Unknown Load Research Articles

Development of a High-Fidelity Framework to Describe the Process-Dependent Viscoelasticity of a Fast-Curing Epoxy Matrix Resin including Testing, Modelling, Calibration and Validation.

Fast-curing epoxy resins enable substantial reduction of cycle times during production of thermoset polymer matrix composites. Due to the snap-cure behaviour, both characterisation and processing of these resins are associated with high complexity which motivates the development of a high-fidelity framework for the prediction of the process-dependent behaviour ranging from experiment to model validation. In order to determine influence of time, temperature, and degree of cure, a multitude of rheometer and dynamic mechanical analysis experiments are conducted and evaluated. Building on the experimental results, a material model based on a generalised Maxwell model is developed. It is calibrated on the results obtained in the tests and shown to describe the material’s behaviour with high accuracy under all investigated conditions. The model’s predictive capabilities are further tested by applying it to a dynamic mechanical analysis, exposing the model to previously unknown loading and temperature conditions. It is demonstrated that the model is capable of predicting such changing boundary conditions with high accuracy.

Disturbance-Observer-Based Feedback Linearization Control for Stabilization and Accurate Voltage Tracking of a DC–DC Boost Converter

The nonminimum-phase property of the dc–dc boost converter can create difficulties in designing a stable, robust, and fast control for the output voltage. In this article, these difficulties are reduced by modifying the state model of the boost converter in such a way that it behaves like a minimum-phase system. More specifically, the control input entering the state equation for the output voltage is treated as an unknown disturbance to make the relative degree of the system equal the system’s order. As a result, the modified model does not exhibit zero dynamics, so that it can be treated as a minimum-phase system for the control design. The modified model is, then, used to derive a dynamic compensator for the output voltage regulation. More explicitly, the dynamic compensator is designed based on combining linearizing feedback control with a disturbance observer. The latter is employed to compensate for model uncertainties and unknown load with a view to ensure asymptotic regulation under the composite controller. The asymptotic regulation is achieved due to the integral action property characterizing the disturbance observer. More interestingly, after simple algebraic manipulation, it turns out that the composite controller reduces to a dynamic state feedback control plus an antiwindup scheme to mitigate the effect of control saturation during transients. The performance of the proposed controller is verified by experimental tests. The experimental results demonstrate the ability of the proposed controller to achieve good transient and steady-state performances.

A machine learning material model for structural adhesives in finite element analysis

The computational analysis of adhesives within finite element simulations is important for many engineering applications, from automotive structures to civil engineering. The underlying material descriptions are closed, analytically formulated models representing the stress-response of the material for given strains. In this publication, we investigate and present a methodology for adopting artificial neural networks for this purpose. The aim is to show feasibility and performance of such models to be able to uncouple the material description from fixed formulations to reap the benefits of neural networks for materials modelling. A methodology is presented for creating training data for a structural adhesive, whereby the data is obtained from specimen and from generic strain paths of a baseline material model. From this, feed-forward neural networks are trained and evaluated using machine learning tools as well as from an engineering perspective by pre-defined test strain paths. It is shown that, given the quality and the quantity of training data is adequate, the model is able to generalize with respect to the response. Especially, the coverage of complete strain space is shown to be crucial. A framework for using machine learning models in a commercial finite element code is presented, integrating models trained through high-level programming interfaces. This enables validating the performance and accuracy of such models in explicit simulations, which is shown to be a crucial step in evaluation of machine learning material models. The model is confirmed to be able to represent structural adhesives with high precision comparable to an analytical model and to generalize for unknown geometries and loading conditions. An approach to predict element failure with the machine learning model is shown and finally, the neural network model response is made explainable from an engineering point of view using feature importance.

Mathematical model and genomics construction of developmental biology patterns using digital image technology.

