Dynamic Motion Model Research Articles

Synthesis of an algorithm for processing radar data with adaptation to a typical trajectory of an aircraft based on the scientific and methodological apparatus for reducing the Lagrange problem to an isoperimetric problem

The constant growth in the tactical and technical characteristics of modern aircraft creates the need to develop more advanced tracking systems, characterized by increased accuracy and speed. In practice, finding a compromise between these parameters is a very difficult task and requires scientific research to find new approaches to solving this problem related to the analysis of the physical characteristics of objects under observation. The well-known optimal Kalman filter algorithm does not always have satisfactory accuracy in conditions of maneuvering an observed object, and the use of algorithms based on the interaction of several models for various types of maneuver leads to unacceptably high computational complexity. One of the approaches to finding a compromise between accuracy and speed is the use of a dynamic motion model in the structure of a quasi-optimal algorithm for processing radar information. To synthesize the model, it is proposed to use the scientific and methodological apparatus of reducing the Lagrange problem to an isoperimetric one. Its key stage is to find conditions for the non-negativity of the asynchronous variation of the extended functional, which is the result of convolution of the accuracy criterion and the action integral. This makes it possible to obtain the necessary conditions for the extremum of the objective functional in the form of a boundary value problem obtained on the basis of reducing the Lagrange problem to an isoperimetric one using the Hamiltonian action integral. The set of trajectories that are the solution to the boundary value problem determines the structure of the quasi-optimal motion model in terms of differential equations and can be successfully used in the construction of recurrent algorithms for adaptive estimation of aircraft motion parameters when moving along standard trajectories with a computational complexity corresponding to the classical Kalman filter algorithm. The procedure for synthesizing a discrete processing algorithm in the work is proposed to be carried out using an approximate solution of the vector-matrix differential equation of the motion model based on the Taylor series expansion of the matrix exponential. Under conditions of a quasi-stationary approximation, this method makes it possible to determine the structure of the transition state matrix and the covariance matrix of the disturbance, which determine the quasi-optimal algorithm for processing radar information with adaptation to a typical trajectory. The paper presents a numerical simulation of the process of estimating the altitude and vertical speed of an aircraft performing a landing approach along a typical descent trajectory. Over the entire trajectory under consideration, the gain of the proposed algorithm relative to the classical Kalman filter is on average 15% in height, 20% in vertical speed, and is achieved by adapting to a typical descent trajectory at equal computational costs.

Distributed driving electric vehicle lateral stability control method considering time delay

This paper introduces a novel control method to address lateral stability challenge in distributed driving electric vehicles. By considering inherent time delay and unknown sensor measurement error generated from design, manufacturing, and external disturbances, the proposed approach achieves better robustness. Firstly, we establish a two-degree-of-freedom (2-DOF) dynamic model for lateral motion of an electric vehicle driven by in-wheel motors (EV-DIM). Based on this model, a control system considering time delay and unknown sensor measurement error is constructed. The control system incorporates a controller based on a dual domination gains observer, and its global asymptotic stability is rigorously proved by the Lyapunov method. Numerical simulations and co-simulations via CarSim and Simulink verify the method’s effectiveness. Compared to the sliding mode control, the proposed method shows better performance which indicates this approach promising for enhancing lateral stability of an electric vehicle.

Finite element and experimental modeling of jaw movement-induced deformations in the human earcanal

As ear-related technologies proliferate, optimizing comfort, retention, and battery life is crucial for enhancing user experience. A thorough understanding of the anatomical interaction between the temporomandibular joint (TMJ) and the earcanal during mouth-opening is essential. This study develops a finite element model and an experimental setup to investigate the biomechanical coupling between the TMJ and the earcanal. We analyze reverse-static deformations, focusing on cartilage-bone junction geometry, mandibular condyle location, and concha mobility. The earcanal geometry is assessed across five cross-sections with seven key dimensions measured. The results indicate that the deformations in cantilever-beam-like models closely match the reference geometry in both approaches, particularly in the lateral region. These findings suggest that a dynamic motion model of the earcanal, accurately simulating its behavior, is feasible.

