Dynamic Parameter Uncertainties Research Articles

Physics-supported Bayesian machine learning for chatter prediction with process damping in milling

Chatter stability of milling operations is a complicated phenomenon causing serious productivity issues in the manufacturing industry, yet a shop-floor implementable solution is lacking. This paper follows a physics-supported Bayesian machine learning approach and incorporates the potential effect of process damping on the stability of the process. Using a likelihood function based on the Nyquist stability criterion, the learning system monitors the actual stability state of the process during arbitrary cuts and refines the underlying model parameter uncertainties in the structural dynamics, cutting force coefficients, as well as the process damping. The framework can operate with limited training data and display the remaining uncertainties in stability predictions to the machine operator. Experimental case studies show the effectiveness of the proposed method and highlight the importance of considering process damping for certain endmills.

Oil and Gas Production Forecasting Using Decision Trees, Random Forst, and XGBoost

Oil and gas production forecasting has always been a hot topic in the petroleum industry. Production forecasting in this sector aims to estimate future production rates, facilitating operational planning, production optimization, and resource allocation for companies. Scientists have traditionally attempted to forecast oil and gas production using methods such as Numerical Reservoir Simulation (NRS) and Decline Curve Analysis (DCA). However, these methods present challenges including time-consuming processes lasting hours or even days, uncertain accuracy, reliance on accurate static models, and uncertainty in dynamic model parameters. In this research, aim to address these limitations by leveraging machine learning models for production forecasting. These models enable faster and more precise decision-making by accurately predicting future outcomes based on historical data. Our study employs three models: Decision Trees (DTR), Random Forest (RFR), and XGBoost. In this reserch utilize the Python programming language and a dataset from wells in New York State, USA. Experimental results demonstrate that the RFR model achieves the highest accuracy (99%) for oil and gas production compared to the XGBoost and DTR models.

A system decomposition method for region tracking control of a non‐holonomic mobile robot with dynamic parameter uncertainties

AbstractThe tracking control problem of non‐holonomic mobile robot systems has been extensively investigated in the past decades, however, most of the existing control strategies were developed specifically for the fixed‐point tracking. This technical note focuses on the region tracking control for a non‐holonomic mobile robot system with parameter uncertainties in the robot dynamics. With the system decomposition and adaptive control method, some restrictions imposed on the angular and linear velocities of the non‐holonomic mobile robot in recent literature are removed, enabling to track dynamic trajectories with any values of the angular and line velocities. The proposed adaptive control scheme can simultaneously solve both the regulation and region tracking problems of a non‐holonomic mobile robot with one passive wheel and two actuated wheels. By utilizing the designed control laws, the mobile robot system is able to globally reach inside a moving region specified by potential functions whose path can be a circular curve, a straight line, or sinusoidal curve, by using a single adaptive controller. Since the dynamic region can be specified arbitrarily small, the fixed‐point tracking can be regarded as a special case of region tracking studied in this paper. Compared with the traditional fixed‐point tracking, region tracking has more flexibility and better robustness. Numerical results are presented to show the effectiveness of the designed strategy.

Data assimilation of PS-InSAR vertical deformation into a frost heave model to analyze subgrade deformation of high-speed railway in northwest China

Temperature changes may cause irregular soil uplift or thawing settlements in frozen soil areas, potentially affecting the safe operation of High-Speed Railways (HSR). Analyzing and predicting these deformation characteristics is thus critical. However, the conventional forecasting and analysis techniques rarely considered factors such as dynamic parameter variations, uncertainties, and measurement errors, which hinder accurate regional scale forecasting. To bridge this gap, this paper introduces a novel time-series coupling method, which integrates post-processing deformation from Interferometric Synthetic Aperture Radar (InSAR) with a frost heave model (FHM), facilitated by the ensemble Kalman filter (EnKF) assimilation algorithm. We obtained deformation observations along the HSR using Persistent Scatterer InSAR (PS-InSAR) technology in combination with time-series post-processing techniques. Considering the causative factors for deformation, we structured the FHM. By integrating FHM with observational data using the EnKF algorithm achieved an efficient upgrade of the posterior distribution of model parameters. This integration significantly improves the predictive accuracy, it facilitates an efficient update to the posterior distribution of model parameters, leading to enhanced prediction accuracy of our model. Our experimental results indicate that the effectiveness of this approach, with observational data assimilation into FHM reducing the average Root Mean Square Error (RMSE) to a mere 0.247 mm. Concurrently, both the Normalized Reduction Error Index (NER) and the Assimilation Efficiency Factor (EFF) values surpassed 0.60 and 0.84 respectively. These underlines signify a successful update of our model parameters, which in turn elevates the accuracy of future deformation predictions, thereby promising safer railway operations.

