Dynamic Multiblock Regression for Process Modelling

An off-grid hybrid PV/diesel model as a planning and design tool, incorporating dynamic and ANN modelling techniques

During the morphing process, the dynamic modeling of the morphing wing aircraft presents the characteristics of rigid‐elastic coupling effects, moving boundaries, and low computational efficiency. Meanwhile, parameters such as aerodynamic forces/moments, center of pressure, center of mass, and moment of inertia would also change significantly, which would seriously affect the flight stability. Therefore, this paper mainly focuses on the rigid‐elastic coupling dynamic modeling of morphing wing aircraft. Based on the virtual power principle, a floating coordinate system and the modal synthesis method are adopted to establish a rigid‐elastic coupling dynamic model that accounts for the elasticity of the wings, which can accurately capture the cross‐scale coupling effects between the rigid‐body motion and the elastic deformation of the wings during the continuously morphing process. Considering the moving boundary problem between the fuselage and the wings during the continuously morphing process, a dynamic constraint modeling technique based on the Craig‐Bampton method and a node's life and death technique describing the constraint states are proposed, which can avoid the complex operations of reestablishing the finite element model of the elastic bodies for traditional methods. Moreover, a method of smoothing model for the rigid–elastic coupling system is developed, which can significantly improve the computational efficiency for the dynamic equation of the morphing wing aircraft. And by changing the value of the smoothing factor, preservation of specific high‐frequency components can be controlled. Finally, the numerical examples are carried out to verify the proposed rigid‐elastic coupling dynamic model for the morphing wing aircraft.

The article presents the results of developing a methodology and implementing dynamic modeling in the SolidWorks Motion 2016 software environment for changing the body roll angle of the Gazelle Next van during active maneuvering and turning using a lever-electromagnetic stabilization system in comparison with a lever stabilizer system. A dynamic modeling technique was developed to analyze the change in the roll angle of the Gazelle NEXT all-metal van in the driving mode when turning and intensive maneuvering with a lever and using a lever-electromagnetic stabilization system). As a result of the research, a decrease in the roll angle of the Gazelle Next all-metal van was revealed in driving modes with active maneuvering and cornering using the lever-electromagnetic stabilization system relative to that installed on the example of the Gazelle NEXT — lever. The proposed method of dynamic modeling proved the effectiveness of using a lever-electromagnetic stabilization system when turning and active maneuvering, compared with standard lever stabilizers based on a comparison of the roll angles of the first and second options.

Classical regression analysis can be used to model time series. However, the assumption that model parameters are constant over time is not necessarily adapted to the data. In phytoplankton ecology, the relevance of time-varying parameter values has been shown using a dynamic linear regression model (DLRM). DLRMs, belonging to the class of Bayesian dynamic models, assume the existence of a non-observable time series of model parameters, which are estimated on-line, i.e. after each observation. The aim of this paper was to show how DLRM results could be used to explain variation of a time series of phytoplankton abundance. We applied DLRM to daily concentrations of Dinophysis cf. acuminata, determined in Antifer harbour (French coast of the English Channel), along with physical and chemical covariates (e.g. wind velocity, nutrient concentrations). A single model was built using 1989 and 1990 data, and then applied separately to each year. Equivalent static regression models were investigated for the purpose of comparison. Results showed that most of the Dinophysis cf. acuminata concentration variability was explained by the configuration of the sampling site, the wind regime and tide residual flow. Moreover, the relationships of these factors with the concentration of the microalga varied with time, a fact that could not be detected with static regression. Application of dynamic models to phytoplankton time series, especially in a monitoring context, is discussed.

We consider the problem of blind calibration of a sensor network, where the sensor gains and offsets are estimated from noisy observations of unknown signals. This is in general a nonidentifiable problem, unless restrictive assumptions on the signal subspace or sensor observations are imposed. We show that if each signal observed by the sensors follows a known dynamic model with additive noise, then the sensor gains and offsets are identifiable. We propose a dynamic Bayesian nonparametric model to infer the sensors' gains and offsets. Our model allows different sensor clusters to observe different unknown signals, without knowing the sensor clusters a priori. We develop an offline algorithm using block Gibbs sampling and a linearized forward filtering backward sampling method that estimates the sensor clusters, gains, and offsets jointly. Furthermore, for practical implementation, we also propose an online inference algorithm based on particle filtering and local Markov chain Monte Carlo. Simulations using a synthetic dataset, and experiments on two real datasets suggest that our proposed methods perform better than several other blind calibration methods, including a sparse Bayesian learning approach, and methods that first cluster the sensor observations and then estimate the gains and offsets.

