This paper presents a constrained horizon predictivecontroller based on a filtered Smith predictor structure (CHSPPC). The proposed algorithm is particulary appropriate to control dead time systems as it exhibits bet...
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This paper presents a constrained horizon predictivecontroller based on a filtered Smith predictor structure (CHSPPC). The proposed algorithm is particulary appropriate to control dead time systems as it exhibits better robustness than others, specially when errors in the dead time estimation are considered. The tuning of the controller parameters includes a low pass filter that is easy defined to obtain a compromise between performance and robustness. Simulation results show that the proposed control strategy allows better results than other approaches.
This paper examines the issue of the generation of optimal control policies where there are explicit constraints upon the control values and there is limited knowledge of the complex economic system. The paper develop...
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This paper examines the issue of the generation of optimal control policies where there are explicit constraints upon the control values and there is limited knowledge of the complex economic system. The paper develops a methodology where the constrained optimal control is based upon a separate model that predicts the policy targets for the economic system. The methodology as applied to a small calibrated macroeconomic model of Australia. (C) 2003 IMACS. Published by Elsevier B.V.. All rights reserved.
In 1996, the Dominican Republic operation of Falconbridge (Falcondo) developed a 5-year Process control Plan in conjunction with the corporate Process control group located in Sudbury, Ontario, Canada. This plan was f...
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ISBN:
(纸本)0873395506
In 1996, the Dominican Republic operation of Falconbridge (Falcondo) developed a 5-year Process control Plan in conjunction with the corporate Process control group located in Sudbury, Ontario, Canada. This plan was further refined into an analysis of the areas of the highest potential economic return for the company with the reduction shaft furnace operation being the most promising. Historically, the reduction plant, which comprises 12 shaft furnaces, has been the bottleneck of ferronickel production. Since 1996 there has been a history of control development, and the current status is that all 12 furnaces are using advanced model-based multivariable predictivecontrol. The combination of advanced process control (APC), improved control system performance and improved process information management has resulted in an increased throughput of 6% per shaft furnace and a reduction in operating costs of 4.5 US cents/lb of Ni. This paper reviews some of the major milestones that have been achieved in this development.
This paper examines the issue of the generation of optimal control policies where there are explicit constraints upon the control values and there is limited knowledge of the complex economic system. The paper develop...
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This paper examines the issue of the generation of optimal control policies where there are explicit constraints upon the control values and there is limited knowledge of the complex economic system. The paper develops a methodology where the constrained optimal control is based upon a separate model that predicts the policy targets for the economic system. The methodology as applied to a small calibrated macroeconomic model of Australia. (C) 2003 IMACS. Published by Elsevier B.V.. All rights reserved.
An Artificial Neural Network (ANN) is an adequate tool for modeling nonlinear systems and can be applied straightforward in the predictive functional control. New structure of ANN multi-step prediction that is differe...
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An Artificial Neural Network (ANN) is an adequate tool for modeling nonlinear systems and can be applied straightforward in the predictive functional control. New structure of ANN multi-step prediction that is different from cascade or parallel is presented, at the same time, the nonlinear predictive functional control using this ANN model has been developed in this paper. The useful of this control strategy is evaluated by applying it to a Continuous Stirred Tank Reactor (CSTR). The simulation results indicate that it is more effective than PID control.
modelbasedpredictivecontrol (MPC) is a control technique that is widely used in chemical process industry. In the past decade, stability of MPC has been an intensive research area, resulting in the general acceptanc...
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modelbasedpredictivecontrol (MPC) is a control technique that is widely used in chemical process industry. In the past decade, stability of MPC has been an intensive research area, resulting in the general acceptance of a theoretical MPC stability framework introducing a terminal cost and terminal constraint to the classic MPC formulation. Although guaranteeing stability, issues regarding optimality and feasibilit.y remain. In this paper, an LMI-based constrained MPC scheme for linear systems is introduced which guarantees stability by use of a time-varying terminal cost and terminal constraint. The online calculation of the terminal cost results in improved performance and feasibility compared to MPC schemes with fixed terminal cost. Finally, the technique is illustrated on a copolymerization reactor.
The paper presents an evolutionary predictivecontrol algorithm. The model of the process (linear or nonlinear) is used in order to forecast the process outputs at future time instants. At each sample time, an evoluti...
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The paper presents an evolutionary predictivecontrol algorithm. The model of the process (linear or nonlinear) is used in order to forecast the process outputs at future time instants. At each sample time, an evolutionary algorithm calculates the optimal control action, subject to the imposed cost functions and the specified constraints. The multiobjective optimisation is efficiently solved using special rules for fitness values' computation. The proposed method is able to support a flexible formulation of the design specifications. Assuming that the controlled system is slow and robust, the approach offers satisfactory results. The algorithm can efficiently cope with the environmental changes and the known variations of the process model.
This paper presents a modified form of model based predictive control that exploits the concept of non-trivial terminal state weighting in the cost function. While maintaining simplicity of implementation, the perform...
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This paper presents a modified form of model based predictive control that exploits the concept of non-trivial terminal state weighting in the cost function. While maintaining simplicity of implementation, the performance of this algorithm is not sacrificed and the computational burden compared with traditional approaches is greatly reduced. This paper describes the proposed algorithm in detail and through application to several systems, including a benchmark fluidised catalytic cracker unit, demonstrates its advantages over traditional model based predictive control algorithms.
The paper presents an efficient control algorithm applied on a two-link robot manipulator with input constraints. The algorithm proposes a sub optimal solution to the predictivecontrol problem with infinite predictio...
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ISBN:
(纸本)0780367332
The paper presents an efficient control algorithm applied on a two-link robot manipulator with input constraints. The algorithm proposes a sub optimal solution to the predictivecontrol problem with infinite prediction horizon, by mean of interpolations between the unconstrained optimal solution and other constrained solutions. The control strategy is based on inserting the predictivecontroller in an adaptive perturbation scheme. The efficiency of the proposed strategy is shown by simulation.
This paper examines the generation of optimal control policies where there are explicit constraints upon the control values and their rates of change, and there is limited knowledge of the complex economic system. The...
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This paper examines the generation of optimal control policies where there are explicit constraints upon the control values and their rates of change, and there is limited knowledge of the complex economic system. The paper develops a methodology using quadratic programming where the constrained optimal control policies are based upon a learning model that predicts the policy targets for the economic system. A subset of the control policies is applied to the economic system and from the response of the system a new predictivemodel and resultant optimal controls are generated. The methodology is then repeated. A numeric example of the methodology as applied to a macroeconomic model is presented in the paper.
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