First-principles density functional theory (DFT) with plane wave (PW) basis set is the most widely used method in quantum mechanical material simulations due to its advantages in accuracy and universality. However, a perceived drawback of PW-based DFT calculations is their substantial computational cost and memory usage, which currently limits their ability to simulate large-scale complex systems containing thousands of atoms. This situation is exacerbated in the new Sunway supercomputer, where each process is limited to a mere 16 GB of memory. Herein, we present a novel parallel implementation of plane wave density functional theory on the new Sunway supercomputer (PWDFT-SW). PWDFT-SW fully extracts the benefits of Sunway supercomputer by extensively refactoring and calibrating our algorithms to align with the system characteristics of the Sunway system. Through extensive numerical experiments, we demonstrate that our methods can substantially decrease both computational costs and memory usage. Our optimizations translate to a speedup of 64.8x for a physical system containing 4,096 silicon atoms, enabling us to push the limit of PW-based DFT calculations to large-scale systems containing 16,384 carbon atoms.

The state of energy (SoE) is a critical metric for battery management systems, complementing the state of charge (SoC) in providing comprehensive battery management insights. This paper addresses SoE estimation for lithium-ion batteries by designing a sliding mode observer that utilizes sliding mode control technology to enhance the robustness of the SoE estimation algorithm. Leveraging the correlation between SoC and SoE, this study develops a method to accurately estimate SoC and determine SoE. Additionally, we introduce a sliding mode observer based on the Super-twisting algorithm, which stabilizes the observer by carefully selecting parameters. The effectiveness of the proposed SoE estimation algorithm is validated using Dynamic Stress Test (DST) data, confirming its practical utility.

The trajectory tracking performance of flexible joint robots (FJRs) is adversely affected in the presence of measurement noise, unmodelled system dynamics, external disturbances, and parametric variations. This paper proposes singular perturbation (SP) based continuous sliding mode controller (CSMC) schemes for the FJRs system to attain superior tracking accuracy. Firstly, the SP is applied to split the high-order FJRs system into two second-order quasi-steady-state and boundary layer models, which relaxes the requirements of high derivatives of system states in the control design. Secondly, two state-differentiators are developed for both subsystems to estimate unavailable velocity and acceleration signals. Then, based on estimated signals, the two CSMC schemes are constructed for both subsystems to attain the asymptotic convergence of tracking errors and fast variables, reduce chattering, and ensure robustness against external disturbance, measurement noise, and system uncertainties. Furthermore, the internal model method is applied to ease the tuning process of the sliding surface gains in the CSMC for both models. More importantly, the bandwidth of a fast control is chosen based on a slow subsystem bandwidth using Ruth Hurwitz stability criteria. Thus, the hectic gains tuning process is hugely simplified by applying an internal model based tuning on both subsystems. Finally, the provided simulation results verify the effectiveness of our proposed control in terms of measurement noise suppression, less chattering, robustness, and high tracking accuracy comparatively.

GW approximation is a powerful approach to accurately describe the excited-state of semiconductors. However, GW incurs high computational cost $\mathcal{O}\left(N^{4}\right)$ and large memory usage $\mathcal{O}\left(N^{3}\right)$, limiting its applications to thousands of (2,742) atoms even on leadership supercomputers. Herein we present a massively parallel implementation of accurate and efficient cubic-scaling plane-wave GW calculations by using low-rank approximations and high-performance computing on leadership supercomputers. By using a series of low rank approximations, we can reduce the expensive GW calculations to the cubic-scaling computational cost $\mathcal{O}\left(N^{3}\right)$ and quadratic memory usage $\mathcal{O}\left(N^{2}\right)$. With the help of parallel and communication optimization, the plane-wave GW calculations gain an overall speedup of over 70x and efficiently scale up to 13,824 atoms within a few minutes using 449,280 cores on new Sunway supercomputer. This accomplishment paves the way for excited-state quantum mechanical material simulations at mesoscopic scale (10K atoms) and for the design of next-generation semiconductor devices.

