Search

In the presence of system coupling and dynamic uncertainties, extensive research has been conducted on the precise motion control of industrial manipulators with general reference trajectories. Since repetitive operations are common tasks in industrial applications, it is an essential and practical problem to further improve the control accuracy by taking advantage of the periodicity of the reference trajectory. In this paper, a desired compensation adaptive robust repetitive control is proposed for multi-DoFs industrial manipulators to perform repetitive tasks. Specifically, the link dynamics identified offline is compensated directly to decouple the system and capture the main characteristics of the link effect. Then, the uncertain friction is dealt with through an online adaptation scheme, in which the desired compensation is utilized to avoid measurement noise and chattering at low speed. And periodic disturbances are approximated by Fourier series expansion with unknown Fourier coefficients, which will be learned online. Finally, the robust feedback is designed to guarantee transient control accuracy and robustness against dynamic uncertainties. Comparative experiments on an industrial manipulator show that the proposed controller possesses better transient and steady-state control accuracy and error convergence rate.

The minimum-time path for intercepting a moving target with a prescribed impact angle is studied in the paper. The candidate paths from Pontryagin’s maximum principle are synthesized, so that each candidate is related to a zero of a real-valued function. It is found that the real-valued functions or their first-order derivatives can be converted to polynomials of at most fourth degree. As a result, each candidate path can be computed within a constant time by embedding a standard polynomial solver into the typical bisection method. The control strategy along the shortest candidate eventually gives rise to the time-optimal guidance law. Finally, the developments of the paper are illustrated and verified by three numerical examples.

The independent metering system decouples the meter-in and meter-out flows of the chamber, giving extra control flexibility and can reducing energy consumption while keeping motion control accuracy. The control performance of the independent metering system is significantly determined by the valve characteristics. Valve nonlinearities, such as deadband, nonlinear flow gain, and leakage, may restrict the control accuracy. To address the issue and improve system control precision, the nonlinearities of the valve need to be further modeled. In this study, a nonlinear valve flow model is proposed to accurately depict the relationships between the flow rate, pressure drop, and input voltage. The valve deadband is compensated by the static inverse function, while a certain polynomial projection function is used to mimic the nonlinear flow curve outside the deadband. To deal with uncertainties and nonlinearities in the independent metering system, an adaptive robust control approach is synthesized. The least squares estimation law is adopted to online adapt the valve parameter and other system parameters. Comparative experiments were carried out to illustrate the effectiveness of the proposed approach. The results show that the valve flow nonlinearities are further captured by the proposed model, and the independent metering system performed a higher-level control precision.

In active object tracking, given a visual observation as input, the goal is to lockup the target by autonomously adjusting camera’s position and posture. Previous works on active tracking assume that there is only one object (person) in the environment without distractors. In this work, towards realistic setting, we move forward to a more challenging scenario, where the tracker moves freely in 3D space like unmanned aerial vehicles (UAV) to track a person in various complex scenes with multiple distractors. To this end, we propose a novel end-to-end anti-distractor active object tracking framework by introducing multiple attention modules. On one hand, we take the target template to learn an embedding as channel-wise attention for current observation to distinguish the target from the distractors. On the other hand, temporal attention is introduced to fuse the observation history to extract a feature representation, which is then fed into a reinforcement learning network to output the action of the tracker. To evaluate our method, we build several multi-object 3D environments in Unreal Engine and extensive experiments demonstrate the effectiveness of our approach.

This research proposed a robust Cooperative Adaptive Cruise Control (CACC) to overcome the shortcoming of the existing CACC controllers in dealing with unexpected events, such as malware attack, phishing attack, and DNS tunneling attack, where perception and data received via communication contradict with the reality. The proposed controller combines the advantage of two information flow topologies - All-Predecessor Following (APF) and Predecessor-Leader Following (PLF) control methods - to improve the capability of CACC platoons. The string stability of the proposed CACC controller was proven. The robustness to time delay switch (TDS) attacks was assessed using simulation. The normal cruise was simulated to show the capability of the proposed controller in a wide range of speeds. TDS attacks, which encompassed four different unexpected events, were tested to verify the robustness of the proposed CACC. Results confirmed that the proposed CACC controller is string stable and robust against these TDS attacks without crashing or causing jerks. It also showed that the proposed CACC controller outperformed a state-of-the-art CACC controller.

The multi-source electromechanical coupling renders energy management of plug-in hybrid electric vehicles (PHEVs) highly nonlinear and complex. Furthermore, the complicated nonlinear management process highly depends on knowledge of driving conditions, and hinders the control strategies efficiently applied instantaneously, leading to massive challenges in energy saving improvement of PHEVs. To address these issues, a novel learning based model predictive control (LMPC) strategy is developed for a serial-parallel PHEV with the reinforced optimal control effect in real time application. Rather than employing the velocity-prediction based MPC methods favored in the literature, an original reference-tracking based MPC solution is proposed with strong instant application capacity. To guarantee the optimal control effect, an online learning process is implemented in MPC via the Gaussian process (GP) model to address the uncertainties during state estimation. The tracking reference in LMPC based control problem in PHEV is achieved by a microscopic traffic flow analysis (MTFA) method. The simulation results validate that the proposed method can optimally manage energy flow within vehicle power sources in real time, highlighting its anticipated preferable performance.

This paper proposes a novel guidance law based on model predictive control for protecting an aircraft from a homing missile. Considering that in the target‐missile‐defender engagement the attacking missile is pursuing the target aircraft by employing a known linear guidance law, a non‐linear predictive model for the engagement is established. This predictive model allows converting the three‐body engagement to a one‐on‐one engagement. The time‐to‐go for the defender towards the future position of the incoming missile is analytically derived, indicating that the optimal time for the defender to intercept the incoming missile is a fixed‐point of a real‐valued function. As a result, the optimal interception duration can be obtained by a standard fixed‐point iteration method, and thus the interception point can be readily predicted via integrating the predictive model. The guidance command is generated by directly leading the defender to the predicted intercept point. Numerical simulations in different scenarios validate the algorithm to be computationally efficient, and demonstrate that the proposed strategy outperforms previous techniques in terms of miss distance, engagement duration, and the control effort.

