Prior experiments have shown that human gait synchronizes to periodic torque pulses applied about the hip and ankle joints by robotic exoskeletons. Importantly, entrainment occurred even when the pulse period differed slightly from the user’s preferred stride period, making it a viable approach to increase gait speed. As gait speed is an important outcome of gait therapy, gait entrainment to mechanical perturbations may serve as a promising new method of robot-aided therapy. Still, an understanding of the underlying neuromechanical processes that give rise to gait entrainment is needed to fully evaluate its therapeutic potential. To gain such insight, the goal of this paper was to evaluate whether an existing neuromechanical model of human locomotion exhibited entrainment behavior similar to that observed in the prior human experiments. Simulation results showed that the model entrained to pulses applied at both the ankle and hip joints. The convergence of relative phase between model gait and hip perturbations was similar to that observed with the human gait, but differed slightly for ankle perturbations. Thus, models that can more accurately describe neuromechanical interactions between human gait and robotic exoskeletons are still needed. Nevertheless, the simulation results support the notion that the limit-cycle behavior observed during locomotion does not require supra-spinal control or a self-sustaining oscillatory neural network, which has important implications for improving gait therapy.

Restoring healthy kinematics is a critical component of assisting and rehabilitating impaired locomotion. Here we tested whether spatiotemporal gait patterns can be modulated by applying mechanical impedance to hip joints. Using the Samsung GEMS-H exoskeleton, we emulated a virtual spring (positive and negative) between the user's legs. We found that applying positive stiffness with the exoskeleton decreased stride time and hip range of motion for healthy subjects during treadmill walking. Conversely, the application of negative stiffness increased stride time and hip range of motion. These effects did not vary over long nor short repeated exposures to applied stiffness. In addition, minimal transient behavior was observed in spatiotemporal measures of gait when the stiffness controller transitioned between on and off states. These results suggest that changes in gait behavior induced by applying hip stiffness were purely a mechanical effect. Together, our findings indicate that applying mechanical impedance using lower-limb assistive devices may be an effective, minimally-encumbering intervention to restore healthy gait patterns.

This paper proposes an offset calibration method for dynamic comparators, which trims the effective transconductance of transistors. The proposed offset calibration method requires additional switches only on the fixed-voltage nodes; therefore, it has negligible impact on the comparator's key performances, such as comparison and reset speed, input referred noise, and power consumption. The proposed offset calibration method is verified with a 14-nm FinFET technology. The simulation results show that the standard deviation of the comparator offset is reduced from 2.07 mV to 0.26 mV. With a 6-bit digital controller, the offset tuning range and step size are 20.8 mV and 0.3 mV, respectively. The offset tuning range is approximately 5 times wider than the uncalibrated offset standard deviation. The degradation of the speed, noise, and power efficiency due to the calibration circuits are less than 0.72%.

The aim of this paper is to construct a theoretical framework for stability analysis relevant to standing balance of humanoids on top of the linear inverted pendulum model, in which their dynamics between the center of mass (CoM) and the zero moment point (ZMP) is dealt with. Based on the well-known sufficient condition that the contact between the ground and the support leg is stable if the corresponding ZMP is always inside the supporting region, this paper aims at characterizing three types of the associated stability regions. More precisely, assuming no external force disturbances affecting the motion of the humanoids, the stability region of the initial CoM position and velocity values can be explicitly computed by solving a finite number of linear inequalities. The stability regions of time-invariant force disturbances such as impulsive force and constant force disturbances are also dealt with in this paper, where the former is exactly obtained through a finite number of linear inequalities while the latter is approximately derived by using an idea of truncation. Furthermore, time-varying force disturbances of finite energy and finite amplitude are concerned with, and their maximum admissible${l} _{2}$and${l} _{\infty }$norms are computed in this paper, where the former can be exactly obtained by solving the discrete-time Lyapunov equation while the latter is approximately derived through an idea of truncation. It is further shown for both the truncation ideas that the approximately obtained stability regions converge to the exact stability regions with an exponential order of${N}$, where${N}$is the truncation parameter. Finally, the effectiveness of the computation methods proposed in this paper is demonstrated through some simulation results.

The locomotion and balance capabilities of bipedal robots have greatly improved in recent years. However, maintaining balance on difficult terrain still poses a significant challenge. In this paper, we examined how humans maintain mediolateral balance when standing on a narrow beam with bare feet and wearing rigid soles. Our results show that foot-beam interaction dynamics critically influence balancing behavior. Importantly, this suggests that differences in human balancing behavior across different support surfaces may not solely result from changes in their neural control strategy. They may also result from changes in foot-ground interaction. Thus, the altered foot-ground interaction dynamics must be considered to accurately capture changes in the human controller across different support surfaces. A simplified model of foot-beam interaction was added to a double inverted pendulum model for human balancing. This extended model could replicate the change in human behavior across different foot contact conditions (bare feet vs. rigid feet). A better understanding of how humans coordinate whole-body behavior across a range of conditions may inform the development of balance controllers for bipedal robots.

