Parameter Mismatch Research Articles

Maximum Convergence Rate Control of a Switched Electrical Network with Symmetries

The purpose of the present research endeavor is to propose a novel control strategy for a DC-DC electrical converter realized as a switched circuit. The present endeavor is based on an early work by Leonard and Krishnaprasad where a prototypical DC-DC converter was modeled as a state space dynamical system and controlled by an open-loop strategy based on Lie group theory. In this work, we shall rather introduce a closed-loop control strategy based on maximum convergence rate, study some features of the novel method, and illustrate its behavior by numerical simulations. A numerical comparison of the two methods complements the paper. The numerical comparison shows how the proposed feedback control method outperforms the static one in terms of convergence rate and resiliency against parameters mismatch.

Analysis of GNSS and IMU Sensor Data Fusion Using the Unscented Kalman Filter Method on Medical Drones in Open Air

Accurate and efficient position estimation is an important element in flight missions for autonomous aerial vehicles such as Unmanned Aerial Vehicles (UAVs). UAVs rely on sensors that provide information regarding position, speed, and orientation. GPS and IMU are sensors that can provide navigation information, where each sensor has characteristics that can complement each other’s shortcomings. Research related to the integration of GPS and IMU sensors for positioning with increasingly higher accuracy has been widely applied, one of which is the Kalman Filter method. Kalman filter not only performs digital signal filtering but is also capable of smoothing and predicting recursion to obtain the most accurate estimation results. Along with the development of the modification of the Kalman Filter, the Unscented Kalman Filter (UKF) method is a solution in overcoming nonlinear systems with estimation results approaching the actual value, so that the positioning accuracy is higher and convergent. This study intends to analyze the fusion performance of GPS and IMU sensor data using the UKF method on Medical Drones in an open space in order to obtain a position with high accuracy. UKF simulations on Medical Drones were carried out using a mathematical model on a 6 DOF (Degree of Freedom) UAV. The sensor data fusion performance was tested by comparing the results of the UKF method with the EKF on the Geopointer software. The results showed that the UKF simulation gave a positional accuracy of 0.022 m to the measurement data. The UKF simulation results in a better positioning accuracy when compared to EKF processing on the Geopointer software which only achieves a position accuracy of 8.467 m. The low accuracy of the EKF processing results is due to the mismatch of the IMU parameters obtained with the actual Medical Drone parameters.

Robust virtual‐vector model predictive control of permanent‐magnet motor considering D‐Q axis inductance parameter uncertainty

AbstractTo suppress parameter mismatch and improve the output performance of model predictive control (MPC), a new robust virtual‐vector MPC strategy is proposed for a five‐phase permanent‐magnet motor in this study. Firstly, the incremental predictive model is applied to remove the impact of flux mismatch. Then the d‐q axis inductance parameter sensitivity of MPC is analysed, which produces the predictive current error between the normal parameter and mismatched parameter. Based on the current error, a new cost function is designed to select the voltage vector more accurately when the d‐q axis inductance parameter mismatch occurs. Thus, good robustness to the parameter's variation can be guaranteed. Afterwards, the duty cycle modulation technology is applied to allocate the duration time of two adjacent vector‐vectors and zero vector. So the motor current harmonics and torque ripple can be considerably suppressed. Finally, the experimental results are provided to show the effectiveness of this proposed method.

Automated advanced imaging in acute ischemic stroke. Certainties and uncertainties

The purpose of this is study was to review pearls and pitfalls of advanced imaging, such as computed tomography perfusion and diffusion-weighed imaging and perfusion-weighted imaging in the selection of acute ischemic stroke (AIS) patients suitable for endovascular treatment (EVT) in the late time window (6–24 h from symptom onset). Advanced imaging can quantify infarct core and ischemic penumbra using specific threshold values and provides optimal selection parameters, collectively called target mismatch. More precisely, target mismatch criteria consist of core volume and/or penumbra volume and mismatch ratio (the ratio between total hypoperfusion and core volumes) with precise cut-off values. The parameters of target mismatch are automatically calculated with dedicated software packages that allow a quick and standardized interpretation of advanced imaging. However, this approach has several limitations leading to a misclassification of core and penumbra volumes. In fact, automatic software platforms are affected by technical artifacts and are not interchangeable due to a remarkable vendor-dependent variability, resulting in different estimate of target mismatch parameters. In addition, advanced imaging is not completely accurate in detecting infarct core, that can be under- or overestimated. Finally, the selection of candidates for EVT remains currently suboptimal due to the high rates of futile reperfusion and overselection caused by the use of very stringent inclusion criteria. For these reasons, some investigators recently proposed to replace advanced with conventional imaging in the selection for EVT, after the demonstration that non-contrast CT ASPECTS and computed tomography angiography collateral evaluation are not inferior to advanced images in predicting outcome in AIS patients treated with EVT. However, other authors confirmed that CTP and PWI/DWI postprocessed images are superior to conventional imaging in establishing the eligibility of patients for EVT. Therefore, the routine application of automatic assessment of advanced imaging remains a matter of debate. Recent findings suggest that the combination of conventional and advanced imaging might improving our selection criteria.

