Online Parameter Research Articles

Fractional Order Adaptive Fixed-Time Sliding Mode Controller for Synchronization of Fractional Order Chaotic Permanent Magnet Synchronous Motors

This paper presents a fractional-order adaptive fixed-time sliding mode controller for the synchronization of chaotic dynamics in permanent magnet synchronous motors (PMSMs). PMSMs, commonly used in electric vehicles, robotics, and aerospace, are prone to chaotic behavior under parameter variations and external disturbances, which can degrade performance and stability. Existing control strategies, such as conventional sliding mode control (SMC) and fractional-order controllers, have limitations, including chattering, slow convergence, and sensitivity to uncertainties. The proposed controller integrates fractional calculus into the sliding mode framework to improve control performance by accounting for the memory effects of PMSM dynamics. The controller ensures fixed-time convergence, guaranteeing that the system reaches the desired state within a fixed-time, regardless of initial conditions. Additionally, an adaptive mechanism adjusts the control parameters online, providing robustness against disturbances and parameter uncertainties. Simulation results demonstrate the superior performance of the proposed controller compared to existing methods, showing faster convergence, improved stability, and reduced chattering. The proposed controller proves effective in both synchronization and control scenarios, making it a promising solution for chaotic suppression in PMSMs across various operating conditions.

Observer Design for State and Parameter Estimation for Two-Time-Scale Nonlinear Systems

The design and calculation of nonlinear observers for parameter estimation in multi-time-scale nonlinear systems present significant challenges due to the inherent complexity and stiffness of such systems. This study proposes a framework for designing observers for two-time-scale nonlinear systems, with the objective of overcoming the aforementioned challenges. The design procedure involves reducing the original two-time-scale nonlinear system to a lower-dimensional model that captures only the slow dynamics while approximating the fast states through the use of an algebraic slow motion invariant manifold function. Subsequently, an exponential observer can be devised for this reduced system, which is valid for both state and parameter estimation. By employing the output from the original system, this observer can be adapted for online state and parameter estimation for the detailed two-time-scale system. The challenges associated with estimating parameters in two-time-scale nonlinear systems, the complexities of designing observers for such systems, and the computational burden associated with computing observers for ill-conditioned systems can be effectively addressed through the application of this design framework. A rigorous error analysis validates the convergence of the proposed observer towards the states and parameters of the original system. The viability and necessity of this observer design framework are demonstrated through a numerical example and an anaerobic digestion process. This study presents a practical approach for state and parameter estimation with observers for two-time-scale nonlinear systems.

Self-Tuning Oxygen Excess Ratio Control for Proton Exchange Membrane Fuel Cells Under Dynamic Conditions

Reasonable and effective control of a cathode air supply system is conducive to improving the dynamic response, operating efficiency, and reliability of fuel cell systems. This paper proposes a novel data-driven adaptive oxygen excess ratio (OER) control strategy based on online parameter identification for fuel cell systems. The proposed control scheme employs a second-order active disturbance rejection controller (ADRC) derived from the proportional-integral-derivative tuning rule to effectively deal with model uncertainties and external disturbances. Online parameter identification continuously translates the cathode air supply system into the second-order model, enabling the real-time adaptation of controller parameters to varying operating conditions. Simulation results demonstrate that the OER control strategy proposed significantly improves voltage stability and system efficiency under dynamic conditions compared to traditional methods. The innovation of this paper is that, based on consideration of the nonlinear slow time-varying characteristics of a PEMFC and the frequent disturbance of load current, adaptive control under system dynamic conditions can be considered. Combining the parameter identification scheme, an adaptive online self-tuning scheme is designed for the identified system model, which avoids the tediousness of a complex modeling process and has promotion value in practical applications.