DYNAMIC MODEL OF VECTOR MOTION AND ITS APPLICATION IN SPACECRAFT UNIAXIAL ORIENTATION PROBLEMS

The object of study is the spacecraft attitude control system. The subject of the study is the quaternion dynamic equation of motion of an arbitrary normalized vector and methods for constructing on its basis algorithms to control the spacecraft’s uniaxial orientation. In this work, a new dynamic model of vector motion in a body-fixed frame is obtained, its properties are investigated, and methods for solving uniaxial orientation problems using this model are considered. This model application significantly simplifies the synthesis control task, which, in this case, is reduced to control synthesis for a system that is a set of second-order integrating links. In many cases, the synthesis problem has an analytical solution for such systems. The resulting control algorithms are much simpler to imple- ment than the ones obtained using the traditional model. The results of numerical simulation, which confirm the effectiveness of the proposed algorithm, are presented.

Ship Autonomous Berthing Strategy Based on Improved Linear-Quadratic Regulator

There has been significant interest in the research field of ship automatic navigation, particularly in the area of autonomous berthing. To address the key challenges of path planning and control during ship berthing, we propose an enhanced Linear−Quadratic Regulator (LQR) control approach, reinforced by the Covariance Matrix Adaptation Evolution Strategy (CMA−ES), along with an adaptive berthing strategy decision model. This integrated framework encompasses ship motion control, path planning, and berthing strategy selection to facilitate adaptive and autonomous ship berthing. Initially, a dynamic mathematical model of ship motion is established, taking into account wind and current interference effects. Subsequently, an adaptive environment−aware berthing strategy model is introduced to enable automatic selection of berthing strategies based on spatial relationships between environmental factors and the berth. By utilizing the refined LQR method, autonomous motion control for ship berthing is achieved. To validate the effectiveness of our controller, comprehensive simulation analyses are conducted under varying operating conditions to encompass crucial factors such as large drift angle characteristics of ships, shallow water effects, and bank effects across seven diverse working conditions. The simulation results underscore the robustness of our proposed method in responding to environmental interference while demonstrating its capability to select appropriate berthing strategies based on varying operational scenarios.

Research on relative reachable domain in target orbit for maneuvering spacecraft

PurposeLots of successful space missions require that the maneuvering spacecraft can reach the target spacecraft. Therefore, research on relative reachable domain (RRD) in target orbit for maneuvering spacecraft is particularly important and is currently a hot-debated topic in the field of aerospace. This paper aims at analyzing and simulating the RRD in target orbit for maneuvering spacecrafts with a single fixed-magnitude impulse and continuous thrust, respectively, to provide a basis for analyzing the feasibility of spacecraft maneuvering missions and improving the design efficiency of spacecraft maneuvering missions.Design/methodology/approachBased on the kinematics model of relative motion, RRD in target orbit for maneuvering spacecraft with a single fixed-magnitude impulse can be calculated via analyzing the relationship between orbital elements, position vector and velocity vector of spacecrafts, and relevant studies are introduced to compare simulation results for the same case and validate the method proposed in the paper. With analysis of the dynamic model of relative motion, the calculation of RRD in target orbit for maneuvering spacecraft with continuous thrust can be transformed as the solution of the optimal control problem, and example emulations are carried out to validate the method.FindingsFor the case with a single fixed-magnitude impulse, simulation results show preliminarily that the method is in agreement with the method in Ref. (Wen et al., 2016), which treats the same case and thus is plausibly correct and feasible. For the case with continuous thrust, analysis and simulation results confirm the validity of the proposed method. The methods based on relative motion in this paper can efficiently determining the RRD in target orbit for maneuvering spacecraft.Originality/valueBoth theoretical analyses and simulation results indicate that the method proposed in this paper is comparatively simple but efficient for determine the RRD in target orbit for maneuvering spacecraft swiftly and precisely.