This paper studies the interplay between device-to-device (D2D) communications and real-time monitoring systems in a cellular-based Internet of Things (IoT) network. In particular, besides the possibility that the IoT devices communicate directly with each other in a D2D fashion, we consider that they frequently send time-sensitive information/status updates (about some underlying physical processes observed by them) to their nearest cellular base stations (BSs). Specifically, we model the locations of the IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another independent PPP. For this setup, we characterize the performance of D2D communications using the average network throughput metric whereas the performance of the real-time applications is quantified by the Age of Information (AoI) metric. The IoT devices are considered to employ a distance-proportional fractional power control scheme while sending status updates to their serving BSs. Hence, depending upon the maximum transmission power available, the IoT devices located within a certain distance from the BSs can only send status updates. This association strategy, in turn, forms the Johnson-Mehl (JM) tessellation, such that the IoT devices located in the JM cells are allowed to send status updates. The average network throughput is obtained by deriving the mean success probability for the D2D links. On the other hand, the temporal mean AoI of a given status update link can be treated as a random variable over space since its success delivery rate is a function of the interference field seen from its receiver. Thus, in order to capture the spatial disparity in the AoI performance, we characterize the spatial moments of the temporal mean AoI. In particular, we obtain these spatial moments by deriving the moments of both the conditional success probability and the conditional scheduling probability for status update links. Our results provide useful design guidelines on the efficient deployment of future massive IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications.

Autonomous underwater vehicles (AUVs) find many applications in oceanography, environmental research, and inspection and maintenance of subsea energy assets. Subsea Resident AUVs remain in subsea for extended periods of time which could last for several months. Maintaining depth using traditional hard actuators (HAs) is very energy expensive. Mimicking aquatic creatures, it is shown in this letter that HAs and proposed soft actuators (SAs) can collaborate in a novel way. This collaboration can stabilize AUVs at any desired depth with minimum energy consumption at steady state. This is demonstrated using a laboratory AUV for which a nonlinear dynamic model was developed that uses experimentally validated system parameters. The AUV uses HAs to quickly reach any desired depth, while SAs generate volume change to adjust the system’s buoyancy to maintain neutral buoyancy at the desired depth. In the neutral buoyancy state, the HAs shut off while the SAs stabilize and maintain the depth with virtually zero energy consumption. A control algorithm architecture is developed to manage the HA and SA collaboration. The HAs use a proportional controller with a dead-band, while the SAs use a proportional-derivative-acceleration (PDA) feedback controller. The ability of both types of actuators to mitigate disturbance forces are explored and analyzed. Simulation results show that SAs alone can reject small disturbances while using both SAs and HAs in collaboration can reject large disturbances. Simulation results demonstrate that combining traditional HAs with SAs leads to dynamic performance and very low energy consumption capabilities that cannot be achieved by either one alone.

In recent years, deep convolutional neural networks have gained significant performance in single image de-raining. However, this progress is contributing to their complicated model design. These complicated models generally contain a huge number of parameters, resulting in high memory footprints and low efficiency. To handle this issue, we propose a novel Lightweight Multi-scale High-order Feedback Network (LMHFNet) for single image de-raining. First, we regard the de-raining problem as a multi-stage task and combine a high-order feedback mechanism with global residual learning to assist the network training. This combination brings obvious performance improvement and avoids increasing additional parameters. Then, we design a novel Lightweight Multi-scale (LM) block as the core component of our network by utilizing the depthwise separable convolution. Next, we propose a novel Lightweight Multi-scale ConvLSTM (LM-ConvLSTM) module to integrate the deep features generated by the feedback mechanism. Last, we discuss the influence of different factors (i.e., loss function and network input/output) to tap the maximum potential of our lightweight network. Our LMHFNet could achieve competitive performance compared with the latest state-of-the-art methods (i.e., RCDNet and DRDNet), and bring a 28- or 46- times compression at the same time. The extensive experiments demonstrate the effectiveness and efficiency of our model in both quantitative assessments and visual quality.

As an important part of the Internet of Things, mobile wireless sensor network (MWSN) will generate traffic with different service quality requirements due to the multi-sensor integration of nodes and application diversity. Due to the topological changes, resource constraints and self-organizing characteristics of MWSN, the Per-Hop Behavior (PHB) approach in the traditional Diff-Serv model has many challenges in providing differential service. In this paper, a path reservation multipath routing (PRMR) protocol is proposed, which can provide a suitable path for each type of traffic with service requirements. PRMR protocol includes path discovery algorithm and packet scheduling algorithm. In addition, the path scheduling model and scheduling algorithm in PRMR solve the problem of load imbalance among reserved paths caused by different types of traffic. In scenarios with different number of nodes, three types of traffic, integrity sensitive data, delay sensitive data and normal data, are used to verify the performance of PRMR differential service and load balancing. Simulation experiments not only compare the quality of service provided by each reserved path in PRMR, but also compare the differential service performance between PRMR and several multipath routing algorithms. Simulation results show that PRMR routing algorithm can guarantee high packet delivery rate and low delay for integrity-sensitive data and delay-sensitive data, respectively. In addition, the simulation results of the average residual energy index show that the protocol can achieve network energy balance.

Resource allocating mechanisms draw much attention from various areas, and exploring the truthfulness of these mechanisms is a very hot topic. In this paper, we focus on the max-min fair allocation in a centralized resource system and explore whether the allocation is truthful when a node behaves strategically. The max-min fair allocation enables nodes receive appropriate resources, and we introduce an efficient algorithm to find out the allocation. To explore whether the allocation is truthful, we analyze how the allocation varies when a new node is added to the system, and we discuss whether the node can gain more resources if it misreports its resource demands. Surprisingly, if a node misrepresents itself by creating several fictitious nodes but keeps the sum of these nodes’ resource demands the same, the node can achieve more resources evidently. We further present some illustrative examples to verify the results, and we show that a node can achieve 1.83 times resource if it misrepresents itself as two nodes. Finally, we discuss the influence of node’s misrepresenting behavior in tree graph: some child nodes gain fewer resources even if their parent node gains more resources by creating two fictitious nodes.

