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IEEE/ASME Transactions on Mechatronics

IEEE/ASME Transactions on Mechatronics

Archives Papers: 941
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Development of a Multisensory Underactuated Prosthetic Hand With Fully Integrated Electronics
Ming ChengLi JiangShaowei FanBin YangJinghui DaiHong Liu
Keywords:Prosthetic handSensorsSensor systemsThumbMechatronicsDC motorsGraspingIntegrationmodularmultisensoryprosthetic handunderactuated
Abstracts:The compliance and manipulability are the crucial properties for prosthetic hands design, especially considering both the usefulness and versatility. This study presents a novel mechatronic design of an underactuated prosthetic hand named HIT-VI hand. It consists of four modular underactuated fingers, one opposite thumb, and a set of multisensory embedded control system. Each modular finger uses one dc motor to drive its two active joints and one passive joint. Due to the use of an underactuated nine-bar mechanism, it can execute anthropomorphic coupled motion and self-adaptive motion. The hand contains 39 distributed force sensors, 10 angular position sensors, 6 current sensors, 1 temperature sensor, and 1 inertial measurement unit (IMU). The multisensory system enables the prosthetic hand to achieve intelligent position/force/current control. The fully integrated mechatronic design makes the prosthetic hand similar to an adult hand in size, weight, and appearance. Based on the kinematics and statics, the stability analysis of the prosthetic hand is carried out. Furthermore, the experiments executing the activities of daily living validate that the proposed hand has a respectable compliance and manipulability.
Model Predictive Variable Impedance Control of Manipulators for Adaptive Precision-Compliance Tradeoff
Zhehao JinDongdong QinAndong LiuWen-an ZhangLi Yu
Keywords:ImpedanceTask analysisRobotsPredictive modelsAerospace electronicsPrediction algorithmsCost functionModel predictive controloptimizationprecision-compliance tradeoffvariable impedance control (VIC)
Abstracts:Impedance control (IC) is widely used in contact-rich manipulator tasks since it provides manipulators with both operation precision and contact compliance, and trades them off by impedance parameters. However, fixed impedance parameters limit the applicability of IC in complex tasks during which the focus on the operation precision or the contact compliance is variable, such as physical human–robot collaboration and complex assembly tasks. This article presents model predictive variable impedance control approaches for adaptive precision-compliance tradeoff to satisfy variable task requirements. Specifically, we establish a novel impedance model, which transforms the variable impedance law design problem into a control law design problem, and allows us to consider novel impedance constraints that can determine manipulators' extreme precision and compliance properties. According to whether state constraints are further considered, one-step (OS) and multistep (MS) model predictive control approaches are proposed to solve these transformed control problems, and the corresponding optimization problem of the OS MPC can be established as a QP problem due to the special form of the novel impedance model. A tank-based approach is further proposed to correct the variable impedance laws for the system passivity concern. Various comparative experiments are conducted to validate the effectiveness of the presented approaches.
Mathematical Model to Predict Thrust Ripple in Double Salient Flux-Modulated Linear Permanent for Five-Axis Gantry Robot Under Field Oriented Control
Sarbajit PaulJunghwan Chang
Keywords:Mathematical modelsLoad modelingGearsStator windingsActuatorsRobotsElectromagneticsDouble saliency $dqn model$ five-axis gantry robotflux modulated permanent magnet linear synchronous motormathematical modeling thrust ripple
Abstracts:This article proposes a detailed mathematical <italic>dqn</italic> axes dynamic modeling of a double saliency flux modulated permanent magnet linear synchronous motor used as the linear actuator for the five-axis gantry robot system to study thrust ripple characteristics. To derive the model, the machine double saliency is considered by analyzing the effect of the cross-coupling inductances into the dynamic voltage equation in <italic>dqn</italic> axes. The electromagnetic performances such as the electromagnetic thrust and the thrust ripple is formulated to check the maximum limit of the thrust ripple at the initial design stage using the derived model. The whole process is validated using 2-D, 3-D finite element analysis, and manufactured prototype under driving conditions.