To obtain higher performance for an industrial hybrid robot, the dynamic control method is utilized to control the robot. For dynamic control, the control performance is directly affected by the accuracy of the dynamic model. This paper investigated a method to establish and identify an accurate dynamic model. First, based on the Lagrangian dynamic equation and the Stribeck friction model, the unidentified dynamic model of the five-DOF hybrid robot is established. Second, identification experiments are carried out. Each of the driving joints performs frequent reciprocating motions individually. In the meantime, the moving speed is gradually increased to obtain driving torques of the respective joint at different moving speeds. Then the dynamic parameters with lower coupling are identified by using the standard deviation index and the least squares methods until all parameters are gradually determined. Finally, the hybrid robot moves a typical trajectory, while the currents of each joint are collected to obtain the driving forces. The actual driving forces, the identified dynamic model, and the unidentified dynamic model are compared. The results show that the identified method could significantly improve the accuracy of the dynamic model. The method proposed in this paper is general and can be applied to the other robots without adding any sensors.

In order to precisely predict the PFC choke inductance and its losses, a dynamic ripple-based inductor modeling technique is proposed considering the real-time current ripple over each switching cycle. The realistic inductor model is synthesized considering the operation temperature, instantaneous current magnitude, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{flux}$</tex> density. Three factors are investigated: (i) the non-linearity of the inductor material; (ii) the switching frequency components in the input current; and (iii) the time variation of the duty cycle. Thus, a function to accurately compute real-time inductance is obtained. To validate the proposed model, simulation studies and experiments are conducted using a single-phase totem-pole PFC rectifier. The theoretical prediction exhibits good agreement with the simulation result with 10% error. It is experimentally verified that the real-time measured inductance matches with the model-predicted inductance at 1 kW output power.

A methodology is introduced for identifying dynamic regression or distributed lag models relating two time series. First, specification of a bivariate time-series model is discussed, and its relationship to the usual dynamic regression model is indicated. Then, a two-stage identification procedure is presented which involves fitting univariate time-series models to each series, and identifying a dynamic shock model relating the two univariate model innovation series. The models obtained at these two stages are combined to identify a dynamic regression model, which may then be fitted in the usual ways. Two systems of economic time series illustrate the methodology.

Purpose: This study aims to analyze a school’s crisis management and explore emerging aspects of its response to a school crisis. Traditional linear modes of analysis often fail to address complex crisis situations. The present study applied a dynamic crisis life cycle model that draws on chaos and complexity theory to a crisis management case, and further imbued the dynamic model with core aspects emerging from the school’s crisis response to understand crisis management. Method and Analysis: The study was conducted at one Midwestern PK-12 school. A combination of case study design to guide data collection in a systemic manner and grounded theory to guide data analysis was administered. Multiple data sources were collected through semistructured interviews, focus group discussion, and review of crisis plan from members of the crisis management team and selected non–team members. Open coding, axial coding, and selective coding strategies were employed to allow for emerging themes with which a constant comparative analysis was used to compare against existing theoretical frame. Strategies for enhancing trustworthiness were discussed. Findings: Findings suggest that (a) the dynamic crisis life cycle model is useful in perceiving and addressing the school crisis and its aftereffects but it also has potential constraints in the sequential design and (b) flexibility, collaboration, and self-correcting mechanism emerge as important aspects of crisis response in strengthening existing understanding of crisis management from which a dynamic responsiveness model is developed. Discussion of the findings, implications for research and practice, and limitations of the results are provided.

An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning

Digital smartphone messaging can be used to promote physical activity to large populations with limited cost. It is not clear which psychological constructs should be targeted by digital messages to promote physical activity. This gap presents a challenge for developing optimal content for digital messaging interventions. The aim of this study is to compare affectively framed and social cognitively framed messages on subsequent changes in physical activity using dynamical modeling techniques. We conducted a secondary analysis of data collected from a digital messaging intervention in insufficiently active young adults (18-29 years) recruited between April 2019 and July 2020 who wore a Fitbit smartwatch for 6 months. Participants received 0 to 6 messages at random per day across the intervention period. Messages were drawn from 3 content libraries: affectively framed, social cognitively framed, or inspirational quotes. Person-specific dynamical models were identified, and model features of impulse response and cumulative step response were extracted for comparison. Two-way repeated-measures ANOVAs evaluated the main effects and interaction of message type and day type on model features. This early-phase work with novel dynamic features may have been underpowered to detect differences between message types so results were interpreted descriptively. Messages (n=20,689) were paired with valid physical activity monitoring data from 45 participants for analysis. Received messages were distributed as 40% affective (8299/20,689 messages), 39% social-cognitive (8187/20,689 messages), and 20% inspirational quotes (4219/20,689 messages). There were no statistically significant main effects for message type when evaluating the steady state of step responses. Participants demonstrated heterogeneity in intervention response: some had their strongest responses to affectively framed messages, some had their strongest responses to social cognitively framed messages, and some had their strongest responses to the inspirational quote messages. No single type of digital message content universally promotes physical activity. Future work should evaluate the effects of multiple message types so that content can be continuously tuned based on person-specific responses to each message type.

