The prediction of projectile-target intersection for moving tank based on adaptive robust constraint-following control and interval uncertainty analysis

To improve the hit probability of tank at high speed,a prediction method of projectile-target intersection based on adaptive robust constraint-following control and interval uncertainty analysis is proposed.The method proposed provides a novel way to predict the impact point of projectile for moving tank.First,bidirectional stability constraints and stability constraint-following error are constructed using the Udwadia-Kalaba theory,and an adaptive robust constraint-following controller is designed considering uncertainties.Second,the exterior ballistic ordinary differential equation with uncertainties is integrated into the controller,and the pointing control of stability system is extended to the impact-point control of projectile.Third,based on the interval uncertainty analysis method combining Chebyshev polynomial expansion and affine arithmetic,a prediction method of projectile-target intersection is proposed.Finally,the co-simulation experiment is performed by establishing the multi-body system dynamic model of tank and mathematical model of control system.The results demonstrate that the prediction method of projectile-target intersection based on uncertainty analysis can effectively decrease the uncertainties of system,improve the prediction accuracy,and increase the hit probability.The adaptive robust constraint-following control can effectively restrain the uncertainties caused by road excitation and model error.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

A Novel Trajectory Tracking Control of AGV Based on Udwadia-Kalaba Approach

Dear Editor,This letter is about an automated guided vehicle(AGV)trajectory tracking control method based on Udwadia-Kalaba(U-K)approach.This method provides a novel,concise and explicit motion equation for constrained mechanical systems with holonomic and/or nonholonomic constraints as well as constraints that may be ideal or nonideal.In this letter.

Adaptive robust control for triple avoidance - striking - arrival performance of uncertain tank mechanical systems

This paper puts forward an unprecedented avoidance-striking-arrival problem aiming to address the need for tank's uncertain mechanical systems on the intelligent battlefield.The associated system uncertainties(possibly rapid)are time-varying but bounded(possibly unknown).The goal is to design a controller that enables the tank to aim at and attack the enemy tank while keeping itself(out of the enemy fire zone).The tank maintains this condition until reaching the predefined region.In this paper,an approximate constraint following control method is adopted to solve this problem,and the original constraints are creatively divided into two categories:the avoidance-tracking constraint and the striking-arrival constraint.An adaptive robust control method is proposed and consequently verified through simulation experiments.It is proved that the system fully obeys the avoidance-tracking-constraint and strictly obeys the striking-arrival constraint under the control input.Besides,the control of the tank vehicle running system and tank gun bidirectional stabilization system are unified to deal with the control signal delay caused by complex uncertainties on the battlefield.Overall,this paper reduced the delay of signal transmission in the system while solved the avoidance-striking-arrival problem.

Modeling and Adaptive Neural Network Control for a Soft Robotic Arm With Prescribed Motion Constraints

This paper presents a dynamic model and performance constraint control of a line-driven soft robotic arm.The dynamics model of the soft robotic arm is established by combining the screw theory and the Cosserat theory.The unmodeled dynamics of the system are considered,and an adaptive neural network controller is designed using the backstepping method and radial basis function neural network.The stability of the closed-loop system and the boundedness of the tracking error are verified using Lyapunov theory.The simulation results show that our approach is a good solution to the motion constraint problem of the line-driven soft robotic arm.

Neural-Network-Based Adaptive Finite-Time Control for a Two-Degree-of-Freedom Helicopter System With an Event-Triggering Mechanism

Helicopter systems present numerous benefits over fixed-wing aircraft in several fields of application.Developing control schemes for improving the tracking accuracy of such systems is crucial.This paper proposes a neural-network(NN)-based adaptive finite-time control for a two-degree-of-freedom helicopter system.In particular,a radial basis function NN is adopted to solve uncertainty in the helicopter system.Furthermore,an event-triggering mechanism(ETM)with a switching threshold is proposed to alleviate the communication burden on the system.By proposing an adaptive parameter,a bounded estimation,and a smooth function approach,the effect of network measurement errors is effectively compensated for while simultaneously avoiding the Zeno phenomenon.Additionally,the developed adaptive finite-time control technique based on an NN guarantees finitetime convergence of the tracking error,thus enhancing the control accuracy of the system.In addition,the Lyapunov direct method demonstrates that the closed-loop system is semiglobally finite-time stable.Finally,simulation and experimental results show the effectiveness of the control strategy.

Evaluation of the Effect of Multiparticle on Lithium-Ion Battery Performance Using an Electrochemical Model

Dear editor,This letter focuses on modeling the electrode heterogeneity by extending the pseudo-two-dimensional model(P2D)with actual particle-size distributions(PSD).The effects of different particle characterization techniques,including the area-weighted,volume-weighted,and number-based methods on cell dynamics are compared.