Voltage regulation in distribution networks by electrical vehicles with online parameter estimation

The integration of a large number of electric vehicles (EVs) offers a new perspective for providing voltage regulation services for the operation of distribution networks. The flexible charging and discharging capabilities of EVs can help mitigate voltage fluctuations and improve grid stability. In this paper, we utilize EV clusters by controlling the discharging power to realize voltage regulation of distribution networks. We formulate a feedback-based optimization problem with the objectives of minimizing voltage mismatch as well as reducing the cost of voltage regulation services provided by EV clusters. Then we propose an algorithm with online resistance estimation to find the optimal solution without requiring complete information about distribution networks. The convergence of the proposed algorithm is guaranteed by the oretical proof. Numerical results in 33-bus system validate the performance of the algorithm. The results validate the applicability of the proposed approach in distribution networks, highlighting the potential of EV clusters as a flexible and cost-effective solution for voltage regulation.

Time-Delay-Based Sliding Mode Tracking Control for Cooperative Dual Marine Lifting System Subject to Sea Wave Disturbances

Dual marine lifting systems are complicated, fully actuated mechatronics systems with multi-input and multi-output capabilities. The anti-swing cooperative lifting control of dual marine lifting systems with dual ships’ sway, heave, and roll motions is still open. The uncertainty regarding system parameters makes the task of achieving stable performance more challenging. To adjust both the attitude and position of large distributed-mass payloads to their target positions, this paper presents a time-delay-based sliding mode-tracking controller for cooperative dual marine lifting systems impacted by sea wave disturbances. Firstly, a dynamic model of a dual marine lifting system is established by using Lagrange’s method. Then, a kinematic coupling-based cooperative trajectory planning strategy is proposed by analyzing the coupling relationship between the dual marine lifting system and dual ship motion. After that, an improved sliding mode tracking controller is proposed by using time-delay estimation technology, which estimates unknown system parameters online. The finite-time convergence of full-state variables is rigorously proven. Finally, the simulation results verify the designed controller in terms of anti-swing control performance. The hardware experiments revealed that the proposed controller significantly reduces the actuator positioning errors by 83.33% compared with existing control methods.

Output-only time-varying modal parameter online identification method and engineering application based on TARMA model

Abstract The modal parameters of long-range cruise weapon systems are typically obtained through finite element analysis and ground vibration testing. However, due to the inability to simulate the system’s time-varying characteristics under flight conditions, it is customary to conduct finite element modelling or ground vibration testing based on several representative operating scenarios. Subsequently, the flight modal frequency of the system is derived through numerical interpolation. This repetitive modelling, analysis, and ground testing process is both time-consuming and resource-intensive. This paper delves into the data-driven output-only identification approach, developing a recursive identification method solely based on the output, which incorporates a time-varying auto-regressive moving average (TARMA) model. By introducing a forgetting factor, the method effectively tracks the system’s time-varying characteristics, enabling rapid and accurate acquisition of the time-varying mode of long-range guided missiles even under unknown excitation conditions. Focusing specifically on the time-varying modal identification challenge posed by a long-range cruise missile, this study performs a frequency spectrum analysis of flight telemetry data. By comparing this analysis with ground vibration experimental data, the disparities between the in-flight and ground modes are elucidated. Leveraging the recursive identification method, the time-varying modal parameters of the flight telemetry data are successfully identified. The alignment between the identified results and the spectral analysis outcomes validates the effectiveness of the proposed online identification technique for time-varying modal parameters, thereby serving the engineering requirements for finite element model refinement and attitude control system design in long-range cruise missiles.

Ultra-low frequency active vibration isolation system with quasi-zero stiffness characteristic using self-tuning filter-based feedforward control

Purpose of studyThis study aims to resolve the compromise between low suspension stiffness and high load-bearing capability in vibration isolation and enhance the suppression performance of ultra-low frequency vibration through advanced active feedforward control methods, featuring high-performance precision isolation for large-scale ultra-precision instruments. Describe methodsAn air-magnetic hybrid parallel configuration of positive and negative stiffness is employed to achieve adjustable stiffness, endowing the vibration isolation system with quasi-zero stiffness characteristics. A novel self-tuning feedforward control strategy is proposed through a self-tuning filter to update the controller parameters online, minimizing the loss caused by model uncertainties. ResultsThe vertical and horizontal natural frequencies of the system exhibit a remarkably low resonance frequency of lower than 0.5 Hz. With the self-tuning feedforward strategy, the maximum vibration attenuation reaches 78 dB in the vertical direction and 70 dB in the horizontal direction, reducing the cumulative power at 100 Hz by 61.4 % and 47.8 %, respectively. The above results showcase the proposed approach with excellent performance in isolating low-frequency vibrations. Conclusions/DiscussionThe ultra-low frequency active vibration isolation system designed in this paper achieves exceptionally low suspension stiffness. The implemented self-tuning feedforward controller isolates large precision machinery from broadband floor vibrations and significantly enhances the vibration isolation performance of the system.

