Justifying the Stabilization of a Marginally Stable Ship

As a precursor to capsize, marginal stability, resulting from incorrect loading conditions and crew negligence, poses a serious danger to ships. Therefore, as a benchmark problem for preventing capsize, the use of an actively controlled pendulum for the stabilization of a marginally stable ship was analyzed. Lyapunov stability criteria and closed loop eigenvalues were used to evaluate the extent to which a proposed pendulum controller could cope with different ship stability conditions. Equations of motion were solved to observe the controller’s performance under different damping conditions. The behavior of the controller yielded the following results: a marginally stable ship can be stabilized, as long as there is no right hand plane zero; energy dissipation is key to the stabilization of a marginally stable ship; the controller must have knowledge of the ship’s stability to prevent controller-induced excitation; and a stabilized tilted ship is more robust to external disturbances than a stabilized upright ship.

Reducing Trajectory Tracking Error of Flexible Mobile Robots Using Command Shaping With Error-Limiting Constraints

The ability to track a trajectory without significant error is a vital requirement for mobile robots. Numerous methods have been proposed to mitigate tracking error. While these trajectory-tracking methods are efficient for rigid systems, many excite unwanted vibration when applied to flexible systems, leading to tracking error. This paper analyzes a modification of input shaping, which has been primarily used to limit residual vibration for point-to-point motion of flexible systems. Standard input shaping is modified using error-limiting constraints to reduce transient tracking error for the duration of the system’s motion. This method is simulated with trajectory inputs constructed using line segments and Catmull-Rom splines. Error-limiting commands are shown to improve both spatial and temporal tracking performance and can be made robust to modeling errors in natural frequency.

Spatial Filter Design for Dual-Stage Systems

Increasing demand for high precision positioning systems has motivated significant research in this field. Within this field, dual-stage nanopositioning systems have the unique potential to offer high-speed and long-range positioning by coupling a short-range, high-speed actuator with a long-range, low-speed actuator. In this paper, design considerations for a spatial filter are presented. The spatial filter allows for control allocation based on range of the signal as opposed to more commonly used frequency-based control allocation. In order to understand the spatial filtering approach more fully, this paper analyzes the filter in detail to understand limitations and give the user a more clear understanding of the approach. Simulation results are included to illustrate aspects of the discussion.

Crane Lift-Off Control of Inclined Payloads

It is difficult for crane operators to lift and maneuver payloads without causing significant, uncontrolled motion. Consequently, research in the area of crane operation has focused on designing controllers to minimize payload swing. However, lifting long and slender payloads (e.g., steel I-beams) from a non-level surface (e.g., like many outdoor construction sites) has not been addressed in much detail. This paper evaluates the amplitude of residual swing and robustness of two different control methodologies while hoisting a slender payload up into the air from an inclined surface. A semi-automatic approach, where the crane operator controls the lift direction and a proportional-integral-derivative (PID) controller adjusts the overhead trolley position, was developed. Experimental tests demonstrate that this method reduces the peak amplitude of residual vibration by about 80% for most non-zero incline angles.

A Data-Driven Exploratory Approach for Level Curve Estimation With Autonomous Underwater Agents

This contribution presents a method to estimate environmental boundaries with mobile agents. The agents sample a concentration field of interest at their respective positions and infer a level curve of the unknown field. The presented method is based on support vector machines (SVMs), whereby the concentration level of interest serves as the decision boundary. The field itself does not have to be estimated in order to obtain the level curve which makes the method computationally very appealing. A myopic strategy is developed to pick locations that yield most informative concentration measurements. Cooperative operations of multiple agents are demonstrated by dividing the domain in Voronoi tessellations. Numerical studies demonstrate the feasibility of the method on a real data set of the California coastal area. The exploration strategy is benchmarked against random walk which it clearly outperforms.