Biological pattern formation ensures that tissues and organs develop in the correct place and orientation within the body. A great deal has been learned about cell and tissue staining techniques, and today’s microscopes can capture digital images. A light microscope is an essential tool in biology and medicine. Analyzing the generated images will involve the creation of unique analytical techniques. Digital images of the material before and after deformation can be compared to assess how much strain and displacement the material responds. Furthermore, this article proposes Development Biology Patterns using Digital Image Technology (DBP-DIT) to cell image data in 2D, 3D, and time sequences. Engineered materials with high stiffness may now be characterized via digital image correlation. The proposed method of analyzing the mechanical characteristics of skin under various situations, such as one direction of stress and temperatures in the hundreds of degrees Celsius, is achievable using digital image correlation. A DBP-DIT approach to biological tissue modeling is based on digital image correlation (DIC) measurements to forecast the displacement field under unknown loading scenarios without presupposing a particular constitutive model form or owning knowledge of the material microstructure. A data-driven approach to modeling biological materials can be more successful than classical constitutive modeling if adequate data coverage and advice from partial physics constraints are available. The proposed procedures include a wide range of biological objectives, experimental designs, and laboratory preferences. The experimental results show that the proposed DBP-DIT achieves a high accuracy ratio of 99,3%, a sensitivity ratio of 98.7%, a specificity ratio of 98.6%, a probability index of 97.8%, a balanced classification ratio of 97.5%, and a low error rate of 38.6%.

Soft formation control for unmanned surface vehicles under environmental disturbance using multi-task reinforcement learning

This paper proposes a distributed soft formation collision avoidance strategy to address the problem of formation obstacle avoidance in complex scenario under the environment interference where unmanned surface vehicles (USVs) are restricted in observation and possess no global reference frame for localisation. A multi-task training framework for formation control is also developed based on the motion characteristics of USVs and the leader–follower method. The soft actor–critic (SAC) reinforcement learning algorithm is adapted to construct agents. With elaborated auxiliary tasks, the soft formation algorithm demonstrates strong resilience to disturbances caused by unknown environmental loads or obstacle avoidance needs with only partial information about the environmental state, thus maintaining the formation shape. The proposed trajectory communication system and policy sharing mechanism allow USVs to approach the destination and avoid collisions whilst maintaining a noncompact formation that can be broken up temporarily to improve obstacle avoidance and can be restored quickly when the obstacle is avoided. Trained agents can be applied to different formations of varying sizes. Simulation results demonstrate the feasibility and effectiveness of the proposed method in different complex sea scenarios and its robustness to unknown environmental disturbances.

A learning-based model predictive control scheme and its application in biped locomotion

This paper proposes a learning-based model predictive control scheme. This scheme divides the predictive model into a known nominal model and an unknown model residual. Model residual is learned using Gaussian process regression. The learned stochastic model is solved quickly using differential dynamic programming, taking into account control input constraints. The simulation results show that compared with state of art optimal control methods, this scheme has good robustness to model residual, accelerates the solution of high-dimensional problems, and can strictly constrain the control inputs according to the actual situation. Based on this learning-based model predictive control scheme, this paper also proposes an online learning gait generator for the uncertainty problem in the locomotion control of biped robots. The zero moment point is strictly constrained during training to ensure safety. The simulation results show that the gait generator is robust to unknown load and unknown external force.

Wavelet neural network sliding mode control of two rigid joint robot manipulator

To solve the problems of low accuracy and poor stability due to uncertainties, external disturbances and unknown load, which exist in the position control of rigid joint robot manipulator, this article is to propose Non-Singular Fast Terminal Sliding Mode Control strategy with Wavelet neural networks observer (NSFTSMCW). The wavelet observer is designed using the online approximation capability of the neural network, which is used to online estimate the modeling error, external disturbances and uncertainties generated by the dynamic surface control of the joint robot online. Combining the above strategies, the robot manipulators position controller is designed. The stability of this control strategy is demonstrated by stability analysis using the Lyapunov criterion. Simulations on the 2-Link Rigid Joint (2LRJ) robot show that the control strategy can overcome the chattering phenomena ensures the accuracy and stability of the joint robot position control.