Control Algorithm for Two-Tank System using Multiparametric Programming

The two-tank system consists of two connected tanks, where the goal is to control the liquid levels in both tanks by manipulating the inflow rates. This study proposes a control algorithm for a two-tank system based on multiparametric programming. In this work, the flow rate is determined using multiparametric programming. The simulation studies demonstrate the proposed work’s effectiveness by comparing its performance with and without noise. Results show accurate and robust regulation of liquid levels, minimizing control effort and making it suitable for systems with dynamic variations or parameter uncertainties. In conclusion, the implementation of multiparametric programming is able to estimate the value of the output for the control algorithm of the two-tank system.

Adaptive backstepping fast terminal sliding mode control of dynamic positioning ships with uncertainty and unknown disturbances

Considering the complexity of the operating environment of marine ships, higher demands are placed on the positioning accuracy and positioning performance of Dynamic Positioning (DP) systems. This paper designs an Adaptive Backstepping Fast Terminal Sliding Mode Control (ABFTSMC) to solve the problems of low positioning accuracy, slow convergence, and poor anti-interference performance in ship positioning due to uncertainty of dynamic model parameters and unknown time-varying environmental disturbances. A Fast Terminal Sliding Mode Control (FTSMC) is combined with backstepping techniques to ensure the robustness of the system to uncertainties and disturbances. Meanwhile, an adaptive law is used to estimate the unknown uncertainty terms. Moreover, the Lyapunov stability criterion is used to prove the convergence and stability of the designed controller. Multiple simulation results show that ABFTSMC has better control performance compared with Adaptive Backstepping Control (ABC) and Adaptive Backstepping Sliding Mode Control (ABSMC). All the comparisons could fully verify the effectiveness and superiority of the proposed control method.

An Integrated Trajectory Planning and Motion Control Strategy of a Variable Rotational Speed Pump-Controlled Electro-Hydraulic Actuator

This article proposes an integrated two-loop motion control strategy of an electro-hydraulic actuator, in which the cylinder actuator is driven by a variable speed pump to track a desired trajectory. The control strategy combines constrained trajectory planning (outer loop) with nonlinear motion control (inner loop) to achieve accurate trajectory tracking of electro-hydraulic actuators under constraints. A nonlinear filter-type trajectory planner is utilized as the outer loop to synthesize the optimal trajectory, reaching the desired reference with the minimum time while fulfilling the constraints. A full-state constraints assignment approach is proposed, including motion and pressure state constraints. Furthermore, to accurately track the synthesized trajectory of the outer loop, a model-based adaptive robust backstepping motion controller is designed as the inner loop, which can realize guaranteed robustness and high accuracy of the trajectory tracking in the presence of dynamic nonlinearities and parametric uncertainties of the electro-hydraulic actuator. Experiments with contrast control strategies and practical trajectories are conducted, which comprehensively verify the advantages of the proposed integrated two-loop control strategy.

Collision-probability-based hopping trajectory optimization on hazardous terrain of small bodies

This paper studies the safe motion of a hopping rover on hazardous terrains of small bodies in the presence of initial state errors and dynamic parameter uncertainties. The collision probability is adopted to optimize the hopping trajectory. Firstly, the hazardous terrain is characterized by the least square median method, and the feasible path point zone is defined for hopping motion. Meanwhile, the hopping trajectory optimization is converted into sequence path point planning and optimal trajectory design problems based on the classical path planning algorithm. Then, considering the effects of errors and uncertainties, a performance index is derived to design a continuous hopping trajectory of sequence path points. Taking the collision probability as an index to indicate safety, local path points are optimized through path point decomposition and replacement strategy. Therefore, a collision-probability-based continuous optimal hopping trajectory can be generated to realize safe and precise motion. Finally, with the simulated hazardous terrain based on the small body 433 Eros, the effectiveness of the proposed method is verified by numerical analysis.

