Design and development of a dextrous manipulator

This paper develops a novel method of actuation for robotic hands. The solution employs Bowden cable routed to each joint as the means by which the finger is actuated. The use of Bowden cable is shown to be feasible for this purpose, even with the changing frictional forces associated with it's use. This method greatly simplifies the control of the hand by removing the coupling between joints, and allows for direct and accurate translation between the joints and the motors driving the Bowden wires. The design also allows for two degrees of freedom (with the same centre of rotation) to be realised in the largest knuckle of each finger, meaning biological finger kinematics are more accurately emulated.

Design of redundant drive joint with adjustable stiffness and damping mechanism to improve joint admittance

Improving admittance of robotic joints is the key issue for making rehabilitation robots safe. This paper describes a design of Redundant Drive Joint (RD-Joint) which allows greater flexibility in the design of robotic mechanisms. The design strategy of the RD-Joint employs a systematic approach which consists of 1) adopting a redundant joint mechanism with internal kinematical redundancy to reduce effective joint inertia, and 2) adopting an adjustable admittance mechanism with a novel Cross link Reduction Mechanism and mechanical springs and dampers as a passive second actuator. First, the basic concepts used to construct the redundant drive joint mechanism are explained, in particular the method that allows a reduction in effective inertia at the output joint. The basic structure of the RD-Joint is introduced based on the idea of reduced inertia along with a method to include effective stiffness and damping. Then, the basic design of the adjustable admittance mechanism is described. Finally, a prototype of RD-joint is described and its expected characteristics are discussed.

A design of e-procurement infrastructure model for public service sector

Procurement is one of major business operations in public service sector. The advance of information and communication technology (ICT) pushes this business operation to increase its efficiency and foster collaborations between the organization and its suppliers. This leads to a shift from the traditional procurement transactions to an e-procurement paradigm. Such change impacts on business process, information management and decision making. E-procurement involves various stakeholders who engage in activities based on different social and cultural practices. Therefore, a design of e-procurement system may involve complex situations analysis. This paper describes an approach of using the problem articulation method to support such analysis. This approach is applied to a case study from UAE.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Design and development of a dextrous manipulator

This paper describes a novel method of actuation for robotic hands. The solution employs a Bowden cable routed to each joint. The use of a Bowden cable is shown to be feasible for this purpose, ever, with the changing frictional forces associated with it. This method greatly simplifies the control of the hand by removing the coupling between joints, and provides for direct and accurate translation between the joints and the servo motors driving the cables. The design also allows for two degrees of freedom with the same centre of rotation to be realized in the largest knuckle of each finger; thus biological finger kinematics are more closely emulated.

Experimental design and model construction algorithms for radial basis function networks

New construction algorithms for radial basis function (RBF) network modelling are introduced based on the A-optimality and D-optimality experimental design criteria respectively. We utilize new cost functions, based on experimental design criteria, for model selection that simultaneously optimizes model approximation, parameter variance (A-optimality) or model robustness (D-optimality). The proposed approaches are based on the forward orthogonal least-squares (OLS) algorithm, such that the new A-optimality- and D-optimality-based cost functions are constructed on the basis of an orthogonalization process that gains computational advantages and hence maintains the inherent computational efficiency associated with the conventional forward OLS approach. The proposed approach enhances the very popular forward OLS-algorithm-based RBF model construction method since the resultant RBF models are constructed in a manner that the system dynamics approximation capability, model adequacy and robustness are optimized simultaneously. The numerical examples provided show significant improvement based on the D-optimality design criterion, demonstrating that there is significant room for improvement in modelling via the popular RBF neural network.

