Synchronization Of Trajectories Research Articles

Percolation-induced explosive synchronization in pinning control

Percolation and synchronization are two paradigmatic examples of phase transitions that can take place in network dynamical systems. In this paper, we show how percolation can induce an explosive, first-order transition to synchronization in pinning control, a classical feedback strategy that exploits the network topology to drive the network dynamics to a desired synchronous solution. When the number of control signals is limited by physical or economical constraints, only a fraction of the network nodes can be effectively synchronized onto the desired trajectory. Determining the sensitivity of this fraction to the number of pinning control signals is then key to decide if it is worth adding additional control signals. We find that when the network graph percolates, that is, it becomes endowed with a giant strongly connected component (GSCC), an explosive transition to synchronization occurs as we increase the fraction of nodes where we can inject the pinning signals. Motivated by this numerical observation, we exploit the probabilistic conditions that ensure the presence of a GSCC to predict the number of pinning signals such that all of its nodes will converge to the desired synchronous trajectory. We test the validity and robustness of our analytical derivations through numerical simulations on both synthetic and real networks, proving the benefit of such analysis in supporting decision-making for control design.

Research on dynamic target steady and quasi-grasping method of coal gangue sorting robot based on global planning and local vision

ABSTRACT Using robots instead of manual sorting of coal gangue can significantly improve the sorting rate. However, the stable grasping of the target gangue by the manipulator is the key to the efficient sorting of robot. The coal gangue on the belt conveyor has the characteristics of high speed and high quality. In the process of grasping, the velocity difference between the end of the manipulator and the coal gangue will cause impact load. In addition, the belt speed fluctuation and deviation also lead to the inaccurate location of the gangue, resulting in excessive position error of the manipulator during grasping, leading to grasping failure. For this reason, a synchronous tracking trajectory planning method of manipulator based on global planning and local visual servo is proposed in this paper. In this method, the manipulator is controlled by the BPNG method to reach the top of the target gangue quickly, and the accurate location of the target gangue is completed by the extended FDSST tracking algorithm. The manipulator uses the “position-velocity-acceleration” three-loop PID square method to realize the synchronous tracking trajectory planning of the target gangue. The experimental results show that this method can achieve stable and accurate grasping of target gangue by manipulator.

Synchrony or asynchrony: development of facial expression recognition from childhood to adolescence based on large-scale evidence.

The development of facial expression recognition ability in children is crucial for their emotional cognition and social interactions. In this study, 510 children aged between 6 and 15 participated in a two forced-choice task of facial expression recognition. The findings supported that recognition of the six basic facial expressions reached a relatively stable mature level around 8-9 years old. Additionally, model fitting results indicated that children showed the most significant improvement in recognizing expressions of disgust, closely followed by fear. Conversely, recognition of expressions of happiness and sadness showed slower improvement across different age groups. Regarding gender differences, girls exhibited a more pronounced advantage. Further model fitting revealed that boys showed more pronounced improvements in recognizing expressions of disgust, fear, and anger, while girls showed more pronounced improvements in recognizing expressions of surprise, sadness, and happiness. These clear findings suggested the synchronous developmental trajectory of facial expression recognition from childhood to adolescence, likely influenced by socialization processes and interactions related to brain maturation.

Trajectory Planning for Coal Gangue Sorting Robot Tracking Fast-Mass Target under Multiple Constraints.

Aiming at the problems of grab failure and manipulator damage, this paper proposes a dynamic gangue trajectory planning method for the manipulator synchronous tracking under multi-constraint conditions. The main reason for the impact load is that there is a speed difference between the end of the manipulator and the target when the manipulator grabs the target. In this method, the mathematical model of seven-segment manipulator trajectory planning is constructed first. The mathematical model of synchronous tracking of dynamic targets based on a time-minimum manipulator is constructed by taking the robot's acceleration, speed, and synchronization as constraints. The model transforms the multi-constraint-solving problem into a single-objective-solving problem. Finally, the particle swarm optimization algorithm is used to solve the model. The calculation results are put into the trajectory planning model of the manipulator to obtain the synchronous tracking trajectory of the manipulator. Simulation and experiments show that each joint of the robot's arm can synchronously track dynamic targets within the constraint range. This method can ensure the synchronization of the position, speed, and acceleration of the moving target and the target after tracking. The average position error is 2.1 mm, and the average speed error is 7.4 mm/s. The robot has a high tracking accuracy, which further improves the robot's grasping stability and success rate.

