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.
Applications of optimization in the field of process systems engineering often involve dealing with nonlinearity and uncertainty, necessitating the Solution Of Stochastic nonlinear programming (SNLP) problems. However...
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Applications of optimization in the field of process systems engineering often involve dealing with nonlinearity and uncertainty, necessitating the Solution Of Stochastic nonlinear programming (SNLP) problems. However, the existing algorithms to solve such problems suffer from various limitations. The L-shaped BONUS algorithm, which is an integration of the BONUS (Better Optimization of nonlinear Uncertain Systems) algorithm and the sampling-based L-shaped method, has been recently proposed to overcome some of these problems. It is shown to have desirable computational properties. This work further investigates the properties of the algorithm by applying it to the environmental problem of pollutant (nutrient) trading in the Christina River Basin. With environmental concerns being heightened, pollution abatement-related decisions are important for industry, making efficient solution techniques invaluable. The results confirm the computational efficiency of the L-shaped BONUS algorithm. Simultaneously, interesting aspects of the environmental trading problem are also explored, pointing toward the scope of implementing stochastic programming techniques for better decision making in the field of industrial ecology.
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.
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.
Process flexibility is important to ensure that an operation can be both profitable and manipulative to changes in internal or external process parameters. A new flexibility metric (FIV) is defined based on the hyperv...
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Process flexibility is important to ensure that an operation can be both profitable and manipulative to changes in internal or external process parameters. A new flexibility metric (FIV) is defined based on the hypervolume ratio of the feasible region and the region containing all combinations of expected uncertain parameters. The FIV evaluation requires a hypervolume determination which may not be straightforward for multidimensional systems or systems with nonconvex feasible regions. Thus, an auxiliary vector approach is developed to estimate the feasible space's size. Compared to the existing methodologies, this new method is able to provide a reliable and facile flexibility evaluation. In addition, this approach can also offer indications of the possible future changes to enhance the overall flexibility. This information is useful for making reasonable and rational engineering decisions to retrofit the designs.
The development of a two-timescale discretization scheme for collocation is presented. This scheme allows a coarser discretization to be used for slowly varying state variables and a second finer discretization to be ...
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The development of a two-timescale discretization scheme for collocation is presented. This scheme allows a coarser discretization to be used for slowly varying state variables and a second finer discretization to be used for state variables having higher-frequency dynamics. That is, the discretization scheme can be tailored to the dynamics of the particular state variables. Consequently, the size of the overall nonlinear programming problem can be reduced significantly. Two two-timescale discretization architecture schemes are described. Comparison of results between the two-timescale method and conventional single-timescale collocation shows very good agreement. When the two-timescale discretization is used in combination with the sparse nonlinear optimizer SNOPT, a significant reduction (by more than 60%) in the number of nonlinear programming parameters required for the transcription of the problem and iterations necessary for convergence can he achieved without sacrificing solution accuracy.
Configurations of a four-column simulated moving bed chromatographic process are investigated by multi-objective optimization. Various existing column configurations are compared through a multi-objective optimization...
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Configurations of a four-column simulated moving bed chromatographic process are investigated by multi-objective optimization. Various existing column configurations are compared through a multi-objective optimization problem. Furthermore, an approach based on an SMB superstructure is applied to find novel configurations which have been found to outperform the standard SMB configuration. An efficient numerical optimization technique is applied to the mathematical model of the SMB process. It has been confirmed that although the optimal configuration highly depends on the purity requirement, the superstructure approach is able to find the most efficient configuration without exploring various existing configurations.
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.
This paper describes a new algorithm for solving nonlinear programming problems with equality constraints. The method introduces the idea of using trust cylinders to keep the infeasibility under control. Each time the...
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This paper describes a new algorithm for solving nonlinear programming problems with equality constraints. The method introduces the idea of using trust cylinders to keep the infeasibility under control. Each time the trust cylinder is violated, a restoration step is called and the infeasibility level is reduced. The radius of the trust cylinder has a nonincreasing update scheme, so eventually a feasible (and optimal) point is obtained. Global and local convergence of the algorithm are analyzed, as well as its numerical performance. The results suggest that the algorithm is promising.
Evolutionary algorithms are robust and powerful global optimization techniques for solving large-scale problems that have many local optima. However, they require high CPU times, and they are very poor in terms of con...
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Evolutionary algorithms are robust and powerful global optimization techniques for solving large-scale problems that have many local optima. However, they require high CPU times, and they are very poor in terms of convergence performance. On the other hand, local search algorithms can converge in a few iterations but lack a global perspective. The combination of global and local search procedures should offer the advantages of both optimization methods while offsetting their disadvantages. This paper proposes a new hybrid optimization technique that merges a genetic algorithm with a local search strategy based on the interior point method. The efficiency of this hybrid approach is demonstrated by solving a constrained multi-objective mathematical test-case. (C) 2007 Elsevier B.V. All rights reserved.
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