In the research of lower extremity exoskeleton, how to achieve synchronization between human and machine is quite significant. The intention recognition, which can be divided into three categories including EMG-based, EEG-based and biomechanics-based, is one of the effective implementation methods. In this paper, a new biomechanics-based method to realize the intention recognition is proposed. Compared with the mainstream, this method identifies the characteristic value of stride and frequency during walking, which describes human intention mathematically and concretizes the intention of human movement, improving the accuracy of recognition result and streamlining the algorithm. In addition, the impedance model is designed to further correct the recognition error. The main contents of this paper can be roughly summarized as follows. Gait feature event points are detected according to the angular signals of exoskeleton joints and the pressure signals of foot sole during the wearer’s walking process. Then the whole gait cycle is segmented by the identified gait feature event points, which is used to identify the wearer’s gait step and frequency in the gait cycle and output the trajectory transformed from standard gait trajectory by the recognized stride and frequency. Moreover, the interactive force signal collected by the three-dimensional force sensors mounted on the four-legged bar is provided as input to the designed impedance controller to adjust the transformed trajectory again. Also, the final trajectory is input to the Proportion Integral and Differential (PID) controller to realize the motion function of the lower extremity exoskeleton based on the wearer’s intention recognition result. Moreover, a simple hardware platform of lower limb exoskeleton is designed and built for practical experimental verification, which involves three kinds of gait respectively having constant stride, constant frequency and time-varying stride and frequency. The feasibility and reliability of the proposed algorithm can be concluded by analyzing the satisfactory experiment result.

Direct-driven electro-hydraulic systems have a wide range of applications owing to their advantages of energy-savings and relatively high control flexibilities in comparison with classic variable displacement pump-controlled hydraulic systems. However, the control accuracy is limited by the inherent nonlinear hydraulic dynamics. Additionally, the pump flow rate may become nonlinear at low pump speeds, causing large pressure-related flow deviations; thereby, limiting the improvement of motion control accuracy. Unfortunately, the pump flow nonlinearity has been ignored or oversimplified without effective modeling in most studies so far. To improve the control accuracy of direct-drive hydraulic systems, a high-precision control strategy must be designed to deal with the nonlinear characteristics and resolve the issue of nonlinear pump flow at low speeds. This article proposes an adaptive robust motion control strategy for a direct-driven electro-hydraulic system with adaptive pump flow rate model compensation. A backstepping integrated direct/indirect adaptive robust controller is designed to deal with the dynamic nonlinearities and uncertainties, which guarantees the stability of the entire hydraulic system. Furthermore, a parameterized polynomial fitting modeling strategy is proposed to accurately describe the nonlinear characteristics of the pump flow rate. Therefore, the uncertain parameters are adjusted in real-time, achieving satisfactory parameter estimations and model compensation for asymptotic motion tracking. Theoretical proof and comparative experiments demonstrate the advantages of the proposed control strategy with adaptive polynomial fitting model compensation for high-precision motion control.

Autonomous underwater vehicles (AUVs) outfitted with manipulators are useful for performing various tasks. As the AUVs manipulate tools, they deviate from neutral buoyancy, and must constantly actuate to maintain depth and orientation. This results in large energy consumption. Furthermore, AUVs continuously oscillate when thrusters are unable to fix minor motion discrepancies. This letter investigates the use of reversible fuel cells–enabled buoyancy control devices (BCDs, soft actuators) with existing thrusters (hard actuators) to correct the heave and tilt motion during tool manipulation. A state-space model that describes an AUV equippped with a manipulator and actuated with thrusters and BCDs is derived. A set of control laws are proposed to allow for a novel collaboration between the two actuators (thrusters and BCDs) for station keeping. Different scenarios are simulated using the model with experimentally identified parameters to analyze the motion and energy performance of the control law. Simulation results show that synergizing the two forms of actuation (soft/hard) improves performance and energy efficiency with the thrusters providing quick depth and orientation correction while BCDs adjust buoyancy and small maneuvering.

In the coalitional game, the agents (nodes) cooperate or compete with each other to maximize their own benefits, thereby creating different coalitions. Designing a fair resource-allocation mechanism can reduce conflicts among agents and enable an all-win scenario. One popular resource-exchange mechanism, the max-min fairness-based coalitional (MMFC) mechanism, achieves fair allocation of resource by finding the max-min resource-exchange rate in a resource exchange network. However, this mechanism cannot ensure envy-free allocation of resource among the agents. To solve this problem, we proposed an envy-free resource-allocation mechanism called the envy-free max-min fairness-based coalitional (EFMMFC) mechanism, in which the resource-exchange rates of all agents constitute a max-min fair vector. EFMMFC mechanism opens new research directions in developing simple and efficient protocol that can produce envy-free allocation subject to the max-min resource exchange rate in coalitional game.

Accurate estimation of the state of charge (SOC) of lithium-ion battery packs remains challenging due to inconsistencies among battery cells. To achieve precise SOC estimation of battery packs, first, a long short-term memory (LSTM) recurrent neural network (RNN)-based model is constructed to characterize the battery electrical performance, and a rolling learning method is proposed to update the model parameters for improving the model accuracy. Then, an improved square root-cubature Kalman filter (SRCKF) is designed together with the multi-innovation technique to estimate the battery cell’s SOC. Next, to cope with inconsistencies among battery cells, the SOC estimation values from the maximum and minimum cells are combined with a smoothing method to estimate the pack SOC. The robustness and accuracy of the proposed battery model and the cell SOC estimation method are verified by exerting the experimental validation under time-varying temperature conditions. Finally, real operation data are collected from an electric-scooter (ES) monitoring platform to further validate the generalization of the designed pack SOC estimation algorithm. The experimental results manifest that the SOC estimation error can be limited to 2% after convergence.

