Parameter Identification Method Research Articles

Parameters identification and trajectory optimization of free-floating space robots

There is a strong dynamic coupling between the base of free-floating space robot and the space manipulator. In order to reduce the interference of the movement of the space manipulator on the robot's base, the trajectory of the space manipulator needs to be optimized. In this paper, the joint path of space manipulators is optimized by using the pseudo-spectral method to reduce the impact of the arm movement on the base. For space robots, parameters identification is the premise of trajectory planning. A parameters identification method is proposed, which only needs to measure the base's angular speed. Numerical simulation results verify the validity of the proposed method. The attitude angle of floating base returns to the initial state after maneuvers of space manipulators.

Adaptive Control Method for Conically Shaped Dielectric Elastomer Actuator With Different Loads

In this paper, we present an adaptive control method for a conically shaped dielectric elastomer actuator (CSDEA) with different loads to achieve its tracking control objective. Firstly, a dynamic model of the CSDEA is constructed to describe its asymmetrical, rate-dependent hysteresis behavior and creep behavior simultaneously. Then, an offline parameter identification method based on the nonlinear-least-squares algorithm is presented to obtain the nominal values of the model parameters of the CSDEA corresponding to the load of 200 (g). The obtained values are regarded as the initial values of the adaptive online parameter identification. Next, an adaptive online parameter identification method (AOPIM) based on the least-mean-square algorithm is proposed, which can dynamically calculate and update the model parameter values to cope with the load changing of the CSDEA. Lastly, based on two kinds of analytical inverses of the dynamic model and the proposed AOPIM, two adaptive inverse compensators are respectively designed to realize the tracking control of the CSDEA. The control experiments with different desired trajectories and different loads are implemented to demonstrate the effectiveness of the presented adaptive control method. Since the root-mean-square errors of the results of all control experiments are lower than 2.7%, the presented method is remarkable from the perspective of the practical application. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The dielectric elastomer material has beneficial properties of high energy density, low mass density, large deformation, fast response and good biological compatibility. Thus, the soft actuator based on the dielectric elastomer material (SADE) has shown great potentials for being used in soft robots. Nevertheless, the SADE has the complex nonlinear behaviors, which brings a big challenge for its precision control. To deal with this issue, some approaches have been presented in previous literatures. However, the load of the SADE is usually fixed in these studies. This paper focuses on the tracking control of the SADE with different loads. To this end, a dynamic model of the CSDEA is constructed, whose parameter values are dynamically calculated and updated to cope with the load changing of the CSDEA. Through calculating the analytical inverse of the dynamic model, two adaptive inverse compensators are developed. The control experimental results demonstrate that the presented adaptive control method is effective. Considering that the load of the SADE is usually diverse in practical applications, this study pays the way for the application of the SADE.

Applying AI and fuzzy modeling to optimize advertising effectiveness in digital marketing strategies for the energy sector

The purpose of the study is to formulate the main aspects of the use of artificial intelligence in advertising and determine its effectiveness in the energy sector. The intensity of development of artificial intelligence technologies and its application to ensure the effectiveness of advertising activities of companies in the energy sector is emphasized. The ethical and practical aspects of using artificial intelligence in advertising are presented. An analysis of the efficiency factors of the advertising process was carried out based on the classification of tools and methods of advertising automation using artificial intelligence technologies. Practical recommendations for the use of neural networks based on artificial intelligence have been formed. The provisions of the classical and meta-heurastic methods of parametric identification of a fuzzy system, which is a model of the marketing process of companies in the energy sector, are substantiated. The effectiveness of advertising was determined using the methodology of modeling fuzzy systems and the sequence of computational steps of a fuzzy neural system. The practical significance of the results lies in the fact that metaheuristic methods make it possible to assess the effectiveness of advertising based on the use of artificial intelligence tools by companies in the energy sector.

Lithium battery model online parameter identification method based on multi-innovation least squares

Lithium battery model online parameter identification method based on multi-innovation least squares

PMSWG Parameter Identification Method Based on Improved Operator Genetic Algorithm

PMSWG Parameter Identification Method Based on Improved Operator Genetic Algorithm

Lithium battery model online parameter identification method based on multi-innovation least squares

Lithium battery model online parameter identification method based on multi-innovation least squares

Optimal Parameter Identification of Energy Harvesters Using Vector Impedance Quantum Genetic Algorithm: A Case Study With Current Transformer