Optimized FCS-MPCC based on disturbance feedback rejection for IPMSMs under demagnetization fault in high-speed trains

This paper proposes an optimized finite control set model predictive current control (FCS-MPCC) strategy based on disturbance feedback rejection control (DFRC) method for interior permanent magnet synchronous motors (IPMSM) employed in high-speed trains. The strategy aims to mitigate the impact of parameter mismatch caused by permanent magnet demagnetization faults. Firstly, a discrete predicting model is established for IPMSMs, considering the mismatch in flux linkage, resistance, and inductance. The analysis is conducted to evaluate the impact of disturbances caused by demagnetization faults on the traditional FCS-MPCC. Secondly, second-order sliding mode disturbances observers (SMDOs) are utilized to detect real-time disturbances under demagnetization faults. The stability analysis of the second-order SMDOs is conducted using a Lyapunov function. Furthermore, disturbance controllers are developed to regulate the disturbances and generate compensating values for the FCS-MPCC. Lastly, experimental validation is performed on two IPMSMs to demonstrate the effectiveness of the proposed DFRC-based FCS-MPCC strategy against parameter mismatch issues caused by demagnetization faults.

Synchronization in networked systems with large parameter heterogeneity

Systems that synchronize in nature are intrinsically different from one another, with possibly large differences from system to system. While a vast part of the literature has investigated the emergence of network synchronization for the case of small parametric mismatches, we consider the general case that parameter mismatches may be large. We present a unified stability analysis that predicts why the range of stability of the synchronous solution either increases or decreases with parameter heterogeneity for a given network. We introduce a parametric approach, based on the definition of a curvature contribution function, which allows us to estimate the effect of mismatches on the stability of the synchronous solution in terms of contributions of pairs of eigenvalues of the Laplacian. For cases in which synchronization occurs in a bounded interval of a parameter, we study the effects of parameter heterogeneity on both transitions (asynchronous to synchronous and synchronous to asynchronous.).

Forced Symmetry-Breaking Bifurcations in Networks with Disordered Parameters

Emergent behavior in complex networks can be predicted and analyzed via the mechanism of spontaneous symmetry-breaking bifurcation, in which solutions of related bifurcation problems lose symmetry as some parameters are varied, even though the equations that such solutions satisfy retain the full symmetry of the system. A less common mechanism is that of forced symmetry-breaking, in which either a bifurcation problem has symmetry on both the state variables and the parameters, or one where the equations have less symmetry when a certain parameter is varied. In this manuscript, it is shown that in certain networks with parameter mismatches the governing equations remain unchanged when the group of symmetries acts on both the state variables and the parameter space. Based on this observation we study the existence and stability of collective patterns in symmetric networks with parameters mismatches from the point of view of forced symmetry-breaking bifurcations. Treating the parameters as state variables, we perform center manifold reductions, which allow us to understand how the disorder in parameters affects the bifurcation points as well as the stability properties of the ensuing patterns. Theoretical results are validated with numerical simulations.

Projective lag quasi-synchronization of coupled systems with mixed delays and parameter mismatch: a unified theory

This paper investigates the projective lag quasi-synchronization by feedback control of a coupled dynamical system with delays and parameter mismatches on arbitrary time domains. Being formulated on time scales, our results are valid simultaneously for continuous- and discrete-time models as well as for any non-standard time domain. Furthermore, the controller design respects the structure of the equations so that we can characterize the stabilization by a limited controller action. Our proofs rely on the unified matrix-measure theory and the generalized Halanay inequality on time scales. We validate our theoretical results with several simulation examples on various time domains.