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Autonomous Landing of Small Unmanned Aerial Rotorcraft Based on Monocular Vision in GPS-denied Area

Focusing on the low-precision attitude of a current small unmanned aerial rotorcraft at the landing stage, the present paper proposes a new attitude control method for the GPS-denied scenario based on the monocular vision. Primarily, a robust landmark detection technique is developed which leverages the well-documented merits of supporting vector machines (SVMs) to enable landmark detection. Then an algorithm of nonlinear optimization based on Newton iteration method for the attitude and position of camera is put forward to reduce the projection error and get an optimized solution. By introducing the wavelet analysis into the adaptive Kalman filter, the high frequency noise of vision is filtered out successfully. At last, automatic landing tests are performed to verify the method's feasibility and effectiveness. © 2014 Chinese Association of Automation.

Avoiding Non-Manhattan Obstacles Based on Projection of Spatial Corners in Indoor Environment

Monocular vision-based navigation is a considerable ability for a home mobile robot. However, due to diverse disturbances, helping robots avoid obstacles, especially nonManhattan obstacles, remains a big challenge. In indoor environments, there are many spatial right-corners that are projected into two dimensional projections with special geometric configurations. These projections, which consist of three lines,might enable us to estimate their position and orientation in 3 D scenes. In this paper, we present a method for home robots to avoid non-Manhattan obstacles in indoor environments from a monocular camera. The approach first detects non-Manhattan obstacles. Through analyzing geometric features and constraints,it is possible to estimate posture differences between orientation of the robot and non-Manhattan obstacles. Finally according to the convergence of posture differences, the robot can adjust its orientation to keep pace with the pose of detected non-Manhattan obstacles, making it possible avoid these obstacles by itself. Based on geometric inferences, the proposed approach requires no prior training or any knowledge of the camera’s internal parameters,making it practical for robots navigation. Furthermore, the method is robust to errors in calibration and image noise. We compared the errors from corners of estimated non-Manhattan obstacles against the ground truth. Furthermore, we evaluate the validity of convergence of differences between the robot orientation and the posture of non-Manhattan obstacles. The experimental results showed that our method is capable of avoiding non-Manhattan obstacles, meeting the requirements for indoor robot navigation.

Adaptive robust feedback control of moving target tracking for all-Electrical tank with uncertainty

A moving target tracking control problem for marching tank based on adaptive robust feedback control scheme is addressed.A series of preparations is needed for tank gun before shooting a target,the purpose of this paper is to design a control system to fulfill two requirements in this process:the turretbarrel system of tank needs to be adjusted from off-target position to command position and point to the moving target stably when there are strong uncertainties(modeling error,uncertain disturbance with unknown boundaries and road excitation) in the system.Considering the characteristic of coupled interaction,the first thing we do in this paper is to build a coupled analysis model of turret-barrel system with uncertainty term in state-space form.Second,an adaptive robust feedback control scheme is proposed by adding adaptive law to overcome the uncertainty.Third,multi-body dynamics software is used to establish the mechanical mechanism of the tank,and DC-motor module is established in SIMULINK environment,thus the target information and tracking error of the control system is collected and transferred,the gear-ball screw is derived directly by the output torque of the DC-motor module.Finally,the control system and the 3D model are combined together by means of Recur Dyn/SIMULINK co-simulation,the turret-barrel system of tank can approximately track the moving target in a certain range.With the adaptive robust feedback control,the target action is completely followed when the target location is constantly changing.

Singularity analysis of Duffing-van der Pol system with two bifurcation parameters under multi-frequency excitations

Bifurcation properties of a Duffing-van der Pol system with two parameters under multi-frequency excitations are studied. Three cases are discussed： （1） λ 1 is considered as bifurcation parameter, （2） λ 2 is considered as bifurcation parameter, and （3） λ 1 and λ 2 are both considered as bifurcation parameters. According to the definition of transition sets, the whole parametric space is divided into several different persistent regions by the transition sets for different cases. The bifurcation diagrams in different persistent regions are obtained, which provides a theoretical basis for optimal design of the system.

Secure Model of Medical Data Sharing for Complex Scenarios

In order to secure the massive heterogeneous medical data for the complex scenarios and improve the information sharing efficiency in healthcare system,a distributed medical data ledger model(DMDL)is proposed in this paper.This DMDL model has adopted the blockchain technology including the function decoupling,the distributed consensus,smart contract as well as multi-channel communication structure of consortium blockchain.The DMDL model not only has high adaptability,but also meets the requirements of the medical treatment processes which generally involve multientities,highly private information and secure transaction.The steps for processing the medical data are also introduced.Additionally,the methods for the definition and application of the DMDL model are presented for three specific medical scenarios,i.e.,the management of the heterogeneous data,copyright protection for medical data and the secure utilization of sensitive data.The advantage of the proposed DMDL model is demonstrated by comparing with the models which are being currently adopted in healthcare system.