Energy harvesting technology, as an emerging energy technology, has contributed outstandingly to the application of online monitoring sensors in power systems. The accurate parameter identification of the harvesters is crucial for their power supply applications. This work aims to investigate the parameter identification of harvesters with complex equivalent circuit models, here taking a current transformer as an example. However, previous studies focused on optimizing algorithms rather than data sources. This paper demonstrates a vector impedance quantum genetic algorithm (QGA) parameter identification method to identify the amplitude and phase information of the impedance responses. By comparing the results of the proposed method with the genetic algorithm and PSO based on impedance responses and QGA based on load resistance responses, it is proved that the proposed vector impedance QGA identification method is optimal in terms of accuracy, speed, and robustness. The root mean square errors of the output voltage and the phase difference with the primary current for the proposed method are 5.884 × 10−5 V and 4.473 × 10−4 ms. Moreover, the practical applicability of this method is validated, demonstrating its effectiveness in real‐life and industrial settings. The proposed identification method in this paper changes the source data so that the sample data are small in volume and extensive in information, which enables faster and more accurate parameter identification. This study provides a new idea for parameter identification researches.

Nonparametric identification of a MEMS resonator actuated by levitation forces

This work presents nonparametric identification of a Micro-Electro-Mechanical Systems (MEMS) beam subjected to a non-classical nonlinear electrostatic (levitation) force. The approach is based on the Restoring Force Surface method and the Chebyshev polynomials. The study examines the main challenges associated with the used approach as applied to small-scale systems, such as the inability to measure the restoring force and acceleration. In this work, we use a mix of analytical techniques, experimental data, and Lagrange polynomial interpolation to estimate the restoring forces of the system and its nonlinearities. The extracted results are compared with the measurements, which show excellent agreement. Results are shown for several cases of forcing strength (voltage loads). The results reveal for some cases quadratic terms for the velocity indicating nonlinear damping, which cannot be revealed using parametric identification methods with a priori assumed forms. It is shown that the extracted nonlinear model is robust enough to predict the dynamical behavior of the beam even when the voltage load is increased to nearly 300 % more than the one used for model identification. The presented approach can be applied to other micro and nano systems to identify their characteristics and reveal their nonlinear stiffness and damping parameters.

A fractional-order modelling and parameter identification method via improved driving training-based optimization for piezoelectric nonlinear system

The accurate identification of the parameters of fractional-order systems is still a challenging problem. The purpose of this paper is to establish a fractional hysteresis model based on a Bouc-Wen differential equation and an improved driving training-based optimization (DTBO) parameter identification method to accurately characterize the nonlinear characteristics of the piezoelectric hysteresis system. In this paper, there are two contributions. On the one hand, an accurate fractional-order hysteresis model is proposed and its rate-dependent property is proved based on the definition of fractional calculus; on the other hand, an improved DTBO optimization method with enhanced performance is proposed to achieve excellent precision and robustness for the purpose of parameter identification. The unique features of the improved DTBO algorithm include an instructor-learner separation strategy, a new update mechanism of instructor position, and an improved phase of the learner group. The results show that the output of the model is consistent with that of the actual piezoelectric platform. Compared with the standard DTBO algorithm and Antlion Optimization algorithm, the improved DTBO algorithm has better performance. The novelty of this paper is that a new high-performance heuristic optimization method is proposed for the particular problem of the parameter identification of fractional-order hysteresis models. The proposed method holds promise as a good candidate in the field of hysteresis modelling and identification.

Synchronous Vibration Parameter Recognition of Constant-Speed Blades Based on Blade Tip Clearance Measurement

A new method for the synchronous vibration parameter identification of constant-speed rotating blades based on blade tip clearance (BTC) measurement and blade tip timing (BTT) is introduced. A BTC sensor is used to measure the BTC when the blade tip passes through each sensor. The BTT method is used to determine whether the blade tip arrives in advance or lags. The geometric model between the BTC and the blade tip vibration displacement (BTVD) is established, and the BTVD of the blade tip passing through each sensor is obtained. Then, the nonlinear least squares method is used to determine the synchronous vibration parameters of the constant-speed rotating blade. The results show that with an increase in amplitude, the higher the accuracy of the vibration parameter identification proposed in this paper; with a decrease in the random error of the BTC measurement, the higher the accuracy of the vibration parameter identification proposed in this paper; with a decrease in the random error in the measurement of the blade disk dimensions, the higher the accuracy of the vibration parameter identification proposed in this paper. In addition, the smaller the ratio of the blade length to the blade disk radius, the higher the accuracy of the vibration parameter identification method introduced in this paper. Because the structure of the gas turbine compressor and turbine blade disk has a small blade disk ratio, the method proposed in this paper is suitable for the simultaneous vibration parameter identification of gas turbine compressor blades and turbine blades.