Research on a New Control System Based on L1 Adaptive Control Scheme for the Voice Coil Motor

With the continuous development of voice coil motors, it has also been widely used in today’s sports competitions. For example, the Hawk-Eye system in tennis matches uses voice coil motors to focus on the camera to capture the trajectory of the tennis ball. Therefore, in order to better solve the problem of dynamic parameter uncertainty, external load disturbance, and tracking control of the voice coil motor servo system in motion, this paper proposes for the first time the strategy of using L1 adaptive control algorithm to control the voice coil motor servo system. First, it briefly analyzes the working principle and control method of the voice coil motor, and then constructs the uncertain parameter model according to the mathematical model of the voice coil motor. The closed-loop system of voice coil motor is simulated and analyzed by L1 adaptive controller. The results show that the L1 adaptive control method effectively suppresses the high-frequency interference caused by the voice coil motor in operation, can control the tracking error to gradually converge and so the tracking effect is better, and has strong robustness to the perturbation of system parameters. It can be applied to voice coil motor servo control.

A simplified method for quantifying the progressive collapse fragility of multi-story RC frames in China

Multi-story reinforced concrete (RC) frames are one of the most widely-used structural systems in engineering practice. To prevent the progressive collapse of RC frames under disastrous events, it is desirable to systematically investigate the progressive collapse fragility of the structures under column loss scenarios. This study proposed a simplified procedure for quantifying the progressive collapse fragility of multi-story RC frames, taking into consideration the influences of dynamic effects and parameter uncertainties. Nine typical RC frames were designed according to the Chinese building codes and investigated by the proposed method. The effects of seismic design intensity and the number of stories on the progressive collapse fragility of the RC frames were investigated. The results indicate that the progressive collapse resistance of multi-story RC frames depends on the combining effects of the structural loads and the bearing capacity of the beams. The structural resistance generally improves when increasing the number of stories. In contrast, it improves with the increase of seismic design intensity only when the seismic load is involved in the controlled load combination. For the RC frames that have various componential dimensions along the height, the stories with a changed componential dimension tend to collapse more likely under column removal scenarios. The outcomes of this study can provide references for quantifying the progressive collapse resistance and recommending appropriate load increase factors for the engineering design of multi-story RC frames in China.

Task-Space Cooperative Tracking Control for Networked Uncalibrated Multiple Euler–Lagrange Systems

Task-space cooperative tracking control of the networked multiple Euler–Lagrange systems is studied in this paper. On the basis of establishing kinematic and dynamic modeling of a Euler–Lagrange system, an innovative task-space coordination controller is designed to deal with the time-varying communicating delays and uncertainties. First, in order to weaken the influence of the uncertainty of kinematic and dynamic parameters on the control error of the system, the product of the Jacobian matrix and the generalized spatial velocity are linearly parameterized; thus, the unknown parameters are separated from known parameters. The online estimation of uncertain parameters is realized by designing parameters and by proposing new adaptive laws for the dynamic and kinematic parameters. Furthermore, to describe the transmission of time-varying delay errors among networked agents, a new error term is introduced, obtained by adding the observation error and tracking error, and the coefficient of the network mutual coupling term related to the time-varying delay rate is added with reference to the generalized space velocity and task-space velocity of the Lagrange systems. In the end, the influence of the time-varying delay on the cooperative tracking control error of the networked multiple Euler–Lagrange systems is eliminated. With the help of Lyapunov stability theory, the tracking errors and synchronization errors of this system are calculated by introducing the Lyapunov–Krasovskii functional; the asymptotic convergence results rigorously prove the stability of the adaptive cooperative control systems. The simulation results verify the excellent performance of the controller.

Adaptive control of time delay teleoperation system with uncertain dynamics.

A bilateral adaptive control method based on PEB control structure is designed for a class of time-delay force feedback teleoperation system without external interference and internal friction to study the uncertainty of dynamic parameters and time delay. The stability and tracking performances of the closed-loop constant time delay teleoperation system are analyzed by Lyapunov stability theory. Finally, the controller designed in this paper is successfully applied to the teleoperation system composed of a two-degree of freedom rotating manipulator as the master robot and the slave robot. The simulation is carried out in no operator and environment force or with operator and environment force. The adaptive bilateral control method's control performance is compared with that of the traditional time-delay teleoperation system. Finally, it is verified that the method has good control performance.

Estimation of Vs30 Bounds From Limited Soil Data Considering Uncertainty

According to design standards, uncertainty of dynamic foundation parameters are accounted in soil structure interaction (SSI) analysis by considering best estimate, upper and lower bounds. Site specific variability (coefficient of variation: Cv) cannot be directly established from limited site measurements. Codes prescribe a value of unity for such cases, which results in wide and often unrealistic range of foundation parameters for SSI. A Monte Carlo (MC) simulation based approach is proposed for calculation of site specific Cv from limited measurements. Along with 12% reduction of mean value, Cv could be reduced to 0.5, thereby enabling reduction of the aforementioned range. Adoption of code specified minimum Cv could accommodate 50% inaccuracy of input shear wave velocity, which justifies use of correlation equations (with inaccuracies reported between 20% and 35%) for generation of synthetic shear wave velocity profiles as substitute for or augmentation of site measurements.

