D-RM Builder: A software tool for generating fast and accurate nonlinear dynamic reduced models from high-fidelity models

Servo electric cylinders have been widely adopted in high-performance linear drive applications such as aerospace systems, robotic servo systems, medical equipment, advanced manufacturing, precision testing, and high-end equipment due to their advantages, including high cleanliness, compact structure, high transmission efficiency, and ease of achieving precise control. However, under complex operating conditions, system performance is influenced not only by control strategies but also closely related to factors such as friction, clearance, transmission flexibility, structural vibrations, and modeling accuracy. This paper reviews mainstream control strategies and dynamic simulation methods for servo electric cylinders, providing structured analysis and systematic evaluation of representative research. In terms of control strategies, key approaches, including classical PID control, robust nonlinear control, intelligent and learning-based control, and active disturbance rejection control, are discussed, with comparative analysis of their characteristics and limitations in tracking accuracy, robustness, adaptability, and engineering feasibility. Regarding dynamic modeling and simulation, methods such as multibody dynamics, finite element analysis, rigid-flexible coupling, and multi-domain collaborative simulation are reviewed, examining their applicability in nonlinear mechanism characterization, local structural response assessment, and high-fidelity system modeling. Current research indicates that servo cylinder control is evolving from single-method improvements toward integrated and composite approaches, while dynamic modeling has progressed from low-order simplified analyses to system-level, multi-level, and high-fidelity descriptions. Existing studies still face challenges, including insufficient unified evaluation criteria, inadequate cross-method comparisons, and insufficient integration between control design and high-fidelity models. Future research should focus on enhancing control-model co-design, experimental validation under complex conditions, and system-level optimization oriented toward intelligent and high-reliability systems.

With the development of digital power systems, the establishment of digital twin models for transformers is of great significance in enhancing power system stability. Consequently, greater demands are placed on the real-time performance and accuracy of thermal-fluid-coupled transient multi-physics field calculations for transformers. However, traditional numerical methods, such as finite element or computational fluid dynamics techniques, often require days or even weeks to simulate full-scale high-fidelity transformer models containing millions of grid nodes. The high computational cost and long runtime make them impractical for real-time simulations in digital twin applications. To address this, this paper employs the dynamic mode decomposition (DMD) method in conjunction with Koopman operator theory to perform data-driven reduced-order modeling of the transformer’s thermal–fluid-coupled multi-physics field. A fast computational approach based on extended dynamic mode decomposition (EDMD) is proposed to enhance the modal decomposition capability of nonlinear systems and improve prediction accuracy. The results show that this method greatly improves computational efficiency while preserving accuracy in high-fidelity models with millions of grids. The errors in the thermal and flow field calculations remain below 3.06% and 3.01%, respectively. Furthermore, the computation time is reduced from hours to minutes, with the thermal field achieving a 97-fold speed-up and the flow field an 83-fold speed-up, yielding an average speed-up factor of 90. This enables fast computation of the transformer’s thermal–fluid-coupled field and provides effective support for the application of digital twin technology in multi-physics field simulations of power equipment.

This study presents a comprehensive analysis of aeroelastic stability in multi-stage turbine rotors mounted on nonlinear supports. A high-fidelity dynamic model is developed by coupling the structural behavior of a rotating shaft–disk–blade assembly with quasi-steady aerodynamic forces. The system incorporates nonlinear stiffness and damping in the bearing supports, and the governing equations of motion are derived using the Lagrangian method. Aerodynamic forces are modeled using cascade theory for incompressible subsonic flow and integrated with structural dynamics through coordinate transformation. The resulting nonlinear system is solved using the Runge-Kutta method, and its stability characteristics are investigated via bifurcation diagrams and Poincaré maps. A detailed parametric study is conducted to examine the influence of aerodynamic parameters, structural parameters and support characteristics on rotor response. Results show that nonlinear supports significantly alter stability boundaries, reduce critical flutter speeds, and introduce multi-periodic dynamic behavior. These findings provide valuable insights into the design and tuning of support systems to enhance the dynamic robustness of turbomachinery.

A high-fidelity dynamic finite element model of a one-pulley belt-drive system is used to study how Coulomb friction coefficient and adhesion stress affect belt stick-slip events over driver and driven pulleys. The model shows that at Coulomb friction coefficient below a certain critical value, the belt undergoes pure sliding in the slip arc on driver and driven pulleys with no stick-slip dynamic events. Above that critical friction coefficient value dynamic stick-slip events start to occur in both driver and driven pulleys resulting in torque pulses, which for practical belt-drives may cause nonsmooth operation, excessive belt noise and/or excessive belt wear. For driver pulleys the dynamic stick-slip events are in the form of Schallamach waves, while for driven pulleys the dynamic stick-slip events are in the form of expansion pulses. The model results are validated using recently generated experimental results.