Optimal Fractional-Order Controller for the Voltage Stability of a DC Microgrid Feeding an Electric Vehicle Charging Station

Charging stations are regarded as the cornerstone of electric vehicle (EV) development and utilization. Electric vehicle charging stations (EVCSs) are now energized via standalone microgrids that utilize renewable energy sources and reduce the stress on the utility grid. However, the control and energy management of EVCSs are challenging tasks because they are nonlinear and time-varying. This study suggests a fractional-order proportional integral (FOPI) controller to improve the performance and energy management of a standalone EVCS microgrid. The microgrid is supplied mainly by photovoltaic (PV) energy and utilizes a battery as an energy storage system (ESS). The FOPI’s settings are best created utilizing the grey wolf optimization (GWO) method to attain the highest performance possible. The grey wolf is run for 100 iterations using 20 wolves. In addition, after 80 iterations for the specified goal function, the GWO algorithm almost discovers the ideal values. For changes in solar insolation, the performance of the proposed FOPI controller is compared with that of a traditional PI controller. The Matlab/Simulink platform models and simulates the EVCS’s microgrid. The results demonstrate that the suggested FOPI controller significantly improves the transient responsiveness of the EVCS performance compared to the standard PI controller. Despite all PV insolation disruptions, the EV battery continues to charge while the ESS battery precisely stores and balances PV energy changes. The results support the suggested FOPI control’s robustness to parameter mismatches. The microgrid’s efficiency fluctuations with the insolation level and state of charge of the EV battery are discussed.

Quasi-synchronization of multi-layer delayed neural networks with parameter mismatches via impulsive control

This paper is concerned with the quasi-synchronization of delayed neural networks with multi-layer network structure and parameter mismatches. The topologies of different layers are independent of each other, and the individual dynamics of different nodes are different as well. Noting that uncertain parameters are taken into account as it is inevitable during the implementation of synchronization. In order to save the control cost, impulsive control that only controls network nodes at discrete times is proposed. Through the impulsive comparison principle, the quasi-synchronization criteria are established under the synchronizing impulses and desynchronizing impulses, respectively. In addition, the theoretical error bounds are estimated. The effectiveness of the theoretical results are verified by numerical simulation.

Adaptive Integral Extended State Observer-Based Improved Multistep FCS-MPCC for PMSM

In this article, an improved multi-step finite control set model predictive current control (FCS-MPCC) based on adaptive integral extended state observer (AIESO) is proposed for permanent magnet synchronous motor (PMSM). The improved multi-step FCS-MPCC based on sector is introduced in the current loop. The elements of the voltage vector set are reduced with different sector division method, so it could reduce the computational burden to some extent. Meanwhile, considering that the high gain extended state observer (ESO) will obtain faster convergence, better tracking accuracy in theory and the system disturbance rejection can be enhanced, but the noise suppression performance will become poor. Thus, the adaptive extended state observer (AESO) is proposed to balance the disturbance rejection and noise suppression. When system subject to disturbance, the adaptive gain will increase to enhance the system disturbance rejection and become small to improve the noise suppression in steady-state. However, disturbed by time-varying disturbance, the small gain in the steady state will lead to poor steady-state tracking accuracy. To improve the steady-state tracking accuracy, AIESO is proposed by adding the integral term into AESO. Finally, in order to avoid the sector misjudgment caused by parameter mismatch, the strategy based on extended state observer is used for disturbance compensation. However the selection of inductance is closed related to the estimated burden of the observer. Thus, the inductance parameter estimation method is proposed to update the initial inductance value to reduce the estimated burden, which can help to suppress the parameter mismatches with a smaller estimated burden of the observer. The simulation and experimental results verify the effectiveness of the proposed method.

An Improved Integral Sliding Mode Control for PMSM Drives Based on New Variable Rate Reaching Law With Adaptive Reduced-Order PI Observer

This paper focuses on speed control in a permanent-magnet synchronous motor (PMSM). To promote the speed tracking performance and anti-disturbance property of PMSM speed regulation system against various unknown disturbances, such as parameter mismatch, load torque variation, friction force, and so on, an improved integral sliding mode control (IISMC) method based on a new variable rate reaching law (NVRRL) and an adaptive reduced-order proportional integral observer (AROPIO) is proposed. The NVRRL method can reduce chattering, and convergence time of the system state at the same time compared to the existing sliding mode reaching law. An anti-windup sliding mode surface is proposed to overcome the overshoot problem of the integral sliding mode surface when the reference speed is large and rises in steps or large ramps. To further improve the system tracking accuracy and anti-disturbance ability, an AROPIO is used to estimate lumped disturbances and provides feedforward compensation for the IISMC. The simulation and experimental results show that the proposed IISMC+AROPIO method not only achieves high speed tracking accuracy and fast convergence speed but also achieves strong anti-interference ability.