We focus on the problems of the accurate time delay estimation, the design of training pilots, and hybrid matrix optimization within the large-scale antenna array Terahertz (THz) broadband communication system. In contrast to the existing researches based on narrow-band arrays, we hereby shed light on the time delay estimation of broadband arrays. In THz broadband communication systems, the data symbol duration is relatively short when comparing with the dimension of the antenna array. In large-scale antenna systems, signals received in each antenna are no longer different phase-shifted copies of the same symbol, but completely different symbols in which occasion traditional narrow-band structure is no longer suitable. Based on the above conclusion, firstly, we put forward a system model based on large-scale antenna arrays and Time delay line (TDL) structure. Secondly, we deduce the Cramer-Rao lower bound (CRLB) of the time delay estimation, and present a time delay estimation algorithm that could reach the CRLB. Thirdly, by minimizing the CRLB, we address the design of the training pilot and optimized TDL structure under the condition of constant envelope training pilot and modulus TDL structure. Finally, we disclose the numerical simulation results. According to the simulation results, the aforementioned method is workable in reaching the CRLB, the TDL structure can significantly surpass that of the traditional model, and the optimal pilot design method outperforms the pseudo-random pilot structure.

Machine learning is widely used in our daily lives. Large amounts of data have been continuously produced and transmitted to the cloud for model training and data processing, which raises a problem: how to preserve the security of the data. Recently, a secure machine learning system named SecureML has been proposed to solve this issue using two-party computation. However, due to the excessive computation expenses of two-party computation, the secure machine learning is about 2× slower than the original machine learning methods. Previous work on secure machine learning mostly focused on novel protocols or improving accuracy, while the performance metric has been ignored. In this article, we propose a GPU-based framework ParSecureML to improve the performance of secure machine learning algorithms based on two-party computation. The main challenges of developing ParSecureML lie in the complex computation patterns, frequent intra-node data transmission between CPU and GPU, and complicated inter-node data dependence. To handle these challenges, we propose a series of novel solutions, including profiling-guided adaptive GPU utilization, fine-grained double pipeline for intra-node CPU-GPU cooperation, and compressed transmission for inter-node communication. Moreover, we integrate architecture specific optimizations, such as Tensor Cores, into ParSecureML. As far as we know, this is the first GPU-based secure machine learning framework. Compared to the state-of-the-art framework, ParSecureML achieves an average of 33.8× speedup. ParSecureML can also be applied to inferences, which achieves 31.7× speedup on average.

Real time path planning from image information is of vital importance in the fields of robots as it has various applications in real time navigation, autonomous driving, robot arm manipulation and human robot cooperation and so on. To achieve this task, a two stage approach is proposed. At the first stage, a novel change point detection approach is proposed to process the image to extract the shape of the obstacles. At the second stage, several novel approximations are adopted to make the path planning problem tractable. Firstly, the irregular shapes of the obstacles in the environment are approximated as lines and circles, which simplify the distance constraint significantly. Secondly, the non‐convex path planning problem is iteratively decomposed as a sequence of subproblems and alternating minimisation method is proposed to efficiently solve the subproblem. To improve the quality of the solution, good initial points obtained by A* algorithm is provided. Both numerical experiments and real experiments are conducted to demonstrate the effectiveness of the proposed algorithm.

Bio-mimetic robots can provide better camouflage effect to explore the seabed by mimicking the swimming locomotion of aquatic creatures. Jellyfish-like robot is a type of unmanned underwater robot can help us explore marine ecosystems and complex underwater ecology efficiently since they can swim steadily with its symmetrical structure that has been optimized in nature and generate sufficient jet propulsion with low power input. The developed jellyfish robot in this paper exhibits contracting muscle-like behaviour. It combines a dielectric elastomer (DE) diaphragm actuator with a transmission mechanism, which can provide compliant thrust force to propel the jellyfish robot to transit through water. This paper presents the design, fabrication, modeling, and experimental characterization of the novel jellyfish robot. A sawtooth signal with specific amplitude and frequency is chosen as input signal that leads to expansion and shrinking of the jellyfish bell. The simulation results from the observed two steps in a cycle of movement are quantitatively similar with the experimental outputs. A data-driven model is developed to capture the vibration in the first step. The process of contracting the bell and producing thrust force is captured by a physical model in the second step. The preliminary results show the jellyfish robot can swim underwater effectively in the vertical direction. The average speed of the robot is about 5 mm/s when a sawtooth signal with 5 kV amplitude and 2 Hz frequency is applied to the DE actuator.

With the developments of intelligent and autonomous robotic technology, robots are usually designed to confront sophisticated tasks such as automated assembly, which requires both high-speed positioning capabilities and compliance with unknown contact environments. As we know, a high-performance motion tracking control in free space can achieve efficient and accurate positioning, while impedance or force control shows superior performance in terms of sensitive force and compliance with unknown contact environments. However, it is still challenging to achieve both high-performance motion tracking and compliance within one single control framework, especially in unknown contact environments. To this end, in this article, a unified motion/force/impedance approach for unknown contact environments is proposed by robust model-reaching control with dynamic trajectory adaptation. Specifically, the overall control scheme includes two loops: the outer loop replans the trajectories of motion and force in real time to meet the environmental constraints, which are estimated by the recursive least squares estimation law; in the inner loop, the robust model-reaching control law is proposed to realize a target model, which is designed to establish a dynamic relationship between the motion and force tracking errors of the replanned trajectories. Then, by changing the matrices of the target model, the compromise between motion tracking and force tracking can be achieved, as well as different control objectives. Experiments are conducted on a seven-degree-of-freedom manipulator to validate the advantages of the proposed scheme.

