In this paper a technical analysis of an ocean thermal energy conversion (OTEC) system is performed. Specifically, we present a general mathematical framework for the synthesis of OTEC power generating systems. The ov...
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In this paper a technical analysis of an ocean thermal energy conversion (OTEC) system is performed. Specifically, we present a general mathematical framework for the synthesis of OTEC power generating systems. The overall synthesis task is to minimize heat exchange area requirements, while generating some fraction of the maximum net power recoverable from hot and cold ocean water. The resulting problem formulation yields a nonlinear, nonconvex mathematical program;however, we show that globally optimal solutions for this program are easily obtained explicitly through a direct optimization approach with minimal computational effort over a wide range of thermodynamic conditions. The proposed analysis is demonstrated on a case study involving the generation of hydrogen by an OTEC system with a pure ammonia working fluid. (c) 2007 Elsevier Ltd. All rights reserved.
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.
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.
In this article we solve a nonlinear cutting stock problem which represents a cutting stock problem that considers the minimization of, both, the number of objects used and setup. We use a linearization of the nonline...
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In this article we solve a nonlinear cutting stock problem which represents a cutting stock problem that considers the minimization of, both, the number of objects used and setup. We use a linearization of the nonlinear objective function to make possible the generation of good columns with the Gilmore and Gomory procedure. Each time a new column is added to the problem, we solve the original nonlinear problem by an Augmented Lagrangian method. This process is repeated until no more profitable columns is generated by Gilmore and Gomory technique. Finally, we apply a simple heuristic to obtain an integral solution for the original nonlinear integer problem.
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.
This paper investigates a discrete-time neural network model for solving nonlinear convex programming problems with hybrid constraints. The neural network finds the solution of both primal and dual problems and conver...
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This paper investigates a discrete-time neural network model for solving nonlinear convex programming problems with hybrid constraints. The neural network finds the solution of both primal and dual problems and converges to the corresponding exact solution globally. We prove here that the proposed neural network is globally exponentially stable. Furthermore, we extend the proposed neural network for solving a class of monotone variational inequality problems with hybrid constraints. Compared with other existing neural networks for solving such problems, the proposed neural network has a low complexity for implementation without a penalty parameter and converge an exact solution to convex problem with hybrid constraints. Some numerical simulations for justifying the theoretical analysis are also given. The numerical simulations are shown that in the new model note only the cost of the hardware implementation is not relatively expensive, but also accuracy of the solution is greatly good. (c) 2007 Elsevier Inc. All rights reserved.
In this paper, a methodology to design fuel-efficient maneuvers for space-based interferometric imaging systems located in near-Earth orbits, under time and imaging constraints, is proposed. The methodology is hierarc...
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In this paper, a methodology to design fuel-efficient maneuvers for space-based interferometric imaging systems located in near-Earth orbits, under time and imaging constraints, is proposed. The methodology is hierarchical and consists of a higher-level nonlinear programming problem and a lower-level linear quadratic tracker. Solutions are obtained for the purpose of quantifying the relationship between the quality of an image obtained by a multispacecraft interferometric imaging system and the dynamic requirements of such imaging maneuvers. These maneuvers are then used for the design of a system capable of obtaining very-high-resolution images from a near-Earth orbital location. To relate the fuel requirements with image quality, the relationship between the imaging process and the error in the final image is studied, and a quality factor is designed to relate the reliability of an image to the trajectory of the spacecraft and, hence, the fuel usage. As an application, a midinfrared imager system located at geostationary orbit is studied and features of the design of such maneuvers are enumerated.
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.
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.
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.
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