Errors In Parameters Research Articles

Crossflow Microfiltration of Aqueous Suspensions with Guar and Xanthan Gums: Identification of Solutions Using Artificial Neural Networks

Artificial Neural Networks (ANNs) are mathematical models used in the computational area that act in an analogous way to the central nervous system of living beings, which possess the ability of acquiring knowledge in a technique called machine learning, allowing them to recognize patterns and stop numerous applications. Therefore, the objective was to develop Neural Networks capable of identifying aqueous solutions with Guar and Xanthan gums (widely used in the food industry) during the crossflow microfiltration process. The networks were trained in the supervised learning algorithms trainscg, trainlm and traingd, all in the 70/15/15 model, for a range of five to fifteen neurons in the hidden layer, whose datasets were found in the literature, referring to temperature, flow velocity, pressure, transmembrane flow rate, time and membrane pore size. The software used to implement the ANNs was MATLAB and the evaluation criteria consisted of the analysis of the parameters confusion matrix, error histogram,performance and ROC curve. In summary, ten ANNs had satisfactory performances, presenting confusion matrices with accuracies above 98.8%, error histogram graphs being Gaussian centered at 0, decaying performance curves with stopping criterion equal to 6 errors in the validation set and ROC graphs similar to a square with vertices at (0,0), (1,0), (0,1) and (1,1), results considered satisfactory in the literature.

Self-error learning framework-based algorithm for parameter recovery of extended Wiener–Hammerstein systems subject to quantised measurements

In this study, a novel estimation scheme is proposed for identifying extended Wiener–Hammerstein systems with hysteresis nonlinearity subject to quantised measurements. The proposed scheme is established in a self-error learning framework to achieve high-performance parameter estimation compared with classic error feedback learning estimation algorithms. Initially, the useful identification data can be extracted from contaminated system data by introducing an adaptive filter. Then, with the help of the filtered data, the identification error expression used to establish the estimator is derived. Subsequently, an online compensation estimation error variable is proposed to eliminate the effect of the regression vector on the convergence performance. A new adaptive law is designed with adaptive recursive gain, considering the compensation estimation error data and parameter initial error data. Under general persistent excitation (PE) condition, the PE condition of the regressor information is verified online, and the estimator convergence is strictly proven. Finally, the statistical results of two illustrated examples and a real-word example are provided to validate the positive features and effectiveness of the proposed estimation scheme.

A novel collision detection method based on current residuals for robots without joint torque sensors: A case study on UR10 robot

Existing model-based collision detection methods rely on accurate torque dynamic parameters identified using measured joint torques. However, for robots lacking joint torque sensors, only joint currents can be measured, and joint torques must be estimated through the linear relationship between joint currents and joint torque constants. This way can lead to cumulative identification errors in torque dynamic parameters, thereby diminishing the performance of model-based collision detection algorithms. To tackle this challenge, this article proposes an innovative collision detection method based on current residuals, which represent the disparities between measured joint currents and predicted joint currents computed by current dynamic parameters. Then, a dynamic threshold method for current residuals is designed to mitigate the impact of modeling errors at zero-speed direction changes on collision detection performance. Additionally, a suppression strategy based on online load identification and compensation is introduced to reduce the interference of non-collision load factors on collision detection signals. The proposed method mitigates the accumulation of errors on torque dynamic identification resulting from inaccuracies in joint torque constants, ultimately enhancing collision detection performance for robots without joint torque sensors. Extensive experiment results validate the correctness and effectiveness of this approach.

Analysis of the operation of the microprocessor control system of a bridge crane using optimum control laws

