Hybrid Dynamic Model Research Articles

Stable Walking of a Biped Robot Controlled by Central Pattern Generator Using Multivariate Linear Mapping.

In order to improve the walking stability of a biped robot in multiple scenarios and reduce the complexity of the Central Pattern Generator (CPG) model, a new CPG walking controller based on multivariate linear mapping was proposed. At first, in order to establish a dynamics model, the lower limb mechanical structure of the biped robot was designed. According to the Lagrange and angular momentum conservation method, the hybrid dynamic model of the biped robot was established. The initial value of the robot's passive walking was found by means of Poincaré mapping and cell mapping methods. Then, a multivariate linear mapping model was established to form a new lightweight CPG model based on a Hopf oscillator. According to the parameter distribution of the new CPG model, a preliminary parameter-tuning idea was proposed. At last, the joint simulation of MATLAB and V-REP shows that the biped robot based on the new CPG control has a stable periodic gait in flat and uphill scenes. The proposed method could improve the stability and versatility of bipedal walking in various environments and can provide general CPG generation and a tuning method reference for robotics scholars.

A dynamic system reliability analysis model on safety instrumented systems

This paper introduces a novel hybrid dynamic model for complex systems reliability assessment. The model synergizes expert knowledge elicitation and an enhanced Dempster-Shafer Theory (DST) with Dynamic Bayesian Networks (DBNs) modeling, aiming to surmount the limitations such as uncertainty and static modeling inherent in traditional methods. The proposed model is deployed on a Safety Instrumented System (SIS) designed to prevent runaway reactions within a Continuously Stirred Tank Reactor (CSTR), considering factors such as system degradation, human interventions, and proof testing on system reliability. The analysis pinpointed the logic solver subsystem as the principal vulnerability within the assessed SIS, leading to targeted recommendations to bolster system reliability. The outcomes offer insights for a wide range of safety-critical systems aiming to augment the safety and efficacy of SISs, thereby advancing safety and resilience management across various complex engineering systems, particularly in contexts where field data is scant.

Enhancing tapping mode atomic force microscopy using AutoResonance control and a hybrid dynamic model

We present a novel method for analyzing tapping mode atomic force microscopy (AFM), enhancing topography reconstruction’s accuracy and speed. Our approach integrates an automatic resonance-tracking control scheme with a hybrid dynamic model that considers interactions between the AFM tip and the sample, and the oscillating sensor dynamics. A key aspect of our method is a simplified model that captures intricate dynamic behavior as a function of the distance from the topography. This model enables the immediate approximation of the distance from the topography, eliminating the necessity to lock onto the nominal distance as done with classical AFMs. To validate the accuracy, we conducted numerical simulations of Van der Waals and capillary interaction forces, as well as experimental measurements of a coin’s topography. The results affirm the effectiveness of our approach in enhancing AFM imaging and characterization capabilities.

Interpersonal strategy for controlling unpredictable opponents in soft tennis

Competition in sports, unlike cooperation in everyday life, does not involve a single solution because individuals aim to behave unpredictably, thereby preventing others from predicting their actions. This study determined how individuals in court-based sports attempted to control others’ unpredictable behaviors, addressing the gap in previous studies regarding the lack of clarity around strategies employed by individuals in competitive situations. We achieved this by applying a switching hybrid dynamics model, considering external inputs to analyze individual behaviors. Consequently, the study indicates that skilled individuals, in contrast to intermediate players, exhibit greater consistency in their behaviors. These skilled individuals lead others to anticipate their consistency and subsequently employ strategies to disrupt these expectations. This strategy exploits the principles of active human inference, implying that competition involves cooperation. This was revealed by an analysis of both human decision-making and behavior in actual matches as discrete and continuous dynamical systems. This interpersonal strategy could assist policymakers in the field of everyday life to enhance competitiveness. This strategy enables policymakers to adopt new policies that promote cooperation with competitors, ultimately increasing competitiveness in various aspects of our daily lives.

Human-in-the-Loop Trajectory Optimization Based on sEMG Biofeedback for Lower-Limb Exoskeleton.