While rehabilitation of upper-limb motor function with human-interactive robots has been met with success, robot-aided locomotor rehabilitation has proven challenging. To inform more effective approaches to robotic gait therapy, it is important to understand neuro-mechanical dynamics and control of unimpaired locomotion. Our previous studies reported that human gait entrained to periodic mechanical perturbations at the ankle when the perturbation period was close to preferred walking cadence. Moreover, entrainment was accompanied by synchronizing the perturbations to a constant gait phase, the same for all subjects, where they provided mechanical assistance. To test the generality of entrainment-based assistance, the present study evaluated the behavior of live unimpaired subjects who walked overground while wearing a hip exoskeleton robot. Periodic torque pulses were applied to the subjects' hips, with a period different from, but close to, their preferred stride cadence. Results indicated that unimpaired subjects entrained their gait to periodic mechanical perturbations at the hip. Convergence of relative phase between gait and perturbations was observed, but clustered around two distinct gait phases, in contrast to the single converged phase observed in entrainment to periodic ankle torques. These entrainment studies quantify important aspects of the nonlinear neuro-mechanical dynamics underlying the control of walking, which will inform the development of effective approaches to robotic walking therapy.

Standing on a beam is a challenging motor skill that requires the regulation of upright balance and stability. In this paper, we analyzed the behavior of humans balancing on a narrow beam without footwear. The results revealed high anti-correlation between lumped upper- and lower-body angular momentum. Despite differences in gross measures of balance, interlimb coordination was consistent between the novice and expert subjects, suggesting that both performances could be described with the same balance controller. By simulating a double inverted pendulum model utilizing different balancing controllers described in the robotics literature, we identified that the whole behavior observed from humans standing on a beam was best replicated with controllers that predominantly utilized hip actuation.

A Ka-band phase shifting low noise amplifier (LNA) using stacked-FET structure in 14nm FinFET CMOS is presented for 5G beam forming array. The gate capacitance of the middle CG-FET is adjusted for phase variation. The additionally stacked CG-FET provides low load impedance to the phase shifting CG-FET for minimizing gain error. Moreover, the higher output isolation for stable phase shifting and power gain can be achieved by additional CG-FET stacking. The designed phase shifting low noise amplifier shows a gain of 15 dB and noise figure of 5 dB with less than 2 dB gain variation while the phase shifting can be controlled up to 22.5 degrees from 30 GHz to 40 GHz. The use of the developed phase shifting LNA can give a better phase and gain performance resulting in the improved array factor in the 5G beam-forming array network.

Based on the liner inverted pendulum model (LIPM) for the dynamics of biped humanoid robots, an analytical method for computing stability regions relevant to standing balance of the biped humanoid robots is introduced in this paper. More precisely, two types of the stability regions are discussed in this paper with the consideration of that the zero moment point (ZMP) should be located in the supporting region to guarantee stable standing of the biped humanoid robots. First, assuming no external disturbances affecting the motion of the biped humanoid robots, the set of the initial values of the center of mass (CoM) position and velocity with which the location of the ZMP is limited to be inside the supporting region can be explicitly obtained by solving a finite number of linear inequalities. Second, two admissible sets of external force disturbances (impulse and finite energy) with which the ZMP does not deviate from the supporting region are characterized by solving finite number of linear inequalities or the discrete-time Lyapunov equation, respectively. The validity and effectiveness of the analytical method proposed in this paper are verified through a simulation result.

Feedback controller for robotic bipedal walking models can be multi-layered, consisting of low-level continuous-time controller and high-level event-based controller. Stimulated by the success in our preceding study that demonstrates the validity of the l∞-induced norm as an adequate performance measure, we suggest a systematic methodology to design optimal event-based feedback controller for bipedal models walking on rough terrains. More precisely, we first assume that the system is already equipped with a low-level continuous-time feedback controller, capable of stable flat-ground-walking, and then formulate the design problem of the high-level event-based feedback control as the l1-optimal control problem for discrete-time linear systems defined on the linearized Poincarè map. In order to validate the proposed methodology, nonlinear dynamic simulations are conducted with a simple biped model walking on rough terrain. The terrain slope randomly varies at each footstep while the magnitude of slope variation is bounded by some maximum value. Simulation results indicate that the optimal system equipped with the proposed controller can successfully overcome a rough terrain on which the original system could not walk and fall.

In this paper, we consider the L1optimal proportional-derivative (PD) controller synthesis by which the L∞gain of trajectory tracking systems can be reduced. The notion of input-to-state stability (ISS), which has been equipped with the L2norm of a vector-valued signal, plays important roles in evaluating the effect of disturbances on the system states. In connection with this, we first redefine the conventional ISS with the L∞norm to deal with bounded persistent disturbances because the disturbances should be mathematically regarded as elements of the L∞space. A tractable model for trajectory tracking control of robot systems is then given by using ideas of extended disturbance and composite error. We next introduce a design method of the L1optimal PD controller for such a model, by which the trajectory tracking system satisfies the redefined ISS and the L∞gain is less than a performance level γ. Finally, we examine the effectiveness of the design method through experimental results for a typical robot manipulator.