Stability and Finite-Time Synchronization Analysis for Recurrent Neural Networks with Improved Integral-Type Time-Varying Delays

This paper studies the stability criterion of integral time-varying recurrent neural networks (RNNs) with zero lower bound and finite-time synchronization based on improved sliding mode control (SMC). Firstly, a sufficient criterion for universal asymptotic stability of RNNs with integral time-varying delays is obtained by estimating a tight upper bound of augmented Lyapunov-Krasovskii functional (LKF) derivative with inequality scaling technique and mutually convex combined inequality. Secondly, in order to eliminate the time that error system state trajectory slides along sliding mode flow pattern until convergence at the origin, based on drive response and SMC theory, a suitable sliding mode controller is designed by considering that sliding mode flow pattern is equal to synchronization error. Finally, maximum allowable upper bound of delay under different delay derivatives are obtained by considering trajectory change of input function under different initial value. Synchronization trajectory of drive and response systems with mismatched parameters and activation functions under the influence of controller are studied, and synchronization time which is required for error system to reach stability is obtained. Simulation results show that the introduction of integral delay can be more comprehensive from both difference and area, so that drive system state is eventually steady at equilibrium point and synchronized with response system. Stability criterion of this paper not only has less conservative and computation complexity but also has shorter synchronization control time.

Modeling of swimming posture dynamics for a beaver-like robot

A bionic underwater robot swims to generate its propulsion in water, which in turn directly determines its movement stability. Exploring the relationship between the swimming force and the posture is important. This study aims to analyze and model the swimming posture dynamics of a beaver-like robot. The posture dynamics is decomposed into three parts: leg dynamics, body hydrodynamics, and body posture dynamics. First, the leg dynamic model of the beaver-like robot is established using the rigid-fluid integration method. Then, the overall fluid dynamics of the robot is modeled via numerical calculation methods to obtain the forces of the water on the robot body during swimming. Lastly, the swimming posture dynamic model of the robot is constructed to describe the relationship between the leg movement and body posture. The swimming process of the beaver-like robot with bionic alternating and synchronous trajectory is simulated with ADAMS 2019. The proposed modeling method and the swimming posture dynamic model are verified by comparing the simulation and theoretical calculation results of robot posture, which could be used for the swimming posture control of a bionic underwater robot.

Fully Distributed Optimal Consensus for a Class of Second-Order Nonlinear Multiagent Systems With Switching Topologies

This article proposes a novel optimal consensus protocol for a class of leaderless multiagent systems, where each agent is described by a second-order nonlinear system and agents interact with each other on switching networks. Any global information, including the eigenvalues of the Laplacian matrix, is unavailable in the control scheme development. The reference trajectory is designed for each agent, and the corresponding performance function, which reflects the off-track error evolution and control cost, is proposed. The sufficient condition for the synchronization of reference trajectories, which does not rely on the topology dwell time, is established by constructing an appropriate current topology-independent Lyapunov function. Due to the fact that the nonlinear function in the system dynamical equation of each agent is unknown, an equation termed integral reinforcement learning (IRL) equation is provided, and it is strictly proven that the provided IRL equation is equivalent to the given Hamilton–Jacobi–Bellman equation. The model-free optimal feedback control law is then derived based on the IRL technique. In the implementation of the developed control scheme, the neural network approximation tool is adopted, and the scheme is applied to a numerical system to show its effectiveness.