First-principles time-dependent density functional theory (TDDFT) is a powerful tool to accurately describe the excited-state properties of molecules and solids in condensed matter physics , computational chemistry, and materials science. However, a perceived drawback in TDDFT calculations is its ultrahigh computational cost O ( N 5 ∼ N 6 ) and large memory usage O ( N 4 ) especially for plane-wave basis set, confining its applications to large systems containing thousands of atoms. Here, we present a massively parallel implementation of linear-response TDDFT (LR-TDDFT) and accelerate LR-TDDFT in two different aspects: (1) numerical algorithms on the X86 supercomputer and (2) optimizations on the heterogeneous architecture of the new Sunway supercomputer. Furthermore, we carefully design the parallel data and task distribution schemes to accommodate the physical nature of different computation steps. By utilizing these two different methods, our implementation can gain an overall speedup of 10x and 80x and efficiently scales to large systems up to 4096 and 2744 atoms within dozens of seconds.

This paper is concerned with the secure event-triggered distributed cooperative longitudinal control problem of virtually coupled high-speed trains (VCHSTs) subject to DoS attacks. First, a DoS-aware dynamic event-triggered transmission mechanism (DETM) is proposed to reduce the frequency of train data transmissions over train-to-train (T2T) information flow channels. Via actively prolonging the inter-event times, the resilience of the developed DETM to DoS attacks can be greatly improved. Second, based on the intermittently arrived T2T data, a distributed event-based secure cooperative control protocol is proposed for each train in the convoy. Third, a sufficient condition is derived to guarantee the asymptotic stability of the train tracking error system under the prescribed H∞performance. Furthermore, a co-design method for solving out the desired train controllers and the triggering conditions is developed. Finally, the efficacy of the derived theoretical results is verified through a case study of a realistic railway line.

A reduced-order extended state observer (RESO) based a continuous sliding mode control (SMC) is proposed in this paper for the tracking problem of high order Brunovsky systems with the existence of external perturbations and system uncertainties. For this purpose, a composite control is constituted by two consecutive steps. First, the reduced-order ESO (RESO) technique is designed to estimate unknown system states and total disturbance without estimating an available state. Second, the continuous SMC law is designed based on the estimations supplied by the RESO estimator in order to govern the nominal system part. More importantly, the robustness performance is well achieved by compensating not only the lumped disturbance, but also its estimation error. Finally, the tracking performance is examined by carrying out several simulations on robotic systems with compliant actuators as an application example of the high order systems. In addition, the comparative study is conducted between the proposed SMC method with RESO and a feedback linearization control (FLC) with a full-order ESO to confirm the estimation and tracking performance of the proposed scheme.

This article addresses the problem of synchronization of impulsive networks with switching topologies. A new synchronization framework is established with an emphasis on settling time estimation. The impulsive networks consist of physical nodes and cyber modules. For physical nodes, states are changed impulsively at discrete time instants due to some switching phenomena or unexpected sudden noises. For cyber modules, two cases of switching scenarios are considered for information exchange patterns during specific time intervals. In the first case, cyber modules lose all the communication links with others, resulting in disconnected topologies. Then, a distributed controller is proposed for nodes without intrinsic nonlinear dynamics. A distinguished feature of this controller is its capability to estimate a bound for settling time, beyond which the synchronization with respect to a virtual target is guaranteed. In the second case, cyber modules lose some communication links but build other new ones with the help of a smart communication center to form connected topologies. A distributed controller is further designed for nodes in the presence of intrinsic nonlinear dynamics. Accordingly, a sufficient condition is derived to achieve synchronization with an estimated settling time bound. For both cases, the estimated bounds are able to reveal the relationship between the impulsive strength and the synchronization performance. Finally, numerical examples including a case study on a modified IEEE 34 bus test feeder are provided to demonstrate the effectiveness of the proposed controllers.

Flexible joint robot (FJR) manipulators can offer many attractive features over rigid manipulators, including light weight, safe operation, and high power efficiency. However, the tracking control of the FJR is challenging due to its inherent problems, such as underactuation, coupling, nonlinearities, uncertainties, and unknown external disturbances. In this article, a terminal sliding mode control (TSMC) is proposed for the FJR system to guarantee the finite-time convergence of the systems output, and to achieve the total robustness against the lumped disturbance and estimation error. By using two coordinate transformations, the FJR dynamics is turned into a canonical form. A cascaded finite-time sliding mode observer (CFTSMO) is constructed to estimate states and lumped disturbance in a finite time based on two measurable states, which not only attenuates the measurement noise but also reduces the peaking phenomenon. The closed-loop stability and the finite-time convergence are rigorously proved by using Lyapunov theorem. The upper bound of the finite convergence time is derived for the reaching and sliding phase. Comparative study is conducted experimentally in real time on the FJR manipulator to verify the effectiveness of the proposed control method.