Wideband Series Harmonic Voltage Compensator for Enhancing Stability of Microgrids

A wideband series harmonicvoltage compensator (WSHVC) for mitigating the adverse effect of unknown grid impedance and load condition on the stability of microgrids with multiple grid-connected inverters (GCIs) is presented. The concept is based on extending the concept of series active power filter. A wideband series voltage source inverter is used to compensate for the high-frequency harmonic voltage caused by the impedance at the point of common coupling, thereby creating a virtually zero high-frequency impedance at the output of the GCIs. The system stability is then assured under any operating condition. The bandwidth of the WSHVC ranges from the second harmonic of the grid frequency to 8 kHz, which is sufficiently higher than the cut-off frequency of typical GCIs. Such fast dynamic behavior is realized by a novel fixed-frequency predictive control scheme with nonlinear switching surfaces. Since the WSHVC handles harmonic power only, its volt-ampere rating is lower than that of the entire system. A 500 VA prototype for a 6.5 kVA testbed with three commercial GCIs, nonlinear load, and adjustable grid impedance has been evaluated. The power dissipation of the WSHVC is less than 1% of the VA rating of the testbed. The effectiveness of the WSHVC on improving system stability is studied.

Passivation and Passivity-Based Control of DC–DC Converter With Unknown Constant Power Loads

The loads of dc–dc converter (DDC) are mostly constant power loads (CPLs) in dc microgrid. Due to the negative impedance characteristic, CPLs may lead to the nonpassive factor in DDC. Thus, it may result in DDC, not operating reliably at the desired operating point. In this article, a nonlinear controller based on passivity-based stability theory is proposed for the DDC with CPL, which will improve the stability of the converter. Through damping and interconnection injecting, the alternating component passivity-based controller (PBC) is designed to realize the unified control of output voltage and inductor current. It could realize the passivation of alternating component. Furthermore, this controller can solve the problem of traditional PBCs to control inductor current or output voltage only. Moreover, a power observer is adopted, which could fast estimate the variation of the CPL and improve the adaptability of DDC when CPL changes. The proposed controller not only ensures the stability of DDC under large variation of CPL, but also features fast dynamic response with accurate tracking over wide operating range. Finally, the feasibility and effectiveness of the proposed controller are verified by simulation and experimental results.

A State Observer for Sensorless Control of Power Converters With Unknown Load Conductance

In this article, the sensorless control problem of a large class of power converters with unknown load conductance is investigated. A reduced-order generalized parameter estimation-based observer (GPEBO) is presented to reconstruct the unmeasurable states and estimate the unknown load conductance of the system. Three nice features of this observer are as follows: finite-time convergence (FTC) is guaranteed, an alertness preservation is imposed to be able to estimate a possibly time-varying load, and the required excitation condition is very weak and can be satisfied in normal operation of power converters. Then, replacing the estimated states and parameter, in a certainty equivalent manner, in a PI passivity-based controller, a sensorless control scheme is proposed to stabilize the systems with exponential convergence. By virtue of the FTC property of the GPEBO, the global exponential stability of the overall closed-loop system is established. Simulation and experimental results of the proposed controller with application to the boost and Ćuk converters are given to assess its effectiveness.

Research on control strategy of dual charging pile thermal management system

With the increasing power of the charging pile, the heat dissipation requirements of the charging pile are higher and higher. In this article, the liquid cooling heat dissipation system is used to dissipate the heat of the double charging pile, and the Lyapunov nonlinear control algorithm is used to control the temperature and compensate the unknown heat load. The mathematical model of double charge pile loop cooling system is established and simulated by Simulink. The results show that the designed nonlinear control strategy has the advantages of fast response and small steady-state error.

Learning-Based Model Predictive Control of DC-DC Buck Converters in DC Microgrids: A Multi-Agent Deep Reinforcement Learning Approach

This paper proposes a learning-based finite control set model predictive control (FCS-MPC) to improve the performance of DC-DC buck converters interfaced with constant power loads in a DC microgrid (DC-MG). An approach based on deep reinforcement learning (DRL) is presented to address one of the ongoing challenges in FCS-MPC of the converters, i.e., optimal design of the weighting coefficients appearing in the FCS-MPC objective function for each converter. A deep deterministic policy gradient method is employed to learn the optimal weighting coefficient design policy. A Markov decision method formulates the DRL problem. The DRL agent is trained for each converter in the MG, and the weighting coefficients are obtained based on reward computation with the interactions between the MG and agent. The proposed strategy is wholly distributed, wherein agents exchange data with other agents, implying a multi-agent DRL problem. The proposed control scheme offers several advantages, including preventing the dependency of the converter control system on the operating point conditions, plug-and-play capability, and robustness against the MG uncertainties and unknown load dynamics.

