statistical feature engineering. A combined framework of an long short-term memory (LSTM) network and a hidden Markov model (HMM) was developed to exploit sequential temporal information in raw motion data at two levels: within and between windows. Taking a moving window data segmentation approach, LSTM estimates the dynamic state corresponding to each window by parsing the contiguous raw data points within the window. HMM then links all of the individual window estimations and further improves the overall estimation. A case study with bottlenose dolphins was conducted to demonstrate the approach. The combined LSTM–HMM method achieved a 6% improvement over conventional methods such as K-nearest neighbor (KNN) and support vector machine (SVM), pushing the accuracy above 90%. In addition to performance improvements, the proposed method requires a similar amount of training data to traditional machine learning methods, making the method easily adaptable to new tasks.

Iterative learning control (ILC) is a powerful technique to regulate repetitive systems. Additive manufacturing falls into this category by nature of its repetitive action in building three-dimensional structures in a layer-by-layer manner. In literature, spatial ILC (SILC) has been used in conjunction with additive processes to regulate single-layer structures with only one class of material. However, SILC has the unexplored potential to regulate additive manufacturing structures with multiple build materials in a three-dimensional fashion. Estimating the appropriate feedforward signal in these structures can be challenging due to iteration varying initial conditions, system parameters, and surface interaction dynamics in different layers of multi-material structures. In this paper, SILC is used as a recursive control strategy to iteratively construct the feedforward signal to improve part quality of 3D structures that consist of at least two materials in a layer-by-layer manner. The system dynamics are approximated by discrete 2D spatial convolution using kernels that incorporate in-layer and layer-to-layer variations. We leverage the existing SILC models in literature and extend them to account for the iteration varying uncertainties in the plant model to capture a more reliable representation of the multi-material additive process. The feasibility of the proposed diagonal framework was demonstrated using simulation results of an electrohydrodynamic jet printing (e-jet) printing process.