Development of a Drop Tester for Portable Electronic Products

Portable electronic products are susceptible to accidental drop impact which can cause various functional and physical damage. This paper first presents a patent pending drop tester which allows portable electronic products free drop at any orientation and drop height, and then introduces the drop tester experiment setup and its design principle. Using a cellular phone as an experiment object, we obtain some data such as the impact forces, the impact accelerations, and the strain of an interested spot. By analyzing experiment data the influence of impact to products in various states is investigated with the aim to provide help for the design of products and improvement of reliability.

A Novel Obstacle Avoidance Consensus Control for Multi-AUV Formation System

In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and leader-follower strategy(IAPF-LF). Firstly, the proposed fixed-time control can achieve the desired multi-AUV formation within a fixed settling time in any initial system state. Secondly, an event-triggered communication strategy is developed to govern the communication among AUVs, and the communication energy consumption can be decremented. The time-varying formation obstacle avoidance control algorithm based on IAPF-LF is designed to avoid static and dynamic obstacles, the desired formation is maintained in the presence of external disturbances, and there is no Zeno behavior under the fixed-time event-triggered consensus control strategy.The stability of the system is proved by the Lyapunov function and inequality scaling. Finally, simulation examples and water pool experiments are reported to verify the performance of the proposed theoretical algorithms.

Design of a Bio-inspired,Two-winged,Flapping-wing Micro Air Vehicle with High-lift Performance

In this paper,we present the development of our latest flapping-wing micro air vehicle(FW-MAV),named Explobird,which features two wings with a wingspan of 195 mm and weighs a mere 25.2 g,enabling it to accomplish vertical take-off and hover flight.We devised a novel gear-based mechanism for the flapping system to achieve high lift capability and reliability and conducted extensive testing and analysis on the wings to optimise power matching and lift performance.The Explobird can deliver a peak lift-to-weight ratio of 1.472 and an endurance time of 259 s during hover flight powered by a single-cell LiPo battery.Considering the inherent instability of the prototype,we discuss the derivatives of its longitudinal system,underscoring the importance of feedback control,position of the centre of gravity,and increased damping.To demonstrate the effect of damping enhancement on stability,we also designed a passive stable FW-MAV.Currently,the vehicle is actively stabilised in roll by adjusting the wing root bars and in pitch through high-authority tail control,whereas yaw is passively stabilised.Through a series of flight tests,we successfully demonstrate that our prototype can perform vertical take-off and hover flight under wireless conditions.These promising results position the Explobird as a robust vehicle with high lift capability,paving the way towards the use of FW-MAVs for carrying load equipment in multiple tasks.

Regulation Flexibility Assessment and Optimal Aggregation Strategy of Greenhouse Loads in Modern Agricultural Parks

With advances in modern agricultural parks,the rural energy structure has undergone profound change,leading to the emergence of an agricultural energy internet.This integrated system combines agricultural energy utilization,the information internet,and agricultural production.Accordingly,this study proposes a regulation flexibility assessment approach and optimal aggregation strategy of greenhouse loads(GHLs)for modern agricultural parks.First,taking into account the operational characteristics of typical GHLs,refined load demand models for lighting,humidification,and temperature-controlled loads are established.Secondly,the recursive least squares method-based parameter identification method is designed to accurately determine key GHL model parameters.Finally,based on the regulation flexibility of quantitatively evaluated GHLs,GHLs are optimally aggregated into multiple flexible aggregators considering minimal operational cost and greenhouse environmental constraints.The results indicate that the proposed regulation flexibility assessment approach and optimal aggregation strategy of GHLs can alleviate the peak regulation pressure on power grids by flexibly shifting the load demands of GHLs.

Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in.To improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic participants.The integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and trajectory.The dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference performance.Based on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax function.Finally,the comparative analysis is performed to validate the proposed method using the naturalistic driving data.The results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

CDP-GAN:Near-Infrared and Visible Image Fusion Via Color Distribution Preserved GAN

Dear Editor,This letter is concerned with dealing with the great discrepancy between near-infrared(NIR)and visible(VS)image fusion via color distribution preserved generative adversarial network(CDP-GAN).Different from the global discriminator in prior GAN,conflict of preserving NIR details and VS color is resolved by introducing an attention guidance mechanism into the discriminator.Moreover。