Regression based D-optimality experimental design for sparse kernel density estimation

This paper derives an efficient algorithm for constructing sparse kernel density (SKD) estimates. The algorithm first selects a very small subset of significant kernels using an orthogonal forward regression (OFR) procedure based on the D-optimality experimental design criterion. The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm. Unlike most of the SKD estimators, the proposed D-optimality regression approach is an unsupervised construction algorithm and it does not require an empirical desired response for the kernel selection task. The strength of the D-optimality OFR is owing to the fact that the algorithm automatically selects a small subset of the most significant kernels related to the largest eigenvalues of the kernel design matrix, which counts for the most energy of the kernel training data, and this also guarantees the most accurate kernel weight estimate. The proposed method is also computationally attractive, in comparison with many existing SKD construction algorithms. Extensive numerical investigation demonstrates the ability of this regression-based approach to efficiently construct a very sparse kernel density estimate with excellent test accuracy, and our results show that the proposed method compares favourably with other existing sparse methods, in terms of test accuracy, model sparsity and complexity, for constructing kernel density estimates.

Design and fabrication of infrared filters for remote sounding instrumentation

Cooled infrared filters have been used in pressure modulation and filter radiometry to measure the dynamics, temperature distribution and concentrations of atmospheric elements in various satellite radiometers. Invariably such instruments use precision infrared bandpass filters and coatings for spectral selction, often operating at cryogenic temperatures. More recent developments in the use of spectrally-selective cooled detectors in focal plane arrays have simplified the optical layout and reduced the component count of radiometers but have placed additional demands on both the spectral and physical performance requirements of the filters. This paper describes and contrasts the more traditional radiometers using discrete detectors with those which use focal plane detector array technology, with particular emphasis on the function of the filters and coatings in the two cases. Additionally we discuss the spectral techniques and materials used to fabricate infrared coatings and filters for use in space optics, and give examples of their application in the fabrication of some demanding long wavelength dichroics and filters. We also discuss the effects of the space environment on the stability and durability of high performance infrared filters and materials exposed to low Earth orbit for 69 months on the NASA Long Duration Exposure Facility (LDEF).

Design of narrowband filters in the infrared region

A method of designing multi-cavity narrowband filters is presented, which is based on a Tschebyshev optical prototype bandpass filter, the equivalent index concept and the variation of phases through the filter structure. Some design results are given.

Simplified parameter adaptive control

A simple parameter adaptive controller design methodology is introduced in which steady-state servo tracking properties provide the major control objective. This is achieved without cancellation of process zeros and hence the underlying design can be applied to non-minimum phase systems. As with other self-tuning algorithms, the design (user specified) polynomials of the proposed algorithm define the performance capabilities of the resulting controller. However, with the appropriate definition of these polynomials, the synthesis technique can be shown to admit different adaptive control strategies, e.g. self-tuning PID and self-tuning pole-placement controllers. The algorithm can therefore be thought of as an embodiment of other self-tuning design techniques. The performances of some of the resulting controllers are illustrated using simulation examples and the on-line application to an experimental apparatus.

Tschebyshev optical filter design

A method of designing multi-cavity infrared narrowband filters for bandwidth between 10% and 20% is presended: The method is based on a Tschebyshev prototype. The theoretical indices from these are simulated by Herpin equivalent layers, the outer layers may be also simulated by Herrmann's asymetrical tri-layer. The new algorithm of filter design can easily be implemented in any microcomputer.

Neurofuzzy design and model construction of nonlinear dynamical processes from data

A common problem in many data based modelling algorithms such as associative memory networks is the problem of the curse of dimensionality. In this paper, a new two-stage neurofuzzy system design and construction algorithm (NeuDeC) for nonlinear dynamical processes is introduced to effectively tackle this problem. A new simple preprocessing method is initially derived and applied to reduce the rule base, followed by a fine model detection process based on the reduced rule set by using forward orthogonal least squares model structure detection. In both stages, new A-optimality experimental design-based criteria we used. In the preprocessing stage, a lower bound of the A-optimality design criterion is derived and applied as a subset selection metric, but in the later stage, the A-optimality design criterion is incorporated into a new composite cost function that minimises model prediction error as well as penalises the model parameter variance. The utilisation of NeuDeC leads to unbiased model parameters with low parameter variance and the additional benefit of a parsimonious model structure. Numerical examples are included to demonstrate the effectiveness of this new modelling approach for high dimensional inputs.