Localizing Mobile Target using Kalman Filtering and Least Square Boosting Ensemble Learning based Approach

Trilateration-based target localization using received signal strengths (RSS) within wireless sensor networks (WSN) often results in inaccurate location estimates due to the considerable fluctuations in RSS measurements encountered in indoor environments. Enhancing the precision of RSS-based localization systems has been a primary area of interest in extensive research efforts. This study introduces two range-free algorithms, Ensemble Learning (EL) and EL+KF, which leverage RSS measurements for localization. In contrast to trilateration, the EL- based localization approach enables the direct estimation of target locations based on field measurements, eliminating the need for distance calculations. Notably, unlike other cutting-edge localization and tracking (L&T) scheme like the support vector regression (SVR), the LSBoost (Least Squares Boosting) EL based localization architecture can be trained very quickly using RSS measurements to determine the mobile target's position. Furthermore, the proposed EL-based localization scheme incorporates the Kalman filter (KF) to achieve additional refinement in target location estimates. To assess the localization effectiveness of these proposed algorithms in the presence of noisy RF channels and dynamic target motion models, rigorous simulations were conducted. Thanks to the robust generalization capabilities of EL, the simulation results reveal that the presented EL-based localization algorithms exhibit superior performance compared to trilateration and the SVR-based localization scheme, particularly in terms of indoor localization accuracy.

A dynamic scale-mixture model of motion in natural scenes.

Some of the most important tasks of visual and motor systems involve estimating the motion of objects and tracking them over time. Such systems evolved to meet the behavioral needs of the organism in its natural environment, and may therefore be adapted to the statistics of motion it is likely to encounter. By tracking the movement of individual points in movies of natural scenes, we begin to identify common properties of natural motion across scenes. As expected, objects in natural scenes move in a persistent fashion, with velocity correlations lasting hundreds of milliseconds. More subtly, but crucially, we find that the observed velocity distributions are heavy-tailed and can be modeled as a Gaussian scale-mixture. Extending this model to the time domain leads to a dynamic scale-mixture model, consisting of a Gaussian process multiplied by a positive scalar quantity with its own independent dynamics. Dynamic scaling of velocity arises naturally as a consequence of changes in object distance from the observer, and may approximate the effects of changes in other parameters governing the motion in a given scene. This modeling and estimation framework has implications for the neurobiology of sensory and motor systems, which need to cope with these fluctuations in scale in order to represent motion efficiently and drive fast and accurate tracking behavior.

Model of Electric Locomotive Simulator Cabin Excitations

Striving to increase the speed of rail vehicles and thus improve the comfort of traveling passengers at the same time, undertakes activities in the sphere of ensuring an appropriate level of safety of rail, passenger, and freight transport. One of the elements of activities in this area is the training of train drivers. Until recently, this training consisted of a theoretical and practical part on the vehicle, alongside an experienced train driver. Considering the increasing level of automation of railway traffic control systems and locomotive equipment, as well as training costs and requirements related to the introduction of TSI, it is becoming an increasingly common requirement to conduct practical training on railway vehicle traffic simulators, while the conditions in the simulator cabin and the trainee’s feelings should correspond to the actual driving conditions. A locomotive driving simulator is a system consisting of a cabin of a suitable type of locomotive or EMU, mapped in 1:1 scale, coupled with a motion excitation system and computer programs connected together forming the software of the cab visualization and dynamics system. The basic program simulating the dynamics and kinematics of the cabin’s motion is a program containing a motion dynamics model that generates signals forcing the movement of the exciters on which the cabin’s platform is mounted. The correct operation of the simulation model depends on the created mathematical model, which can be built in several ways. This article presents the issue of building a mathematical model describing the dynamics of the rail vehicle motion, which can then be used in the simulation model of the simulator cabin motion. Two ways of proceeding in the process of approaching the construction of a mathematical model of rail vehicle motion dynamics will be presented, with the possibility of later use in creating a simulation model of the motion of the locomotive simulator cabin. One of the possible routes was used in the past in the construction of the EP09 locomotive simulator.

