Cycling road racing is a popular sport for people all over the world. Different types of cycling road races require different abilities from the riders. Flexible riding strategies also play an important role in these ...
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To tackle the dilemma between adaptability and performance in grasping estimation solutions, this paper presents a robotic grasping scheme featuring an object-gripper motion space between gripper-agnostic fingertip es...
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A nonlinear programming approach, along with finite element implementation, has been developed to perform plastic limit analysis for materials under non-associated plastic flow, so the plastic stability condition of n...
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A nonlinear programming approach, along with finite element implementation, has been developed to perform plastic limit analysis for materials under non-associated plastic flow, so the plastic stability condition of non-standard materials can be directly calculated. In the framework of Radenkovic's theorems, a decoupled material model with non-associated plastic flow was introduced into kinematic limit analysis so that the classic limit theorems were extended for non-standard plastic flow materials, such as cohesive-frictional materials. A non-associated plastic dissipation power is derived, and a purely kinematic formulation is obtained for limit analysis. Based on the mathematical programming theory and the finite element method, the numerical implementation of kinematic limit analysis is formulated as a nonlinear programming problem subject only to one equality constraint. An extended direct iterative algorithm is proposed to solve the resulting programming problem. The developed method has a wide applicability for limit analysis. The effectiveness and efficiency of the proposed method are validated through numerical examples and the influence of non-associated plastic flow on stability conditions of structures is numerically investigated.
This paper constructs a centralized noncooperative game model of power sales with grid's risk avoidance and its renewable energy power's investment and production strategy considering renewable energy power pr...
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This article proposes a hybrid optimization algorithm based on a modified BFGS and particle swarm optimization to solve medium scale nonlinear programs. The hybrid algorithm integrates the modified BFGS into particle ...
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This article proposes a hybrid optimization algorithm based on a modified BFGS and particle swarm optimization to solve medium scale nonlinear programs. The hybrid algorithm integrates the modified BFGS into particle swarm optimization to solve augmented Lagrangian penalty function. In doing so, the algorithm launches into a global search over the solution space while keeping a detailed exploration into the neighborhoods. To shed light on the merit of the algorithm, we provide a test bed consisting of 30 test problems to compare our algorithm against two of its variations along with two state-of-the-art nonlinear optimization algorithms. The numerical experiments illustrate that the proposed algorithm makes an effective use of hybrid framework when dealing with nonlinear equality constraints although its convergence cannot be guaranteed. (C) 2012 Elsevier Ltd. All rights reserved.
We present a class of trust region algorithms that do not use any penalty function or a filter for nonlinear equality constrained optimization. In each iteration, the infeasibility is controlled by a progressively dec...
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We present a class of trust region algorithms that do not use any penalty function or a filter for nonlinear equality constrained optimization. In each iteration, the infeasibility is controlled by a progressively decreasing upper limit and trial steps are computed by a Byrd-Omojokun-type trust region strategy. Measures of optimality and infeasibility are computed, whose relationship serves as a criterion on which the algorithm decides which one to focus on improving. As a result, the algorithm keeps a balance between the improvements on optimality and feasibility even if no restoration phase which is required by filter methods is used. The framework of the algorithm ensures the global convergence without assuming regularity or boundedness on the iterative sequence. By using a second order correction strategy, Marato's effect is avoided and fast local convergence is shown. The preliminary numerical results are reported. (C) 2012 Elsevier Ltd. All rights reserved.
We propose a new class of incremental primal-dual techniques for solving nonlinear programming problems with special structure. Specifically, the objective functions of the problems are sums of independent nonconvex c...
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We propose a new class of incremental primal-dual techniques for solving nonlinear programming problems with special structure. Specifically, the objective functions of the problems are sums of independent nonconvex continuously differentiable terms minimized subject to a set of nonlinear constraints for each term. The technique performs successive primal-dual increments for each decomposition term of the objective function. The primal-dual increments are calculated by performing one Newton step towards the solution of the Karush-Kuhn-Tucker optimality conditions of each subproblem associated with each objective function term. We show that the resulting incremental algorithm is q-linearly convergent under mild assumptions for the original problem.
This paper presents a numerical upper bound limit analysis using radial point interpolation method (RPIM) and a direct iterative method with nonlinear programming. By expressing the internal plastic dissipation power ...
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This paper presents a numerical upper bound limit analysis using radial point interpolation method (RPIM) and a direct iterative method with nonlinear programming. By expressing the internal plastic dissipation power with a kinematically admissible velocity field obtained through RPIM interpolation, the upper bound problem is formulated mathematically as a nonlinear programming subjected to single equality constraint which is solved by a direct iterative method. To evaluate the integration of internal power dissipation rate without any background integral cell, a new meshless integration technique based on Cartesian Transformation Method (CTM) is employed to transform the domain integration first as boundary integration and then one-dimensional integration. The effectiveness and accuracy of the proposed approach are demonstrated by two classical limit analysis problems. Further discussion is devoted to optimal selection of relevant parameters for the computation. (C) 2013 Published by Elsevier Ltd.
In this work, preliminary results of human motion synthesis are presented. Specifically, a single stride motion (consisting of two steps) of a human lower-body model is obtained. The optimal control problem was reform...
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ISBN:
(纸本)9798350309799
In this work, preliminary results of human motion synthesis are presented. Specifically, a single stride motion (consisting of two steps) of a human lower-body model is obtained. The optimal control problem was reformulated as a nonlinear programming problem using the differential inclusion method. The main goal of this study is to compare the performance of trigonometric and polynomial (B-spline) discretization methods. The obtained results indicate that the trigonometric spline method performs similarly to the B-spline method and results in a smooth motion.
State-of-the-art finite time convergence conditions for the sliding mode controllers rely on bounds on perturbation terms. These bounds are often over-approximated, leading to conservative designs, i.e., high gains th...
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ISBN:
(纸本)9781665467612
State-of-the-art finite time convergence conditions for the sliding mode controllers rely on bounds on perturbation terms. These bounds are often over-approximated, leading to conservative designs, i.e., high gains that amplify undesired behaviors such as chattering. This paper proposes to evaluate precisely the bounds on the perturbation terms to avoid conservative designs by using branch-and-bound algorithms dedicated to nonlinear programming. This leads to non-linear, a priori non-convex, non-differentiable constraints on the controller's gains, which is shown to be solvable using a modern black-box optimization algorithm. We propose a new methodology employing branch-and-bound and blackbox solvers to generate gains as small as possible ensuring finite time convergence for the twisting algorithm. It is investigated using both a classical and a recently proposed sufficient conditions for finite time convergence. The applicability of the approach is illustrated over a numerical example.
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