This note examines the classic passive earth resistance of cohesionless soil by using two newly developed numerical procedures based on finite element formulations of the bound theorems of limit analysis and non-linea...
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This note examines the classic passive earth resistance of cohesionless soil by using two newly developed numerical procedures based on finite element formulations of the bound theorems of limit analysis and non-linear programming techniques. Solutions using upper and lower bounds are presented to complement the previous studies of this problem. The parameters studied are soil-wall interface friction, wall inclination, backfill surface configuration and the wall's weight.
The dynamic behavior of many processes is characterized by time delays due to measurement delays, which put strict limitations on the performance of the control system. In this paper a time-delay factorization strateg...
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The dynamic behavior of many processes is characterized by time delays due to measurement delays, which put strict limitations on the performance of the control system. In this paper a time-delay factorization strategy for the nonlinear model predictive control (NMPC) and state estimation of multiple-input multiple-output (MIMO), nonlinear, open-loop unstable processes having output-measurement delays, and subject to unmeasured disturbances is proposed. At first, the NMPC algorithm based on a nonlinear programming approach is presented. Then, on-line parameter-identification and state-estimation schemes are combined with the NMPC algorithm to maintain the process at a steady-state which is unstable for the open-loop system. Finally, the effectiveness of the proposed method is demonstrated via simulation on the control of a catalytic continuous stirred tank reactor (CSTR). (c) 2007 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
In this paper, we present a new optimization system (GENLS), for nonlinear system of equations. Our approach has two characteristic features. Firstly, nonlinear system of equations is transformed into a nonlinear prog...
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In this paper, we present a new optimization system (GENLS), for nonlinear system of equations. Our approach has two characteristic features. Firstly, nonlinear system of equations is transformed into a nonlinear programming problem (NLP) with additional parameter e to de. ne initial precision of the system. That is, the objective is to reduce the violation of the constraints to an acceptable level (desired precision epsilon*) by minimizing a function that measures the maximum violation of the constraints. Secondly, efficient co-evolutionary algorithm is implemented for solving the resulting NLP, which combines concept of co-evolution, repairing procedure and elitist strategy. Finally, we report numerical results in order to establish the actual computational burden of the proposed method and to assess its performances with respect to classical approaches for solving nonlinear system of equations. (c) 2007 Elsevier Inc. All rights reserved.
Considering a generic nonlinear programming problem (NLPP), this paper provides a family of linear infinite problems (LIPs) or linear semi-infinite problems (LSIPs), and establishes the connection between optimality i...
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Considering a generic nonlinear programming problem (NLPP), this paper provides a family of linear infinite problems (LIPs) or linear semi-infinite problems (LSIPs), and establishes the connection between optimality in the respective NLPP and that in the provided LIPs (LSIPs). (C) 2007 Elsevier B.V. All rights reserved.
Dynamic adaptation of transmission power has been researched as a technique for improving the performance and capacity of wireless networks. In this paper an estimator-based algorithm is presented for distributed powe...
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Dynamic adaptation of transmission power has been researched as a technique for improving the performance and capacity of wireless networks. In this paper an estimator-based algorithm is presented for distributed power control. The proposed power control policy is optimal with respect to users dynamically allocating transmit power so as to minimize an objective function consisting of the user's performance degradation and the network interference. The policy enables a user to address various user-centric and network-centric objectives by adapting power in either a greedy or energy efficient manner. The algorithm is predictive, with a user performing autonomous interference estimation and prediction prior to adapting transmit power. Also, closed-loop implementation of the algorithm is of reasonable complexity thus allowing for distributed online operation. Subsequently, the robustness of the algorithm to stochastic detriments such as a time varying channel and noisy measurements is investigated.
Multi-scenario optimization is a convenient way to formulate and solve multi-set parameter estimation problems that arise from errorsin-variables-measured (EVM) formulations. These large-scale problems lead to nonline...