Deep Q-Network-Enhanced Self-Tuning Control of Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a widespread evolutionary technique that has successfully solved diverse optimization problems across various application fields. However, when dealing with more complex optimization problems, PSO can suffer from premature convergence and may become stuck in local optima. The primary goal is accelerating convergence and preventing solutions from falling into these local optima. This paper introduces a new approach to address these shortcomings and improve overall performance: utilizing a reinforcement deep learning method to carry out online adjustments of parameters in a homogeneous Particle Swarm Optimization, where all particles exhibit identical search behaviors inspired by models of social influence among uniform individuals. The present method utilizes an online parameter control to analyze and adjust each primary PSO parameter, particularly the acceleration factors and the inertia weight. Initially, a partially observed Markov decision process model at the PSO level is used to model the online parameter adaptation. Subsequently, a Hidden Markov Model classification, combined with a Deep Q-Network, is implemented to create a novel Particle Swarm Optimization named DPQ-PSO, and its parameters are adjusted according to deep reinforcement learning. Experiments on different benchmark unimodal and multimodal functions demonstrate superior results over most state-of-the-art methods regarding solution accuracy and convergence speed.

Closed-loop control strategy design for Caputo–Fabrizio definition-based fractional-order Boost converters considering parametric uncertainties

This paper proposes a dual closed-loop adaptive PI control system based on parameter identification for Caputo–Fabrizio (C-F) definition-based fractional-order boost converters. First, a C-F definition-based fractional-order model of the Boost converter is derived. Afterward, an online component parameter identification method is proposed to obtain the values and orders of capacitors and inductors in the proposed model. Based on these identified parameters, a dual closed-loop adaptive PI control system considering parametric uncertainties for the Boost converters is designed using the frequency domain analysis method. Finally, simulation validations are conducted. The online parameter identification method can accurately identify the values and orders of capacitors and inductors. The identification error is less than [Formula: see text] for the capacitance value and [Formula: see text] for the other parameters. Moreover, the proposed control system can automatically adjust the parameters with changes in the component parameters, effectively improving the robustness. Compared to traditional PI controllers, when the parameters of components change, the proposed control system can reduce voltage overshoot by [Formula: see text] in the case of sudden changes in the input voltage. These results confirm the efficacy of the proposed online parameter identification method and the dual closed-loop adaptive PI control system.

Master-slave coupling scheme for synchronization and parameter estimation in the generalized Kuramoto-Sivashinsky equation.

The problem of estimating the constant parameters of the Kuramoto-Sivashinsky (KS) equationfrom observed data has received attention from researchers in physics, applied mathematics, and statistics. This is motivated by the various physical applications of the equationand also because it often serves as a test model for the study of space-time pattern formation. Remarkably, most existing inference techniques rely on statistical tools, which are computationally very costly yet do not exploit the dynamical features of the system. In this paper, we introduce a simple, online parameter estimation method that relies on the synchronization properties of the KS equation. In particular, we describe a master-slave setup where the slave model is driven by observations from the master system. The slave dynamics are data-driven and designed to continuously adapt the model parameters until identical synchronization with the master system is achieved. We provide a simple analysis that supports the proposed approach and also present and discuss the results of an extensive set of computer simulations. Our numerical study shows that the proposed method is computationally fast and also robust to initialization errors, observational noise, and variations in the spatial resolution of the numerical scheme used to integrate the KS equation.

