Use Of Numerical Optimization Techniques Research Articles

Positioning Accuracy Improvement of Industrial Robots Considering Configuration and Payload Effects Via a Hybrid Calibration Approach

Robot calibration of an industrial robot is of importance for those applications requiring high positioning accuracy. The use of numerical optimization techniques to identify the accurate kinematic parameters and tackle the residual errors for improving positioning accuracy is still challenging. The difficulties in using the conventional optimization techniques is associated with the expensive computation of the second derivative terms in the Hessian matrix and even more linked to the additional improvement of the residual errors induced by the configuration and payload effects for the practical applications. A hybrid calibration procedure, which is robust and efficient, is proposed to handle the said problems in this study. The first step is to utilize the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm together with the quasi-Newton method to optimize the cost function with non-smooth nature established using massive measurement data. In the second step, two kinds of artificial neural network (ANN) algorithms are employed to further improve the nonlinear residual errors induced by payload and configuration effects. After the completion of the hybrid calibration, improvements of 88.1% and 80.1% have been attained, respectively, for the mean and maximum positioning errors of the robot end-effector, i.e., the mean/maximum positioning errors are reduced from 2.613mm/6.294mm to 0.310mm/1.255mm for 840 untrained measurement data. The experimental results also validated the effectiveness of the proposed method.

Simultaneous Cross Section and Launching Nose Optimization of Incrementally Launched Bridges

The conventional design process of the launching nose of an incrementally launched bridge is usually based on parametric models with great limitations and the use of a trial and error technique to improve the design. Facing the disadvantages of traditional approaches, the use of numerical optimization techniques permits the development of objective, rigorous, and complete formulations. This work solves the design optimization problem of an incrementally launched prestressed concrete bridge during construction, searching simultaneously for the optimum cross section dimensions and the prestressing forces of the concrete deck, as well as the most adequate characteristics of the launching nose for the best economic solution. A computer code that allows the generation of a deck was developed, under real design constraints similar to those a bridge designer may encounter in practice. This code is very useful for preliminary phases of a project. The results obtained with this innovative formulation show the benefit of considering numerical optimization tools.

Least-squares support vector machines modelization for time-resolved spectroscopy

By use of time-resolved spectroscopy it is possible to separate light scattering effects from chemical absorption effects in samples. In the study of propagation of short light pulses in turbid samples the reduced scattering coefficient and the absorption coefficient are usually obtained by fitting diffusion or Monte Carlo models to the measured data by use of numerical optimization techniques. In this study we propose a prediction model obtained with a semiparametric modeling technique: the least-squares support vector machines. The main advantage of this technique is that it uses theoretical time dispersion curves during the calibration step. Predictions can then be performed by use of data measured on different kinds of sample, such as apples.

Physically correlated muscle activation for a human head and neck computational model

A computational 50th percentile male head and neck complex model was correlated to physical experimental data. The computational model utilizes 15 muscle pairs represented by the Hill Muscle Model with the complete head/neck system modeled using MADYMO™. The model was used for analysis and optimization of activation and deactivation of muscle activity in flexion and extension. Sensitivity analysis performed using the model shows that, of the multiple parameters within the Hill Model, activation level and timing prove to have the greatest effect on the system kinematics. In addition, the rate by which an activation level is changed becomes an important factor in the simulation. With the use of numerical optimization techniques, a pattern was determined for the applied activation/deactivation rates and timing of flexors and extensors during flexion and extension of the head. The numerical optimization result correlated to within 9% of measured value during the initial flexion of the head. The optimized activation model reflected an activation onset 90 ms after the start of the impulse load, which agrees with published reaction times of muscles. Activation and deactivation rates for the extensors were found to be 1.7 and 0.29%, respectively. While the onset of activation of the flexor muscles occurred before rebound, it was found that muscles, at near the mid-plane, were triggered by the optimized model to abate the flexion. Rates of activation and deactivation of the flexors were found to be 0.9 and 0.3%, respectively. Both the extensors as well as the flexors were found to activate only up to 70% before deactivating. Therefore, it was evident from this study that using the Hill Muscle Model with the activation parameter modeled as binary, 0 or 100%, may lead to inaccurate simulation results.

Survival of the fittest—genetic algorithms versus evolution strategies in the optimization of systems models

The use of numerical optimization techniques on simulation models is a developing field. Many of the available algorithms are not well suited to the types of problems posed by models of agricultural systems. Coming from different historical and developmental backgrounds, both genetic algorithms and evolution strategies have proven to be thorough and efficient methods in identifying the global optimum of such systems. A challenging herd dynamics model is used to test and compare optimizations using binary and real-value genetic algorithms, as well as evolution strategies. All proved successful in identifying the global optimum of this model, but evolution strategies were notably slower in achieving this. As the more successful innovations of each of these methods are being commonly adopted by all, the boundaries between them are becoming less clear-cut. They are effectively merging into one general class of optimization methods now termed evolutionary algorithms.

On the optimal design of switched-capacitor filter circuits for analog and mixed-signal integrated circuit realization

This contribution describes developments in the use of numerical optimization techniques as part of a package whose function is to generate silicon-level layout for general analog functional modules from high-level specifications. The investigation is using switched-capacitor filters as a case study that is representative, in terms of its associated physical layout problems, of many classes of analog circuits.

Aspects of Optimization-based CADCS

With the recent dramatic increase in available computing power, numerical optimization has become an attractive tool for the design of complex engineering systems. Yet, generalized use of numerical optimization techniques in design has been hindered by (i) the difficulty to translate in a faithful manner the actual design problem into any kind of rigid mathematical optimization problem, (ii) the inability of classical optimization tools to efficiently take into account the many distinctive features of optimization problems arising in a design context, and (iii) the unavailability of software tools offering to the designer a powerful as well as congenial environment supporting such capabilities.In this paper, some aspects of these questions are touched upon and avenues are suggested to address them. In particular, a recently proposed interaction driven design methodology is briefly described and numerical optimization schemes satisfying two specific requirements of many design problems are sketched. As an example, the design of a controller for a copolymerization reactor using the Maryland developed CONSOLE system is considered.

The use of numerical optimization techniques in the design of acoustic horn loudspeakers

The use of numerical optimization techniques in the design of acoustic horn loudspeaker systems is considered. Conventional synthesis techniques (i.e., stepped impedance transformer or nonuniform line) are inadequate for horn loudspeaker design due to the frequency‐dependent nature of the radiative load at the mouth and the complex behavior of the driver. In this paper, the horn is modeled as an n‐section commensurate stepped impedance transformer terminated in a one‐port network that represents the complex frequency‐dependent radiation impedance. The driver is modeled as a linear two‐port transducer using measured data as described in an earlier presentation [J. S. McLean and E. L. Hixson, J. Acoust. Soc. Am. Suppl. 1 79, S90 (1986)]. The entire horn/driver system is optimized to exhibit equiripple frequency response using the characteristic impedances of the individual transformer sections as the optimization variables. The system is optimized as a singly terminated filter in order that it may be driven...

ON OPTIMAL DESIGN OF MULTI-PURPOSE TIE-BEAMS

The paper presents a method of solving the problem of optimally designing mulli-purpose tie-beams. The problem is that of minimizing the maximum deflection of a simply supported beam under transverse load. The volume of the beam is prescribed and the maximum allowable longitudinal elongation if the beam were to act as a tie is given. The method of solution presented herein makes use of numerical optimization techniques to obtain a design satisfying specified criteria. The solutions thus obtained are compared with the results of others to show the effectiveness and usefulness of the method of solution presented herein.