This work presents the results of studies on the influence of the error of the main parameters of the bridge crane on the quality of optimal control. The quality of control refers to the exact positioning of the load at the stopping point and the magnitude of the final fluctuations of the load after the crane stops. The concept of optimal control is based on L.S. Pontryagin's maximum principle, which is when the goal is achieved by switching the acceleration-braking control of the load suspension point according to a specific algorithm. To achieve the maximum speed of operation of the crane, certain restrictions and conditions have been removed, namely, the refusal to eliminate load fluctuations during crane acceleration, which simplifies the subsequent elimination of load fluctuations during crane braking. However, this is an unnecessary waste of time, and therefore, acceleration is carried out at the maximum possible acceleration without eliminating load fluctuations. Of course, at the same time, significant fluctuations in the cargo occur, and they continue throughout the entire time the crane is moving. Moreover, the final goal is achieved only at the end of the cycle by switching the acceleration-deceleration modes - the cargo is delivered precisely to the specified point without fluctuations after the crane stops. A PC program that generates optimal control laws for a specific crane has been developed. This program can be used for any lifting machine where the suspension point moves in a straight line, and flexible load suspension is used, such as overhead or gantry cranes, hoists, etc. The results of the research were tested on several objects: a 0.5t crane, a 5t bridge grab crane, and models of bridge cranes. In each case, a good coincidence of theoretical and experimental results was observed, resulting in effective management of the object. The original microprocessor system implemented the optimal control laws, built first based on the MKP-1 microcontroller and then based on Arduino. However, a good result is guaranteed only if the fundamental parameters of the crane coincide with the theoretical ones. This is not easy to achieve during the actual operation of the crane. They vary within a specific range. The article examines how the error in each of the main parameters of the crane affects the result and how the control system can be improved to reduce the negative impact to a minimum. Keywords: optimal control, elimination of load fluctuations, accurate positioning, crane, microprocessor, reloading cycle

Generation of High‐Purity Full Poincaré Beam Arrays via Metasurface Integrated Degenerate Cavity

AbstractRecently, full Poincaré beams (FPBs) with spatially variant polarization states spanning the entire surface of the Poincaré sphere have attracted considerable interests. Expanding the FPBs into an array way has the promising applications from multiparticle manipulation to optical communication. However, how to efficiently generate a high‐purity FPB array with intracavity method remains elusive. Here, a metasurface‐integrated degenerate cavity laser is proposed for efficient generation of 10 × 10 FPB array, where a pair of metasurfaces are adopted to independently modulate the circular polarization state while compensating the mode discrepancies within the cavity. Such a design strategy enables a self‐reproducing intracavity optical field, concurrently ensuring the stabilization of intracavity oscillation. It is numerically demonstrated that the purity of orbital angular momentum (OAM) in the first‐order FPB array can approach to 92.89%, with a root‐mean‐square error (RMSE) of polarization ellipse parameters below 8.17%. This work provides a compact and efficient design platform for the generation of a variety of vector beam arrays and may find promising applications in free space communication and multiple particle manipulation.

Depth Measurement Error Analysis and Structural Parameter Correction of Structured Light Depth Imager

Considering that structured light depth imagers are difficult to use for precision measurements due to their limited measurement accuracy, we propose an innovative method for correcting structural parameters of structured light depth imagers to reduce the depth measurement error caused by structural parameter errors. For the structured light depth imager, the analytical imaging model is established, and the model of depth error caused by structural parameter errors is established based on the analysis of the depth measurement error analysis. Then, structural parameters are corrected according to the depth measurement error analysis and processing based on experimental depth imaging data of the standard reference plane at the maximum depth. As a result, the corrected analytical imaging model and corrected depth measurement values are obtained. Experimental results have demonstrated the success of this proposed method and its simplicity and convenience.

A Multistage Nonlinear Method for Aeroengine Health Parameter Estimation Based on Adjacent Operating Points

Abstract Health parameter estimation is the core of engine gas path analysis (GPA), which is widely adopted for engine safety improvement, as well as for operation and maintenance cost reduction. The major challenge of GPA lies in the contradiction between the high dimensions of parameters under estimation, e.g., health parameters, and the limited measurements obtainable from a small number of sensors. Existent GPA methods for health parameters commonly apply dimension reduction before estimation, leading to information loss and hence inaccurate estimation. To tackle the challenge of limited sensor measurements and to have more system outputs than parameters under estimation, we proposed to augment the output vector of the system model by combining the measurements from multiple adjacent operating points. But the engine model can face the problem of homogenization if using data from adjacent operating points. This can, in turn, lead to a low identifiability of parameters. We analyze the internal mechanism of such large deviation of the parameter estimation results based on linear models and argue for the need of nonlinear method. Hence, we propose a multistage nonlinear parameter estimation method for health parameters, combining biased and unbiased estimation. In our extensive simulations based on 10 output measurements of a JT9D engine, our method can estimate 130% more parameters than the widely used GPA method, while reducing the maximum estimation error of health parameters from 2.2% to 0.1%.