Identification and compensation of position independent geometric errors of dual rotary axes for hybrid-type five-axis machine tool based on unit dual quaternions

This paper presents a novel synchronous motion trajectory based on the unit dual quaternion (UDQ) to identify the position-independent geometric errors (PIGEs) of the dual rotary axes of a hybrid five-axis machine tool using a Double ball-bar (DBB). A general irregular trajectory equipartition method is presented to deal with the asynchronous matching between DBB data sampling and trajectory points. The kinematic model is effectively simplified by redefining the PIGEs of linear/rotary axes. The installation errors of the DBB are obtained by circular/spherical fitting trajectories, which are brought into the error model for elimination. The simulation of the UDQ kinematic model combined with preset error value shows that the non-uniform variation of DBB motion will lead to the deviation of PIGEs decoupling results. A compensation strategy of major orientation errors based on the UDQ is proposed, which is verified by simulation and compensation experiments.

Synchronization characteristics of two vibrator-driven pendulums

In this paper, a dynamic model with two vibrator-driven pendulums is presented. Each pendulum is motivated by a pair of vibrators mounted on it. According to the deduced synchronization criteria of the pendulums and the vibrators, the synchronous states of pendulums are determined by the phase differences of vibrators. The two pendulums achieve anti-phase synchronization when the phase differences of arbitrary-two vibrators are all stabilized around zero. If the phase differences of the two vibrators mounted on the same pendulum are zero whereas those on different pendulums are π, the pendulums will arrive at the in-phase synchronous state. Besides, the system shows a relatively good isolative effect under the anti-phase synchronous motion of the two pendulums. Based on the theoretical results, some numerical qualitative analyses are presented and simulations are implemented for verification. The dynamic model in this paper can offer guidance to design a kind of vibrating jaw crusher. Here, the two pendulums serve as movable jaws and work by the anti-phase synchronous swing motion trajectory.

Theoretical, numerical, and experimental studies on controlled synchronization strategy of four-axis variable trajectory equal-thickness screen

The four-axis variable trajectory equal-thickness screen (FAVTETS) achieves equal-thickness screening by appropriate excitation force generated by four eccentric rotors in synchronized motion. The forced synchronization exciter is characterized by an unsatisfactory synchronization effect due to the wear of transmission parts. Hence, in this paper, the four-axis controlled synchronization of FAVTETS is investigated, and an intelligent control system is proposed. Firstly, research is conducted on the variable trajectory characteristics of FAVTETS. Next, the sliding mode, fuzzy, and PID controllers are designed and compared to obtain an ideal control effect. Moreover, the influence of torsional stiffness of flexible coupling on control accuracy is considered. The results show that the sliding mode controller has a good control effect. Based on the aforementioned, the simulation model of a four-axis controlled synchronization structure is established via MATLAB/Simulink. Finally, Labview is employed to design the four-axis controlled synchronization software system. Then, the physical experiment is carried out. The four-axis controlled synchronization and vibration trajectory measurement results verify the effectiveness and robustness of the intelligent control system.

A New Approach to Stability Analysis for Stochastic Hopfield Neural Networks With Time Delays

This article is devoted to the existence and the global stability of stationary solutions for stochastic Hopfield neural networks with time delays and additive white noises. Using the method of random dynamical systems, we present a new approach to guarantee that the infinite-dimensional stochastic flow generated by stochastic delay differential equations admits a globally attracting random equilibrium in the state-space of continuous functions. An example is given to illustrate the effectiveness of our results, and the forward trajectory synchronization will occur.

