Search results

The designs of salient pole generators may differ considerably from one hydroelectric plant to another. Automatic optimization procedure is highly desirable, because the designer may have little experience with a similar machine. The presented approach defines the design space by 12 variables which have the largest influence on the goal function. The design space is constrained by a number of linear and nonlinear constraints. The optimization process is based on successive linearizations of the goal function and the nonlinear constraints followed by a simplex procedure. The process is highly effective because the goal function is heavily constrained, so the optimum is virtually always on the boundary of the feasibility region. The procedure has been tested on a number of earlier designs. The goal function could have been reduced on average by some 8 percent, had this software been available at the time of the design of these machines.

In this paper, an online convex optimization method for the exoatmospheric ascent trajectory of space interceptors is proposed. The purpose of this paper is to transform the original trajectory optimization problem into a sequence of convex optimization subproblems.

For convenience in calculating accuracy and efficiency, the complex nonlinear terminal orbital elements constraints are converted into several quadratic equality constraints, which can be better computed by a two-step correction method during the iteration. First, the nonconvex thrust magnitude constraint is convexified by the lossless convexification technique. Then, discretization and successive linearization are introduced to transform the original problem into a sequence of one convex optimization subproblem, considering different flight phases. Parameters of trust-region and penalty are also applied to improve the computation performance. To correct the deviation in real time, the iterative guidance method is applied before orbit injection.

Numerical experiments show that the algorithm proposed in this paper has good convergence and accuracy. The successive progress can converge in a few steps and 3–4 s of CPU time. Even under engine failure or mission change, the algorithm can yield satisfactory results.

The convex optimization method presented in this paper is expected to generate a reliable optimal trajectory rapidly in different situations and has great potential for onboard applications of space interceptors.

The originality of this paper lies in the proposed online trajectory optimization method and guidance algorithm of the space inceptors, especially for onboard applications in emergency situations.

The purpose of this study is to examine the nonlinear relationship between financial constraints and the chief executive officer (CEO) stock options compensation and to analyze whether the impact of financial constraints on the CEO stock options compensation changes at certain level of financial constraints or not.

This study is based on a sample of 90 French firms for the period extending from 2008 to 2019. To deal with the non-linearity, the authors use a panel threshold method.

Using different measures of financial constraints [KZ index (Baker et al., 2003), SA index (Hadlock and Pierce, 2010) and FCP index (Schauer et al., 2019)], the results reveal that the impact of the financial constraints (SA index and FCP index) is positive below the threshold value and it becomes negative above.

The non-linearity between financial constraints and CEO stock options shows that the level of financial constraints can be a major determinant of the CEO compensation structure. More specifically, this study sheds light on the key role played by the level of financial constraints and how this latter influence management decisions.

This paper is the first to the best of the authors' knowledge to examine the nonlinear relationship between financial constraints and the CEO stock options compensation using a panel threshold model.

Contact problems are among the most difficult ones in mechanics. Due to its practical importance, the problem has been receiving extensive research work over the years. The finite element method has been widely used to solve contact problems with various grades of complexity. Great progress has been made on both theoretical studies and engineering applications. This paper reviews some of the main developments in contact theories and finite element solution techniques for static contact problems. Classical and variational formulations of the problem are first given and then finite element solution techniques are reviewed. Available constraint methods, friction laws and contact searching algorithms are also briefly described. At the end of the paper, a bibliography is included, listing about seven hundred papers which are related to static contact problems and have been published in various journals and conference proceedings from 1976.

The purpose of this paper is to present numerical experimentation of three constraint detection methods to explore their main features and drawbacks in infeasibility detection during the design process.

Three detection methods (deletion filter, additive method and elasticity method) are used to find the minimum intractable subsystem of constraints in conflict. These methods are tested with four enhanced NLP solvers (sequential quadratic program, multi-start sequential quadratic programing, global optimization solver and genetic algorithm method).

The additive filtering method with both the multistart sequential quadratic programming and the genetic algorithm solvers is the most efficient method in terms of computation time and accuracy of detecting infeasibility. Meanwhile, the elasticity method has the worst performance.

The research has been carried out for only inequality constraints and continuous design variables. This research work could be extended to develop computer-aided graphical user interface with the capability of including equality constraints and discrete variables.

These proposed methods have great potential for finding and guiding the designer to detect the infeasibility for ill-posed complex design problems.

The application of the proposed infeasibility detection methods with their four enhanced solvers on several mechanical design problems reduces the number of constraints to be checked from full set to a much smaller subset.

The purpose of this paper is to present a new method for solving parametric programming problems; a new scheme of constraints fuzzification. In the proposed approach, constraints are learned based on deductive learning.

