Joint Dynamic Model Research Articles

Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation

With the rapid development of Internet and Web techniques, Cross-Domain Recommendation (CDR) models have been widely explored for resolving the data-sparsity and cold-start problem. Meanwhile, most CDR models should utilize explicit domain-shareable information (e.g., overlapped users or items) for knowledge transfer across domains. However, this assumption may not be always satisfied since users and items are always non-overlapped in real practice. The performance of many previous works will be severely impaired when these domain-shareable information are not available. To address the aforementioned issues, we propose the Joint Preference Exploration and Dynamic Embedding Transportation model (JPEDET) in this paper which is a novel framework for solving the CDR problem when users and items are non-overlapped. JPEDET includes two main modules, i.e., joint preference exploration module and dynamic embedding transportation module. The joint preference exploration module aims to fuse rating and review information for modelling user preferences. The dynamic embedding transportation module is set to share knowledge via neural ordinary equations for dual transformation across domains. Moreover, we innovatively propose the dynamic transport flow equipped with linear interpolation guidance on barycentric Wasserstein path for achieving accurate and bidirectional transformation. Our empirical study on Amazon datasets demonstrates that JPEDET significantly outperforms the state-of-the-art models under the CDR setting.

Research on Zero Force Control Strategy for Flexible Joints of Industrial Robot Arms

<p>In order to achieve the control of flexible joints in industrial robotic arms, this article first establishes a zero force control mathematical model of the joint, including a simplified joint model and a joint dynamics model. Then, the fuzzy PID control algorithm is used to solve the robot mathematical model, achieving control of joint response time and speed. In order to verify the effectiveness of the joint control strategy, this article uses ABB Robot 1200 as the basic simulation model of the robot. After three-dimensional modeling of the flexible joint, it is linked to the ABB robot. Finally, the control accuracy of the control strategy is verified through a simulation environment.</p> <p> </p>

Purchasing Power Parity and Uncovered Interest Parity Revisited: Evidence From G7 Exchange Rates Using a Dynamic Joint Model

Purchasing Power Parity and Uncovered Interest Parity Revisited: Evidence From G7 Exchange Rates Using a Dynamic Joint Model

Bayesian sparse joint dynamic topic model with flexible lead-lag order

Currently, text documents from multiple sources have become available in many fields. It is of great interest to study the relationship between documents from different sources and uncover the underlying causality. Zhu et al. (2021) proposed a joint dynamic topic model (JDTM). They classified all topics into three groups and used the “shared topics” with a fixed time lag order to characterize the shared information between two corpora. Although JDTM is a powerful tool for discovering the lead-lag relationship, there are two potential shortcomings. First, different shared topics should have distinct meanings, which should lead to different time lag orders between the two corpora. Second, for dynamic documents, not all topics are represented in each time slice, and thus topic sparsity should be considered. To address these two problems, we propose a sparse joint dynamic topic model (SJDTM) with a flexible lead-lag order. We assume a birth-and-death mechanism for all topics and a flexible lead-lag order for different shared topics. The performance of SJDTM is evaluated using both synthetic data and two real text corpora consisting of conference papers and journal papers.

Joint dynamic topic model for recognition of lead-lag relationship in two text corpora

Joint dynamic topic model for recognition of lead-lag relationship in two text corpora

Finite-time sliding mode control for UVMS via T-S fuzzy approach

<p style='text-indent:20px;'>In order to solve the control problem of Underwater Vehicle with Manipulator System (UVMS), this paper proposes a finite-time sliding mode control strategy via T-S fuzzy approach. From the general dynamic model of UVMS and considering the influence between the manipulator and the underwater vehicle, hydrodynamic damping, buoyancy and gravity as the fuzzy items, we establish global fuzzy dynamic model and design a closed-loop fuzzy sliding mode controller. We prove the model in theory from two aspects: the reachability of sliding domain and the finite-time boundedness. We also give the solution of the controller gain. A simulation on the actual four joint dynamic model of UVMS with two fuzzy subsystems is carried out to verify the effectiveness of this method.</p>