Coordinating illness and insurance trajectories: Evidence from a post-acute care unit

This article examines how healthcare practitioners incorporate patients' insurance coverage and financial situation into their professional judgment. It does so by introducing the concept of an “insurance trajectory” that healthcare workers must coordinate with their medical management of illness and recovery. Drawing on 15 months of ethnography and 16 in-depth interviews at a post-acute care unit in New York City, this article argues that providers engage in anticipation work to align the tempo of recovery with the timeline of insurance coverage, in order to maximize revenue for the organization and minimize costs for patients. It identifies three modalities of anticipation work from intake to discharge: the creation of roadmaps on which illness and insurance trajectories intersect to predict an ideal discharge date, the synchronization of trajectories to avoid denials of coverage during rehabilitation, and the projection of futures to prevent illness and insurance trajectories from decoupling once patients are discharged. These findings expand our understanding of the effects of managed care on healthcare workers' practices and decision-making.

Output-Feedback Self-Synchronization of Directed Lur'e Networks via Global Connectivity.

In this article, we generalize the results on self-synchronization of Lur'e networks diffusively interconnected through dynamic relative output-feedback from the undirected graph case in Zhang et al. 2016 to the general directed graph case. A linear dynamic self-synchronization protocol of the same structure is adopted as the one proposed in Zhang et al. 2016. That is, the Lur'e-type nonlinearity is not involved in our self-synchronization protocol. It is in fact unknown and only assumed to be incrementally sector bounded within a given sector. In the absence of a leader Lur'e system defining the synchronization trajectory, we construct a novel self-synchronization manifold in order to derive the self-synchronization error dynamics. Meanwhile, the connectivity of the general directed graph having a directed spanning tree is quantified by the global connectivity, instead of the so-called general algebraic connectivity used in the directed graph case under static relative state feedback. The global connectivity plays a crucial role in handling self-synchronization problems of directed nonlinear networks via dynamic relative output feedback, including directed networks with the Lipschitz nonlinear node dynamics, which is also discussed in this article. The protocol parameter matrix design is performed by solving the obtained LMI conditions in sequence. In addition, some discussions are complemented on the important technical details in our self-synchronization protocol design along with extensions. Finally, our theoretical results are illustrated through numerical simulations over a directed nonlinear dynamical network.

A Clustering-Anonymity Approach for Trajectory Data Publishing Considering both Distance and Direction

Trajectory data contains rich spatio-temporal information of moving objects. Directly publishing it for mining and analysis will result in severe privacy disclosure problems. Most existing clustering-anonymity methods cluster trajectories according to either distance- or direction-based similarities, leading to a high information loss. To bridge this gap, in this paper, we present a clustering-anonymity approach considering both these two types of similarities. As trajectories may not be synchronized, we first design a trajectory synchronization algorithm to synchronize them. Then, two similarity metrics between trajectories are quantitatively defined, followed by a comprehensive one. Furthermore, a clustering-anonymity algorithm for trajectory data publishing with privacy-preserving is proposed. It groups trajectories into clusters according to the comprehensive similarity metric. These clusters are finally anonymized. Experimental results show that our algorithm is effective in preserving privacy with low information loss.

On linear-quadratic optimal transition in synchronization of homogeneous multi-agent systems

Abstract In this paper, a new approach for the optimal synchronization of homogeneous multi-agent systems is presented. In contrast to existing approaches, we offer an intuitive cost for the transition of each agent to a common synchronization trajectory, which facilitates tuning of the cost weights. The introduced LQ optimal tracking problem is solved by reducing it to a LQ regulator problem. By choosing a linear parameterization of the synchronization trajectory, we enable the distributed implementation of the optimal synchronization controller. We give examples how such a parameterization can be chosen and how it influences the required communication between the agents. Furthermore, we derive an optimal communication topology that minimizes the weighted sum of all agents’ transition costs. Finally, we demonstrate our results in a simulation example.