An Analytical Method for Accuracy Analysis of Close Proximity Rendezvous in Space Based on an Improved C-W Equation under Perturbation Effects

In order to address the issue of quantitatively solving the impact of perturbation forces and initial errors on rendezvous accuracy, traditional analytical methods are not suitable, and the calculation steps for statistical methods based on Monte Carlo simulation analysis are complex and computationally time-consuming. To overcome these challenges, this paper proposes a method for analysing rendezvous accuracy based on an improved relative motion dynamics model. Describing the relative motion between a sub-spacecraft and a target spacecraft by an improved Clohessy-Wiltshire (C-W) equation. By employing the derived state transition matrix from the model, it is possible to calculate the error propagation during the rendezvous process and express the rendezvous accuracy analytically. Through simulation examples, the proposed method proves to be both accurate and efficient in analysing the influence of perturbation forces and random errors such as initial position and velocity on rendezvous accuracy. This study verifies the correctness and effectiveness of the approach.

3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR).

Objective. 3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly under-sampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly under-sampled data.Approach. STINR-MR used a joint reconstruction and deformable registration approach to achieve a high acceleration factor for cine volumetric imaging. It addressed the ill-posed spatiotemporal reconstruction problem by solving a reference-frame 3D MR image and a corresponding motion model that deforms the reference frame to each cine frame. The reference-frame 3D MR image was reconstructed as a spatial implicit neural representation (INR) network, which learns the mapping from input 3D spatial coordinates to corresponding MR values. The dynamic motion model was constructed via a temporal INR, as well as basis deformation vector fields (DVFs) extracted from prior/onboard 4D-MRIs using principal component analysis. The learned temporal INR encodes input time points and outputs corresponding weighting factors to combine the basis DVFs into time-resolved motion fields that represent cine-frame-specific dynamics. STINR-MR was evaluated using MR data simulated from the 4D extended cardiac-torso (XCAT) digital phantom, as well as two MR datasets acquired clinically from human subjects. Its reconstruction accuracy was also compared with that of the model-based non-rigid motion estimation method (MR-MOTUS) and a deep learning-based method (TEMPEST).Main results. STINR-MR can reconstruct 3D cine-MR images with high temporal (<100 ms) and spatial (3 mm) resolutions. Compared with MR-MOTUS and TEMPEST, STINR-MR consistently reconstructed images with better image quality and fewer artifacts and achieved superior tumor localization accuracy via the solved dynamic DVFs. For the XCAT study, STINR reconstructed the tumors to a mean ± SD center-of-mass error of 0.9 ± 0.4 mm, compared to 3.4 ± 1.0 mm of the MR-MOTUS method. The high-frame-rate reconstruction capability of STINR-MR allows different irregular motion patterns to be accurately captured.Significance. STINR-MR provides a lightweight and efficient framework for accurate 3D cine-MRI reconstruction. It is a 'one-shot' method that does not require external data for pre-training, allowing it to avoid generalizability issues typically encountered in deep learning-based methods.

Mathematical framework for snake robot motion in a confined space

Snake robots have redundant kinematic structure which predetermines them for motion in rough and unstructured terrain with obstacles, narrow spaces or environments with different ground levels. Dynamic modeling of snake robots is an important element in the development of control strategies for them. This paper, deals with the dynamic model of a snake robot moving in a pipeline. The model is based on Newton's mechanics with consideration of the viscous as well as Coulomb friction model. The paper uses Hertz theory for modeling of contact between a snake robot and side walls of a pipeline. Based on optimization process results, the friction effects as well as analyses of snake robot propulsive force are investigated. The main contributions of the paper are the developed dynamic model for snake robot motion in a pipeline, open source code of the developed model in MATLAB, and analyses of individual parameters of snake robot motion.