Adaptive control of A class of nonlinear systems with guaranteed parameter estimation: A concurrent learning based approach

AbstractRecent advances in concurrent learning based adaptive controllers have relaxed the persistency of excitation condition required to achieve exponential tracking and parameter estimation error convergence. This was made possible via the use of additional concurrent learning stacks in the parameter estimation algorithm. However, the proposed concurrent learning components, that is, the history stacks, needed to be filled with “selected” values dependent on the actual system states. Therefore, the previously proposed concurrent learning adaptive controllers required the system to be stable initially for a finite time so that the corresponding history stacks can be filled (finite excitation condition). In this work, motivated to remove the finite excitation condition, a novel desired system state based concurrent learning adaptive controller is proposed. In order to remove the system state dependencies in the controller and estimation algorithms, a filtered version of the dynamics and a novel prediction error formulation have been designed. The overall exponential stability, parameter error convergence and boundedness of the system states during closed loop operations are ensured via Lyapunov based arguments. The main advantages of the proposed method are its dependence on the desired system states and the overall stability results that paved the way in removing the need for finite excitation condition. Numerical studies performed on a two link robotic device are also presented to illustrate the feasibility of the proposed method.

Аналіз точності самоналаштування динамічної моделі газотурбінного двигуна

One of the most promising directions in the development of aircraft engines is related to the introduction of adaptive automatic control systems (ACS). The defining element of these systems are dynamic mathematical models of engines capable of self-adjustment based on engine operating parameters measured in flight. A number of leading researchers have developed a concept of using such models called STORM (Self-Tuned On-board Real-time Model). However, in the corresponding works, clearly insufficient attention is paid to solving the problem of checking the sufficiency of the information used to ensure the necessary accuracy of the models. This check must be performed a priori (to predict the composition of engine operating modes, and the volume of registered information), as well as posteriori. The subject of this study is the process of forming dynamic mathematical models (MM) of gas turbine engines using real data for the subsequent use of these models to solve problems related to the control and diagnostics of on-board systems. The goal of this study is to determine the dependence of estimation errors of dynamic parameters of mathematical models on influencing factors. Tasks considered in the work: forming the structure of a mathematical model, dividing the identification process into stages according to the structure of the model (estimating the parameters that determine the static and dynamic properties of the object), forming the least-square functional for the assessment task, determining the errors in estimating dynamic coefficients, analyzing influencing factors, and determining dependencies between factors and errors. For this purpose, the methods of the theory of air-jet engines, the theory of linear dynamic systems, and statistical evaluation are used. The following results were obtained: a mathematical model of a turboshaft engine with a reciprocating gas generator was formed, and ratios were obtained that allow determining the errors in estimating the time constant of a reciprocating engine or gas generator. Scientific and practical innovation: for the first time, a ratio was obtained that determines the errors in estimating the time constant based on the specified values of the measurement errors, the intensity of the jump-like change in fuel consumption, and the frequency and duration of observation. These relations are presented in dimensionless coordinates, which makes them universal and able to be applied to any single-stroke turbojet engine or single-stroke gas generator during a priori or a posteriori analysis of results, as well as planning experiments and debugging on-board self-tuning algorithms of models.

Precision Optimization Analysis of Online Measurement System for Laser Melting Pool Morphology in Additive Manufacturing Process

Selective Laser Melting (SLM), as the most precise and highest quality processing technology in additive manufacturing, is widely used in fields such as aerospace. Online measurement of the laser melting pool, the basic unit of the SLM process, reflects the precision and process stability of SLM. The accuracy of the online measurement system for laser melting pool morphology, which is based on the principle of visual measurement, is crucial for accurate evaluation of the laser melting pool morphology. This paper firstly conducts research on visual calibration technology for the online measurement system of laser melting pool morphology, achieving precise positioning of the visual system and effectively calculating target-related feature information, which is then converted into corresponding digital information for further processing and analysis. Secondly, the paper completes the system error analysis and parameter optimization design, effectively improving the accuracy of the visual measurement system. Finally, theoptimized online measurement system is verified for accuracy and evaluated for precision, proving the feasibility and effectiveness of the methods presented in this paper.

Auxiliary-cavity-enhanced quantum estimation of optorotational-coupling strength.

A scheme is proposed to achieve significantly enhanced quantum estimation of optorotational-coupling (ORC) strength by coupling a driven auxiliary cavity to a Laguerre-Gaussian (L-G) rotational cavity, where the ORC originates from the exchange of orbital angular momentum between a L-G light and rotational mirror. The results indicate that, by appropriately designing the auxiliary-cavity mechanism, the estimation error of the ORC parameter is significantly reduced, and revealing the estimation precision has a much stronger thermal noise and dissipation robustness in comparison with the unassisted case. Our study paves the way toward achieving high-precision quantum sensors.