The permanent magnetic synchronous motors (PMSMs) are nonlinear and subject to different kinds of uncertainties, which brings challenges to design a controller for the robustness and accurate tracking. This paper presents a phase-lead repetitive control (PLRC) with field-oriented feedback linearization and a disturbance observer to improve the robustness and the speed tracking for a PMSM system. Firstly, feedback linearization is designed to cancel the nonlinearity in the field-oriented based PMSM model, in which the field-oriented control (FOC) decouples the magnetic loop and the torque loop in the PMSM system to simplify the PMSM control design. Secondly, the PLRC is designed to decrease the speed fluctuations caused by the external periodic disturbance, while the disturbance observer (DO) is employed to estimate and compensate the non-periodic uncertainties including the payload. Finally, the proposed control is validated by numerical simulations. The results show the proposed control has a superior speed tracking performance than its counterparts.

As one type of heating furnaces, the electric heating furnace (EHF) typically suffers from time delay, non-linearity, time-varying parameters, system uncertainties, and harsh en-vironment of the furnace, which significantly deteriorate the temperature control process of the EHF system. In order to achieve accurate and robust temperature tracking performance, an integration of robust state feedback control (RSFC) and a novel sliding mode-based disturbance observer (SMDO) is proposed in this paper, where modeling errors and external disturbances are lumped as a lumped disturbance. To describe the characteristics of the EHF, by using convection laws, an integrated dynamic model is established and identified as an uncertain nonlinear second order system. The SMDO is newly designed to estimate the lumped disturbance, where the estimation error converges to zero asymptotically. The estimation of the disturbances is then used in the control law of the RSFC to reject the system's lumped disturbance. The analytical results demonstrate that the proposed method is asymptotically stable with guaranteeing the tracking error convergence to zero even in the presence of external disturbances. Finally, the comparative simulation study shows the effectiveness of proposed method for the temperature control tracking of the considered furnace application.

This paper is concerned with the event-triggered control problem of vehicle active suspension systems subject to uncertain actuator faults. Firstly, networked modeling of the vehicle suspension control system is presented and a dynamic event-triggered communication mechanism is developed to reduce some unnecessary data transmissions between networked sensor and controller, thus enabling efficient usage of the limited communication resources for the in-vehicle network. Different from most existing event-triggered mechanisms, the threshold parameter of the proposed triggering condition is adaptively regulated in accord with a dynamic rule. Secondly, by Lyapunov functional method, a co-design criterion of the desired event-triggering parameter and H∞controller gain is derived so as to guarantee the asymptotic stability of the resulting closed-loop system and preserve several performance requirements of the suspension system, including ride comfort, road holding and suspension deflection/stroke limitation. Finally, simulation results are provided to show the effectiveness of the proposed method.

This article proposes a continuous adaptive integral-type sliding-mode control approach for a class of mechatronic systems by taking into consideration matched and unmatched uncertainties, and uncertainty in the control gain. Four different sliding-mode controllers are designed to enable: 1) the avoidance of the singularity by preventing differentiating the system states with fractional power; and 2) the attenuation of the chattering by utilizing full-order sliding manifolds. The control gain adaptation in the full-order sliding-mode controller is presented to avoid the overestimation of the gain. With the continuous control in place, the mechatronic systems have a fast response with high precision. The soften action from carefully designed controllers with the gain adaptation guarantees the trajectories of the mechatronic systems to move smoothly and prevents damage to the mechanical components of the systems. Both the simulation and experimental results demonstrate the effectiveness and feasibility of the proposed control approach.