Adaptive Control of Robot Series Elastic Drive Joint Based on Optimized Radial Basis Function Neural Network

For the social robot with serial elastic actuator, the joint dynamics model has the problems of strong coupling and high nonlinear, and the traditional PD control algorithm cannot achieve accurate trajectory tracking effect on the joint position of social robot using series elastic actuator. Therefore, an optimized Radial basis function (RBF) neural network adaptive control algorithm was proposed. The method based on RBF neural network approximates the social robot joint model parameters, an adaptive law was designed to estimate the weights of the neural network and the joint model online. The dynamic plane method is combined to improve the robustness of the control algorithm. The simulation results show that the trajectory tracking error peak of PD control algorithm is 0.2 rad. Compared with PD control algorithm, the trajectory tracking error peak of RBF neural network adaptive control algorithm based on dynamic surface optimization is reduced to ± 0.05 rad, which realizes accurate approximation of the parameters of social robot joint model, and accurate dynamics model approximation provides a theoretical basis for further research on human–robot interaction (HRI) of social robots.

A Joint Dynamic Pricing, Advertising, and Production Model with Inventory-Level-Dependent Goodwill

Inventory level has a significant impact on the goodwill of products to customers, which seldom becomes the focus of previous studies. In this paper, joint dynamic pricing, advertising, and production decision-making problem is investigated, where the demand rate depends on sales price and goodwill. The inventory and backlog as well as advertisement are considered as goodwill-building factors. The optimal dynamic pricing, advertising, and production policies are derived by using Pontryagin’s maximum principle. Numerical examples are provided to demonstrate the obtained results, and sensitivity analysis of main system parameters is carried out to obtain some managerial insights. We find that when the initial goodwill is relatively high, the firm’s profit first decreases and then increases with respect to the impact intensity of inventory on goodwill; otherwise, the firm always benefits from a higher impact intensity of inventory on goodwill. Furthermore, the optimal production and advertising policies are complementary caused by the feature of inventory-dependent goodwill.

Modeling and Identification of an Industrial Robot with a Selective Modal Approach

The stiffness properties of industrial robots are very important for many industrial applications, such as automatic robotic assembly and material removal processes (e.g., machining and deburring). On the one hand, in robotic assembly, joint compliance can be useful for compensating dimensional errors in the parts to be assembled; on the other hand, in material removal processes, a high Cartesian stiffness of the end-effector is required. Moreover, low frequency chatter vibrations can be induced when low-stiffness robots are used, with an impairment in the quality of the machined surface. In this paper, a compliant joint dynamic model of an industrial robot has been developed, in which joint stiffness has been experimentally identified using a modal approach. First, a novel method to select the test configurations has been developed, so that in each configuration the mode of vibration that chiefly involves only one joint is excited. Then, experimental tests are carried out in the selected configurations in order to identify joint stiffness. Finally, the developed dynamic model of the robot is used to predict the variation of the natural frequencies in the workspace.

Comprehensive modeling and identification of nonlinear joint dynamics for collaborative industrial robot manipulators

For collaborative robots, the ability to accurately predict the actuator torques required to realize the desired task is highly important. This will improve and guarantee the safety, motion and force control performance, and smooth lead-through programming experience. Thus, this paper presents the investigation towards comprehensive modeling and identification of nonlinear joint dynamics for collaborative robots. The proposed joint dynamics model and identification describes the most dominant dynamic characteristics of robot joints that comprise strain-wave transmissions, such as nonlinear friction, nonlinear stiffness, hysteresis, and kinematic error. Position-dependent backlash characteristics is observed and quantified using our proposed identification method and the Generalized Maxwell-Slip friction model is extended to describe the observed phenomena. The developed dynamic modeling and identification procedures provides insightful guidance for the design and model-based control of collaborative robots.