Leadership Style and Practitioner Motivation

Leaders play an important role in leading followers to achieve organizational goals effectively. They need to communicate with employees in detail. and manage human, financial and marketing capital wisely, simply put Leadership is the process by which individuals motivate or influence others to achieve organizational goals. Therefore, the leadership of executives is very important in making the organization achieve its goals effectively. The researcher therefore conducted a study. Leadership style and motivation of workers by collecting data from a group of 515 corporate executives in the private sector and using questionnaires as a tool. Statistics used in data analysis include. Frequency, Percentage, Mean and Standard Deviation, Chi-square statistic, p-value, Chi-square/df Harmony Level Index (GFI), Adjusted Fit Index (AGFI), Criterion Fit Index (NFI), Comparative Fit Index (CFI), and Parameter Error in Estimation Index (RMSEA). Statistical analysis. Inferential Statistics is the analysis of structural equation models. The results found that Hypothesis 1(H1)Leadership style and motivation of workers. Supportive leadership, directive leadership, achievement- oriented leadership Participatory leadership influences work performance, including work quantity, work quality, time spent on work, DE=0.18*. Accept the research hypothesis. and Hypothesis 2 (H2) work motivation consists of success in work. Being respected, nature of work, responsibilities Progress has an influence on work performance l DE=0.42*,IE=0.47*Accept the research hypothesis. Accept the research hypothesis. Leadership styles and worker motivation Supportive leadership, directive leadership, achievement-oriented leadership Participatory leadership and Work motivation consists of success in work. Being respected, nature of work, responsibilities Progress results in work performance, influencing work performance, including work quantity, work quality, time spent performing work. Statistically significant.

Prediction of Gold price with comparison of forecasting methods

Gold has emerged as an extra famous and very beneficial commodity in phrases of investment. Gold has been considered, as a country wide reserved commodity for many years, which leads to very integral for the economy of any country. Most people and traders believe that gold is a protected investment from uncertainty and political chaos. The rate of motion of gold helps the buyers from the centre of attention in their investments; they make use of the year by year information from Indian Gold Council. The analysis of the data was taken from 1964 to 2020. This paper's motto is to analyze and summarize different algorithms for predicting the rate of gold. The procedures utilized to fit the data were from the Time Series analysis Auto Regressive Integrated Moving Average (ARIMA) and Neural Network models; Multi-Layer Perception (MLP) and Extreme Learning Machine (ELM). The test data were utilized for the analysis, and then the outcome was exhibited with the help of error parameters. ELM is best as compared to ARIMA and MLP. The error measures are RMSE (1634.975) and MAPE (3.002). The error measurements have been represented in the tables for ARIMA and MLP. The best prediction of Gold price was given by the ELM, which is be efficient and accurate model.

A Novel Methodology for Measuring Ambient Thermal Effects on Machine Tools.

Large machine tools are critically affected by ambient temperature fluctuations, impacting their performance and the quality of machined products. Addressing the challenge of accurately measuring thermal effects on machine structures, this study introduces the Machine Tool Integrated Inverse Multilateration method. This method offers a precise approach for assessing geometric error parameters throughout a machine's working volume, featuring a low level of uncertainty and high speed suitable for effective temperature change monitoring. A significant innovation is found in the capability to automatically realise the volumetric error characterisation of medium- to large-sized machine tools at intervals of 40-60 min with a measurement uncertainty of 10 µm. This enables the detailed study of thermal errors which are generated due to variations in ambient temperature over extended periods. To validate the method, an extensive experimental campaign was conducted on a ZAYER Arion G™ large machine tool using a LEICA AT960™ laser tracker with four wide-angle retro-reflectors under natural workshop conditions. This research identified two key thermal scenarios, quasi-stationary and changing environments, providing valuable insights into how temperature variations influence machine behaviour. This novel method facilitates the optimization of machine tool operations and the improvement of product quality in industrial environments, marking a significant advancement in manufacturing metrology.

An EGO-based online model updating method for hybrid simulation

Hybrid simulation combines experimentally testing of physical substructures with computational simulation of numerical substructures to replicate structural responses under earthquakes. When numerical substructure involves parts that are similar to physical substructure, measured responses of physical substructure are often used to update similar parts within numerical substructure, aiming to improve accuracy of seismic assessment. This study proposes an efficient global optimization based approach for online model updating in hybrid simulation (HSMU-EGO) involving multiple key components yet with only one physically tested in laboratory. Kriging meta-model is constructed for the response error between the numerical model for updating and the experimental substructure, while efficient global optimization and optimal Latin hypercube design are integrated for parameter estimation to minimize the response error. Computational simulations are conducted for a simple steel braced frame and a complex concrete continuous bridge to comprehensively evaluate the performance of HSMU-EGO in the presence of constitutive parameter and model errors. The proposed method is demonstrated to effectively and efficiently improve the accuracy of hybrid simulation in almost all cases. Compared with Unscented Kalman Filter (UKF), this proposed HSMU-EGO provides more suitable alternative when prior information is limited for the physical substructure.