This paper is concerned with the reliable filtering for discrete-time nonlinear systems with abnormal measurements and probabilistic distributed time-delays. Two binary stochastic sequences are employed to model stochastic occurring nonlinearities and probabilistic distributed time-delays, respectively. The considered abnormal measurements could be outliers or injected data resulting from cyber-attackers. A factitious saturation constraint on innovation is adopted to remove these abnormal measurements in the designed filter. By resorting to the stochastic analysis combined with Lyapunov stability theory, a sufficient condition is proposed to check whether or not the augmented system is bounded in mean square. Furthermore, the desired filter gain depends on the solution of a linear matrix inequality. Finally, an illustrative example is adopted to verify the effectiveness of the developed design scheme.

This paper is concerned with the distributed target tracking for a moving target of discrete time-varying nonlinear dynamics over a wireless sensor network. A number of spatially distributed sensors are deployed to measure the state of the target, calculate local state predictions as well as local state estimates, and further exchange local information with their underlying neighboring sensors. Due to adversarial attacks, a subset of sensors are deliberately manipulated and thus misbehaving. Accordingly, information exchanges from the misbehaving sensors to their neighbors become antagonistic rather than cooperative as in normal operation. First, a novel distributed target tracking scheme in terms of local state predictors and state estimators is developed for each sensor over a partially misbehaving sensor network. Second, criteria for designing the desired distributed target tracking scheme and the time-varying adjacency matrix are derived such that two ellipsoidal prediction and estimation sets can be recursively computed. It is analytically proved that these two sets guarantee the containment of the true target state at every instant of time regardless of misbehaving sensors as well as unknown-but-bounded process and measurement noises. Third, based on the proposed design criteria, optimization methods are put forward to minimize the calculated ellipsoids. Finally, an application to vehicle tracking is given to show the effectiveness of the results.

This paper presents a reduced order discrete extended state observer (RODESO) based repetitive controller (RC) with application to a piezoelectric actuator (PEA) based nano-positioning system. The RODESO-based RC proposed in this paper is capable of rejecting both periodic and aperiodic disturbances. Moreover, RODESO can be tuned using only two parameters and the model free approach of RODESO-based RC makes it an ideal solution to overcome the challenges of piezoelectric actuator control. Various types of periodic and aperiodic disturbances are used in the simulation to demonstrate the disturbance rejection capability of the proposed algorithm. The comparison studies demonstrate that the RODESO-based RC has superior performance over the phase lead RC.

The flexible joint robot manipulators provide various benefits, but also present many control challenges such as nonlinearities, strong coupling, vibration, etc. This paper proposes optimal second order integral sliding mode control (OSOISMC) for a single link flexible joint manipulator to achieve robust and smooth performance. Firstly, the integral sliding mode control is designed, which consists of a linear quadratic regulator (LQR) as a nominal control, and switching control. This control guarantees the system robustness for the entire process. Then, a nonsingular-terminal sliding surface is added to give a second order integral sliding mode control (SOISMC), which reduces chartering effect and gives the finite time convergence as well. Simulation results show superiority of the proposed algorithm over LQR and ISMC in terms of tracking performance and chattering mitigation.

Identifying the underlying model in a set of data contaminated by noise and outliers is a fundamental task in computer vision. The cost function associated with such tasks is often highly complex, hence in most cases only an approximate solution is obtained by evaluating the cost function on discrete locations in the parameter (hypothesis) space. To be successful at least one hypothesis has to be in the vicinity of the solution. Due to noise hypotheses generated by minimal subsets can be far from the underlying model, even when the samples are from the said structure. In this paper we investigate the feasibility of using higher than minimal subset sampling for hypothesis generation. Our empirical studies showed that increasing the sample size beyond minimal size ( p ), in particular up to p+2, will significantly increase the probability of generating a hypothesis closer to the true model when subsets are selected from inliers. On the other hand, the probability of selecting an all inlier sample rapidly decreases with the sample size, making direct extension of existing methods unfeasible. Hence, we propose a new computationally tractable method for robust model fitting that uses higher than minimal subsets. Here, one starts from an arbitrary hypothesis (which does not need to be in the vicinity of the solution) and moves until either a structure in data is found or the process is re-initialized. The method also has the ability to identify when the algorithm has reached a hypothesis with adequate accuracy and stops appropriately, thereby saving computational time. The experimental analysis carried out using synthetic and real data shows that the proposed method is both accurate and efficient compared to the state-of-the-art robust model fitting techniques.