A Novel Virtual Sensor for Estimating Robot Joint Total Friction Based on Total Momentum

Robot joint friction is an important and complicated issue in improving robot control performance. In this paper, a virtual sensor based on the total generalized momentum concept is proposed to estimate the total friction torque, including both the motor-side and link-side friction, of robot joints without joint torque sensors. The proposed algorithm only requires a robot joint dynamics model and not a complex friction model dependent on factors such as time and velocity. By compensating for the estimated friction torque with a robot joint controller, the trajectory tracking performance of the controller, especially the velocity tracking performance, can be improved. To verify the effectiveness of the developed algorithm, 2-DOF planar manipulator simulations and single-joint system experiments are conducted. The simulation and experimental results show that the designed virtual sensor can effectively estimate the total joint friction disturbance and that the controller trajectory tracking performance is improved after observed friction compensation. However, the position tracking performance improvement of the controller is less than that for the velocity tracking performance improvement during the experiments. In addition, the velocity step response ability and velocity tracking performance of the controller are improved more at low velocities than that at high velocities in the experiments. The proposed algorithm has engineering and theoretical significance for estimating robot joint friction and improving the performance of robot joint controllers.

Joint Manufacturing and Onsite Microgrid System Control Using Markov Decision Process and Neural Network Integrated Reinforcement Learning

Onsite microgrid generation systems with renewable sources are considered a promising complementary energy supply system for manufacturing plant, especially when outage occurs during which the energy supplied from the grid is not available. Compared to the widely recognized benefits in terms of the resilience improvement when it is used as a backup energy system, the operation along with the electricity grid to support the manufacturing operations in non-emergent mode has been less investigated. In this paper, we propose a joint dynamic decision-making model for the optimal control for both manufacturing system and onsite generation system. Markov Decision Process (MDP) is used to formulate the decision-making model. A neural network integrated reinforcement learning algorithm is proposed to approximately estimate the value function given policy of MDP. A case study based on a manufacturing system as well as a typical onsite microgrid generation system is conducted to validate the proposed MDP model as well as the solution strategy.

A dynamic parameter identification method of industrial robots considering joint elasticity

Considering the joint elasticity, a novel dynamic parameter identification method is proposed for general industrial robots only with motor encoders. Firstly, the unknown parameters of the elastic joint dynamic model are analyzed and divided into two types. The first type is the motion-independent parameter only including the joint stiffness, which can be identified by the static force/torque-deformation experiments without the dynamic model. The second type is the motion-dependent parameter composed of the rest of the parameters, which needs the dynamic excitation experiments. Therefore, these two types of parameters can be identified separately. Meanwhile, it is found that the rotor inertia parameters can be obtained from the manufacturer, which reduces the identification difficulty of other parameters. After obtaining the rotor inertia and joint stiffness, an approximate processing algorithm is proposed considering the motor friction to establish the linear identification model of other parameters. Hence, the least squares can be employed to identify the parameters, and the independence of the inertia and joint viscous friction parameters are not affected. Meanwhile, the exciting trajectories can be optimized throughout the robot workspace, which reduces the effect of measurement noise on identification accuracy. With the proposed separated identification strategy and approximate processing algorithm, the dynamic parameters can be obtained precisely without double encoders on each joint. Finally, a series of simulations are conducted to evaluate the good performance of the proposed method.

Stiffness and Damping Formulations for Jointed Deployable Space Truss Structure

In this paper, the dynamic behaviors of a elastic rod connected by a joint with clearance of deployable truss structures are investigated. A one-dimensional cantilever with joint support is examined in the deployable truss structure. These formulations consist of the nonlinear joint dynamic stiffness model and damping models are formulated and the dynamic characteristics of free-vibrating structures are estimated. The natural frequency transitions are investigated by considering the contact and noncontact conditions of the clearances, then the formulation of dynamic stiffness model is obtained. The friction models are mathematically formulated and simplified to make them easy to calculate. The impact damping models are formulated using the linear envelope of the time response and it can be negligible when the speed is small. The dynamic characteristics can be estimated by integrating these models and formulations using the energy loss factors. Experimental evaluations show the dynamic characteristics and demonstrate the validity of the formulations.