Battery parameter identification method of a battery module based on a multi-physical measurement system

The secondary utilization of retired electric vehicle batteries is beneficial for improving resource utilization efficiency. Capacity and internal resistance are battery parameters that can reflect the battery state. To identify the parameters of a single battery in a battery module, it is usually necessary to disassemble the battery module. The process is complex, time-consuming, and unsafe. In this paper, a battery parameter identification method without disassembling the battery module is developed based on a multi-physical measurement system. First, a multi-physical electrical-thermal-spatial measurement system is constructed to measure the physical parameters of the battery module during charging and discharging. Then, the electrical model and the thermal model are developed. To obtain the capacity and internal resistance of each cell within the battery module, a battery parameter identification model is established with temperature and total battery current as input parameters and battery capacity and internal resistance as output parameters. Batteries with different capacities and internal resistances are connected in series and parallel to simulate retired battery modules for electric vehicles. The battery parameters are identified using the method presented in this paper. According to the experiments, the relative errors of parameter identification for battery capacity and internal resistance are both within 9 %. This method can improve the efficiency of the battery screening process as well as the reliability of the battery system.

Parametric identification method for an absorption air conditioning parabolic trough collector solar plant

In this work is established a parametric identification method for an absorption air conditioning solar plant. A scaled thermal plant, consisting of a thermal capacitor and a flow line that acts as a capacitor and thermal energy radiator is used. As the mathematical model of the scaled plant is structurally identical to that of the solar plant the first is used to determine the methodology that can be used later for the identification of the PTC solar plant. Parametric identification is a necessary step that allows to determine the unknown parameters of the mathematical model of any solar/thermal plant. This model then can be used to analyze the plant characteristics and design an appropriate control algorithm. Although the system model is nonlin-ear it can be expressed in the form of a linear regressor in the parameters. This permits to use the least squares method as the identification method. The method is applied to the thermal plant to identify the useful form that the covariance matrix and excitation signals should have to ensure that when applied to the solar plant its unknown parameters can be properly estimated. Once the solar plant parameters are properly estimated model can be used to analyze and simulate the operation of the ab-sorption air conditioning system.

Identification and modelling of parameters for the information-physical-social convergence characteristics of user-side flexible resources

The use of flexible resource information on the user side helps to increase system efficiency. Power system power variation becomes more pronounced with the access to renewable resources. Therefore, the study proposes a parameter identification and modeling method for the physical and social integration characteristics of flexible resource information on the user side. Taking the user’s air conditioning load as the object, the thermal dynamic model of the air conditioning building is constructed using equivalent thermal parameters, and the variable frequency air conditioning load is embedded in the battery model. The model parameter identification is carried out using high-dimensional model expression technology. According to the experimental data, in options 2 and 3, the system operator makes power purchases based on the storage status of the lithium battery or virtual battery, increasing the number of power purchases when the price of electricity is low and decreasing the number of power purchases when the price of electricity is high. This effectively reduces the system operator’s electricity costs. The error of multiple linear regression modelling varies widely, with relative errors up to 0.75 and an average relative error of 15.1%. The relative error of modelling based on the high-dimensional model expression technique is in the range of 0 to 0.2, with an average relative error of 5.5%. The results show that compared with multiple linear regression models, high-dimensional model representation technology has higher modeling accuracy and can accurately identify the parameters of the air conditioning load aggregation model, solving the problem of difficult parameter calculation in the practical application of the air conditioning load aggregation model, and providing technical support for power system regulation.

An improved dynamic model identification method for small unmanned helicopter

PurposeThe purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.Design/methodology/approachThis method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.FindingsThe algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.Originality/valueThis paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.

Vibration-Based Recognition of Wheel-Terrain Interaction for Terramechanics Model Selection and Terrain Parameter Identification for Lugged-Wheel Planetary Rovers.

Identifying terrain parameters is important for high-fidelity simulation and high-performance control of planetary rovers. The wheel-terrain interaction classes (WTICs) are usually different for rovers traversing various types of terrain. Every terramechanics model corresponds to its wheel-terrain interaction class (WTIC). Therefore, for terrain parameter identification of the terramechanics model when rovers traverse various terrains, terramechanics model switching corresponding to the WTIC needs to be solved. This paper proposes a speed-independent vibration-based method for WTIC recognition to switch the terramechanics model and then identify its terrain parameters. In order to switch terramechanics models, wheel-terrain interactions are divided into three classes. Three vibration models of wheels under three WTICs have been built and analyzed. Vibration features in the models are extracted and non-dimensionalized to be independent of wheel speed. A vibration-feature-based recognition method of the WTIC is proposed. Then, the terrain parameters of the terramechanics model corresponding to the recognized WTIC are identified. Experiment results obtained using a Planetary Rover Prototype show that the identification method of terrain parameters is effective for rovers traversing various terrains. The relative errors of estimated wheel-terrain interaction force with identified terrain parameters are less than 16%, 12%, and 9% for rovers traversing hard, gravel, and sandy terrain, respectively.