A timely inspection of pipelines is of great importance to prevent oil and gas from leaking. This paper develops a novel robotic system that can automatically grab a flange of pipelines and perform bolt looseness inspection autonomously. The robotic system consists of a 4-DOF robotic arm with a stereo camera system on a mobile platform and a pair of piezoceramic transducers (PZTs) mounted on two fingers of the robotic arm. The PZTs act as a smart touch sensor for bolt looseness inspection when the fingers grab a flange. An onboard visual tracking algorithm is developed to provide the 3D position of the flange. The flange is first recognized and then localized in a 2D image by YOLO region-based convolutional neural networks. The 2D position in the image is then transferred to the 3D position through a set of calibrated stereo camera parameters. The robotic arm receives proportional gain control signals based on the distance between the finger center and the flange. In the experiment, we install four cameras, apply a feature-based location method, and calculate the average value of history positions to reduce the detection error. When the robotic arm grabs the flange, a stress wave signal is generated by one of the PZTs mounted on the fingers, and the signal received from the other PZT indicates if the flange is tightly bolted or not. Experimental results have validated this autonomous robotic inspection approach.

Robot navigation with noisy perception is a fundamental but challenging task. We present a framework for creating navigable space from cluttered point clouds generated by low-end sensors with high sparsity and noise. Our method incrementally seeds and creates local convex regions free of obstacle points along robot's trajectory. Then a dense version of the point cloud is reconstructed through a map point regulation process where the original noisy map points are first projected onto a series of local convex hull surfaces, after which those points falling inside the convex hulls are culled. We have tested our proposed framework using a public autonomous driving dataset and a manually built structured environment for various performance evaluation, as well as inside our research building for ground robot navigation tasks. Our results reveal that the reconstructed navigable space has small volume loss (error) comparing with the ground truth, and the method is highly efficient, allowing real-time navigation computation.

Plug-in hybrid electric vehicles (PHEVs) unite the advantages of the engine and electric motor which could provide great potential in saving energy. However, the fuel economy performance of the PHEVs is highly associated with the driving condition, especially for parallel PHEVs because they could not decouple the engine work status from the driving condition. Meanwhile, fuel economy performance is not only a longitudinal issue but also related to lane selection. Lane selection is an important driving behavior and the algorithm of lane selection is necessary for the development progress of intelligent connected vehicles. Energy consumption cost is an important part of the vehicle’s using consumption cost. Therefore, lane selection strategies must consider this point. With the development of intelligent connected vehicle technology, such as V2X (Vehicle to Everything), the potential of energy consumption performance of intelligent connected PHEVs could be improved by taking environment information from V2X and smart sensors into lane selection. In this paper, a neural network (NN) based method is proposed to predict the future status of the local vehicle using the information from V2X, and then another network is used to estimate the future energy consumption of each lane. The lane selection is decided on energy consumption estimation. Lastly, the effectiveness of the method is validated by simulation using Matlab combined with SUMO (Simulation of Urban MObility).

Pump controlled hydraulic actuators are wildly used in the aerospace industry owing to the advantages of energy-saving and integrated configurations. Negative loads may occur to actuators due to external force loads or the inertial force when the actuator decelerates significantly. Uncertain negative load working conditions may cause cavitation, actuator vibration, and even instability to the motion control if the actuator is without sufficient meter-out damping. Various types of hydraulic configuration schemes have been proposed to deal with negative loads of hydraulic actuators. However, few of them can simultaneously achieve energy saving and high control accuracy. This study proposes an energy-saving and accurate motion tracking strategy for a hydraulic actuator with uncertain negative loads. The actuator’s motion is driven by a servomotor pump, which gives full play to the advantage of energy-saving. The meter-out pressure is controlled by proportional valves to provide the optimized meter-out damping. The nonlinear adaptive robust control law is designed, which guarantees the control stability and achieve high tracking accuracy. An integrated direct/indirect adaptation law obtains satisfactory parameter estimations and model compensation for asymptotic motion tracking. Comparative experiments under different working conditions were performed to validate the advantages of the proposed control strategy.

In order to solve the problems of high cost and difficult management of traditional agricultural planting, internet of things (IoT) technology was applied to realize real-time detection and intelligent management of crop growth and remote control of equipment, and change the traditional agricultural planting mode. The research results show that in MyEclipse development environment, using B/S (Browser/Server) architecture, Java and JavaScript language to design, Tomcat built server to publish information and complete the function of data storage and query, users can access the monitoring center in the local area network (LAN). When the detected data exceed the set threshold range, the control instructions issued by the monitoring center are transmitted to the main control chip through ethernet, and then the switching operation of the relay is controlled. The real-time monitoring of crop growth environment can be realized.

Digitally programmable metasurfaces are of potential use in wireless multiplexing techniques because they can encode and transmit information without using traditional radio-frequency components such as antennas or mixers. Space–time-coding digital metasurfaces can, in particular, manipulate the propagation direction and harmonic power distribution of electromagnetic waves, making them suitable for space- and frequency-division multiplexing. However, while digital metasurfaces have been used for wireless communication, these systems could implement signal modulation only in the time domain. Here, we report a wireless communication scheme that uses space–time-coding digital metasurfaces to implement both space- and frequency-division multiplexing. By encoding space–time-coding matrices through multiple channels, digital messages can be directly transmitted to different users at different locations simultaneously, without the need for digital-to-analogue conversion and mixing processes. To illustrate this approach, we have built a dual-channel wireless communication system based on a two-bit space–time-coding digital metasurface and use it to transmit two different pictures to two users simultaneously in real time. Space–time-coding digital metasurfaces can be used to implement secure and low-cost space- and frequency-division multiplexing in a dual-channel wireless communication system.