Lower-limb exoskeletons (LLEs) can provide rehabilitation training and walking assistance for individuals with lower-limb dysfunction or those in need of functionality enhancement. Adapting and personalizing the LLEs is crucial for them to form an intelligent human-machine system (HMS). However, numerous LLEs lack thorough consideration of individual differences in motion planning, leading to subpar human performance. Prioritizing human physiological response is a critical objective of trajectory optimization for the HMS. This paper proposes a human-in-the-loop (HITL) motion planning method that utilizes surface electromyography signals as biofeedback for the HITL optimization. The proposed method combines offline trajectory optimization with HITL trajectory selection. Based on the derived hybrid dynamical model of the HMS, the offline trajectory is optimized using a direct collocation method, while HITL trajectory selection is based on Thompson sampling. The direct collocation method optimizes various gait trajectories and constructs a gait library according to the energy optimality law, taking into consideration dynamics and walking constraints. Subsequently, an optimal gait trajectory is selected for the wearer using Thompson sampling. The selected gait trajectory is then implemented on the LLE under a hybrid zero dynamics control strategy. Through the HITL optimization and control experiments, the effectiveness and superiority of the proposed method are verified.

Minimum-energy switching geometric filter on lie groups for differential-drive wheeled mobile robots

Accurate state estimation plays a critical role in various applications, such as tracking, regulation, and fault detection in robotic and mechanical systems. Typically, the Kalman–Bucy filter is used as a linear state observer for this purpose. However, real-world robots often exhibit complex behavior, characterized by a combination of dynamics, making it essential to employ hybrid filters. In this context, the Switching Kalman filter stands out as a well-established solution. In this article we aim to generalize the Brownian-Markov Stochastic Model, a hybrid dynamic model for differential-drive wheeled mobile robots, to the case of a mobile robot whose center of mass is not aligned to the wheels axle middle point, and to design a geometric hybrid state estimator by exploiting the Lie groups theory. The Brownian-Markov Stochastic Model features two modes: “grip” and “slip”. These modes correspond to ideal grip and lateral slippage, with transitions governed by a state-dependent Markov chain. To validate the proposed switching filter, we conduct MATLAB® simulations of the robot’s motion in a scenario prone to lateral grip loss, comparing the state estimates produced by the switching geometric filter with those obtained using the switching Kalman filter.

A dynamic hybrid model of supercritical once-through boiler-turbine unit including recirculation mode and once-through mode

Supercritical once-through boiler-turbine units play a paramount role in maintaining grid stability owing to their exceptional efficiency and flexibility. Nevertheless, achieving precise modeling of this device under various operational conditions remains a significant challenge. This study conducts modeling and simulation of an actual supercritical once-through coal-fired power plant. The findings signify that the hybrid model proposed herein adeptly encompasses all operational modes of supercritical once-through units, including start-up recirculation mode and once-through mode. To bolster model precision, an optimization algorithm grounded on weighted mean of vectors (INFO) has been employed for parameter identification, while the INFO-XGBoost was utilized to fashion a machine learning model for static parameters. It has been validated that the developed model accurately captures the dynamic characteristics of the unit. Furthermore, the utilization of the INFO-XGboost to model static parameters has been proved to enable the errors in principal outputs of coordinated control system to be within 0.35 % of MAPE across the entire operational process, thus facilitating the design of more refined and intelligent control systems.

Hybrid dynamical modeling of shape memory alloy actuators with phase kinetic equations

Shape memory alloy morphing actuators are a type of composite soft actuator with many attractive properties such as large deformation, small form factor, self-sensing ability, and physical reservoir computing potential. These actuators are composed of active shape memory alloy wires and a passive material to magnify the overall deflection. However, the dynamic modeling of these actuators is difficult due to both shape memory alloy characteristics and the nonlinearity of the passive layer. Here, a hybrid dynamical model is proposed that couples the phase kinetics and thermal modeling for the shape memory alloy with a dynamic Cosserat beam model. This hybrid model is benchmarked against experimental linear and morphing actuators resulting in a root mean squared error of 0.87 mm for the linear actuator and root mean squared error of 1.34 and 1.42 mm for the two morphing actuator configurations evaluated in this work. This model applies continuous phase kinetic equations in a comprehensive hybrid dynamical model to accurately simulate the hysteretic transition of the alloy, which is then coupled to a high deformation beam model. This work can expand the capability and design of novel morphing actuators to achieve specified dynamic characteristics for increased application in robotic fields.