Reshaping 3PL Operations: Machine Learning Approaches to Mitigate and Manage Damage Parameters

In the third-party logistics (3PL) environment, it is very important to reduce damage parameters, increase operational efficiency and reduce costs. This study aims to develop strategies for reshaping 3P operations by analyzing the parameters involved in damage control with machine learning. The logistics sector is gradually growing in the world and the potential of the sector is better understood over time. Damage to products in the logistics sector, especially during transportation and storage, not only causes financial losses but also affects customer productivity and operational efficiency. With the use of artificial intelligence techniques, it is possible to determine consumer expectations, predict damage losses, and develop innovative strategies by applying machine learning algorithms. At the same time, options such as driverless vehicles, robots used in storage and shelves, and the easy use of big data within the system, which have emerged with artificial intelligence, minimize errors in the logistics sector. Thanks to the use of artificial intelligence in the logistics sector, businesses are more efficient. This study includes an estimation study in the field of error parameters for the logistics service sector with machine learning methods. In the application, real data of a 3PL company for the last 5 years is used. For the success of 3PL companies, warehousing and undamaged delivery of products are of great importance. The fewer damaged products they send, the more they increase their value. The company examined in the study kept its damage data and wanted it to be analyzed so that it could take precautions accordingly and follow a more profitable path. For this reason, the study focuses on data on errors and damages. This study shows what kind of problems can occur in such a company and how the 3PL company can evaluate the problems to increase customer service quality and cost efficiency.

Accurate error compensation method for multi-axis parallel machine via singularized jacobi geometric parameter correction and coupling error evaluation

The Jacobian model is a prevalent tool for error compensation in multi-axis parallel mechanisms. However, discrepancies between the model's nominal and actual geometrical parameters, combined with equivalent replacements and high-order rounding in the modeling process, lead to equation solving challenges and modeling errors. These inaccuracies result in residual errors in the Jacobian model compensation. To address these problems, this paper proposes an optimal Jacobian correction approach. This is based on a geometrical parameter singularized Jacobian correction model, and a module for the evaluation of coupling errors for multi-axis parallel mechanisms was incorporated. Instead of relying on iterative processes, a singularized geometrical error solution method (SESM) was developed. Through this method, precise derivation of the Jacobian correction parameters is ensured, effectively addressing the indefinite equation challenge and partial posture non-solution problem. Moreover, modeling errors resulting from equivalent infinitesimal replacements and the overlooking of high-order minor values are compensated for by the SESM. It was observed that varying singularized geometrical parameters in the Jacobian model can produce different coupling effects and compensation outcomes. Therefore, a sensitivity-based error predictive evaluation method (EPEM) was introduced. By this method, the optimal correction parameter of the Jacobian model across the entire workspace is identified, ensuring precise pose error compensation. The proposed method was validated using a three-axis parallel mechanism. Through these tests, its superior efficacy was revealed. In comparison to the traditional uncorrected Jacobian compensation, reductions in position and orientation errors by 64.93% and 55.29%, respectively, were achieved. This method provides a new approach for error modeling, equation solving, and parameter correction for multi-axis mechanism error compensation and precision equipment development.