Tracking the Impact of Media on Voter Choice in Real Time: A Bayesian Dynamic Joint Model

ABSTRACTCommonly used methods of evaluating the impact of marketing communications during political elections struggle to account for respondents’ exposures to these communications due to the problems associated with recall bias. In addition, they completely fail to account for the impact of mediated or earned communications, such as newspaper articles or television news, that are typically not within the control of the advertising party, nor are they effectively able to monitor consumers’ perceptual responses over time. This study based on a new data collection technique using cell-phone text messaging (called real-time experience tracking or RET) offers the potential to address these weaknesses. We propose an RET-based model of the impact of communications and apply it to a unique choice situation: voting behavior during the 2010 UK general election, which was dominated by three political parties. We develop a Bayesian zero-inflated dynamic multinomial choice model that enables the joint modeling of: the interplay and dynamics associated with the individual voter's choice intentions over time, actual vote, and the heterogeneity in the exposure to marketing communications over time. Results reveal the differential impact over time of paid and earned media, demonstrate a synergy between the two, and show the particular importance of exposure valence and not just frequency, contrary to the predominant practitioner emphasis on share-of-voice metrics. Results also suggest that while earned media have a reducing impact on voting intentions as the final choice approaches, their valence continues to influence the final vote: a difference between drivers of intentions and behavior that implies that exposure valence remains critically important close to the final brand choice. Supplementary materials for this article are available online.

A joint dynamic pricing and production model with asymmetric reference price effect

Reference price plays a significant role in influencing purchase decisions of customers. Due to loss aversion, the asymmetric reference price effect on market demand should be taken into account. This paper develops a joint dynamic pricing and production model with asymmetric reference price effect. In a finite planning horizon, the demand rate is time-varying and depends on price as well as reference price. The decision-making problem with the asymmetric reference price effect turns to be a nonsmooth optimal control problem, which cannot be solved by standard optimal control method. As a special case, we first obtain the joint optimal dynamic pricing and production strategy with symmetric reference price effect by solving the corresponding standard optimal control problem based on Maximum principle. For the case of asymmetric reference price effect, we propose a systematical method on basis of optimality principle to solve the nonsmooth optimal control problem, and obtain the joint strategy. Numerical examples are employed to illustrate the effectiveness of the proposed method. In addition, we assess the sensitivity analysis of system parameters to examine the impacts of asymmetric reference price on optimal pricing and production strategies and total profits.

Failure treatment strategy and fault-tolerant path planning of a space manipulator with free-swinging joint failure

Aiming at a space manipulator with free-swinging joint failure, a failure treatment strategy and fault-tolerant path planning method is proposed in this paper. This method can realize failure treatment of a space manipulator with free-swinging joint failure through determination of the optimal locked joint angle and dynamics model reconfiguration. Fault-tolerant path planning is realized by the establishment of the degraded workspace with integrated kinematics performance (DWWIKP) and an improved A-Star (A∗) algorithm. This paper has the following contributions. The determination of the optimal locked joint angle can ensure that the manipulator is able to continue follow-up tasks while maximizing the workspace of the manipulator after locking the fault joint. Underactuated control of a high degree-of-freedom (DOF) manipulator can be effectively solved through dynamics model reconfiguration. The analysis process of the dynamics coupling relationship can be applied to cases where the active joint and the passive joint are parallel or perpendicular to each other. The establishment of the DWWIKP can demonstrate the kinematics performance of the manipulator in both joint space and operation space comprehensively. The improved A∗ algorithm based on the integrated kinematics performance index (IKPI) can search a fault-tolerant task trajectory that satisfies the requirements of reachability and the overall kinematics performance simultaneously. The method proposed in this paper is verified by a 7-DOF manipulator, and it is available to any DOF manipulator with free-swinging joint failure.