Experimental validation and numerical investigation of virtual strain sensing methods for steel railway bridges

Railway bridges are subjected to intermittent excitations induced by gravity loads. The monitoring of stress time histories at fatigue prone areas (i.e., “hot spots”) for steel, heavy haul railway bridges is vital for cost-effective maintenance and, most importantly, for maintaining safety. In this regard, strain measurements are crucial for determining fatigue susceptibility and, possibly, damage to steel bridges under daily, cyclical loading conditions. While, in the limit, sensing the response of a steel bridge under operational conditions is the best way to identify hot spots to prevent damage and, possibly, collapse, it is impractical if not impossible to install strain sensors at every susceptible location. To address this issue, the current study focuses on virtual sensing of strain time histories on steel railway bridges using sparse response measurements. An augmented Kalman filter (AKF) method was adopted for input-state estimation. Since AKF estimates unknown load and response using a physical model, it is crucial to assess the effects of modeling uncertainties on estimation results. In addition to AKF, Modal Expansion (ME) was adopted for extrapolation of measured response to unmeasured locations. In contrast to AKF, which requires a full physical model, ME only relies on vibration modes. A novel application of the Singular Value Decomposition (SVD) method that facilitates data-driven strain prediction by relying on data obtained from field measurements or models was also developed and examined. The study was completed using simulated experiments and strains measured from an in-service, through girder, steel railway bridge. The three methods were compared and strengths and limitations of each were highlighted.

A Novel Subspace-Aided Fault Detection Approach for the Drive Systems of Rolling Mills

This brief proposes a subspace-aided fault detection approach for the drive systems of strip rolling mills. Considering the impact of the unknown periodic load generated by the strip rolling process, the primary contributions are concluded as follows. First, this brief presents an approach to describe the subspace of the unknown/unmeasurable periodic load. Second, a fundamental frequency identification approach for the drive systems is proposed and then the subspace of the unknown periodic load can be constructed by the fundamental frequency. Third, this brief presents a subspace-aided fault detection approach to identify the data-driven stable kernel representation (SKR) of the closed-loop system by projecting the input–output (I/O) process data, so as to obtain a robust residual against the unknown periodic load. In addition, the effectiveness and performance of the approaches are verified by numerical examples and experimental data of the test rig for the drive systems of strip rolling mills. The results show that a robust residual generation against the unknown periodic load in the drive systems can be obtained and the robust subspace-aided fault detection can be achieved. Compared with the traditional method, the proposed approaches can improve the fault detection rate more effectively and be applied to the drive systems of strip rolling mills reliably.

Modeling and Position Control of the HASEL Actuator via Port-Hamiltonian Approach

This paper deals with the modeling and control problem of a Hydraulically Amplified Self-healing Electrostatic (HASEL) actuator based on the port-Hamiltonian framework. A nonlinear spring-damper system is used to approximate the mechanical deformation of the actuator due to the motion of the fluid while a nonlinear capacitance is used to approximate the electric behavior of the system. The actuator position control strategy is investigated based on the Interconnection Damping Assignment-Passivity Based Control (IDA-PBC) method, with further Integral Actions (IA) added to cope with load uncertainties. The proposed model and control laws are validated on an experimental benchmark. The experimental tests demonstrate that the proposed model is accurate up to 94% of fitness. The controllers allow assigning the actuator position with ramp and sinusoidal references with the relative error less than 5%. At last, the robustness of the proposed IDA-PBC controller with IA has been shown with the experimental result for the unknown load disturbance rejection.