The peptide quantitative structure-activity relationship (QSAR), also known as the quantitative sequence-activity model (QSAM), has attracted much attention in the bio- and chemoinformatics communities and is a well developed computational peptidology strategy to statistically correlate the sequence/structure and activity/property relationships of functional peptides. Amino acid descriptors (AADs) are one of the most widely used methods to characterize peptide structures by decomposing the peptide into its residue building blocks and sequentially parametrizing each building block with a vector of amino acid principal properties. Considering that various AADs have been proposed over the past decades and new AADs are still emerging today, we herein query the following: is it necessary to develop so many AADs and do we need to continuously develop more new AADs? In this study, we exhaustively collect 80 published AADs and comprehensively evaluate their modeling performance (including fitting ability, internal stability, and predictive power) on 8 QSAR-oriented peptide sample sets (QPSs) by employing 2 sophisticated machine learning methods (MLMs), totally building and systematically comparing 1280 (80 AADs × 8 QPSs × 2 MLMs) peptide QSAR models. The following is revealed: (i) None of the AADs can work best on all or most peptide sets; an AAD usually performs well for some peptides but badly for others. (ii) Modeling performance is primarily determined by the peptide samples and then the MLMs used, while AADs have only a moderate influence on the performance. (iii) There is no essential difference between the modeling performances of different AAD types (physiochemical, topological, 3D-structural, etc.). (iv) Two random descriptors, which are separately generated randomly in standard normal distribution N(0, 1) and uniform distribution U(-1, +1), do not perform significantly worse than these carefully developed AADs. (v) A secondary descriptor, which carries major information involved in the 80 (primary) AADs, does not perform significantly better than these AADs. Overall, we conclude that since there are various AADs available to date and they already cover numerous amino acid properties, further development of new AADs is not an essential choice to improve peptide QSAR modeling; the traditional AAD methodology is believed to have almost reached the theoretical limit nowadays. In addition, the AADs are more likely to be a vector symbol but not informative data; they are utilized to mark and distinguish the 20 amino acids but do not really bring much original property information to these amino acids.

Achieving a fast transient response and high steady-state tracking accuracy simultaneously has been a challenging issue for the linear motor system in the presence of kinematic and dynamic constraints, various parametric uncertainties, and uncertain nonlinearities. To this end, a two-loop control structure is developed to maximize the converging speed of the transient response and achieve high steady-state accuracy. Specifically, in the outer loop, an online trajectory replanning algorithm is devised to force the replanned trajectory to merge into the desired trajectory in minimum time under the system’s kinematic and dynamic constraints. In the inner loop, an adaptive robust controller is synthesized to handle the parametric uncertainties and uncertain nonlinearities effectively so that high steady-state tracking accuracy is guaranteed. Particularly, the interaction between the two loops is intuitive since a feedforward term is optimized in the outer loop and then fed into the inner loop to make the model compensation. Comparative experiments are carried out on a linear-motor-driven system. Experimental results confirm that the proposed control structure can achieve a minimum-time transient response without violating the kinematic and dynamic constraints and also guarantee the excellent steady-state tracking accuracy.

Lithium is a key resource in global efforts toward decarbonization. However, like the extraction process associated with this soft, white metal, the lithium story is complex. Ignoring this complexity in pursuit of a low-carbon future risks compromising other sustainability and equality goals. A holistic approach is needed to successfully navigate the lithium challenge.

The rapid industrial development in the fields, such as robotics and high-tech manufacturing, has put forward much higher requirements for the application of high-performance electrohydraulic system, which should not only achieve high control accuracy but also work in a highly energy-efficient way. As we know, a valve control method could achieve good accuracy, but the low energy efficiency is its essential drawback. Conversely, a pump control method could achieve good energy efficiency, but the achievable accuracy is the drawback. Since neither individual valve control nor individual pump control method can meet the dual requirements, a novel idea of advanced valves and pump coordinated hydraulic control design is developed in this article. Within the control details, the high-performance adaptive robust control law is synthesized to handle the nonlinearities and uncertainties of the electrohydraulic system, and the optimized command generation approach for the valves and the pump is developed to attain the high level of energy efficiency while considering the control performance. Essentially, the control command of the pump is based on the desired model compensation to provide the majority amount of flow in an energy-efficient way, and control commands of the valves are generated directly by tracking errors to guarantee the control accuracy. The theoretical performance of the overall system control design is analyzed. Comparative experiments are conducted, and impressive results are obtained, showing that the proposed system could achieve even better tracking accuracy than the typical valve-controlled system, while the energy efficiency reached a high level. The advantages of the advanced system control design can be verified both in theory and experiments.

Despite the enormous applicability, the traditional, oversimplified in vitro two-dimensional (2D) monolayer cell culture models for preclinical drug screening towards evaluating pharmacological as well as toxicological properties fail to recapitulate structural, biological, and functional attributes of their in vivo counterparts. In this regard, biomimetic, three-dimensional (3D) microarchitectures have been fabricated using diverse microengineering strategies to reiterate the in vivo microenvironment in vitro by mimicking the sophisticated tissue-level extracellular matrix-like organization, including the cell-cell and cell-matrix interactions. These 3D microarchitectures, with the additional advantages of ease of fabrication and cost-effectiveness, can be applied for predicting the responses of diverse pharmaceutical agents, for instance, anticancer drugs, towards rapid progress in drug development. In this article, we emphasize on various 3D cellularized polymeric microarchitectures as in vitro models, alternative to traditional 2D models for drug screening. Further, we discuss diverse microengineering strategies used for the fabrication of such cellularized polymeric microcarriers and various architectural designs towards evaluating the efficacy of drug molecules in vitro, highlighting the pros and cons of the structure-, as well as performance-related attributes. Finally, we provide a summary of the perspectives, highlighting the lessons learned so far and outlook towards their further development.

In this paper, an improved online particle swarm optimization (PSO) is proposed to optimize the traditional search controller for improving the operating efficiency of the permanent magnet synchronous motor (PMSM). This algorithm combines the advantages of the attraction and repulsion PSO and the distributed PSO that can help the search controller to find the optimal d - axis air gap current quickly and accurately under non-stationary operating conditions, thereby minimizing the air gap flux and then improving the motor efficiency. To verify the effectiveness and stability of this proposed algorithm, the operating efficiency of PMSM as using this proposed algorithm is compared with that of traditional search controller under non-stationary operating conditions. The results show that the proposed algorithm can improve the operating efficiency of PMSM by 6.03% on average under non-stationary operation conditions. This indicates that the search controller based on the improved PSO has a better adaptation to the variation of external operating conditions, and can improve the operation efficiency of PMSM under non-stationary condition.