Dynamic Skill Learning From Human Demonstration Based on the Human Arm Stiffness Estimation Model and Riemannian DMP
Zhiwei LiaoGedong JiangFei ZhaoYuqiang WuYang YueXuesong Mei
Keywords:RobotsQuaternionsManifoldsEllipsoidsTask analysisImpedanceFeature extractionDynamic movement primitive (DMP)human-like variable impedance (HVI)multispace skillsRiemannian manifoldskill transfer
Abstracts:Traditional skill transfer frameworks mainly focus on kinematics, while there has been a lack of studies on dynamics. This article develops a Riemannian-based dynamic movement primitive (DMP) framework for learning and generalizing multispace data, including position, orientation, and stiffness from human demonstration. A simplified geometric configuration of the human arm skeleton is adopted to extract its endpoint stiffness. The dynamic skills of a variable stiffness are obtained in real time and then transferred to robots. The effectiveness of the presented approach is verified by two experiments in real scenarios on the Franka Emika Panda robot. The experimental results indicate that both kinematic and dynamic skills can be learned and generalized by the extended DMP framework with high accuracy and strong correlation. The human-like variable impedance control of a robot can be successfully realized by using the proposed approach. Thus, the proposed approach, including the stiffness estimation model and the skill learning and generalization framework, is suitable for applications of human&#x2013;robot collaboration, contact operation, and teleoperation, where the position, orientation, and stiffness need to be considered simultaneously.
Differential Evolution-Based Three Stage Dynamic Cyber-Attack of Cyber-Physical Power Systems
Kang-Di LuZheng-Guang WuTingwen Huang
Keywords:Mathematical modelsPower system dynamicsMechatronicsGeneratorsForecastingTime measurementReactive powerConstrained differential evolutioncyber-physical power system (CPPS)cyber-physical system (CPS)dynamic attackstate forecasting
Abstracts:With the rapid development of communication, control, and computer technology, traditional power systems have evolved into cyber-physical power system (CPPS). However, CPPS not only affords convenience but also introduces more cyber-attacks. Among many types of cyber-attacks, false data injection attacks (FDIAs) have drawn much attention in the CPPS security domain due to their stealthiness. Most FDIAs are based on the static model on a single snapshot and often ignore the reality of dynamically time-evolving CPPS. Accordingly, this article proposes a novel three-stage dynamic false data injection attack (DFDIA) model in CPPS by considering potential dynamic behaviors. To consider both attack location and attack amplitude, the designing DFDIA is formulated as two constrained single-objective optimization problems. Two versions of constrained differential evolution are presented as the solver to determine the attack location and optimize the attack vector for collaboratively altering the meter measurements. Then, an interval state forecasting-based countermeasure is proposed to detect the established DFDIA. In this detector, the variation bounds of state values are determined via ensemble deep learning-based state forecasting method. Finally, extensive simulation results on several IEEE bus systems demonstrate the feasibility of DFDIA and the effectiveness of the defense mechanism.
Effects of Mechanical Scanning Speeds on Eddy-Current Detection of Cavity Defects
Zhengya GuoKok-Meng LeeZhenhua Xiong
Keywords:ConductorsComputational modelingPerturbation methodsInspectionSensorsProbesMechatronicsDefect detectioneddy current (EC)magnetic flux density (MFD)modelingscanning speed
Abstracts:This article presents a distributed current-source (DCS) model considering the perturbation of the eddy-current (EC) field and its EC-generated magnetic flux density (MFD) caused by the defect in a moving conductor. The perturbed EC field, which is formulated into two subproblems (with and without a defect) in state space, is iteratively solved for the transient responses of a 3-D EC system and used to analyze the parametric effects on the fine and coarse detection of cavity defects. The DCS model improves the computational efficiency by reducing the solution domain and the number of experiments for parameter optimization, leading to the development of an EC probe consisting of an MFD sensor and a pair of rectangular electromagnets. Experimentally evaluated on a testbed configured on a computer numerical control (CNC) lathe for 2-D area scanning, the findings demonstrate the feasibility of mechanical scanning at a high speed to improve EC detection efficiency.
Robotic Polishing of Unknown-Model Workpieces With Constant Normal Contact Force Control
Jian LiYisheng GuanHaowen ChenBing WangTao Zhang
Keywords:RobotsSolid modelingRobot kinematicsForceForce controlSurface treatmentService robotsConstant normal contact forceintelligent manufacturingmacro-mini robotic systemrobotic polishingunknown-model workpiece
Abstracts:Industrial robots are increasingly being considered for application in performing polishing operations. Most research has been performed on workpieces with a priori (CAD) model, and used impedance control or hybrid force-position control to achieve constant force operation. For workpieces without priori models, it is still difficult to maintain a constant normal contact force during the polishing process. To address this challenge, this paper proposes a cooperative force-position control method consisting of three parts: constructing a model between contact forces and robotic poses during polishing operations and obtaining a real-time pose deviation of the current tool-workpiece contact state and the desired (normal) contact state; based on the deviation, a method of real-time adjustment of 2D predefined polishing paths to 3D actual polishing paths is proposed; finally, constant normal contact force control is achieved by a direct force control algorithm based on the results obtained from the above method. The proposed method is based on a macro-mini robotic system with an active force control polishing device, and its effectiveness was tested by various unknown-model workpiece polishing scenarios. The results demonstrate that the proposed method can achieve stable normal force tracking and high-quality surfaces during unknown-model workpiece polishing.