Robust trajectory tracking control of non-holonomic wheeled mobile robots using an adaptive fractional order parallel fuzzy PID controller

Non-holonomic wheeled mobile robots (WMRs) are highly uncertain, multi-input multi-output (MIMO), non-linear dynamic systems that are expected to perform under varying environment and structural reservations. An Adaptive Fractional Order Parallel Fuzzy Proportional-Integral-Derivative (AFO−PFPID) controller is proposed and investigated on WMR to meet the above challenges. Computer simulations were carried out under the effects of dynamic parameter variations, noise, forced displacement, time delay, and uncertainty in the pose to thoroughly assess the controller's performance. Further, to evaluate its relative assessment, the AFO−PFPID controller's performance is compared with its integer counterpart Adaptive Integer Order Parallel Fuzzy Proportional-Integral-Derivative (AIO−PFPID) controller. Both the controllers were tuned with the Multi-Objective Grey Wolf Optimization Algorithm to minimize the positional and velocity profile errors with an overall goal to attain effective trajectory tracking. Though both the controllers effectively performed tracking goals, the AFO−PFPID controller has offered a significantly robust performance even under the model uncertainties and disturbances. Therefore, based on the presented investigations, it is concluded that the AFO−PFPID controller is a superior control technique for non-holonomic WMRs trajectory tracking application.

Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver

This paper presents a sliding mode fuzzy control approach for industrial robots at their static and near static speed (linear velocities less than 5 cm/s). The extended Kalman filter with its covariance resetting is used to translate the coordinates from Cartesian to joint angle space. The translated joint angles are then used as a reference signal to control the industrial robot dynamics using a sliding mode fuzzy controller. The stability and robustness of the proposed controller is proven using an appropriate Lyapunov function in the presence of parameter uncertainty and unknown dynamic friction. The proposed controller is simulated on a 6-DOF industrial robot, namely the Universal Robot-UR5, considering the maximum allowable joint torques. It is observed that the proposed controller can successfully control UR5 under uncertainties in terms of unknown dynamic friction and parameter uncertainties. The tracking performance of the proposed controller is compared with that of the sliding mode control approach. The simulation results demonstrate superior performance of the proposed approach over the sliding mode control method in the presence of uncertainties.

A long short-term memory based deep learning algorithm for seismic response uncertainty quantification

The application of metamodeling technique to overcome computational challenge of Monte Carlo simulation (MCS) technique for response uncertainty quantification under stochastic earthquake load is a difficult task due to the high-dimensional nature of stochastic load. Recent developments in the sequential models for forecasting and prediction have opened a new avenue in this regard. Various deep learning algorithms, particularly the convolutional neural network and recurrent neural network are quite suitable for response uncertainty quantification of nonlinear stochastic dynamic system. However, most of the existing studies consider stochastic load as the only source of uncertainty assuming the parameters characterizing a structure as deterministic. The present study proposes a long short-term memory (LSTM) based deep learning algorithm for seismic response uncertainty quantification by duly addressing both the stochastic nature of dynamic load and structural system parameter uncertainty. The functional application program interface feature of Keras that allows layers sharing to form more complex model is explored to form a response approximation model. It incorporates more than one input source i.e., stochastic dynamic excitation sequence as well as structural system parameter uncertainty. The proposed algorithm is elucidated through two numerical examples i.e., a proof-of-concept example and one realistic structural engineering problem by considering the direct MCS based results as the benchmark. The results of accuracy matrices, regression analysis results, comparison of seismic response statistics and reliability results with the results of direct MCS technique clearly revealed the enhanced prediction capability of the proposed LSTM model.

Climate change effects Management with the approach of the uncertainty of Atmosphere-Ocean General Circulation Models in Hamadan Province, Iran