In this paper, we analyze the impact of caching on the performance of a cache enabled system with heterogeneous traffic where one of the users need to be served with confidential data. In this setup, a wireless helper system always serves a dedicated user and it can also serve a user requesting cached content. A cellular network access point is also available to serve the latter user if it cannot retrieve the requested data from the helper’s cache. The impact of caching and secrecy on throughput and delay performance for each user is then examined when the access point can deploy superposition coding to serve both users simultaneously. Two decoding schemes are considered in this work. The first decoding scheme treats interference from parallel transmissions as noise while the second one utilizes the parallel transmission to apply successive decoding for the intended data. Furthermore, network and cache related factors are identified and their impact on the overall performance of the system are analyzed. In order to find the optimal transmission power allocations, two distinct optimization problems are set in this context comparing the two decoding schemes. This will assist to identify the benefits of the considered decoding schemes for each user satisfying the secrecy requirements of the dedicated user and reducing its impact on the overall performance of the system. Funding: Swedish Foundation for Strategic Research (SSF)Swedish Foundation for Strategic Research; Swedish Research Council (VR)Swedish Research Council; Center for Industrial Information Technology (CENIIT); Excellence Center at Linkoping-Lund in Information Technology (ELLIIT); Indo-Sweden Project, Department of Science and Technology, India

This paper addresses the finite-time three-dimensional path-following control problem for underactuated autonomous airship with error constraints and uncertainties. First, a five degrees-of-freedom path-following error model in the Serret-Frenet coordinate frame is established. By applying the finite-time stability theory, a virtual guidance-based finite-time adaptive neural backstepping path-following control approach is proposed. Barrier Lyapunov functions (BLFs) are introduced to deal with attitude error constraints. Neural networks (NNs) are presented to compensate for the uncertainties. To prevent the “explosion of complexity” in the design of the backstepping method, a finite-time convergent differentiator (FTCD) is introduced to estimate the time derivatives of virtual control signals. Stability analysis showed that all closed-loop signals are uniformly ultimately bounded, the constrained requirements on the airship attitude errors are never violated, and the path-following errors converge to a small neighborhood of the origin in a finite time. At last, simulation studies are provided to demonstrate the effectiveness of the proposed control approach.

Reconfigurable intelligent surface (RIS) is a new paradigm that has great potential to achieve cost-effective, energy-efficient information modulation for wireless transmission, by the ability to change the reflection coefficients of the unit cells of a programmable metasurface. Nevertheless, the electromagnetic responses of the RISs are usually only phase-adjustable, which considerably limits the achievable rate of RIS-based transmitters. In this paper, we propose an RIS architecture to achieve amplitude-and-phase-varying modulation, which facilitates the design of multiple-input multiple-output (MIMO) quadrature amplitude modulation (QAM) transmission. The hardware constraints of the RIS and their impacts on the system design are discussed and analyzed. Furthermore, the proposed approach is evaluated using our prototype which implements the RIS-based MIMO-QAM transmission over the air in real time., This paper aims to investigate the feasibility of using RIS for MIMO wireless transmission for higher-order modulation by presenting an analytical modeling of the RIS-based system and providing experimental results from a prototype which has been built

Pump control hydraulic systems can achieve high efficiency by the advantages of no throttling loss and high power-to-volume ratio. However, low tracking accuracy and slow frequency response are main drawbacks for the applications of pump control hydraulic systems, because of the existing high-order dynamics, uncertainties, and highly nonlinear dynamics. Recently, the advent of servomotor-pump direct-drive electrohydraulic systems shows a good prospect for this issue, and the design of the control algorithm is the key to achieve high motion accuracy. In this article, to achieve precision motion control, an adaptive robust control with a backstepping design is proposed for an electrohydraulic system, where the cylinder actuator is direct-driven by a servomotor pump. Considering the high-order dynamics and nonlinearities of hydraulic systems, the controller is processed in two steps: position tracking step and pressure step. Besides, the pump flow deviation under low speed is another important limitation for good control performance. Thus, a nonlinear pump flow rate mapping is proposed by practical fitting and used into the controller design by the proper nonlinearity compensation of the desired pump flow. Comparative experiment results show that the proposed control strategy achieves high motion control performances in spite of the nonlinearities and uncertainties.

In this study, we focus mainly on the problem of finding the minimum-length path through a set of circular regions by a fixed-wing unmanned aerial vehicle. Such a problem is referred to as the Dubins traveling salesman problem with neighborhoods (DTSPN). Algorithms developed in the literature for solving DTSPN either are computationally demanding or generate low-quality solutions. To achieve a better trade-off between solution quality and computational cost, an efficient gradient-free descent method is designed. The core idea of the descent method is to decompose DTSPN into a series of subproblems, each of which consists of finding the minimum-length path of a Dubins vehicle from a configuration to another configuration via an intermediate circular region. By analyzing the geometric properties of the subproblems, we use a bisection method to solve the subproblems. As a result, the descent method can efficiently address DTSPN by successively solving a series of subproblems. Finally, several numerical experiments are carried out to demonstrate the descent method in comparison with several existing algorithms.

Along with the traditional motion control demands, human–machine collaboration properties are becoming increasingly important in modern robotic control system. The collaboration requires both robot's dynamics and payload dynamics, in which the former can be identified offline, whereas the latter has to be estimated online. However, the accurate payload estimation cannot be guaranteed by the traditional adaptive control where the adaptation law and the control law are synthesized together to achieve the same goal of reducing tracking errors. To achieve precision motion control and accurate payload estimation simultaneously, this article first developed an identification method for a 6-DoFs industrial robot, which simplifies the excitation trajectory optimization and captures the main dynamics using fewer parameters; and then proposed an integrated direct/indirect adaptive robust control (DIARC) algorithm. Specifically, the proposed DIARC consists of a generalized momentum-based indirect adaptation law to estimate the payload online, a fast direct term to compensate for the adaptation transients, and a robust feedback to attenuate the uncertain nonlinearities. Finally, comparative experiments show that the payload is estimated to the true value and better tracking performances can be achieved by the proposed controller.

