Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult то control using conventional techniques. Here, a novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling input displacement of a new adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Since the conventional control strategy is a very challenging task, fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS controller, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.     

Intelligent robotic gripper with adaptive grasping force

The on-off control robot gripper is widely employed in pick-and-place operations in Cartesian space for handling hard objects between two positions. Without contact force monitoring, it can not be applied in fragile or soft objects handling. Although, an appropriate grasping force or gripper opening for each target could be searched by trial-and-error process, it needs expensive force/torque sensor or an accurate gripper position controller. It has too expensive and complex control strategy disadvantages for most of industrial applications. In addition, it can not overcome the target slip problem due to mass uncertainty and dynamic factor. Here, an intelligent gripper is designed with embedded distributed control structure for overcoming the uncertainty of object’s mass and soft/hard features. A communication signal is specified to integrate both robot arm and gripper control kernels for executing the robotic position control and gripper force control functions in sequence. An efficient model-free intelligent fuzzy sliding mode control strategy is employed to design the position and force controllers of gripper, respectively. Experimental results of pick-and-place soft and hard objects with grasping force auto-tuning and anti-slip control strategy are shown by pictures to verify the dynamic performance of this distributed control system. The position and force tracking errors are less than 1 mm and 0.1 N, respectively.

     

Support vector fuzzy adaptive network in regression analysis

Neural-fuzzy systems have been proved to be very useful and have been applied to modeling many humanistic problems. But these systems also have problems such as those of generalization, dimensionality, and convergence. Support vector machines, which are based on statistical learning theory and kernel transformation, are powerful modeling tools. However, they do not have the ability to represent and to aggregate vague and ill-defined information. In this paper, these two systems are combined. The resulting support vector fuzzy adaptive network (SVFAN) overcomes some of the difficulties of the neural-fuzzy system. To illustrate the proposed approach, a simple nonlinear function is estimated by first generating the training and testing data needed. The results show that the proposed network is a useful modeling tool.     

Diversity of quantum optimizations for training adaptive support vector regression and its prediction applications

Three kinds of quantum optimizations are introduced in this paper as follows: quantum minimization (QM), neuromorphic quantum-based optimization (NQO), and logarithmic search with quantum existence testing (LSQET). In order to compare their optimization ability for training adaptive support vector regression, the performance evaluation is accomplished in the basis of forecasting the complex time series through two real world experiments. The model used for this complex time series prediction comprises both BPNN-Weighted Grey-C3LSP (BWGC) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) that is tuned perfectly by quantum-optimized adaptive support vector regression. Finally, according to the predictive accuracy of time series forecast and the cost of the computational complexity, the concluding remark will be made to illustrate and discuss these quantum optimizations.     

Support vector ordinal regression

In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples. The sequential minimal optimization algorithm is adapted for the resulting optimization problems; it is extremely easy to implement and scales efficiently as a quadratic function of the number of examples. The results of numerical experiments on some benchmark and real-world data sets, including applications of ordinal regression to information retrieval, verify the usefulness of these approaches.     

支持向量回归方法用于取代芳烃急性毒性的预测

采用支持向量回归方法建立了取代芳烃对斜生栅列藻的急性毒性值(-lgEC_50)与分子体积(v)、近似表面积(ASA)、网格表面积(GSA)、水化能(HE)、疏水系数(LogP)间的定量关系,用留一法预报了取代芳烃对斜生栅列藻的急性毒性值(- lgEC_50),相对误差绝对值平均为5．803％,其结果优于偏最小二乘法建模所得结果(相对误差绝对值平均为9．49％)。     

Support vector regression and multilayer feed forward neural networks for non-exercise prediction of VO2max