Real-time correction of gain nonlinearity in electrostatic actuation for whole-angle micro-shell resonator gyroscope

MEMS gyroscopes are well known for their outstanding advantages in Cost Size Weight and Power (CSWaP), which have inspired great research attention in recent years. A higher signal-to-noise ratio (SNR) for MEMS gyroscopes operating at larger vibrating amplitudes provides improved measuring resolution and ARW performance. However, the increment of amplitude causes strong nonlinear effects of MEMS gyroscopes due to their micron size, which has negative influences on the performance. This paper carries out detailed research on a general nonlinear mechanism on the sensors using parallel-plate capacitive transducers, which is called the gain nonlinearity in electrostatic actuation. The theoretical model established in this paper demonstrates the actuation gain nonlinearity causes the control-force coupling and brings extra angle-dependent bias with the 4th component for the whole-angle gyroscopes, which are verified by the experiments carried out on a micro-shell resonator gyroscope (MSRG). Furthermore, a real-time correction method is proposed to restore a linear response of the electrostatic actuation, which is realized by the gain modification with an online parameter estimation based on the harmonic-component relationship of capacitive detection. This real-time correction method could reduce the 4th component of the angle-dependent bias by over 95% from 0.003°/s to less than 0.0001°/s even under different temperatures. After the correction of actuation gain nonlinearity, the bias instability (BI) of whole-angle MSRG is improved by about 3.5 times from 0.101°/h to 0.029°/h and the scale factor nonlinearity (SFN) is reduced by almost one order of magnitude from 2.02 ppm to 0.21 ppm.

Adaptive robust motion control for hydraulic load sensitive systems considering displacement dynamic compensation

Hydraulic load-sensitive systems (HLSS) are widely used for high power density and energy efficiency. This study introduces an adaptive, energy-efficient HLSS with a valve-controlled variable motor. The system faces challenges from non-linearities, including internal higher-order dynamics due to displacement changes and external unknown disturbances, which hinder precision applications. To address this issue, this study explores HLSS principles to develop an accurate system model. Subsequently, an adaptive robust motion control that considers displacement compensation (DCARC) is proposed using the established model. DCARC can learn unknown parameters online and compensate the model more accurately to improve control accuracy. Experiments show that considering the higher order dynamic effects caused by displacement in the system can improve model accuracy and effectively reduce the burden of parameter adaptation and robust feedback terms. High-precision and energy-efficient HLSS motion is verified and realized in the study. The control accuracy of DCARC is 19.4% higher than that of conventional adaptive robust control (ARC). Under experimental conditions, the proposed system can improve energy efficiency by up to five times compared to valve-controlled fixed displacement motor systems (VFDS).

Adaptive fuzzy neural super-twisting control of micro gyroscope sensor

To maintain the vibrations of the gyroscope proof mass, an adaptive super-twisting sliding mode control (STSMC) of a micro gyroscope based on a two-loop recursive fuzzy neural network (TLRFNN) is designed. In order to estimate the unknown parameters of the nonlinear system online, an adaptive technology is adopted based on the parameter linearization of the nonlinear model. In response to the problem of system chattering caused by ordinary sliding mode control, the STSMC with the advantages of high order sliding mode and conventional sliding mode is introduced. Aiming at the problem of uncertainty in system parameters, a two-loop recursive fuzzy neural network approximation with the competence over the storage of a priori information is utilized. All adaptive laws are obtained under the Lyapunov stability framework which contributes to the stability of the system. The simulation research shows the system exhibits good tracking performance and maintains a small tracking error under the control of the suggested control system. The effect is measured by calculating the root mean square error (RMSE) parameter of the tracking error and the proposed control system achieves a tracking effect of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{1}}{\ ext{0.8590}} \ imes {\ ext{1}}{{\ ext{0}}^{ - 4}}$$\\end{document} and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{2}}{\ ext{0.0831}} \ imes {\ ext{1}}{{\ ext{0}}^{ - 4}}$$\\end{document} in the x and y directions, respectively.