A Finite-Impulse-Response-Based Approach to Control Acoustic-Caused Probe-Vibration in Atomic Force Microscope Imaging

In this paper, we propose a finite-impulse-response (FIR)-based feedforward control approach to mitigate the acoustic-caused probe vibration during atomic force microscope (AFM) imaging. Compensation for the extraneous probe vibration is needed to avoid the adverse effects of environmental disturbances such as acoustic noise on AFM imaging, nanomechanical characterization, and nanomanipulation. Particularly, residual noise still exists even though conventional passive noise cancellation apparatus has been employed. The proposed technique exploits a data-driven approach to capture both the noise propagation dynamics and the noise cancellation dynamics in the controller design, and is illustrated through the experimental implementation in AFM imaging application.

Maximum Correntropy Criterion Constrained Kalman Filter

Non-Gaussian noise may degrade the performance of the Kalman filter because the Kalman filter uses only second-order statistical information, so it is not optimal in non-Gaussian noise environments. Also, many systems include equality or inequality state constraints that are not directly included in the system model, and thus are not incorporated in the Kalman filter. To address these combined issues, we propose a robust Kalman-type filter in the presence of non-Gaussian noise that uses information from state constraints. The proposed filter, called the maximum correntropy criterion constrained Kalman filter (MCC-CKF), uses a correntropy metric to quantify not only second-order information but also higher-order moments of the non-Gaussian process and measurement noise, and also enforces constraints on the state estimates. We analytically prove that our newly derived MCC-CKF is an unbiased estimator and has a smaller error covariance than the standard Kalman filter under certain conditions. Simulation results show the superiority of the MCC-CKF compared with other estimators when the system measurement is disturbed by non-Gaussian noise and when the states are constrained.

Limit Cycle Analysis and Control of the Dissipative Chaplygin Sleigh

There are many types of systems in both nature and technology that exhibit limit cycles under periodic forcing. Sometimes, especially in swimming robots, such forcing is used to propel a body forward in a plane. Due to the complexity in studying a fluid system it is often useful to investigate the dynamics of an analogous land model. Such analysis can then be useful in gaining insight about and controlling the original fluid system. In this paper we investigate the behavior of the Chaplygin sleigh under the effect of viscous dissipation and sinusoidal forcing. This is shown to behave in a similar manner as certain robotic fish models. We then apply limit cycle analysis techniques to predict the behavior and control the net translational velocity of the sleigh in a horizontal plane.

Modeling and Experimental Testing of Hoverboard Dynamic Behavior

Self-balancing human transporters are naturally unstable. However, when coupled with sophisticated control laws, these machines can provide mobility within a finite stability envelope. Challenging environmental conditions, or unanticipated operator action, can cause these machines to exhibit unexpected behavior. In an effort to better understand the behavior of these systems inside and outside the stability envelope, a dynamic model of a hoverboard is presented. Motion-capture data is also presented in which an operator’s interactions with the hoverboard were recorded.

Stochastic Dynamics of a Piezoelectric Energy Harvester Subjected to Lévy Flight Excitations

Vibration energy harvesting seeks to exploit the energy of ambient random vibration for power generation, particularly in small scale devices. Piezoelectric transduction is often used as a conversion mechanism in harvesting and the random excitation is typically modeled as a Brownian stochastic process. However, non-Brownian excitations are of potential interest, particularly in the nonequilibrium regime of harvester dynamics. In this work, we investigate the averaged power output of a generic piezoelectric harvester driven by Brownian as well as (non-Brownian) Lévy stable excitations both in the linear and the Duffing regimes. First, a coupled system of stochastic differential equations that model the electromechanical system are presented. Numerical simulation results (based on the Euler-Maruyama scheme) that show the average power output from the system under Brownian and Lévy excitations are presented for the cases where the mechanical degree of freedom behaves as a linear as well as a Duffing oscillator. The results demonstrate that Lévy excitations result in higher expectation values of harvested power. In particular, increasing the noise intensity leads to significant increase in power output in the Levy case when compared with Brownian excitations.