For electromagnetic suspension (EMS) type urban Maglev vehicles using a U-shaped electromagnet, the levitation and guidance forces are generated by only one electromagnet. Although the levitation force is actively controlled by changing the voltage of the electromagnet, the guidance force is passively determined by the levitation force. In addition, the curve negotiation performance of EMS-type urban Maglev vehicles using a U-shaped electromagnet must be considered, because an urban guideway may have some curves with shorter radii. It is, therefore, necessary to predict the curving performance with the greatest accuracy possible, in order to improve electromagnetic suspension and establish guideway design specifications. The objective is to establish a new dynamic modelling technique, so as to achieve more realistic curving simulation and thus to more accurately evaluate the curving performance of an EMS-type Maglev vehicle. The use of a full vehicle multibody dynamic model is proposed, and is applied to the evaluation of curving performance. Design changes are also investigated to obtain the bogie design directions for minimising variation in the lateral air gap, which is a criterion for curving performance.

In order to improve the effect of dance teaching action analysis, this paper uses dance action skill feature identification method to analyze dance teaching action and combines teaching and technology to simulate dance action. Moreover, this paper conducts dynamic modeling of dance movements and analyzes the typical properties of several dynamic modeling techniques. At the same time, this paper takes the double-joint dance movement limb as the control object for comparative analysis. The comparison shows that the fractional sliding mode approaching law has better smoothing properties. In addition, this paper selects the control method combining fractional calculus and sliding mode control theory to improve the tracking speed and following effect of the double joints of dance movements. Finally, this paper studies the variance virtual spindle cross-coupling control method based on fractional-order sliding mode to further improve the synchronization accuracy of dance movement limb joints. From the experimental data, it can be seen that the application effect of dance movement skill feature recognition in dance teaching movement analysis is very good.

Dynamic models play a critical role in the design of model-based controllers, which has a significant effect on the dynamic characteristics of motion equipment. This paper mainly focuses on the dynamic modeling and parameter identification for a gantry-type automated fiber placement (AFP) machine. First, a dynamic modeling process combining prismatic axes and revolute axes is conducted by Newton-Euler method, in which the effects of friction and hydraulic balance system are also considered. Then, as the convenience for parameter identification and the application in linearity control, the methods of dynamic model linearization and determination of minimum inertial parameters based on the multi-body system (MBS) theory are proposed, with which a dynamic model in the form of linearized minimum inertial parameters is consequently established. To identify the parameters in the model, key issues regarding excitation trajectory, filtering, and identification algorithm are discussed in detail. Finally, corresponding experiments are performed on the AFP machine, and experimental results show that there is a good agreement between the prediction of the model and the measurement in actuality. Data analysis shows that except for Z-axis, the relative error rates of the others are not greater than 5%, which proves the effectiveness of the established dynamic model and the identified parameters.

Offline robot dynamic identification methods are based on the use of the inverse dynamic identification model (IDIM), which calculates the joint forces/torques (estimated as the product of the known control signal (the input reference of the motor current loop) with the joint drive gains) that are linear in relation to the dynamic parameters, and on the use of the linear least squares technique to calculate the parameters (IDIM-LS technique). However, as actuation-redundant parallel robots are overactuated, their IDIM has an infinity of solution for the force/torque prediction, depending on the value of the desired overconstraint that is a priori unknown in the identification process. As a result, the IDIM cannot be used as it is for such a class of parallel robots. This paper proposes a procedure for the dynamic parameter identification of actuation-redundant parallel robots. The procedure takes advantage of two possible modified formulations for the IDIM of actuation-redundant robots that can be used for identification purpose. The modified IDIM formulations project some or all input torques/forces onto the robot bodies, thus leading to a unique solution of the model that can then be used in the identification process. A systematic and straightforward way to compute these modified IDIM is presented. The identification of the inertial parameters of a planar parallel robot with actuation redundancy, the DualV, is then carried out using these modified IDIM. Experimental results show the validity of the methods.