Synchronization-Free Multivariate Statistical Process Control for Online Monitoring of Batch Process Evolution

Synchronization of variable trajectories from batch process data is a delicate operation that can induce artifacts in the definition of multivariate statistical process control (MSPC) models for real-time monitoring of batch processes. The current paper introduces a new synchronization-free approach for online batch MSPC. This approach is based on the use of local MSPC models that cover a normal operating conditions (NOC) trajectory defined from principal component analysis (PCA) modeling of non-synchronized historical batches. The rationale behind is that, although non-synchronized NOC batches are used, an overall NOC trajectory with a consistent evolution pattern can be described, even if batch-to-batch natural delays and differences between process starting and end points exist. Afterwards, the local MSPC models are used to monitor the evolution of new batches and derive the related MSPC chart. During the real-time monitoring of a new batch, this strategy allows testing whether every new observation is following or not the NOC trajectory. For a NOC observation, an additional indication of the batch process progress is provided based on the identification of the local MSPC model that provides the lowest residuals. When an observation deviates from the NOC behavior, contribution plots based on the projection of the observation to the best local MSPC model identified in the last NOC observation are used to diagnose the variables related to the fault. This methodology is illustrated using two real examples of NIR-monitored batch processes: a fluidized bed drying process and a batch distillation of gasoline blends with ethanol.

Design and implementation of multi-axis real-time synchronous look-ahead trajectory planning algorithm

Design and implementation of multi-axis real-time synchronous look-ahead trajectory planning algorithm

Why are synchronised linear multi-agent systems sensitive to parameter deviations?

The paper investigates the robustness of synchronised linear systems with respect to parameter deviations. It shows that for oscillator networks even infinitesimally small changes of the agent parameters make the overall system to become asymptotically stable and, hence, not synchronisable with respect to a non-trivial synchronous trajectory. This phenomenon raises the question: Why is the property of synchrony so sensitive with respect to the agent parameters? The paper gives an explanation by showing that the synchronous behaviour of any linear multi-agent system appears in a part of the state space that is unobservable by the networked controller. As the overall system is not completely observable but structurally observable, any parameter deviation generally makes the unobservable part to become observable. Hence, this part is moved into the control loop of the networked controller and, for oscillator networks, it gets asymptotically stabilised, which destroys the synchrony of the overall system.

Position - Velocity Control for Two PMDC Motors Connected by a Cross-Coupling Technique with Butterflies Optimization Algorithm

In this paper, two contributions are presented. the first is to design two cascade controllers to control the velocity and position for two Permanent Magnet DC motors (PMDC) working together at the same time for use in many applications such as CNC machines, robotics, and others. Furthermore, the cross-coupling technique is used to connect these motors and adjust the precise synchronization of their movement on the axes. The second contribution is the use of the butterfly’s optimization algorithm (BOA) with the objective function Integral Time Absolute Error (ITAE) to extract the optimal parameter values for the two cascade controllers and the synchronization controller in order to obtain the best accurate results. The simulation results showed high accuracy to reach the desired position at a regular velocity of both the PMDC motors with accurate synchronization and tracking trajectory on the axes. In addition, a very small position deviation of 0.021 rad was observed, and the system returned to a steady-state after 2 seconds of applying the full load.

Soft sensor development based on kernel dynamic time warping and a relevant vector machine for unequal-length batch processes

The unequal-length problem in batch process data directly affects the performance of data-driven soft sensors. Meanwhile, the nonlinearity and high dimensionality of batch process data make the unequal-length problem more serious, and the development of effective soft sensors for unequal-length batch processes has become a challenge. To fully address this challenge, an effective soft sensor based on kernel dynamic time warping and a relevant vector machine is proposed in this paper. The proposed soft sensor consists of trajectory synchronization and online prediction modeling. First, combining the kernel trick, we design a kernel DTW (KerDTW) algorithm to effectively solve the synchronization of unequal-length trajectories with high dimensionality and strong nonlinearity characteristics. Meanwhile, a novel synchronization performance combination index (SPCI) is proposed to realize adaptive selection of the optimal parameter of the KerDTW algorithm. Then, based on the synchronized batch trajectories using the KerDTW algorithm, an online prediction model is established using an RVM to achieve online quality prediction of nonlinear process data. The effectiveness of the proposed soft sensor is illustrated through a penicillin fermentation process.