Dynamic Hybrid Models With Active Sampling and Adaptive Selection of Double-Domain Features for the Tuning of Microwave Cavity Filters.

Microwave cavity filters are essential electromechanical coupling devices in communication systems. Structural-parameter tuning by experienced operators improves the filter performance but is demanding and time-consuming. The automatic tuning method has received extensive research attentions using data-driven modeling approaches. However, two main issues affect the accuracy and efficiency of the model construction: 1) features of tuning processes, as model inputs, have limited adaptability and extraction accuracy to different resonant states and 2) models require plentiful training data and the training process is time-consuming. Thus, dynamic hybrid models are developed in this study with self-selected inputs, self-organized samples, and a self-learning structure. First, spatial features are extracted to flexibly depict the tuning characteristic, and double-domain (spatial or circuital) features are selected adaptively to accommodate distinct resonance states. Second, a trustworthiness-curiosity-driven active sampling method is exploited to attain fewer and better-training data. Third, an improved glsms broad learning system acrlong BLS is developed using new modules of incremental node calculation and weight pruning, characterized by more lightweight and flexible structures. The proposed method is effective and flexible demonstrated by simulations and experiments, and the tuning task of microwave cavity filters is fulfilled in a more accurate and efficient manner.

HTEC foot: A novel foot structure for humanoid robots combining static stability and dynamic adaptability

Passive bionic feet, known for their human-like compliance, have garnered attention for their potential to achieve notable environmental adaptability. In this paper, a method was proposed to unifying passive bionic feet static supporting stability and dynamic terrain adaptability through the utilization of the Rigid-Elastic Hybrid (REH) dynamics model. First, a bionic foot model, named the Hinge Tension Elastic Complex (HTEC) model, was developed by extracting key features from human feet. Furthermore, the kinematics and REH dynamics of the HTEC model were established. Based on the foot dynamics, a nonlinear optimization method for stiffness matching (NOSM) was designed. Finally, the HTEC-based foot was constructed and applied onto BHR-B2 humanoid robot. The foot static stability is achieved. The enhanced adaptability is observed as the robot traverses square steel, lawn, and cobblestone terrains. Through proposed design method and structure, the mobility of the humanoid robot is improved.

Adaptive machine learning for forecasting in wind energy: A dynamic, multi-algorithmic approach for short and long-term predictions

This study elucidates the formulation and validation of a dynamic hybrid model for wind energy forecasting, with a particular emphasis on its capability for both short-term and long-term predictive accuracy. The model is predicated on the assimilation of time-series data from past wind energy generation and employs a triad of machine learning algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN). Empirical data, harvested from a 2 MW grid-connected wind turbine, served as the basis for the training and validation phases. A comparative evaluation methodology was devised to scrutinize the performance of each constituent algorithm across a diverse array of metrics. This evaluation framework facilitated the identification of individual algorithmic limitations, which were subsequently mitigated through the implementation of a dynamic switching mechanism within the hybrid model. This innovative feature enables the model to adaptively select the most efficacious forecasting technique based on historical performance data. The hybrid model demonstrated superior forecasting accuracy in both, short-term energy forecasts at 15-min intervals over a day, and in broad, long-term. It recorded a Normalized Mean Absolute Error (NMAE) of 5.54 %, which is notably lower than the NMAE range of 5.65 %–9.22 % observed in other tested models, and significantly better than the average NMAE found in the literature, which spans from 6.73 % to 10.07 %. Such versatility renders it invaluable for grid operators and wind farm management, aiding in both operational and strategic planning. The study's findings not only contribute to the existing body of knowledge in renewable energy forecasting but also suggest the hybrid model's broader applicability in various other predictive analytics domains.