Dynamic modeling method of the bolted joint with uneven distribution of joint surface pressure

The dynamic characteristics of the bolted joints have a significant influence on the dynamic characteristics of the machine tool. Therefore, establishing a reasonable bolted joint dynamics model is helpful to improve the accuracy of machine tool dynamics model. Because the pressure distribution on the joint surface is uneven under the concentrated force of bolts, a dynamic modeling method based on the uneven pressure distribution of the joint surface is presented in this paper to improve the dynamic modeling accuracy of the machine tool. The analytic formulas between the normal, tangential stiffness per unit area and the surface pressure on the joint surface can be deduced based on the Hertz contact theory, and the pressure distribution on the joint surface can be obtained by the finite element software. Futhermore, the normal and tangential stiffness distribution on the joint surface can be obtained by the analytic formula and the pressure distribution on the joint surface, and assigning it into the finite element model of the joint. Qualitatively compared the theoretical mode shapes and the experimental mode shapes, as well as quantitatively compared the theoretical modal frequencies and the experimental modal frequencies. The comparison results show that the relative error between the first four-order theoretical modal frequencies and the first four-order experimental modal frequencies is 0.2% to 4.2%. Besides, the first four-order theoretical mode shapes and the first four-order experimental mode shapes are similar and one-to-one correspondence. Therefore, the validity of the theoretical model is verified. The dynamic modeling method proposed in this paper can provide a theoretical basis for the accurate dynamic modeling of the bolted joint in machine tools.

Dynamic joint model of capacitive charge pumps and on‐chip photovoltaic cells for CMOS micro‐energy harvesting

SummaryOn‐chip energy harvesting by means of integrated photovoltaic cells in standard CMOS technology can be successfully used to recharge or power‐up integrated circuits with the use of charge pumps for voltage boosting. In this paper, a tool to facilitate the design of such structures is proposed consisting of an accurate model of the joint dynamics of the micro‐photovoltaic cell and a capacitive DC/DC converter in the slow‐switching limit regime. The model takes into account both the top and bottom parasitic capacitances of the flying capacitors. We assume a classical model for the photodiode whose photogenerated current is extracted from device‐level simulations. The joint model is verified by circuit‐level simulations achieving high accuracy and computation time savings of up to 1700×. The joint model shows that the voltage generated by an integrated photovoltaic cell connected to a capacitive DC/DC converter is not constant even under constant illumination. This phenomenon can only be reproduced through the joint model and failing to take it into account results in an error in the estimation of the time needed by the DC/DC converter to reach a given output voltage. We also demonstrate that the maximum output voltage reached by a DC/DC converter in the slow‐switching limit regime when a photovoltaic cell is used as energy transducer depends on the switching frequency. Finally, the applicability of the model is illustrated through the optimization of time response and charge efficiency for the Dickson, Fibonacci, and exponential topologies in the case of implantable devices. Copyright © 2016 John Wiley & Sons, Ltd.

Joint-Structured-Sparsity-Based Classification for Multiple-Measurement Transient Acoustic Signals

This paper investigates the joint-structured-sparsity-based methods for transient acoustic signal classification with multiple measurements. By joint structured sparsity, we not only use the sparsity prior for each measurement but we also exploit the structural information across the sparse representation vectors of multiple measurements. Several different sparse prior models are investigated in this paper to exploit the correlations among the multiple measurements with the notion of the joint structured sparsity for improving the classification accuracy. Specifically, we propose models with the joint structured sparsity under different assumptions: same sparse code model, common sparse pattern model, and a newly proposed joint dynamic sparse model. For the joint dynamic sparse model, we also develop an efficient greedy algorithm to solve it. Extensive experiments are carried out on real acoustic data sets, and the results are compared with the conventional discriminative classifiers in order to verify the effectiveness of the proposed method.