The purpose of this study is to develop non-exercise (N-Ex) VO₂max prediction models by using support vector regression (SVR) and multilayer feed forward neural networks (MFFNN). VO₂max values of 100 subjects (50 males and 50 females) are measured using a maximal graded exercise test. The variables; gender, age, body mass index (BMI), perceived functional ability (PFA) to walk, jog or run given distances and current physical activity rating (PA-R) are used to build two N-Ex prediction models. Using 10-fold cross validation on the dataset, standard error of estimates (SEE) and multiple correlation coefficients (R) of both models are calculated. The MFFNN-based model yields lower SEE (3.23 ml kg^?1 min^?1) whereas the SVR-based model yields higher R (0.93). Compared with the results of the other N-Ex prediction models in literature that are developed using multiple linear regression analysis, the reported values of SEE and R in this study are considerably more accurate. Therefore, the results suggest that SVR-based and MFFNN-based N-Ex prediction models can be valid predictors of VO₂max for heterogeneous samples.     

A flat surface robotic gripper for handling limp material

A prototype flat-surface, single-chambered and multi-chambered gripper, based on the operational principle of suction and pressure differential has been designed, implemented and experimentally tested. The prototype grippers are proven sufficient to pick and place fabric material accurately and reliably without causing any distortion and/or folding of the fabric. Both prototype grippers have been mounted on AdeptOne and AdeptThree robot arms for experimental and reliability analysis. They both meet requirements as set by the US apparel industry, related to pick and place single cut plies of several types of fabric     

Correlation-aided support vector regression for forex time series prediction

Market is often found behaving surprisingly similar to history, which implies that correlation exists significant for market trend analysis. In the context of Forex market analysis, this paper proposes a correlation-aided support vector regression (cSVR) for time series application, where correlation data are extracted through a graphical channel correlation analysis, compensated by a parameterized Pearson’s correlation to exclude noise meanwhile minimize useful information lost. The effectiveness of cSVR against SVR is confirmed by experiments on 5 contracts (NZD/AUD, NZD/EUD, NZD/GBP, NZD/JPY, and NZD/USD) exchange rate prediction within the period from January 2007 to December 2008.     

Support vector regression for anomaly detection from measurement histories

This research focuses on the analysis of measurements from distributed sensing of structures. The premise is that ambient temperature variations, and hence the temperature distribution across the structure, have a strong correlation with structural response and that this relationship could be exploited for anomaly detection. Specifically, this research first investigates whether support vector regression (SVR) models could be trained to capture the relationship between distributed temperature and response measurements and subsequently, if these models could be employed in an approach for anomaly detection. The study develops a methodology to generate SVR models that predict the thermal response of bridges from distributed temperature measurements, and evaluates its performance on measurement histories simulated using numerical models of a bridge girder. The potential use of these SVR models for damage detection is then studied by comparing their strain predictions with measurements collected from simulations of the bridge girder in damaged condition. Results show that SVR models that predict structural response from distributed temperature measurements could form the basis for a reliable anomaly detection methodology.     

支持向量回归算法用于烷基苯若干热物性定量预测

烷基苯精馏分离是石油化工重芳烃加工的基本方法,各种烷基苯的热物性智能数据库对重芳烃加工过程优化控制有实用价值。本文研究了烷基苯系化合物若干热物性与化合物结构间的关系。采用新近提出的、特别适合于小样本多变量训练集的支持向量回归（support vector regression,SVR）算法总结了烷基苯系化合物已知物性的实验数据,建立了预报烷基苯系化合物若干物性的数学模型。47个烷基苯系化合物正常沸点、沸点汽化热、临界温度、临界压力和临界体积的SVR留一法（leaving—one—out,LOO）预测的平均相对误差值（mean relative error,MRE）分别为0.370％,1.655％。0.791％．2.069％．0.933％。结果表明,支持向量回归算法预测结果优于人工神经网络（ANN）和偏最小二乘（PLS）算法。     