Cooperative Adaptive PID-Like Voltage Regulation in Inverter-Based Islanded Microgrids Under Unknown Uncertainties

As modern distributed generators (DGs) always involve heterogeneous and unknown disturbances, a lack of accurate knowledge about the entire microgrid (MG) dynamics arises. Hence, an appropriate secondary control strategy is helpful to improve the voltage regulation process in an uncertain and unknown environment. To this aim, in this article, we suggest a novel distributed adaptive proportional–integral–derivative (PID)-like controller able to counteract unknown uncertainties arising from external disturbances and parameter mismatches, as well as unavoidable deviations induced by primary droop control while recovering the desired voltage reference value. By leveraging Lyapunov theory, an adaptive mechanism is provided to adjust control parameters for dealing with different operating conditions. The stability of the entire MG, analytically derived by also exploiting the Barbalat lemma, proves that all DGs within the electrical grid track the reference signal despite the presence of unknown uncertainties, with bounded adaptive control gains in steady-state phases. A detailed simulation analysis confirms the theoretical derivation and the effectiveness of the proposed controller in ensuring the voltage restoration in different troublesome scenarios, where both reference/loads variations and plug-and-play phenomena occur.

Robust Predictive Current Control of PMSG Wind Turbines with Sensor Noise Suppression

Model predictive control (MPC) is an efficient and multi-functional control scheme for synchronous permanent magnet generators (PMSGs). However, the effective management of traditional MPC depends on precise system models. Multiple uncertainties of permanent magnet flux, motor inductance, filter inductance and parameter measurement noise will limit MPC’s performance. The conventional linear extended state observer (ESO) can perform robust predictive control of the ultralocal model of the PMSG system to cope with parameter mismatches. However, the ESO is limited in balancing disturbance rejection with measurement noise attenuation. Since the amplification of high-frequency noise pollution can lead to both poor control performance and system instability, this challenge is of significant importance. To solve the problem, a new hybrid parallel cascaded ESO (PCESO) model-free predictive control framework is proposed using the three-level neutral-point-clamped (NPC) power electronic converter, on both the machine side and grid side. Analytical discussions of the time and frequency domain characteristics of the PCESO demonstrate its superior characteristics over the ESO. The proposed method can effectively balance parameter mismatch, disturbance rejection and high-frequency noise suppression. Finally, the effectiveness of the proposed method, under uncertainties of parameter mismatches, measurement noise and permanent magnet flux, is verified through real-time hardware-in-the-loop tests on a back-to-back grid-tied PMSG interfaced with an NPC power converter.

On the Unique Parameterization of a Single Particle Model Using Electrochemical Impedance Spectroscopy (EIS) and Hybrid Power Pulse Characterization (HPPC) Tests

Prior work has shown that equivalent circuit models (ECM) fitted to match frequency data (EIS) do not necessarily match time data (HPPC). Depending on the fitting methodology, the quality of data, and the ECM order, the parameter mismatch is manageable and can be justified by the simplification of the physical phenomena in an ECM. An investigation of the fitting methods and the sensitivity to noise will be shown using synthetic data of an OCV-R2RC model.The investigation will be expanded in fitting EIS and HPPC synthetic data from an SPMe and an SPM model. The sensitivity of the SPM parameters to the measured EIS and HPPC will clarify long-standing confusion of the uniqueness and even the magnitude of physically-relevant parameters in SPM models.The learnings from the synthetic data will then be demonstrated with EIS and HPPC data obtained from several Graphite/NMC battery pouch cells at different states of charge (SOC) and states of health (SOH) after cycling at a variety of accelerated aging protocols that include c-rates, temperatures, SOC swing, and externally applied pressure.Figure: Voltage Profile of Characterization Protocols(HPPC and EIS) for a single cell at different SOC.Bottom right, Nyquist plot corresponding to first characterization scheme of a fresh and aged cell. Figure 1

Projective Synchronization of Delayed Uncertain Coupled Memristive Neural Networks and Their Application.