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Multi-scenario optimization is a convenient way to formulate and solve multi-set parameter estimation problems that arise from errorsin-variables-measured (EVM) formulations. These large-scale problems lead to nonlinear programs (NLPs) with specialized structure that can be exploited by the NLP solver in order to obtained more efficient solutions. Here we adapt the IPOPT barrier nonlinear programming algorithm to provide efficient parallel solution of multi-scenario problems. The recently developed object oriented framework, IPOPT 3.2, has been specifically designed to allow specialized linear algebra in order to exploit problem specific structure. This study discusses high-level design principles of IPOPT 3.2 and develops a parallel Schur complement decomposition approach for large-scale multi-scenario optimization problems. A large-scale case study example for the identification of an industrial low-density polyethylene (LDPE) reactor model is presented. The effectiveness of the approach is demonstrated through the solution of parameter estimation problems with over 4100 ordinary differential equations, 16,000 algebraic equations and 2100 degrees of freedom in a distributed cluster. (c) 2007 Elsevier Ltd. All rights reserved.
This paper presents a nonlinear model-based predictive controller (NMPC) for trajectory tracking of a four-wheeled omnidirectional mobile robot. Methods of numerical optimization to perform real-time nonlinear minimiz...
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This paper presents a nonlinear model-based predictive controller (NMPC) for trajectory tracking of a four-wheeled omnidirectional mobile robot. Methods of numerical optimization to perform real-time nonlinear minimization of the cost function are used. The cost function penalizes the robot's position error, the robot's orientation angle error, and the control effort. Experimental results of the trajectories following and the performances of the methods of optimization are presented. Copyright (C) 2007 John Wiley & Sons, Ltd.
Augmented Lagrangian methods for large-scale optimization usually require efficient algorithms for minimization with box constraints. On the other hand, active-set box-constraint methods employ unconstrained optimizat...
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Augmented Lagrangian methods for large-scale optimization usually require efficient algorithms for minimization with box constraints. On the other hand, active-set box-constraint methods employ unconstrained optimization algorithms for minimization inside the faces of the box. Several approaches may be employed for computing internal search directions in the large-scale case. In this paper a minimal-memory quasi-Newton approach with secant preconditioners is proposed, taking into account the structure of Augmented Lagrangians that come from the popular Powell-Hestenes-Rockafellar scheme. A combined algorithm, that uses the quasi-Newton formula or a truncated-Newton procedure, depending on the presence of active constraints in the penalty-Lagrangian function, is also suggested. Numerical experiments using the Cute collection are presented.
Two types of uncertainty exist in engineering. Aleatory uncertainty comes from inherent variations while epistemic uncertainty derives from ignorance or incomplete information. The former is usually modeled by the pro...
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Two types of uncertainty exist in engineering. Aleatory uncertainty comes from inherent variations while epistemic uncertainty derives from ignorance or incomplete information. The former is usually modeled by the probability theory and has been widely researched. The latter can be modeled by the probability theory or nonprobability theories and is much more difficult to deal with. In this work, the effects of both types of uncertainty are quantified with belief and plausibility measures ( lower and upper probabilities) in the context of the evidence theory. Input parameters with aleatory uncertainty are modeled with probability distributions by the probability theory. Input parameters with epistemic uncertainty are modeled with basic probability assignments by the evidence theory. A computational method is developed to compute belief and plausibility measures for black-box performance functions. The proposed method involves the nested probabilistic analysis and interval analysis. To handle black-box functions, we employ the first order reliability method for probabilistic analysis and nonlinear optimization for interval analysis. Two example problems are presented to demonstrate the proposed method.
Augmented Lagrangian methods with general lower-level constraints are considered in the present research. These methods are useful when efficient algorithms exist for solving subproblems in which the constraints are o...
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Augmented Lagrangian methods with general lower-level constraints are considered in the present research. These methods are useful when efficient algorithms exist for solving subproblems in which the constraints are only of the lower-level type. Inexact resolution of the lower-level constrained subproblems is considered. Global convergence is proved using the constant positive linear dependence constraint qualification. Conditions for boundedness of the penalty parameters are discussed. The resolution of location problems in which many constraints of the lower-level set are nonlinear is addressed, employing the spectral projected gradient method for solving the subproblems. Problems of this type with more than 3 x 10(6) variables and 14 x 10(6) constraints are solved in this way, using moderate computer time. All the codes are available at http://***/similar to egbirgin/tango/.
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