INTRODUCTION: Since Iran is located in the semi-arid belt, it has faced such issues as drought, dust crisis, and intensified migration. The assessment of the effects of climate change includes identifying some key aspects of uncertainties used to estimate its impacts, such as uncertainties in the context of Atmosphere-Ocean General Circulation Models (AOGCMs): in regional-scale climatology, in statistical or dynamic downscaling methods, and parametric and structural uncertainties in different models. One of the most important sources of uncertainty in climate change is the use of different AOGCMs that produce different outputs for climate variables. METHODS: In this study, to investigate the uncertainty of AOGCM models, the downscaled data of the NASA Earth Exchange Global Daily Downscaled Projections dataset obtained from 21 AOGCMs with medium Representative Concentration Pathway4.5 scenario were downloaded from the NASA site for 81 cells in Hamadan Province, Iran. After the validation of the models, they were evaluated against the criteria of the coefficient of determination and model efficiency coefficient in comparison with the data of the Hamedan synoptic station in the statistical period of 1976-2005. To reduce the uncertainty of AOGCMs, the ensemble performance (EP) of models was used in Climate Data Operators software. FINDINGS: It was revealed that MRI-CGCM3, MPI-ESM-LR, BNU-ESM, ACCESS1-0, MIROC-ESM, MIROC-ESM-CHEM, and MPI-ESM-MR models had better performance than similar models. It was also found that IPSL-CM5A-LR, CNRM-CM5, CSIRO-Mk3-6-0, CESM1-BGC, and GFDL-ESM2M had the lowest correlation between observational and simulation data of mean monthly precipitation. CONCLUSION: According to the results, this method could provide a good estimate in the base period (1976-2005), compared to the data of the Hamedan synoptic station, and was more accurate compared to the single implementation method of each AOGCM model. The results of EP of models in the future period (2020-2049) showed that precipitation will not change considerably in the future and will increase by 0.23 mm. In addition, the average, maximum, and minimum annual temperatures will increase by 1.54°C, 1.7°C, and 1.40°C, respectively.

Quaternion‐based adaptive control for trajectory tracking of quadrotor unmanned aerial vehicles

SummaryThe problem of trajectory tracking of aerial vehicles with four rotors and dynamic parameter uncertainties is addressed in this article. By exploiting the cascade structure of the translational and attitude dynamics of the quadrotor, a nonlinear hierarchical adaptive control is proposed. The attitude control law is designed based on the unit quaternion, therefore, the singularities of the Euler angles and angle‐axis representation are avoided. Instead of relying on the stability properties of time‐varying cascade systems, the stability of the whole closed‐loop system is proven by an appropriate Lyapunov function. In addition, a novel method to compute the desired orientation based on the desired Cartesian trajectories and position control inputs is proposed. A set of simulations results are presented to assess the performance of the proposed adaptive controller.

Adaptive Tracking PID and FOPID Speed Control of an Elastically Attached Load Driven by a DC Motor at Almost Step Disturbance of Loading Torque and Parametric Excitation

Adaptive tracking control of the speed of a very elastically attached circular load driven by a direct current motor accompanied with an adaptive conventional and a fractional-order Proportional Integral Derivative (PID) controller is studied. It refers to improving the closed-loop control system response of elastically coupled components of drivelines. The motor and the load mechatronic models and the block diagrams are constructed. Parameters of the PID controller in the model reference control are both constant, as well as vary in time. The adaptive control method is improved by the application of a new closed-loop control structure canceling error dynamics. A few competing control strategies are tested based on the application of two types low and high frequency stepwise increasing variations of loading torque and damping coefficient of motion. Moreover, the performance of the control strategies is verified by Integral Time-Weighted Absolute Error (ITAE) index, since their robustness is evaluated by applying a sine modulated triangle waves of selected electric parameters. Therefore, a dynamic forcing and parameter uncertainty is applied. Simulation results are compared for checking the proposed methods.

Research on dynamic transmission error of harmonic drive with uncertain parameters by an interval method

Transmission error is one of the most important performance indicators for evaluating harmonic drives, and can have crippling effects on positioning accuracy and stability of industrial robots. However, most of the existing error analysis methods focus on a single factor, and do not consider the uncertainty of dynamic parameters, leading to evident limitations. In the present study, static transmission error (caused by manufacturing and assembly error) and dynamic transmission error (generated by static transmission error and dynamic parameters) of a harmonic drive are modeled. An interval method is developed and used to numerically express uncertain dynamic parameters of the system. Chebyshev polynomials are used to approximate the dynamic differential equations of the harmonic drive, and then the distribution of dynamic transmission error and its relationship with uncertain parameters are discussed in detail. In addition, a global sensitivity analysis is carried out to intuitively demonstrate how much impact each parameter has on dynamic transmission error. Our results suggest that the moment of inertia Jin and the torsional stiffness coefficient k1 have a large influence on dynamic transmission error. Finally, the proposed method is validated by experiment. The method can be adopted to determine the upper and lower bounds of dynamic transmission error of dynamic systems under the influence of uncertain parameters and provides a theoretical basis for transmission error optimization and compensation.