319 Development and Validation of an Artificial Intelligence Model to Accurately Predict Spinopelvic Parameters

OBJECTIVES/GOALS: The correction of spinopelvic parameters is associated with better outcomes in patients with adult spinal deformity (ASD). This study presents a novel artificial intelligence (AI) tool that automatically predicts spinopelvic parameters from spine x-rays with high accuracy and without need for any manual entry. METHODS/STUDY POPULATION: The AI model was trained/validated on 761 sagittal whole-spine x-rays to predict the following parameters: Sagittal Vertical Axis (SVA), Pelvic Tilt (PT), Pelvic Incidence (PI), Sacral Slope (SS), Lumbar Lordosis (LL), T1-Pelvic Angle (T1PA), and L1-Pelvic Angle (L1PA). A separate test set of 40 x-rays was labeled by 4 reviewers including fellowship-trained spine surgeons and a neuroradiologist. Median errors relative to the most senior reviewer were calculated to determine model accuracy on test and cropped-test (i.e. lumbosacral) images. Intraclass correlation coefficients (ICC) were used to assess inter-rater reliability RESULTS/ANTICIPATED RESULTS: The AI model exhibited the following median (IQR) parameter errors: SVA[2.1mm (8.5mm), p=0.97], PT [1.5° (1.4°), p=0.52], PI[2.3° (2.4°), p=0.27], SS[1.7° (2.2°), p=0.64], LL [2.6° (4.0°), p=0.89], T1PA [1.3° (1.1°), p=0.41], and L1PA [1.3° (1.2°), p=0.51]. The parameter errors on cropped lumbosacral images were: LL[2.9° (2.6°), p=0.80] and SS[1.9° (2.2°), p=0.78]. The AI model exhibited excellent reliability at all parameters in both whole-spine (ICC: 0.92-1.0) and lumbosacral x-rays: (ICC: 0.92-0.93). DISCUSSION/SIGNIFICANCE: Our AI model accurately predicts spinopelvic parameters with excellent reliability comparable to fellowship-trained spine surgeons and neuroradiologists. Utilization of predictive AI tools in spine-imaging can substantially aid in patient selection and surgical planning.

Fixed-Time Adaptive Parameter Estimation for Hammerstein Systems Subject to Dead-Zone

The Hammerstein model has proven its ability for modeling many industrial servo systems, but the corresponding parameter estimation remains as a challenging problem, especially for the purpose of achieving fast convergence. This paper presents a fixed-time (FxT) adaptive parameter estimation scheme for Hammerstein systems with an asymmetric dead-zone to ensure the convergence of estimated parameters in fixed-time. A continuous piecewise linear (CPL) function is adopted to model the nonlinear dead-zone dynamics to remedy the use of intermediate variable. To avoid using the system state information, a K-filter is used to construct a relationship between the unknown system parameters and the input/output signals. Then a new adaptive law based on the parameter error and the FxT scheme is developed to online estimate the lumped unknown parameters and ensure the fixed-time convergence, where an online updated auxiliary variable of regressor is suggested to eliminate the impact of the regressor on the convergence performance. Both numerical simulations and experimental results are given to demonstrate the effectiveness of proposed methods.

Error Modeling and Accuracy Analysis of Parallel Robots for Spacecraft Docking Motion Simulation System

Background:: A large number of on-orbit docking dynamics experiments are conducted in the spacecraft docking motion simulation system with the development of Chinese space science and technology, and therefore, the requirement for high-precision parallel robots has increased. The pose accuracy of parallel robots is one of the most important problems in this application. Objective:: In order to evaluate the pose accuracy of parallel robots in advance, a comprehensive pose accuracy analysis method considering major error sources is presented in this paper, and the influence of major error sources on the pose accuracy is also investigated to summarize the statistics and propagation characteristics of the pose error. Methods:: The first-order error model for the parallel robot is established based on a generalized error model for each hydraulic cylinder and a sensitivity analysis method. Using the error model, a statistical approach to the parallel robot pose accuracy analysis is presented, and the influence of different parameter errors and different poses on the pose accuracy of the parallel robot is investigated. Sensitivity analysis is applied to evaluate the contribution of each parameter error to the position and orientation error of the parallel robot. An automated pose accuracy analysis program that computes and graphically displays the position and orientation error distributions and the sensitivity analysis results is developed. Results: The statistical analysis results of the influence of different parameter errors and different poses on pose accuracy are obtained by using the automated pose accuracy analysis program. The means of the position and orientation errors are close to zero. The standard deviations in the x and y directions are larger than those in the z direction, and these standard deviations are amplified with the increase of the parameter errors. For the given elevation, sensitivity analysis to various parameter errors is performed. It is found that the length error sensitivities of the hydraulic cylinders are less than one, and position error sensitivities of the hook joints A4, A5, and A6 are much greater than those of hook joints A1, A2 and A3. Conclusion:: The elemental error sources belong to one of two groups, i.e., those affecting the hydraulic cylinder length and those affecting the hook joints. The distributions of the position and orientation errors are consistent with statistics theories. The parallel robot is more sensitive to the errors affecting the hook joints than those affecting the hydraulic cylinder length. These will help the designers and users of the parallel robot understand the statistics and propagation characteristics of the pose error. Some recent patents on error modeling and kinematic calibration of parallel robots are also discussed in this article.