Dynamic Model-Assisted Bearing Remaining Useful Life Prediction Using the Cross-Domain Transformer Network
Yongchao ZhangKe FengJ. C. JiKun YuZhaohui RenZheng Liu
Keywords:Predictive modelsTransformersRolling bearingsDegradationAdaptation modelsTraining dataLoss measurementDomain adaptationremaining useful life (RUL)rolling bearingsimulated datatransformer
Abstracts:Remaining useful life (RUL) prediction of rolling bearings is of paramount importance to various industrial applications. Recently, intelligent data-driven RUL prediction methods have achieved fruitful results. However, the existing methods heavily rely on the quality and quantity of the available data. For some critical bearings in industrial scenarios, the real run-to-failure data are insufficient, which impair the applicability of data-based methods for industrial practices. To address these issues, this article proposes a novel dynamic model-assisted RUL prediction approach for rolling bearing, in which sufficient simulation data are applied as the training data to solve the problem caused by insufficient real data. More specifically, a dynamic rolling bearing model is introduced for simulating the degradation process of physical structures. Then, a multilayer cross-domain transformer network is developed to implement RUL prediction and adapt the learned prediction knowledge from simulation to the actual measurements. Furthermore, a mutual information loss is utilized to preserve the generalized prediction knowledge of the measured data. The proposed approach can achieve a high RUL prediction accuracy with only limited measured data, which tackles the drawbacks of the existing data-driven methods. The experimental results of the rolling bearing degradation datasets demonstrate the effectiveness and superiority of the proposed RUL prediction approach.
Adaptive Neural Tracking Control for Manipulators With Prescribed Performance Under Input Saturation
Yizhuo SunJianxing LiuYabin GaoZhuang LiuYue Zhao
Keywords:UncertaintyTrajectory trackingArtificial neural networksTorqueTask analysisMechatronicsIEEE transactionsInput saturationneural network (NN)nonsingular terminal sliding mode control (NTSMC)prescribed performance control (PPC)trajectory trackinguncertain robotic systems
Abstracts:In this article, an improved adaptive neural network (NN) nonsingular terminal sliding mode control (NTSMC) scheme is proposed for prescribed-performance trajectory tracking of manipulators with unmodeled dynamics and input saturation. In order to reduce the adverse effect of input saturation due to the conflict between excessive control force and limited motor torque, an auxiliary system is constructed. With the help of prescribed performance functions, we develop an improved NN-based NTSMC strategy to achieve tunable prescribed tracking errors under limited control, where it does not need prior precise knowledge of uncertainties. Theoretically, the uniform ultimate boundedness of the closed-loop system is proved by using the Lyapunov function. Finally, extensive comparative experiments are carried out on a ROKAE platform of a multidegree-of-freedom manipulator, and the improved tracking performance of the proposed scheme is verified.
Fast and Precise Positioning With Coupling Torque Compensation for a Flexible Lightweight Two-Link Manipulator With Elastic Joints
Tran Vu TrungMakoto Iwasaki
Keywords:CouplingsService robotsTorqueVibrationsRobot sensing systemsManipulatorsLoad modelingManipulatorsmotion controlrobot controlvibration control
Abstracts:This study presents an effective two-degrees-of-freedom controller, considering coupling torque compensation, with the aim of achieving fast and precise positioning of a flexible lightweight two-link robot with elastic joints. Coprime factorization-based feedforward control and <inline-formula><tex-math notation="LaTeX">$H_infty$</tex-math></inline-formula> control are cooperatively applied to the quasi-full-closed-loop control structure, forming a fundamental control strategy with improved reference tracking and residual vibration suppression capability, compared with the original proportional&#x2013;proportional integral control. However, satisfactory outcomes for control performance are not realized due to the coupling effect between the robot joints. Previous studies have often addressed the coupling issue for the semiclosed-loop control structure, based on the approximate rigid-body dynamics, but the existing methods are not applicable to the quasi-full-closed-loop control structure. In this research, a flexible three-mass model-based feedforward coupling torque compensation scheme appropriate for the quasi-full-closed-loop control structure is proposed, combined with a position reference filter design. The simulation and experimental results obtained confirm the superiority of the proposed method over the conventional methods, and the control specifications are consistent with those of the rigid manipulators. The proposed controller design is beneficial for industrial robotics applications that demand flexibility but without deterioration in control performance.
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