Contact Force Estimation of Robot Manipulators With Imperfect Dynamic Model: On Gaussian Process Adaptive Disturbance Kalman Filter

This paper is concerned with the contact force estimation problem of robot manipulators based on imperfect dynamic models of the manipulator and the contact force. To handle the imperfect dynamic information of the manipulator, a hybrid model, consisting of the nominal model and the residual dynamics, is established for the manipulator, and the Gaussian process regression (GPR) technique is employed to learn the mean and covariance of the residual dynamics. On this basis, a virtual measurement equation is established for contact force estimation and a Gaussian process adaptive disturbance Kalman filter (GPADKF) is developed where the variational Bayes technique is employed to achieve online identification of the noise statistics in the force dynamics. The GPADKF is capable of decoupling the contact force from residual dynamics and system noises, thereby reducing the dependence on accurate dynamic models of the manipulator and the contact force. Simulation and experimental results demonstrate that the proposed scheme outperforms the state-of-art methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Contact force estimation for robot manipulators can be achieved by fusing the dynamic models of the manipulator and the contact force. When both models are imprecise, the traditional inverse dynamics-based and disturbance Kalman filter-based approaches can no longer provide accurate force estimates. To handle this challenge, a computationally efficient hybrid dynamic model is established for the manipulator, which consists of the nominal model and a residual dynamics compensation term learned from offline data via the GPR. On this basis, an adaptive disturbance Kalman filter is constructed by using the variational Bayes technique to deal with the inaccurate noise covariance matrix in the force dynamic model. Compared with the existing approaches, the force estimate obtained via the proposed scheme is more accurate and reliable, as refined noise covariance matrices (provided by both the GPR and the variational Bayes procedure) have been adopted in the Kalman gain calculation. The proposed GPADKF method is the extension of the composite disturbance filtering (CDF) framework. With the proposed scheme, the dependency on the perfect dynamic models in contact force estimation can be significantly reduced, and this makes our approach especially suitable for contact force estimation problems under unfamiliar and complicated environments.

A machine learning and CFD modeling hybrid approach for predicting real-time heat transfer during cokemaking processes

As the development of green energy and smart industries progresses, concerted efforts are being directed towards the transformation of coking industries. Effective management of heat transfer during the coking process is essential to fortify energy consumption management and augment the quality of coke produced. However, the process during cokemaking is a complex thermochemical conversion process where even minor deviations in operating conditions can lead to instability. Additionally, due to the environmental and chemical conditions inside the coke oven, temperature measurements during operation are difficult. As the fields of Industry 4.0 continue to evolve, numerous industries have begun to leverage intelligent strategies to enhance advanced process management. The digital twin (DG) technique stands as a pivotal technology in the realm of industrial transformation, paving the way for a smarter future. However, given the inherent difficulty in real-time data collection for process operations, the challenge of achieving synchronization becomes increasingly prevalent in the development of DG for the smart coking process. As a result, predicting changes in coal temperature during the cokemaking process is crucial. Currently, computer technology allows for the simulation of complex chemical processes. While some numerical models describe the cokemaking process, these models are limited by the time-consuming characteristic and computing resources. To address this issue, this study proposed a Machine Learning (ML) and Computational Fluid Dynamics (CFD) hybrid model for real-time prediction of heat transfer during the cokemaking process. A CFD model was built based on an industrial-scale coke oven, with cokemaking process data generated through CFD simulation serving as input for the ML models. This study used a total of nine ML models, including ensemble and deep learning models. The hyperparameters of each model were optimized by Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and JAYA algorithms to identify the optimal model structure. Both simulation and empirical experimentation using a coke oven, coupled with industrial data verification, have substantiated the superiority of the proposed model. It simulates the heat transfer during the coking process with high accuracy and efficiency, thereby demonstrating its capacity to significantly reduce energy consumption within the coking industry.