Discrete recoil control system modelling and optimal recoil damping of deepwater drilling riser systems

This study addresses discrete dynamic modelling and the optimal recoil control problem of deepwater drilling riser systems subject to the friction resistance of fluid discharge and platform heave motion. First, a discrete-time exosystem model for friction resistance of drilling fluid discharge is developed; then, a discrete-time dynamic model of platform heave motion is established. Third, a finite-time discrete optimal recoil controller with feedforward compensation mechanisms was designed. The optimal recoil controller gains can be obtained by iteratively solving discrete equations. The simulation results show that under the discrete feedforward and feedback optimal recoil controller and pure feedback optimal recoil controller, the recoil movements of the riser system are significantly restrained. Moreover, the former is more effective than the latter at resisting the riser recoil response.

Human-like acceleration and deceleration control of a robot astronaut floating in a space station

The acceleration and deceleration (AD) motions are the basic motion modes of robot astronauts moving in a space station. Controlling the locomotion of the robot astronaut is very challenging due to the strong nonlinearity of its complex multi-body dynamics in a gravity-free environment. However, after training, humans can move well in space stations by pushing the bulkhead, and the motion mechanism of humans is a good reference for robot astronauts. The contribution of this study is modeling the human AD motion in a microgravity environment and proposing a human-like control method for robot astronauts moving in space stations. Specifically, the movement and contact force data of the human body during AD motion were collected on an air-floating platform. Through human AD modeling analysis, the mechanism of human motion is discovered, and semi-sinusoidal primitives of contact forces are proposed for AD motion. Then, a dynamic guidance model of human AD motion is built to complete motion planning under contact constraints, which is used as the expected model for the AD control of robot astronauts. Benefiting from the force primitives, accurate and safe planning of human-like AD motion can be completed. The characteristics and mechanism of human AD motion have been analyzed from the perspective of optimization. Lastly, based on the proposed dynamic guidance model, the AD motion policy is mapped to the robot astronaut system via a system control method based on the equivalent mapping of dynamic responses (force, velocity and pose). Through comparative analysis with real human motion data and simulation results under different conditions, the proposed AD control method can achieve human-like motion efficiently and stably. Even when confronted with errors in the robot’s contact velocities and inertia parameters, the method can significantly reduce the motion errors while ensuring stability.

A Physics-Informed Low-Shot Adversarial Learning for sEMG-Based Estimation of Muscle Force and Joint Kinematics.

Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot adversarial learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange's equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. A physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM).

Appointed-time safety-guaranteed control for spacecraft flying around non-cooperative target based on fully actuated system theory

Oriented to the control problem of the spacecraft flying around the non-cooperative target, an appointed-time safety-guaranteed control method based on the fully actuated system theory is proposed considering the effects of external disturbances and undesired orbital manoeuvres of the non-cooperative target. First, the relative motion dynamics model is established based on the Line-of-sight (LOS) frame and then transformed into the fully actuated form to reduce the complexity of controller design. Furthermore, an appointed-time safety-guaranteed controller is designed to guarantee the appointed-time convergence and flight safety of the fly-around system. Finally, the effectiveness and robustness of the designed control method are verified by three groups of fly-around simulations.