Despite successful demonstrations of outdoor walking by a few biped robots, performance measure for such robots has not been formally defined yet. The performance measure should be adequately defined by which one can evaluate how well the robot keeps from falling in the presence of disturbance. If such performance measure is suitably determined, designing a sort of optimal controller for stable outside walking would be possible. This paper firstly suggests to adopt the l∞-induced norm defined on the linearized Poincarè map as a novel performance measure for biped robots, in the presence of bounded persistent disturbance. In order to validate the measure, a nonlinear dynamic simulation is conducted by extending an existing simple model to walk on rough terrain, of which height variance is bounded by some maximum value. The measure exploited for the model is compared with the numerical result obtained from nonlinear dynamic simulation. Finally, an example of application of this measure is provided, which successfully predicts whether the system could overcome upcoming terrain roughness.

In this paper, we propose a novel planar biped model which achieves asymptotically stable walking, based on ankle actuation and the virtual pendulum concept. Simple models for walking have provided useful insights in understanding fundamental principles of human locomotion as well as developing controllers for biped robots. Existing simplest walking models include a point mass with either rigid legs or compliant legs. Both models are able to describe different gaits such as walking and running, but are not able to address posture stabilization, which is another important issue in bipedalism. Recently, the virtual pendulum (VP) concept was proposed as an intuitive posture stabilization strategy and successfully demonstrated on the compliant leg scheme. However, the model does not address inherent peripheral mechanics of walking, i.e., ankle actuation and collisional energy loss from foot-ground interaction. The model proposed in this paper consists of a rigid trunk, rigid legs and ankle actuation. The energy dissipation due to collision and compensation by ankle actuation play an essential role for asymptotically stable walking, while the trunk posture is stabilized based on the VP concept. Nonlinear dynamic simulation verifies that the model constructs a stable limit cycle with a small angular oscillation of trunk.

The MIT Cheetah demonstrated a stable 6 m/s trot gait in the sagittal plane utilizing the self-stable characteristics of locomotion. This paper presents a numerical analysis of the behavior of a quadruped robot model with the proposed controller. We first demonstrate the existence of periodic trot gaits at various speeds and examine local orbital stability of each trajectory using Poincar`e map analysis. Beyond the local stability, we additionally demonstrate the stability of the model against large initial perturbations. Stability of trot gaits at a wide range of speed enables gradual acceleration demonstrated in this paper and a real machine. This simulation study also suggests the upper limit of the command speed that ensures stable steady-state running. As we increase the command speed, we observe series of period-doubling bifurcations, which suggests presence of chaotic dynamics beyond a certain level of command speed. Extension of this simulation analysis will provide useful guidelines for searching control parameters to further improve the system performance.

Animals use tails to improve locomotion performance; here we assess how the biomimetic MIT Cheetah robot can do the same. Analysis proves that for a given power and weight, tails can provide greater average torque than reaction wheels for the short times of interest in high speed running. A simple tail controller enables the cheetah to perform aerial orientation maneuvers in simulation. The MIT Cheetah robot's tail rejects an impulsive disturbance from a `wrecking ball' in experiments. This study demonstrates that a tail will help the MIT Cheetah achieve its goal of 30mph locomotion by 2014.

The portfolio management for trading in the stock market poses a challenging stochastic control problem of significant commercial interests to finance industry. To date, many researchers have proposed various methods to build an intelligent portfolio management system that can recommend financial decisions for daily stock trading. Many promising results have been reported from the supervised learning community on the possibility of building a profitable trading system. More recently, several studies have shown that even the problem of integrating stock price prediction results with trading strategies can be successfully addressed by applying reinforcement learning algorithms. Motivated by this, we present a new stock trading framework that attempts to further enhance the performance of reinforcement learning-based systems. The proposed approach incorporates multiple Q-learning agents, allowing them to effectively divide and conquer the stock trading problem by defining necessary roles for cooperatively carrying out stock pricing and selection decisions. Furthermore, in an attempt to address the complexity issue when considering a large amount of data to obtain long-term dependence among the stock prices, we present a representation scheme that can succinctly summarize the history of price changes. Experimental results on a Korean stock market show that the proposed trading framework outperforms those trained by other alternative approaches both in terms of profit and risk management.

A statistical method for compound noun decomposition is presented. Previous studies on this problem showed some statistical information are helpful. But applying statistical information was not so systemic that performance depends heavily on the algorithm and some algorithms usually have many separated steps. In our work statistical information is collected from manually decomposed compound noun corpus to build a Markov model for composition. Two Markov chains representing statistical information are assumed independent: one for the sequence of participants’ lengths and another for the sequence of participants ’ features. Besides Markov assumptions, least participants preference assumption also is used. These two assumptions enable the decomposition algorithm to be a kind of conditional dynamic programming so that efficient and systemic computation can be performed. When applied to test data of size 5027, we obtained a precision of 98.4%.