State and parameter estimation of a dynamic froth flotation model using industrial data

This paper investigates an observable dynamic model of froth flotation circuits aimed at online state and parameter estimation and model-based control. The aim is to estimate the model states and parameters online from industrial data. However, in light of limitations in the plant data, additional model analysis is conducted. It is shown that without online compositional measurements, only the states and parameters of a reduced model can be estimated online. The reduced model lumps all recovery mechanisms into a single empirical equation. The reduced model is used to develop a moving horizon estimator (MHE) which is implemented on the industrial data. The state and parameter estimates from the MHE are used to evaluate the model prediction accuracy over a receding control horizon as would be done in model predictive control (MPC). Given the uncertainty of the available data, unmeasured disturbances and missing online measurements, the estimation and prediction results are reasonably accurate, at least in a qualitative sense. If accurate and reliable online measurements are available for estimation, the reduced model shows potential to be used for long-term model-based supervisory control of a flotation circuit.

Improved online parameter identification coupled with discrete wavelet transform for state-of-charge estimation under noise-riding discharging/charging battery voltage

With the expansion of the battery market and electric vehicles, improved battery-state estimation approaches are required for energy management to meet the requirements for safe usage. The accurate identification of battery parameters significantly influences the state-of-charge (SOC) computation algorithms. In addition, noise from charging and discharging voltages can influence the accuracy of the SOC estimation. This paper proposes a novel methodology combining a discrete wavelet transform (DWT) denoising procedure and an adaptive parameter identification. The DWT uses a multi-resolution analysis (MRA) to decompose noise-riding charging/discharge voltage signals, which are then rebuilt using the inverse DWT (IDWT) based on the rigsure adaptive threshold selection rule. In this study, a dual-resistor-capacitor lithium-ion battery model was examined. A formulation of the modified forgetting factor recursive least squares (MFFRLS) was presented and demonstrated robustness in overcoming data saturation while enhancing parameter tracking ability even under the effect of noise. To verify the effectiveness of the coupled methods for SOC estimation defined in this study, experimental data obtained from various working conditions were employed to estimate the SOC using the extended Kalman filter (EKF). A thorough analysis demonstrated that the proposed MFFRLS combined with the denoising method performed well in terms of SOC estimation accuracy and robustness.

Degradation modeling of serial space lithium-ion battery pack based on online inconsistency representation parameters

Establishing an inconsistency-based degradation model for lithium-ion battery packs is crucial for suppressing the degradation of battery packs by optimizing the inconsistency. This paper proposes a method for modeling the degradation of serial space lithium-ion battery packs based on online inconsistency representation parameters. Firstly, the static inconsistency representation parameters are acquired online by quantifying the difference among voltage intervals of cells through Gaussian distribution, addressing the challenge of acquiring static inconsistency representation parameters online. Simultaneously, dynamic inconsistency representation parameters are serialized representations by quantifying the voltage differences of cells at multiple moments, better capturing the rapid evolutionary process of inconsistency. Secondly, a linear model is used to model the serialized parameters and degradation, simplifying the modeling process while achieving multi-source information fitting. Meanwhile, a nonlinear model is constructed to better fit the nonlinear degradation trend of the battery pack. Then, Kalman filter is utilized for model fusion to achieve complementary advantages and improve modeling accuracy. Cross-validation with laboratory battery pack data confirms that this method outperforms comparison approaches, achieving the mean absolute error and maximum error of less than 1.73 % and 3.31 %, respectively. The method proposed in this paper provides a basis for future work on battery pack life extension.

Vehicle posture wheelbase preview control of in-wheel motors drive intelligent vehicle on potholed road

The in-wheel motors drive intelligent vehicle (IMDIV) has a strong ability of dynamic control, which makes it be a major development path of intelligent vehicles in the future. However, its stability is easily deteriorated by the impact of the road when driving on potholed road. To tackle this issue, a vehicle posture control method based on road feedback and elevation recognition is proposed. Firstly, an elevation recognition algorithm of adaptive Kalman filter (AKF) based on vehicle dynamic response is proposed after considering the flooded road and the system noise uncertainty. Secondly, a vehicle posture controller of fuzzy active disturbance rejection control (FADRC) is designed, with the function of dynamically adjusting the active disturbance rejection control (ADRC) parameters. It is designed to implement front suspension feedback control and rear suspension wheelbase preview control. At last, the road elevation recognition algorithm and vehicle posture control method are proved by Simulink simulation and off-line hardware in the loop test. The results revealed that the AKF can effectively recognize both white noise pavement and potholed road, and the recognition accuracy is greater than 90%. The FADRC controller realizes accurate front suspension feedback control and rear suspension wheelbase preview control by adjusting the real-time online parameters of ADRC. The proposed vehicle posture control method can effectively control the pitch and roll motion, which significantly improves the stability of IMDIV on potholed road. This study can serve as a reference for the posture stability control of IMDIV on potholed road.