Adaptive sensorless control for buck converter with constant power load

The existing results mainly focus on the full-information control for converters with constant power load (CPL). More precisely, it is assumed that all of the states of the systems should be measured. There are no theoretical results on an adaptive sensorless control scheme for the converters feeding CPL with the guaranteed stability. Note that a sensorless control will facilitate the decrease of the overall cost and fault rate as well as the increase of the reliability of the systems. In this paper, the sensorless control problem for DC–DC buck converter with unknown CPL is addressed. The main contribution of this paper is to design a reduced-order generalized parameter estimation-based observer to simultaneously reconstruct the unmeasurable inductor current and unknown power load of the system. Borrowing the dynamic regressor extension and mixing technique, its main idea is to transform the state observation into the parameter estimation problem. Besides, the finite-time convergence of the observer can be ensured. It is noted that the buck converter is only regarded as an application example. As a matter of fact, this observer can be extended to a large class of converters with CPLs. Then, introducing the observed terms into an existing full-information controller, in a certainty equivalent manner, an adaptive sensorless control scheme is achieved. Finally, the performance of the designed controller is assessed via simulation and experimental results.

Enhanced dynamic performance in DC–DC converter‐PMDC motor combination through an intelligent non‐linear adaptive control scheme

A novel neuro-adaptive control scheme is proposed in the context of angular velocity tracking in DC–DC buck converter driven permanent magnet DC motor system. The controller builds upon the idea of backstepping and consists of a fast single hidden layer Hermite neural network (HNN) module equipped with on-board (adaptive) learning to counteract the unknown non-linear time-varying load torque and to ensure nominal tracking performance. The HNN has a simple structure and exhibits promising speed and accuracy in estimating dynamic variations in the unknown load torque apart from being computationally efficient. The proposed method guarantees a rapid recovery of nominal angular velocity tracking under parametric and non-parametric uncertainties. In order to verify the performance of the proposed neuro-adaptive speed controller, extensive experimentation has been conducted in the laboratory under various real-time scenarios. Results are obtained for start-up, time-varying angular velocity tracking and under the influence of highly non-linear unknown load torque. The performance metrics such as peak undershoot/overshoot and settling time are computed to quantify the transient response behaviour. The results clearly substantiate theoretical propositions and demonstrate an enhanced dynamic speed tracking under a wide operating regime, thus confirming the suitability of proposed method for fast industrial applications.

Distributed Secure Estimation Against Unknown FDI Attacks and Load Deviation in Multi-Area Power Systems

This brief studies the distributed estimation problem for multi-area power systems where the sensor measurements encounter with false data injection (FDI) attacks. For the large load frequency control (LFC) power systems with unknown load deviation, a distributed intermediate observer based method is proposed to estimate the system states, FDI attack signals and load deviation of each area in the power system. In this case, the gain of each intermediate observer is obtained by constructing a self-relative linear matrix inequality that can be easily solved by the standard software packages. Notice that the computational cost of each estimator is low even though the scale of power systems is large. Finally, the effectiveness of the proposed method is verified by an illustrative example.

A sensorless approach for tracking control problem of tubular linear synchronous motor

<p>As well-known, linear motors are widely applied to various industrial applications<br />due to their abilities in providing directly straight movement without auxiliary mechanical transmissions. This paper addresses the sensorless control problem of tubular linear synchronous motors, which belong to a family of permanent magnet linear motor. To be specific, a novel velocity observer is proposed to deal with an unmeasurable velocity problem, and asymptotic convergence of the observer error is ensured. Unlike other studies on sensorless control methods for linear motors, our proposed observer is designed by regrading unknown disturbance load in the tracking control problem whereas considering theoretical demonstrations. By adjusting controller parameters properly, the position and velocity tracking error converge in arbitrary small values. Finally, the effectiveness of the proposed method is verified in two illustrative examples.</p>

Cascade-based tracking control for dynamic positioning vessels under unknown sea loads

This paper investigates the trajectory tracking control problem of a dynamic positioning vessel (DPV) subject to tracking performance, time-varying sea loads and inertia uncertainties. Firstly, a novel cascade model of the DPV is built to describe the dynamics. Then, in order to guarantee the performance constraints of the trajectory tracking, a new-type fixed-time performance function is proposed, which can not only guarantee the transient and steady-state performance during task execution, but also achieve convergence at a fixed time. In addition, an adaptive neural network is introduced to handle the problem of parameter uncertainty and unknown sea loads in the system. Furthermore, it is proved by the Lyapunov stability theory that all signals in the closed-loop system are bounded . Finally, numerical simulation results verified the effectiveness of the proposed method and ensure that the tracking error is stable in the interval of 2 m or less within a given time.