Keum-Shik Hong

Robust adaptive boundary control of a flexible marine riser with vessel dynamics

In this paper, boundary control of a flexible marine riser with vessel dynamics is developed to reduce the risers vibrations. To provide an accurate and concise representation of the risers dynamic behavior, the distributed parameter model of the flexible marine riser with vessel dynamics is described by a partial differential equation (PDE) coupled with ordinary differential equations (ODEs) involving functions of space and time. Boundary control is developed at the top boundary of the riser based on Lyapunovs direct method to regulate the risers vibrations. With the proposed boundary control, uniform boundedness is achieved. The boundary control is implementable with actual instrumentation since all the signals in the control can be measured by sensors or calculated by a backwards difference algorithm. Simulations are provided to illustrate the effectiveness of the proposed control.

fNIRS-based brain-computer interfaces: a review.

A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis (ICA), multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine (SVM), hidden Markov model (HMM), artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.

Modified Skyhook Control of Semi-Active Suspensions: A New Model, Gain Scheduling, and Hardware-in-the-Loop Tuning

In this paper, a road adaptive modified skyhook control for the semi-active Macpherson strut suspension system of hydraulic type is investigated. A new control-oriented model, which incorporates the rotational motion of the unsprung mass, is introduced. The control law extends the conventional skyhook-groundhook control scheme and schedules its gains for various road conditions. Using the vertical acceleration data measured, the road conditions are estimated by using the linearized new model developed. Two filters for estimating the absolute velocity of the sprung mass and the relative velocity in the rattle space are also designed. The hydraulic semi-active actuator dynamics are incorporated in the hardware-in-the-loop tuning stage of the control algorithm developed. The optimal gains for the ISO road classes are discussed. Experimental results are included. @DOI: 10.1115/1.1434265#

Sliding-Mode Antisway Control of an Offshore Container Crane

In this paper, a sliding-mode control for an offshore container crane is discussed. The offshore container crane is used to load/unload containers between a huge container ship (called the “mother ship”) and a smaller ship (called the “mobile harbor”), on which the crane is installed. The purpose of the mobile harbor is to load/unload containers in the open sea and transport them to shallower water where they can be offloaded at existing conventional ports, thereby obviating the need for expansive and expensive new facilities. The load/unload control objective is to suppress the pendulum motion (i.e., “sway”) of the load in the presence of the wave- and wind-induced movements (heave, roll, and pitch) of the mobile harbor. A new mechanism for lateral sway control, therefore, is proposed as well. A sliding surface is designed in such a way that the longitudinal sway of the load is incorporated with the trolley dynamics. The asymptotic stability of the closed-loop system is guaranteed by a control law derived for the purpose. The proposed new mechanism can suppress lateral sway, which functionality is not possible with conventional cranes. Simulation results are provided.

Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface

This paper presents a study on functional near-infrared spectroscopy (fNIRS) indicating that the hemodynamic responses of the right- and left-wrist motor imageries have distinct patterns that can be classified using a linear classifier for the purpose of developing a brain-computer interface (BCI). Ten healthy participants were instructed to imagine kinesthetically the right- or left-wrist flexion indicated on a computer screen. Signals from the right and left primary motor cortices were acquired simultaneously using a multi-channel continuous-wave fNIRS system. Using two distinct features (the mean and the slope of change in the oxygenated hemoglobin concentration), the linear discriminant analysis classifier was used to classify the right- and left-wrist motor imageries resulting in average classification accuracies of 73.35% and 83.0%, respectively, during the 10s task period. Moreover, when the analysis time was confined to the 2-7s span within the overall 10s task period, the average classification accuracies were improved to 77.56% and 87.28%, respectively. These results demonstrate the feasibility of an fNIRS-based BCI and the enhanced performance of the classifier by removing the initial 2s span and/or the time span after the peak value.