Sensorless Human–Robot Interaction: Real‐Time Estimation of Co‐Grasped Object Mass and Human Wrench for Compliant Interaction

Human–robot physical interaction in shared object manipulation can fully leverage the strengths of both humans and collaborative robots, achieving better interaction outcomes. However, in typical physical interactions, the inertia parameters of the object are either assumed to be known or ignored, simplifying the interaction to be directly between the human and the collaborative robot. This simplification can lead to inaccuracies in the robot's understanding of human intent due to deviations in object dynamics. This article presents a method that eliminates the need for a force sensor on the human side during human–robot collaboration. Using an extended Kalman filter, the method estimates object parameters online and applies a disturbance observer to determine human force. This approach reduces human effort and improves tracking performance compared to traditional methods. It also avoids the hassle of installing and removing sensors in complex scenarios. By estimating object mass and human force in real time, the robot can adjust its behavior to ensure safer and smoother interactions. Furthermore, the system can handle various objects without requiring specific programming for each case.

Obstacle avoidance shape control of deformable linear objects with online parameters adaptation based on differentiable simulation

The manipulation of deformable linear objects (DLOs) such as ropes, cables, and hoses by robots has promising applications in various fields such as product assembly and surgical suturing. However, DLOs are more difficult to manipulate than rigid objects because their shape changes during manipulation. Furthermore, preventing a DLO from colliding with the environment is important to prevent it from becoming entangled and causing shape control to fail. In this paper, we proposed an obstacle avoidance and shape control scheme for DLOs based on differentiable simulation that does not require prior data or a specialized controller. First, we established a dynamic model of the DLO that allows for both forward dynamics transfer and error backpropagation to obtain gradients. Then, we employed model predictive control to optimize the embedded neural network for predicting the actions that would manipulate the DLO. Finally, the control scheme was made applicable to DLOs with different material properties by allowing online adaptation of the model parameters essential to deformation during manipulation. Simulations and real-world experiments demonstrate that the proposed control scheme could manipulate the DLO stably and accurately to avoid obstacles and achieve the goal state. In addition, the online adaptation of parameters helped mitigate the sim-to-real gap.

Identifying the parameters of ultracapacitors based on variable forgetting factor recursive least square

PurposeUltracapacitors find extensive applications in various fields because of their high energy density and long cycling periods. However, due to the movement of ions and the arrangement patterns on rough/irregular electrode surfaces during the charge and discharge process of ultracapacitors, the parameters of ultracapacitors usually change with the variation of operating conditions. The purpose of this study is to accurately and quickly identify the parameters of ultracapacitors.Design/methodology/approachA variable forgetting factor recursive least square (VFFRLS) algorithm is proposed in this paper for online identifying the equivalent series resistance and capacitance C of ultracapacitors. In this work, a real-time error-based strategy is developed to adaptively regulate the value of the forgetting factor of traditional forgetting factor recursive least square (FFRLS) algorithm. The strategy uses the square of the average time autocorrelation estimation of the prior error and the posterior error between the predicted output and the actual output as the adjustment basis of forgetting factors.FindingsExperiments were conducted using the proposed scheme, and the results were compared with the estimation results obtained by the recursive least squares (RLS) algorithm and the traditional FFRLS algorithm. The maximum root mean square error between the estimated values and actual values for VFFRLS is 3.63%, whereas for FFRLS it is 9.61%, and for RLS it is 19.33%.Originality/valueBy using the proposed VFFRLS algorithm, a relatively high precision can be achieved for the online parameter estimation of ultracapacitors. Besides, the dynamic balance between parameter stability and tracking performance can be validated by dynamically adjusting the forgetting factor.