Impedance control of a cable-driven SEA with mixed H2/H∞H_2/H_\infty synthesis

Purpose: This paper presents an impedance control method with mixed $H_2/H_\infty$ synthesis and relaxed passivity for a cable-driven series elastic actuator to be applied for physical human-robot interaction. Design/methodology/approach: To shape the system's impedance to match a desired dynamic model, the impedance control problem was reformulated into an impedance matching structure. The desired competing performance requirements as well as constraints from the physical system can be characterized with weighting functions for respective signals. Considering the frequency properties of human movements, the passivity constraint for stable human-robot interaction, which is required on the entire frequency spectrum and may bring conservative solutions, has been relaxed in such a way that it only restrains the low frequency band. Thus, impedance control became a mixed $H_2/H_\infty$ synthesis problem, and a dynamic output feedback controller can be obtained. Findings: The proposed impedance control strategy has been tested for various desired impedance with both simulation and experiments on the cable-driven series elastic actuator platform. The actual interaction torque tracked well the desired torque within the desired norm bounds, and the control input was regulated below the motor velocity limit. The closed loop system can guarantee relaxed passivity at low frequency. Both simulation and experimental results have validated the feasibility and efficacy of the proposed method. Originality/value: This impedance control strategy with mixed $H_2/H_\infty$ synthesis and relaxed passivity provides a novel, effective and less conservative method for physical human-robot interaction control.Comment: 11 pages, already published in Assembly Automatio

Inferring Gene Regulatory Network Using An Evolutionary Multi-Objective Method

Inference of gene regulatory networks (GRNs) based on experimental data is a challenging task in bioinformatics. In this paper, we present a bi-objective minimization model (BoMM) for inference of GRNs, where one objective is the fitting error of derivatives, and the other is the number of connections in the network. To solve the BoMM efficiently, we propose a multi-objective evolutionary algorithm (MOEA), and utilize the separable parameter estimation method (SPEM) decoupling the ordinary differential equation (ODE) system. Then, the Akaike Information Criterion (AIC) is employed to select one inference result from the obtained Pareto set. Taking the S-system as the investigated GRN model, our method can properly identify the topologies and parameter values of benchmark systems. There is no need to preset problem-dependent parameter values to obtain appropriate results, and thus, our method could be applicable to inference of various GRNs models.Comment: 8page

Universality of Heisenberg-Ising chain in external fields

Motivated by the recent surge of transverse-field experiments on quasi-one-dimensional antiferromagnets Sr(Ba)Co$_2$V$_2$O$_8$, we investigate the quantum phase transition in a Heisenberg-Ising chain under a combination of two in-plane inter-perpendicular transverse fields and a four-period longitudinal field, where the in-plane transverse field is either uniform or staggered. We show that the model can be unitary mapped to the one-dimensional transverse-field Ising model (1DTFIM) when the $x$ and $y$ components of the spin interaction and the four-period field are absent. When these two terms are present, following both analytical and numerical efforts, we demonstrate that the system undergoes a second-order quantum phase transition with increasing transverse fields, where the critical exponents as well as the central charge fall into the universality of 1DTFIM. Our results naturally identify the 1DTFIM universality of 1D quantum phase transitions observed in the existed experiments in Sr(Ba)Co$_2$V$_2$O$_8$ with transverse field applied along either [100] or [110] direction. Upon varying the tuning parameters a critical surface with 1DTFIM universality is determined and silhouetted to exhibit the general presence of the universality in a much wider scope of models than conventional understanding. Thus our results provide a broad guiding framework to facilitate the experimental realization of 1DTFIM universality in real materials.Comment: 8 pages, 4 figure

Online Energy Management for a Sustainable Smart Home with an HVAC Load and Random Occupancy

In this paper, we investigate the problem of minimizing the sum of energy cost and thermal discomfort cost in a long-term time horizon for a sustainable smart home with a Heating, Ventilation, and Air Conditioning (HVAC) load. Specifically, we first formulate a stochastic program to minimize the time average expected total cost with the consideration of uncertainties in electricity price, outdoor temperature, renewable generation output, electrical demand, the most comfortable temperature level, and home occupancy state. Then, we propose an online energy management algorithm based on the framework of Lyapunov optimization techniques without the need to predict any system parameters. The key idea of the proposed algorithm is to construct and stabilize four queues associated with indoor temperature, electric vehicle charging, and energy storage. Moreover, we theoretically analyze the feasibility and performance guarantee of the proposed algorithm. Extensive simulations based on real-world traces show the effectiveness of the proposed algorithm.Comment: 14 pages, 21 figure

Photon-mediated electronic correlation effects in irradiated two-dimensional Dirac systems

Periodically driven systems can host many interesting and intriguing phenomena. The irradiated two-dimensional Dirac systems, driven by circularly polarized light, are the most attractive thanks to intuitive physical view of the absorption and emission of photon near Dirac cones. Here, we assume that the light is incident in the two-dimensional plane, and choose to treat the light-driven Dirac systems by making a unitary transformation to capture the photon-mediated electronic correlation effects, instead of using usual Floquet theory. In this approach, the electron-photon interaction terms can be cancelled out and the resultant effective electron-electron interactions can produce important effects. These effective interactions will produce a topological band structure in the case of 2D Fermion system with one Dirac cone, and can lift the energy degeneracy of the Dirac cones for graphene. This method can be applicable to similar light-driven Dirac systems to investigate photon-mediated electronic effects in them.Comment: 5 pages, 4 figure

Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently

We propose a family of nonconvex optimization algorithms that are able to save gradient and negative curvature computations to a large extent, and are guaranteed to find an approximate local minimum with improved runtime complexity. At the core of our algorithms is the division of the entire domain of the objective function into small and large gradient regions: our algorithms only perform gradient descent based procedure in the large gradient region, and only perform negative curvature descent in the small gradient region. Our novel analysis shows that the proposed algorithms can escape the small gradient region in only one negative curvature descent step whenever they enter it, and thus they only need to perform at most $N_{\epsilon}$ negative curvature direction computations, where $N_{\epsilon}$ is the number of times the algorithms enter small gradient regions. For both deterministic and stochastic settings, we show that the proposed algorithms can potentially beat the state-of-the-art local minima finding algorithms. For the finite-sum setting, our algorithm can also outperform the best algorithm in a certain regime.Comment: 31 pages, 1 tabl

Computation Load Balancing Real-Time Model Predictive Control in Urban Traffic Networks

Owing to the rapid growth number of vehicles, urban traffic congestion has become more and more severe in the last decades. As an effective approach, Model Predictive Control (MPC) has been applied to urban traffic signal control system. However, the potentially high online computation burden may limit its further application for real scenarios. In this paper, a new approach based on online active set strategy is proposed to improve the real-time performance of MPC-based traffic controller by reducing the online computing time. This approach divides one control cycle into several sequential sampling intervals. In each interval, online active set method is applied to solve quadratic programming (QP) of traffic signal control model, by searching the optimal solution starting at the optimal solution of previous interval in the feasible region. The most appealing property of this approach lies in that it can distribute the computational complexity into several sample intervals, instead of imposing heavy computation burden at each end of control cycle. The simulation experiments show that this breakthrough approach can obviously reduce the online computational complexity, and increase the applicability of the MPC in real-life traffic networks

Analog-to-digital conversion revolutionized by deep learning

As the bridge between the analog world and digital computers, analog-to-digital converters are generally used in modern information systems such as radar, surveillance, and communications. For the configuration of analog-to-digital converters in future high-frequency broadband systems, we introduce a revolutionary architecture that adopts deep learning technology to overcome tradeoffs between bandwidth, sampling rate, and accuracy. A photonic front-end provides broadband capability for direct sampling and speed multiplication. Trained deep neural networks learn the patterns of system defects, maintaining high accuracy of quantized data in a succinct and adaptive manner. Based on numerical and experimental demonstrations, we show that the proposed architecture outperforms state-of-the-art analog-to-digital converters, confirming the potential of our approach in future analog-to-digital converter design and performance enhancement of future information systems

Modulating quantum Fisher information of qubit in dissipative cavity by coupling strength

By using the non-Markovian master equation, we investigate the effect of the cavity and the environment on the quantum Fisher information (QFI) of an atom qubit system in a dissipation cavity. We obtain the formulae of QFI for two different initial states and analyze the effect of the atom-cavity coupling and the cavity-reservoir coupling on the QFI. The results show that the dynamic behavior of the QFI is obviously dependent on the initial atomic states, the atom-cavity coupling and the cavity-reservoir coupling. The stronger the atom-cavity coupling, the quicker the QFI oscillates and the slower the QFI reduces. Especially, the QFI will tend to a stable value not zero if the atom-cavity coupling is large enough. On the other hand, the smaller the cavity-reservoir coupling, the stronger the non-Markovian effect, the slower the QFI decay. In other words, choosing the best parameter can improve the accuracy of parameter estimation. In addition, the physical explanation of the dynamic behavior of the QFI is given by means of the QFI flow.Comment: 7 pages, 4 figure

Uncertainties in the Deprojection of the Observed Bar Properties

In observations, it is important to deproject the two fundamental quantities characterizing a bar, i.e., its length ($a$) and ellipticity ($e$), to face-on values before any careful analyses. However, systematic estimation on the uncertainties of the commonly used deprojection methods is still lacking. Simulated galaxies are well suited in this study. We project two simulated barred galaxies onto a 2D plane with different bar orientations and disk inclination angles ($i$). Bar properties are measured and deprojected with the popular deprojection methods in the literature. Generally speaking, deprojection uncertainties increase with increasing $i$. All the deprojection methods behave badly when $i$ is larger than $60^\circ$, due to vertical thickness of the bar. Thus, future statistical studies of barred galaxies should exclude galaxies more inclined than $60^\circ$. At moderate inclination angles ($i\leq60^\circ$), 2D deprojection methods (analytical and image stretching) and Fourier-based methods (Fourier decomposition and bar-interbar contrast) perform reasonably well with uncertainties $\sim10\%$ in both the bar length and ellipticity. Whereas the uncertainties of the 1D analytical deprojection can be as high as $100\%$ in certain extreme case. We find that different bar measurement methods show systematic differences in the deprojection uncertainties. We further discuss the deprojection uncertainty factors with the emphasis on the most important one, i.e., the 3D structure of the bar itself. We construct two triaxial toy bar models that can qualitatively reproduce the results of the 1D and 2D analytical deprojections; they confirm that the vertical thickness of the bar is the main source of uncertainties.Comment: 11 pages, 18 figures, accepted for publication in Ap