This describes the application of a visual servo control method to the microrobotic manipulation of polymer beads on a two-dimensional fluid interface. A microrobot, actuated through magnetic fields, is utilized to manipulate a non-magnetic polymer bead into a desired position. The controller utilizes multiple modes of robot actuation to address the different stages of the task. A filtering strategy employed in separation mode allows the robot to spiral from the manipuland in a fashion that promotes the manipulation positioning objective. Experiments demonstrate that our multiphase controller can be used to direct a microrobot to position a manipuland to within an average positional error of approximately 8 pixels (64 µm) over numerous trials.

We present a sensorless rotor velocity tracking controller for the full order, nonlinear dynamic model of the permanent magnet stepper motor actuating a mechanical subsystem. Specifically, the structure of the electrical subsystem dynamics is exploited to reconstruct the rotor position and velocity signals from measurements of stator current and stator voltage. This surrogate rotor position signal is then used to design a control strategy that achieves exponential rotor velocity tracking. Experimental results are included to demonstrate the efficacy of the proposed algorithm.

Unmanned surface vehicles (USVs) operating in marine environments are often subjected to external disturbances, such as water waves and currents that might considerably affect system dynamics. Due to the complexity of existing tools used to capture their effects on system dynamics, they are often discarded or at most replaced by a mild form of noise. Indeed, traditional estimation methods often rely on complex procedures involving a battery of experiments conducted both in laboratory settings and outdoors in actual operating environments, followed by intensive hydrodynamics computations. Stochastic noise has been shown in the literature to better capture the uncertainties acting on marine vehicles. In this work, we propose a stochastic model to recreate the disturbances observed on small catamaran USVs moving at lower speed in their operating environment. A maximum likelihood method is proposed to identify the noisy dynamics of the USVs using limited amounts of experimental data gathered in the operating environment. Analytical expressions are derived which reduce the computational effort required to estimate model parameters. The proposed framework is shown to be effective at replicating the distribution of the noise and predicting the future trajectories of the USVs by a few time horizons in actual operating environments.

In this paper, two commonly utilized control methods for active disturbance rejection are designed and evaluated in a column formation (i.e, convoy) application of ground vehicles. Specifically, a variable structure controller (VSC) and a robust sign of the error (RISE) controller are utilized to achieve inter-vehicle distance tracking despite i) not measuring the preceding vehicle's velocity and ii) being subjected to environmental influences. Both controllers utilize headway (i.e., variable inter-vehicle distance based on vehicle velocity) to: i) promote overall convoy stability and ii) utilize only the preceding vehicle's position measurement. The performance of the controllers are evaluated and analyzed on experimental scale vehicles in both on and off-road settings.