预测同源寡聚蛋白质的支持向量机方法

计算实验表明蛋白质一级结构包含着四级结构信息。本文用支持向量机方法从蛋白质一级结构出发区分同源二聚体和非同源二聚体。蛋白质原始序列的子序列分布用于支持向量机的输入向量,从而充分考虑了蛋白质序列的信息。当子序列的长度为3时,10次交叉验证的总预测准确率达到84．9％,在相同的数据集上,比原有的决策树方法提高了15．0％。实验表明残基顺序对同源寡聚蛋白质的识别起重要作用,而支持向量机方法是蛋白质四级结构预测的强有力工具。     

Design fundamentals of a reconfigurable robotic gripper system

Discusses the design and modeling fundamentals of a multi-degree-of-freedom reconfigurable robotic gripper system (RGS), designed to automate the process of limp material handling, reliably and without distortion, deformation, and/or folding. The reconfigurable gripper design draws upon a previously reported flat surfaced, fixed-dimensions gripper system (Kolluru et al., 1998). The design consists of four arms in a cross-bar configuration, with a flat surfaced, fixed dimensions, suction-based gripper unit mounted on each of the arms. The kinematic and dynamic performance of the reconfigurable RGS is analyzed theoretically and then validated using Integrated Design Engineering Analysis Software (I-DEAS) simulation software     

Support vector regression with reduced training sets for air temperature prediction: a comparison with artificial neural networks

Sudden changes in weather, in particular extreme temperatures, can result in increased energy expenditures, depleted agricultural resources, and even loss of life. However, these ill effects can be reduced with accurate air temperature predictions that provide adequate advance warning. Support vector regression (SVR) was applied to meteorological data collected across the state of Georgia in order to produce short-term air temperature predictions. A method was proposed for reducing the number of training patterns of massively large data sets that does not require lengthy pre-processing of the data. This method was demonstrated on two large data sets: one containing 300,000 cold-weather training patterns collected during the winter months and one containing 1.25 million training patterns collected throughout the year. These patterns were used to produce predictions from 1 to 12 h ahead. The mean absolute error (MAE) for the evaluation set of winter-only patterns ranged from 0.514°C for the 1-h prediction horizon to 2.303°C for the 12-h prediction horizon. For the evaluation set of year-round patterns, the MAE ranged from 0.513°C for the 1-h prediction horizon to 1.922°C for the 12-h prediction horizon. These results were competitive with previously developed artificial neural network (ANN) models that were trained on the full data sets. For the winter-only evaluation data, the SVR models were slightly more accurate than the ANN models for all twelve of the prediction horizons. For the year-round evaluation data, the SVR models were slightly more accurate than the ANN models for three of the twelve prediction horizons.     

A robust adaptive sliding-mode control for rigid robotic manipulators with arbitrary bounded input disturbances

In this paper, a robust adaptive sliding-mode control scheme for rigid robotic manipulators with arbitrary bounded input disturbances is proposed. It is shown that the prior knowledge on the upper bound of the norm of the input disturbance vector is not required in the sliding-mode controller design. An adaptive mechanism is introduced to estimate the upper bound of the norm of the input disturbance vector. The estimate is then used as a controller gain parameter to guarantee that the output tracking error asymptotically converges to zero and strong robustness with respect to bounded input disturbances can be obtained. A simulation example is given in support of the proposed control scheme.     

Multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for OCR prediction

This article investigates the feasibility of multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for the prediction of over consolidation ratio (OCR) of clay deposits based on Piezocone Penetration Tests (PCPT) data. MARS uses piece-wise linear segments to describe the non-linear relationships between input and output variables. LSSVM is firmly based on the theory of statistical learning, and uses regression technique. The input parameters of the models are corrected cone resistance (q _t ), vertical total stress (σ_v), hydrostatic pore pressure (u ₀), pore pressure at the cone tip (u ₁), and the pore pressure just above the cone base (u ₂). The developed LSSVM model gives error bar of predicted OCR. Equations have also been developed for prediction of OCR. The performance of MARS and LSSVM models has been compared with the traditional methods for OCR prediction. As the results reveal, the proposed MARS and LSSVM models are robust models for determination of OCR.