Improved sliding mode tracking control for spacecraft electromagnetic separation supporting on-orbit assembly

This paper addresses the electromagnetic separation problem for mass-unknown spacecraft in elliptical orbits that play a significant role in on-orbit assembly and other future space missions. First, the relative motion dynamics model between the platform (target spacecraft) and module (chaser spacecraft) is presented with the lumped disturbance resulting from external disturbances, elliptical eccentricity and unknown mass, and a compliant reference trajectory is designed by applying the idea of trigonometric-function. Then, an improved sliding mode controller is developed to ensure that the tracking errors of relative motion can converge to near the equilibrium point. In order to avoid adverse impacts, the elliptical eccentricity and unknown mass are fully considered by using the developed control approach, and the global asymptotic stability of the closed-loop system is analyzed by strict theoretical proof. Combined with spacecraft electromagnetic docking technology, the electromagnetic separation scheme can make on-orbit assembly easy to achieve with certain advantages, e.g., it doesn’t consume propellant and thus doesn’t cause plume contamination, and has reversible, continuous and non-contact characteristics. Finally, numerical simulations are performed to illustrate the effectiveness and feasibility of the developed control approach.

Dynamic Model of Lower Limb Motion in the Sagittal Plane during the Gait Cycle

Context: This work presents the development of a dynamic model for human lower limb motion in the sagittal plane during the gait cycle. The primary objective of this model is to serve as a powerful tool for the design of rehabilitation and assistive devices, such as exoskeletons, prostheses, and orthoses. It achieves this by facilitating the estimation of joint torques, the detailed analysis of kinematic variables, optimal actuator selection, and the exploration of advanced control techniques. Method: The dynamic model consists of two primary components: (1) the plant model and (2) a closed-loop controller. The plant model represents the forward dynamics of human gait and is based on a multi-mass pendulum composed of three segments of the lower limb (thigh, lower leg, and foot) and three joints (hip, knee, and ankle). It is analyzed using the Euler-Lagrange formulation and the nonlinear second-order differential equations are implemented in MATLAB’s Simulink. To reproduce reference human gait trajectories and simulate the functioning of the neuromusculoskeletal system and the central nervous system, a closed-loop PID controller is incorporated into the plant model. It is noteworthy that the scope of this dynamic model is specifically confined to the sagittal plane. Results: The dynamic model is evaluated in terms of angular displacement tracking using the relative maximum error (RME) and the root mean square error (RMSE) for reference trajectories of healthy adult male human gait as reported in the literature. The model demonstrates tracking with errors below 2.2 [°] in magnitude and 3,5% for all three considered segments (thigh, lower leg, and foot). Conclusions: The quantitative results show that the dynamic model developed in this work is reliable and allows for a precise reproduction of human gait trajectories.

Motion mode distribution of spiral maneuvering target during reentry phase

Obtaining the probability density distribution of target motion mode is the basis for model set design in multi-model tracking methods. During reentry, the target is expected to perform a violent spiral maneuver due to aerodynamic resonance between roll and pitch modes, the motion of target is highly nonlinear and the spiral frequency is a prior unknown random variable. This paper evaluates the probability distribution of spiral frequency of reentry target under three different types of maneuvering control inputs, which can provide guidelines for the design of model sets. This work is based on the dynamic model of reentry spiral motion presented in this paper, whose effectiveness has been verified by using a large number of Monte Carlo simulations.

Design and analysis of a continuum manipulator for use in narrow spaces

PurposeThis paper aims to present a kinematics analysis method and statics based control of the continuum robot with mortise and tenon joints to achieve better control performance of the robot.Design/methodology/approachThe kinematics model is derived by the geometric analysis method under the piecewise constant curvature assumption, and the workspace and dexterity of the proposed robot are analyzed to optimize its structure parameters. Moreover, the statics model is established by the principle of virtual work, which is used to analyze the mapping relationship between the bending deformation and the applied forces/torques. To improve the control accuracy of the robot, a model-based controller is put forward.FindingsResults of the experiments verify the feasibility of the proposed continuum structure and the correctness of the established model and the control method. The force deviation between the theoretical value and the actual value is relatively small, and the mean value of the deviation between the driving forces is only 0.46 N, which verify the established statics model and the controller.Originality/valueThe proposed model and motion controller can realize its accurate bending control with a few deviations, which can be used as the reference for the motion planning and dynamic model of the continuum robot.