Research on railway vehicle modal parameter identification method based on drop impact load

This paper investigates the modal parameter identification technique utilizing a drop impact load. A 12-DOF mathematical model of a Railway Vehicle System (RWVS) was constructed, followed by theoretical calculations, simulation analysis, and tests with four different drop impact loads. The responses to these loads were inspected through FFT, and the modal frequencies of the RWVS were determined by the Peak Picking (P-P) method. The simulation demonstrated that four types of drop impact loads can activate the bounce, pitch, and roll modes of the RWVS. The theoretical calculation and simulation analysis revealed that the maximum error of modal frequency identification is no greater than 6.5%. The experimental results verified that the drop impact excitation method can be used to identify the modal parameters of a complex railway vehicle system, which is highly beneficial for the design and verification of the vehicle structure.

Examination of machine learning method for identification of material model parameters

In this work, we compare two methods of using artificial neural network (ANN) to determine the optimal parameters of material model by results of high-speed plate impact experiments. The first ANN is trained to determine the free surface velocity history based on the impact parameters and material model parameters; this ANN is used as a fast emulator of model to speed-up the statistical Bayesian calibration of the model parameters. The second ANN directly determines the material model parameters based on the impact parameters and the free surface velocity history, because this ANN is trained by inverse data set; this approach for inverse problem is proposed for the first time and can be more preferable for fast-track expert systems in engineering applications. Both approaches are successfully used to determine the optimal parameters of material model in the case of impact of copper and aluminum plates at different temperatures and impact velocities with experimental data from the literature. A simple relaxation plasticity model with a constant relaxation time is used in the present research as an example; more accurate coincidence with the experimental data and wider applicability of the ANN-based approach can be expected for a more realistic plasticity model.

Co-estimation of maximum available capacity and state-of-charge for lithium-ion batteries in multi-operating mode with temperature and degradation state adaptivity

Accurate state of charge (SOC) estimation is critical for battery management systems (BMS). A new SOC estimation framework is developed, containing four modules: battery modeling, parameter identification, SOC estimation, and capacity estimation. It mainly makes three improvements to improve the adaptability. In the parameter identification module, an improved forgetting factor parameters identification method is proposed to obtain model parameters with high precision. For SOC estimation, considering the impact of noise on SOC estimation, an adaptive square root unscented Kalman filter is proposed, which can solve the divergence issue caused by inappropriate noise matrix iteration. Moreover, a weighted recursive least squares algorithm is developed to estimate the maximum available capacity and feed the result back into the SOC estimation module to improve the estimation accuracy. The experimental results show that the proposed framework can obtain high-precision SOC estimation results under different temperatures and aging conditions.

An Accurate Dynamic Model Identification Method of an Industrial Robot Based on Double-Encoder Compensation

Aiming at the challenges to accurately simulate complex friction models, link dynamics, and part uncertainty for high-precision robot-based manufacturing considering mechanical deformation and resonance, this study proposes a high-precision dynamic identification method with a double encoder. Considering the influence of the dynamic model of the manipulator on its control accuracy, a three-iterative global parameter identification method based on the least square method and GMM (Gaussian Mixture Model) under the optimized excitation trajectory is proposed. Firstly, a bidirectional friction model is constructed to avoid using residual torque to reduce the identification accuracy. Secondly, the condition number of the block regression matrix is used as the optimization objective. Finally, the joint torque is theoretically identified with the weighted least squares method. A nonlinear model distinguishing between high and low speeds was established to fit the nonlinear friction of the robot. By converting the position and velocity of the motor-side encoder to the linkage side using the deceleration ratio, the deformation quantity could be calculated based on the discrepancy between theoretical and actual values. The GMM algorithm is used to compensate the uncertainty torque that was caused by model inaccuracy. The effectiveness of the proposed method is verified by a simulation and experiment on a 6-DoF industrial robot. Results prove that the proposed method can enhance the online torque estimation performance by up to 20%.

Dynamic parameter identification based on improved particle swarm optimization and comprehensive excitation trajectory for 6R robotic arm

PurposeRobotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.Design/methodology/approachThis study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.FindingsExperiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.Originality/valueA new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.