Adaptive neural‐fuzzy inference system (ANFIS) is used for constraint learning by generating input and output membership functions and suitable fuzzy rules.

The experimental results show the ability of the proposed approach to model the set of constraints and solve parametric programming. Some notes in the proposed method are clustering of similar constraints, constraints generalization and converting crisp set of constraints to a trained system with fuzzy output. Finally, this idea for modeling of constraint in the support vector machine (SVM) classifier is used and shows that this approach can obtain a soft margin in the SVM.

Properties of the new scheme such as global view of constraints, constraints generalization, clustering of similar constraints, creation of real fuzzy constraints, study of constraint strength and increasing the degree of importance to constraints are different aspects of the proposed method.

Kriging model has been widely adopted to reduce the high computational costs of simulations in Reliability-based design optimization (RBDO). To construct the Kriging model accurately and efficiently in the region of significance, a local sampling method with variable radius (LSVR) is proposed. The paper aims to discuss these issues.

In LSVR, the sequential sampling points are mainly selected within the local region around the current design point. The size of the local region is adaptively defined according to the target reliability and the nonlinearity of the probabilistic constraint. Every probabilistic constraint has its own local region instead of all constraints sharing one local region. In the local sampling region, the points located on the constraint boundary and the points with high uncertainty are considered simultaneously.

The computational capability of the proposed method is demonstrated using two mathematical problems, a reducer design and a box girder design of a super heavy machine tool. The comparison results show that the proposed method is very efficient and accurate.

The main contribution of this paper lies in: a new local sampling region computational criterion is proposed for Kriging. The originality of this paper is using expected feasible function (EFF) criterion and the shortest distance to the existing sample points instead of the other types of sequential sampling criterion to deal with the low efficiency problem.

This paper aims to design an event-triggered adaptive prescribed performance controller for flexible manipulators, with the primary objectives of achieving output performance constraints and addressing communication resource limitations.

The authors propose a novel prescribed performance barrier Lyapunov function (PP-BLF) that considers both output and tracking performance constraints. The PP-BLF ensures that the system's output, transient behavior and steady-state performance, adhere to prescribed constraints. The boundary of the PP-BLF is established by an exponential function that decays over time. Notably, the PP-BLF can be applied seamlessly in unconstrained cases without necessitating controller redesign. Moreover, the controller design incorporates an event-triggered mechanism, effectively reducing the frequency of controller updates and optimizing the utilization of communication resources. Additionally, the authors employ adaptive techniques to estimate the system's unknown parameters and approximate unknown nonlinear functions using radial basis function neural networks (RBFNN). To address the challenge of “complexity explosion”, dynamic surface technology is employed.

Numerical simulations are conducted under five different cases to verify the effectiveness of the proposed controller. The results demonstrate that the controller successfully constrains the output tracking error within the prescribed performance boundary. Moreover, compared with the traditional time-triggered mechanism, the event-triggered mechanism significantly reduces the controller's update frequency, resolving the problem of limited communication resources.

The paper reduces the update frequency of control signals and improves resource utilization through an event-triggered mechanism in the form of relative thresholds. The authors recognize that the event-triggered mechanism may impact the output performance of the system. To address this challenge, the authors propose a prescribed performance Barrier Lyapunov Function (PP-BLF). The PP-BLF is designed to effectively constrain the output performance of the system, ensuring satisfactory control even when the control signal updates are reduced.

The BEKK GARCH class of models presents a popular set of tools for applied analysis of dynamic conditional covariances. Within this class the analyst faces a range of model choices that trade off flexibility with parameter parsimony. In the most flexible unrestricted BEKK the parameter dimensionality increases quickly with the number of variables. Covariance targeting decreases model dimensionality but induces a set of nonlinear constraints on the underlying parameter space that are difficult to implement. Recently, the rotated BEKK (RBEKK) has been proposed whereby a targeted BEKK model is applied after the spectral decomposition of the conditional covariance matrix. An easily estimable RBEKK implies a full albeit constrained BEKK for the unrotated returns. However, the degree of the implied restrictiveness is currently unknown. In this paper, we suggest a Bayesian approach to estimation of the BEKK model with targeting based on Constrained Hamiltonian Monte Carlo (CHMC). We take advantage of suitable parallelization of the problem within CHMC utilizing the newly available computing power of multi-core CPUs and Graphical Processing Units (GPUs) that enables us to deal effectively with the inherent nonlinear constraints posed by covariance targeting in relatively high dimensions. Using parallel CHMC we perform a model comparison in terms of predictive ability of the targeted BEKK with the RBEKK in the context of an application concerning a multivariate dynamic volatility analysis of a Dow Jones Industrial returns portfolio. Although the RBEKK does improve over a diagonal BEKK restriction, it is clearly dominated by the full targeted BEKK model.