A dynamic hybrid model for efficient tuberculosis incidence rate prediction

ABSTRACT Tuberculosis remains a global health challenge, predicting its incidences is crucial for effective planning and intervention strategies. This study combines AutoRegressive Integrated Moving Average (ARIMA) and Nonlinear AutoRegressive with exogenous input (NARX) models as an innovative approach for TB incidence rate prediction. The performance of the proposed model (ARIMA-NARX) was evaluated using standard metrics (MSE, RMSE, MAE, and MAPE), and it was refined to achieve the best average predictive accuracies with an MSE: 0.0622, RMSE: 0.0851, MAE: 0.07520, and MAPE: 0.05535 followed by NARX 0.1597, 0.3189, 0.2724, and 0.3366, and ARIMA (2,0,0) 0.7781, 0.5959, 0.6524, and 0.6080 Models. These findings are expected to shed light on the TB incidence rate, providing valuable information to policymakers such as the World Health Organization (WHO) and health professionals. The developed model can potentially serve as a predictive tool for proactive TB control and intervention strategies in the region and the world at large.

A novel residual hybrid dynamic model for unmanned helicopters: combining both physical and deep learning models

A novel residual hybrid dynamic model for unmanned helicopters: combining both physical and deep learning models

Mechanical behavior and performance evolution of railway ballast track under dynamic stabiliser based on the hybrid MBD-DEM simulation

The uncertainty in local structure and time-variable compaction of ballast aggregates poses challenges in selecting suitable stabilization methods and parameters for ballast tracks in practical engineering, often resulting in suboptimal operational outcomes. To address this issue comprehensively, the spatial hybrid model of the double dynamic stabilisers-ballast track (MBD-DEM) is proposed in this paper. Subsequent numerical simulations of stabilisation operations are conducted to elucidate ballast vibration, migration behavior and the evolution of ballast track resistance under varying initial conditions of the ballast bed. Initially, leveraging multibody dynamics theory (MBD) and discrete element method (DEM), a MBD model of double stabilisers and a DEM ballast track model containing 6 sleepers were established respectively. Using the coupling interface, the real-time hybrid calculation of these two models is achieved through 6 sleepers in the middle of the ballast bed. Subsequently, the model's accuracy was validated by comparing test data from stabilisation vehicle operations on a Kunming Railway Bureau test line with simulation data from the dynamic double stabilisers-ballast track spatial hybrid model. Finally, utilizing the proposed hybrid model, the dynamic behavior, contact states, and movement rules of ballast particles during stabilisation operations on both loose and initially stable ballast beds (LBB and ISBB) were investigated at the mesoscopic scale. Concurrently, an in-depth analysis of lateral resistance changes in the ballast bed before and after the stabilisation operation is presented. The relevant findings effectively unveil the evolution of dynamic behavior in the ballast bed during stabilisation operations, concluding with practical suggestions for on-site operations. Simulation results illustrate that the rotation of ballast particles in the XY plane beneath each sleeper is predominantly characterized by harmonic rotation. With an increase in the depth of the ballast bed, the range of the rotation axis for combined angular velocity diminishes, leading to the phenomenon of rotation axis deflection. To ensure operational safety and efficiency, a minimum of two-times stabilization operations is recommended for loose ballast beds (LBB), with three-times operations considered optimal. For initially stable ballast beds (ISBB), one-time stabilization operation is basically sufficient.

Hybrid Power System Design and Dynamic Modeling for Enhanced Reliability in Remote Natural Gas Pipeline Control Stations

The most rapid and efficient method to transport natural gas from its source to its destination is through a pipeline network. The optimal functioning of control stations for natural gas pipelines depends on the use of electrical devices, including data loggers, communication devices, control systems, surveillance equipment, and more. Ensuring a reliable and consistent power supply proves to be challenging due to the remote locations of these control stations. This research article presents a case study detailing the design and dynamic modeling of a hybrid power system (HPS) to address the specific energy needs of a particular natural gas pipeline control station. The HOMER Pro 3.17.1 software is used to design an optimal HPS for the specified location. The designed system combines a photovoltaic (PV) system with natural gas generators as a backup to ensure a reliable and consistent power supply for the control station. Furthermore, it provides significant cost savings, reducing the cost of energy (COE) by USD 0.148 and the annual operating costs by USD 87,321, all while integrating a renewable energy fraction of 79.2%. Dynamic modeling of the designed system is performed in MATLAB/Simulink R2022a to analyze the system’s response, including its power quality, harmonics, voltage transients, load impact, etc. The experimental results are validated using hardware in the loop (HIL) and OPAL-RT Technologies’ real-time OP5707XG simulator.