A life-cycle dynamic wear degradation model of planetary gear systems

A digital twin for a wind turbine gearbox (WTG-DT) is essential for advancing wind farm intelligence and improving the efficiency of wind turbine operation. This study addresses key limitations of existing condition monitoring systems, such as low accuracy and slow parameter updates, by proposing a long short-term memory (LSTM) network to intelligently calibrate model parameters. This ensures the real-time operation and maintenance of wind turbines. A high-fidelity dynamic model is developed and validated by performing frequency analysis of vibration signals collected through the condition monitoring system (CMS), with wind speed and load data from the supervisory control and data acquisition (SCADA) system as inputs. To simplify the complex finite element analysis process, a parameter sensitivity analysis is conducted, and a GA-PSO optimization algorithm, based on genetic algorithms, is applied. These methods generate sufficient training data for constructing an LSTM-based predictive agent model, which is then used to accurately calibrate the virtual model. When applied to 6 MW turbines, this approach significantly improves real-time performance, accuracy, and reliability, enhancing the overall operational efficiency of wind turbines.

Model predictive control (MPC) is an effective method in lateral-longitudinal integrated control with safety guarantee for autonomous vehicles. But its computational burden is significant, making it challenging to meet real-time requirements. The contribution of this paper is to propose an explicitly solvable autonomous vehicle motion controller with lateral-longitudinal integrated characteristics and safety guarantee, achieved by integrating input-output controllers from exponential control Lyapunov function (ECLF) and exponential control barrier function (ECBF). We performed simulations using a high-fidelity dynamics model in CarSim for simple urban traffic scenarios. The simulation results demonstrate that our designed controller has good trajectory tracking performance, safety guarantee, low computational burden and a certain level of robustness.

Hydrogen–Fuel Cell (HFC) systems have become one of the most promising zero-carbon shipping solutions, with high energy-conversion efficiency, silent operation and potential for full decarbonization when coupled with renewable hydrogen production. Yet, the operational efficiency of HFC marine power systems is significantly affected by nonlinear electrochemical kinetics, interactions between subsystems and extremely wide fluctuating load profiles typical for naval service missions. The resulting complex multi-variable control laws open up trade-offs between efficiency, hydrogen consumption, dynamic response and emission of auxiliary by-products like NOx, unconverted hydrogen and water vapor exhaust. To tackle these issues, the present work puts forward a complete Multi Objective Particle Swarm Optimization (MOPSO) framework for the combined performance and emissions optimization of hydrogen marine–FC propulsion systems. The MOPSO technique is used to optimize decision variables such as fuel cell operating current density, stack temperature, air stoichiometric ratio, compressor control and the proportion at which ESS is lead on hybrid with a Pareto-optimal trade-offs between: (i) system efficiency, (ii) hydrogen consumption, (iii) emissions and by-product reduction, and (iv) component degradation and thermal stress reduction. A high-fidelity dynamic simulation model has been developed, combining PEM fuel cell electrochemical kinetics, onboard power demand information and compressor-humidifier dynamics with marine propulsion load cycles for patrol, ferry and tugboat duties. Results show that the MOPSO optimized control strategy increases net fuel cell efficiency by 12-18%, decreases hydrogen consumption by 15-27%, and decreases auxiliary emissions and stack temperature excursions by up to 35% as compared with the baseline, rule based, and single-objective tuning approaches. Moreover, degradation parameters such as voltage decay rate and membrane hydration instability were dramatically mitigated which enabled extension of the projected stack life time by 20–30%.

One of the main challenges in Systems designs is the ability to integrate real time high fidelity models on suitable and feasible hardware platforms. Because of its inherited parallelism, FPGA (Field Programmable Gate Arrays) technology achieves sample rates which are typically faster than real time. This can be seen as the last line of defence against the increasing requirements given by high fidelity models. But as most of the FPGA applications are specialised and the FPGA toolsets do not support basic control systems blocks, designs are constructed and optimised manually. This leads to significant effort required in finding feasible hardware FPGA implementations. Therefore, the work in this paper describes how to automatically optimise the most time consuming process found in generic FPGA implementations: the optimisation of the pipelining process. This is constructed on a rigorous mathematical model and achieved using drag and drop floating point HDL (Hardware Description Language) control systems blocks, under System Generator, in Simulink.

Process modelling and simulation of bitumen mining and recovery from oil sands

A high-fidelity multibody dynamics model for predicting the transient response of planetary gear trains is presented. The model supports an arbitrary number of gears, stages and arms. The model accurately accounts for the effects of gear tooth stiffness/damping/friction and tooth backlash. The multibody system representing the system is modeled using rigid bodies, revolute joints and rotational actuators. A penalty model is used to impose the joint and normal contact constraints. The normal contact penalty stiffness and damping are used to model the tooth stiffness and damping. The contact model detects contact between discrete points on the surface of a gear tooth (master contact surface) and a polygonal surface representation of the mating gear tooth (slave contact surface). A recursive bounding box/bounding sphere contact search algorithm is used to allow fast contact detection. An asperity friction model or an elasto-hydrodynamic lubrication model can be used for the contact friction forces. The governing equations of motion are solved along with joint/constraint equations using a time-accurate explicit solution procedure. The model is partially validated by comparing its predictions of the resonant frequencies of a planetary gear train to those of a previously published steady-state dynamic model. The model can help improve the design of planetary gear boxes including increasing the range of operating speeds, torque capacity and durability.

Application of Economics Optimizing Control to a Two-step Transesterification Reaction in a Pilot-Scale Reactive Distillation Column

Step responses of nonlinear non-minimum phase systems

High-fidelity numerical simulation of complex dynamics of tethered spacecraft

Identification of a parametric loudspeaker system using an adaptive Volterra filter