Direct adaptive control of parabolic systems: algorithm synthesis and convergence and stability analysis

This paper presents a model reference adaptive control of a class of distributed parameter systems described by linear, n-dimensional, parabolic partial differential equations. Unknown parameters appearing in the system equation are either constant or spatially-varying. Distributed sensing and actuation are assumed to be available. Adaptation laws are obtained by the Lyapunov redesign method. It Is shown that the concept of persistency of excitation, which guarantees the parameter error convergence to zero in finite-dimensional adaptive systems, in infinite-dimensional adaptive systems should be investigated in relation to time variable, spatial variable, and also boundary conditions. Unlike the finite-dimensional case, in infinite-dimensional adaptive systems even a constant input is shown to be persistently exciting in the sense that it guarantees the convergence of parameter errors to zero. Averaging theorems for two-time scale systems which involve a finite dimensional slow system and an infinite dimensional fast system are developed. The exponential stability of the adaptive system, which is critical in finite dimensional adaptive control in terms of tolerating disturbances and unmodeled dynamics, is shown by applying averaging. >

Approximation-Based Adaptive Tracking Control of Pure-Feedback Nonlinear Systems With Multiple Unknown Time-Varying Delays

This paper presents adaptive neural tracking control for a class of non-affine pure-feedback systems with multiple unknown state time-varying delays. To overcome the design difficulty from non-affine structure of pure-feedback system, mean value theorem is exploited to deduce affine appearance of state variables x(i) as virtual controls α(i), and of the actual control u. The separation technique is introduced to decompose unknown functions of all time-varying delayed states into a series of continuous functions of each delayed state. The novel Lyapunov-Krasovskii functionals are employed to compensate for the unknown functions of current delayed state, which is effectively free from any restriction on unknown time-delay functions and overcomes the circular construction of controller caused by the neural approximation of a function of u and [Formula: see text] . Novel continuous functions are introduced to overcome the design difficulty deduced from the use of one adaptive parameter. To achieve uniformly ultimate boundedness of all the signals in the closed-loop system and tracking performance, control gains are effectively modified as a dynamic form with a class of even function, which makes stability analysis be carried out at the present of multiple time-varying delays. Simulation studies are provided to demonstrate the effectiveness of the proposed scheme.

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface.

The hybrid brain-computer interface (BCI)s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, “forward,” “backward,” “left,” and “right.” The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology.

Robust adaptive boundary control of an axially moving string under a spatiotemporally varying tension

In this paper, a vibration suppression scheme for an axially moving string under a spatiotemporally varying tension and an unknown boundary disturbance is investigated. The lower bound of the tension variation is assumed to be sufficiently larger than the derivatives of the tension. The axially moving string system is divided into two spans, i.e., a controlled span and an uncontrolled span, by a hydraulic touch-roll actuator which is located in the middle section of the string. The transverse vibration of the controlled span part of the string is controlled by the hydraulic touch-roll actuator, and the position of the actuator is considered as the right boundary of the controlled span part. The mathematical model of the system, which consists of a hyperbolic partial differential equation describing the dynamics of the moving string and an ordinary differential equation describing the actuator dynamics, is derived by using the Hamiltons principle. The Lyapunov method is employed to design a robust boundary control law and adaptation laws for ensuring the vibration reduction of the controlled span part. The asymptotic stability of the closed loop system under the robust adaptive boundary control scheme is proved through the use of semigroup theory. Simulation results verify the effectiveness of the robust adaptive boundary controller proposed.

Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI.

Functional near-infrared spectroscopy (fNIRS) is an optical imaging method that can be used for a brain-computer interface (BCI). In the present study, we concurrently measure and discriminate fNIRS signals evoked by three different mental activities, that is, mental arithmetic (MA), right-hand motor imagery (RI), and left-hand motor imagery (LI). Ten healthy subjects were asked to perform the MA, RI, and LI during a 10s task period. Using a continuous-wave NIRS system, signals were acquired concurrently from the prefrontal and the primary motor cortices. Multiclass linear discriminant analysis was utilized to classify MA vs. RI vs. LI with an average classification accuracy of 75.6% across the ten subjects, for a 2-7s time window during the a 10s task period. These results demonstrate the feasibility of implementing a three-class fNIRS-BCI using three different intentionally-generated cognitive tasks as inputs.