In this paper, a robust control scheme (RCS) for Steer-by-Wire (SbW) systems with partially known dynamics is proposed. It is shown that an SbW system can be represented by a nominal model and an unknown portion. A nominal feedback controller can then be used to stabilize the nominal model and a sliding mode compensator (SMC) is designed to remove the effects of both the unknown system dynamics and uncertain road conditions on the steering performance. For practical consideration, robust exact differentiator (RED) technique is utilized to estimate the derivatives of the position signals for controller design. It is further shown that the designed RCS is able to guarantee a robust steering performance against system and road uncertainties. The comparative experimental studies are given to verify the excellent performance of the proposed RCS for SbW systems.

Nonrigid image registration techniques using intensity based similarity measures are widely used in medical imaging applications. Due to high computational complexities of these techniques, particularly for volumetric images, finding appropriate registration methods to both reduce the computation burden and increase the registration accuracy has become an intensive area of research. In this paper, we propose a fast and accurate nonrigid registration method for intra-modality volumetric images. Our approach exploits the information provided by an order statistics based segmentation method, to find the important regions for registration and use an appropriate sampling scheme to target those areas and reduce the registration computation time. A unique advantage of the proposed method is its ability to identify the point of diminishing returns and stop the registration process. Our experiments on registration of end-inhale to end-exhale lung CT scan pairs, with expert annotated landmarks, show that the new method is both faster and more accurate than the state of the art sampling based techniques, particularly for registration of images with large deformations.

Wheel speed sensor is commonly used in conventional Antilock Braking Systems (ABS) to measure the wheel rotational speed. This paper proposes a novel architecture for In-Wheel Electric Vehicles (EVs) based on data fusion concept that provides improved accuracy, reliability, redundancy and fault-tolerance for the wheel speed measurement system of the ABS. The proposed method was extensively evaluated using actual ABS hardware. The experimental results showed that the accuracy of the wheel speed estimation based on the proposed data fusion were improved by up to 46 percent in comparison with a commercial ABS sensor. The proposed approach has the potential to substantially improve the performance of the ABS for In-Wheel EVs.

In this paper, a robust learning control is developed for a class of single input single output (SISO) nonlinear systems with T-S fuzzy model. It is seen that the proposed sliding mode learning control with the powerful Lipshitz-like condition can guarantee the stability, convergence and robustness of the closed-loop system without involving any assumptions on uncertain system dynamics. In addition, the concept that the local system with the maximum membership function dominates the system dynamic behaviours helps to greatly simplify the control system design. It will be further seen that the continuous learning control ensures the advantage of chattering-free that may occur in conventional sliding mode systems. Simulation examples are presented to demonstrate the effectiveness of the proposed learning control through the comparison with the H-infinity control.

In this paper, a new robust single-hidden layer feedforward network (SLFN)-based pattern classifier is developed. It is shown that the frequency spectrums of the desired feature vectors can be specified in terms of the discrete Fourier transform (DFT) technique. The input weights of the SLFN are then optimized with the regularization theory such that the error between the frequency components of the desired feature vectors and the ones of the feature vectors extracted from the outputs of the hidden layer is minimized. For the linearly separable input patterns, the hidden layer of the SLFN plays the role of removing the effects of the disturbance from the noisy input data and providing the linearly separable feature vectors for the accurate classification. However, for the nonlinearly separable input patterns, the hidden layer is capable of assigning the DFTs of all feature vectors to the desired positions in the frequency-domain such that the separability of all nonlinearly separable patterns are maximized. In addition, the output weights of the SLFN are also optimally designed so that both the empirical and the structural risks are well balanced and minimized in a noisy environment. Two simulation examples are presented to show the excellent performance and effectiveness of the proposed classification scheme.

Driver distraction test rig is an automotive fascia analog for human machine interface studies, produced in conjunction with Swinburne University of Technology, GM Holden Innovation, and AutoCRC. The prototype will be used in the development and validation of test protocols for evaluating the level of driver distraction imposed by in-vehicle information systems. By means of modular interfacing technologies, spatial reconfiguration of radio/HVAC controls is achieved with robustness and flexibility. This will allow considerable testing to determine the consequences control positioning has with regard to the loss of attention concerning the primary task of driving. The design, manufacture, and implications of the project are discussed in this paper.