Modelling the Dynamics of Outbreak Species: The Case of Ditrupa arietina (O.F. Müller), Gulf of Lions, NW Mediterranean Sea

An outbreak species exhibits extreme, rapid population fluctuations that can be qualified as discrete events within a continuous dynamic. When outbreaks occur they may appear novel and disconcerting because the limiting factors of their dynamics are not readily identifiable. We present the first population hybrid dynamic model that combines continuous and discrete processes, designed to simulate marine species outbreaks. The deterministic framework was tested using the case of an unexploited benthic invertebrate species: the small, serpulid polychaete Ditrupa arietina. This species is distributed throughout the northeast Atlantic Ocean and Mediterranean Sea; it has a life cycle characterised by a pelagic dispersive larval stage, while juveniles and adults are sedentary. Sporadic reports of extremely high, variable densities (from &lt;10 to &gt;10,000 ind.m−2) have attracted attention from marine ecologists for a century. However, except for one decade-long field study from the Bay of Banyuls (France, Gulf of Lions, Mediterranean Sea), observations are sparse. Minimal formulations quantified the processes governing the population dynamics. Local population continuous dynamics were simulated from a size-structured model with a null immigration–emigration flux balance. The mathematical properties, based on the derived hybrid model, demonstrated the possibilities of reaching an equilibrium for the population using a single number of recruits per reproducer. Two extrapolations were made: (1) local population dynamics were simulated over 180 years using North Atlantic Oscillation indices to force recruitment variability and (2) steady-state population densities over the Gulf of Lions were calculated from a connectivity matrix in a metapopulation. The dynamics reach a macroscopic stability in both extrapolations, despite the absence of density regulating mechanisms. This ensures the persistence of D. arietina, even when strong, irregular oscillations characteristic of an outbreak species are observed. The hybrid model suggests that a macroscopic equilibrium for a population with variable recruitment conditions can only be characterised for time periods which contain several outbreak occurrences distributed over a regional scale.

Hybrid dynamic modeling of an industrial reactor network with first-principles and data-driven approaches

Dynamic modeling for complex, hazardous, difficult-to-operate chemical processes can often be challenging. This paper introduces a dynamic model that utilizes a hybrid approach, combining first-principles and data-driven methodologies, for modeling a complex reactor network comprising seven reactors in a counterflow connection. Specifically, a first-principles model is developed through mechanism analysis for each reactor. Real-time measurements are utilized by an unscented Kalman filter (UKF) to facilitate the co-estimation of both model states and parameters. A quadratic programming optimization is performed to address the physical constraints in the model parameters. Finally, a Seq2Seq neural network is employed in a serial configuration to compensate for the unmodeled dynamics by the first-principles model. The performance of the proposed dynamic model is compared with several other methods on an industrial nitration process under load-changing scenarios. The results demonstrate that our proposed model exhibits superior performance in terms of prediction accuracy and interpretability over other methods.

Development of Dynamic Micro- and Macroscopic Hybrid Model for Efficient Highway Traffic Simulation

This paper presents a dynamic hybrid traffic simulation model and its improvement. Here, a hybrid traffic simulation model is defined as a combination of multiple traffic models with different resolutions, which are dynamically switched to each other. The proposed model couples a microscopic and a macroscopic model. In a dynamic hybrid model, the application domains of micro- and macroscopic models can be switched during the simulation to achieve overall speed-up while keeping a high resolution of the area of interest. The authors extended an existing dynamic hybrid model to be applicable to highway merging sections. The proposed model was also validated by data obtained on an actual highway.