In this article, the authors analyzed the nonlinear effects of projective synchronization between coupled memristive neural networks (MNNs) and their applications. Since the complete signal transmission is difficult under parameter mismatch and different projective factors, the delays, which are time-varying, and uncertainties have been taken to realize the projective synchronization of MNNs with multi-links under the nonlinear control method. Through the extended comparison principle and a new approach to dealing with the mismatched parameters, sufficient criteria have been determined under different types of projective factors and the framework of the Lyapunov-Krasovskii functional (LKF) for projective convergence of the coupled MNNs. Instead of the classical treatment for secure communication, the concept of error of synchronization between the drive and response systems has been applied to solve the signal encryption/decryption problem. Finally, the simulations in numerical form have been demonstrated graphically to confirm the adaptability of the theoretical results.

Hybrid Impulsive Control Based Synchronization of Leakage and Multiple Delayed Fractional-Order Neural Networks with Parameter Mismatch

Hybrid Impulsive Control Based Synchronization of Leakage and Multiple Delayed Fractional-Order Neural Networks with Parameter Mismatch

An Improved Model-Free Active Disturbance Rejection Deadbeat Predictive Current Control Method of PMSM Based on Data-Driven

This article proposes an improved model-free active disturbance rejection deadbeat predictive current control(ADRDPCC) method for permanent magnet synchronous motor (PMSM) used in more electric aircraft (MEA) based on the data driven method, which is used to solve the parameter mismatch problem of deadbeat predictive current control (DPCC) and improve the performance of PMSM control system. DPCC model applied to the current loop of the MEA motor is established as the main control strategy of the system. The principle of active disturbance rejection control is combined with DPCC, and the ADRDPCC structure is formed to optimize the control strategy. A specific extended state observer (ESO) of ADRDPCC is designed to track the internal disturbance caused by parameter mismatch and the external disturbance in real time. DPCC is used as the control law of the ADRDPCC structure to predict the current and output the reference voltage based on the observation of ESO. A deep reinforcement learning model based on ADRDPCC is designed and trained based on the data-driven method. The trained model can compensate and optimize ADRDPCC based on the disturbance observed by ESO and the observed control state of PMSM. The simulated and experimental results show the superiority of the proposed method.

Average Model-Based Sliding Mode Control for Quality Improvement of a Grid-Connected PUC Inverter

This paper presents a new Sliding Mode Phase-Shift PWM (SM-PSPWM) based control strategy for a grid-connected Packed U-Cell (PUC) inverter. The proposed control strategy is based on the average model of the PUC inverter while using a multi-carriers phase-shift pulse width modulation (PS-PWM) technique. The use of the PWM strategy, based on the inverter average model, ensures high steady-state performance, low harmonic current, and enhanced thermal properties. In addition, the Sliding Mode Control (SMC) is applied to generate the input signals to achieve a fast dynamical response and high robustness against disturbances. To further improve the robustness under parameter mismatch and distorted grid voltage conditions, an integral term is added to the surface function for better accuracy and stability. To validate the performance of the proposed controller (fast dynamics, global stability, low current THD, ...) shown by the simulation results, a laboratory setup is used to validate the theoretical analysis.

High-Quality Positioning Strategy for Biaxial Contour Machining System With Nonlinear Model Predictive Control

The trajectory tracking ability is an important index to measure the computer numerical control (CNC) machining tools. In this paper, the nonlinear model predictive control strategy (NMPC) is proposed to improve the trajectory tracking ability of the biaxial contour machining system (BCMS). The position-, velocity-, and current-loop of the permanent magnet synchronous motors (PMSMs) of the BCMS are considered as the complete control object to construct a single-loop nonlinear model predictive controller. Meanwhile, the matching non-linear disturbance observer (NL-DOB) is designed to estimate the uncertainty disturbances caused by the parameters mismatch, external load changes and harmonic disturbances in real-time. And the estimated disturbance values are used as feed-forward compensations, which can improve the steady-state performance of the system. At last, the positioning performance comparison between the proposed strategy and the conventional ones are carried on the testbench. The experimental results indicate that the positioning errros can be reduced by up to 40% with the proposed algorithm.