The prevalence of short texts on the Web has made mining the latent topic structures of short texts a critical and fundamental task for many applications. However, due to the lack of word co-occurrence information induced by the content sparsity of short texts, it is challenging for traditional topic models like latent Dirichlet allocation (LDA) to extract coherent topic structures on short texts. Incorporating external semantic knowledge into the topic modeling process is an effective strategy to improve the coherence of inferred topics. In this paper, we develop a novel topic model—called biterm correlation knowledge-based topic model (BCK-TM)—to infer latent topics from short texts. Specifically, the proposed model mines biterm correlation knowledge automatically based on recent progress in word embedding, which can represent semantic information of words in a continuous vector space. To incorporate external knowledge, a knowledge incorporation mechanism is designed over the latent topic layer to regularize the topic assignment of each biterm during the topic sampling process. Experimental results on three public benchmark datasets illustrate the superior performance of the proposed approach over several state-of-the-art baseline models.

Three-dimensionally (3D) maneuverable robotic fish are highly desirable due to their ability to explore and survey the underwater environment. Existing depth control mechanisms are typically focused on using either compressed air or a piston to generate changes in volume. However, this often makes the system bulky and therefore impractical for use in small size underwater robots. In this letter, a small and compact 3D maneuverable robotic fish is developed. Instead of using a compressed air tank, the robot is equipped with an on-board water electrolyzer that generates gases in the required amount, in order to achieve changes in depth. The fabricated robotic fish demonstrates fast diving and rising performance. A servo motor is used to generate asymmetric flapping motion on the caudal fin for two-dimensional (2D) planar motion. A 3D dynamic model is derived for the fabricated robotic fish. This 3D model is then embedded into a relative velocity framework, to develop a guidance and control scheme that enables the robotic fish to maneuver through underwater orifices. These underwater orifices may be either stationary or moving. Simulations are used to demonstrate the efficacy of the developed algorithm.

Modern robots are increasingly being designed to collaborate with human, and the safety of human-robot interaction has become more significant than ever. Series elastic actuator (SEA) is a potential actuator system in force control for various robotic applications with inherent safe property. However, achieving the good performances of SEA torque control especially with unknown load-side dynamics is still challenging. In this article, a high performance decoupled torque controller is proposed to control the SEA as an ideal torque source that is suitable for various application scenarios. A more general decoupled dynamics of the SEA system is first established to fit the various scenarios by considering the large parametric uncertainties, nonlinearities, and unmodeled uncertainties. The adaptive robust control algorithm with an effective compensation mechanism is then developed to attenuate the uncertainties. Theoretically, the proposed approach guarantees the stability and high performance of the SEA system. Comparative experiments are carried out on a SEA testbed, and the experimental results demonstrate that the proposed approach has significant performance improvements over previous approaches.

This brief addresses the problem of collision avoidance by robotic fish that have a single caudal fin. The fish is hybrid-tailed in that the caudal fin is driven by a double-jointed mechanism. One joint is driven by a servomotor and the second joint is driven by an ionic polymer–metal composite (IPMC) actuator, which is often called an artificial muscle. For this type of robotic fish, collision avoidance is performed by employing a collision cone approach. Within the framework of the collision cone approach, Lyapunov-based methods are employed to determine analytical expressions of nonlinear guidance laws with which cooperative collision avoidance can be achieved. It is shown how these cooperative collision avoidance laws can be made optimal in the sense of minimizing an energy-like performance index. The conditions under which the developed guidance laws are robust to sensor measurement errors are determined. Simulations and experiments are performed to validate the guidance laws.

Artificial intelligence is regarded in the context of the tasks that are delegated to it during the development of technical projects and the features of the implementation of tasks that must be performed in real time without the possibility of interruption. The relevance of the study is determined by the fact that artificial intelligence sees as its main task the formalisation of decision-making with little or no human involvement. The basis of the methodological search are biological and prognostic methods. The current stage of development of socio-humanitarian technologies, and in particular, systems with artificial intelligence, prompts people to conduct such socio-technical projects, for the implementation of which the properties of the human biological brain are no longer enough. The result of research on artificial intelligence is the desire to understand the work of the human brain, to reveal the secrets of human consciousness and the problem of creating machines with a certain level of human intelligence. The novelty of the study is determined by the fact that the understanding of artificial intelligence is primarily aimed at creating an environment that can distinguish artificial intelligence from the entire array of technological devices not only as a platform of knowledge and program code. The practical significance of the study is determined by the necessity for standardisation and the formation of attitudes and the acceptability of the use of artificial intelligence in tasks that will subsequently affect social development.

To realize high-performance contouring tracking of dual-linear-motor-driven (DLMD) gantry, both motion coordination of multiaxes and synchronization of redundant actuators are indispensable factors. However, earlier contouring methods of multiaxes systems only focus on motion coordination, leading to certain inherent limitations and uncertainties when applied to DLMD gantry. Especially, the usually ignored synchronization issue may directly affect the normal operation of system. To this end, this paper proposes a novel contouring control method by integrating both motion coordination between axes and synchronization of redundant actuators. With the knowledge of the complete system dynamics, corresponding modification and extension have been done within the framework of global task coordinate frame (GTCF). Subsequently, both associated issues are effectively dealt with in the task space to simultaneously obtain efficient motion coordination ability and high-level synchronization, along with more accurate contouring error calculation. The proposed method is compared with the earlier GTCF-based contouring control method without consideration of a synchronization issue. The experimental results fully indicate the effectiveness and superiority of the proposed method to indeed guarantee and further improve the contouring performance.