The development of amine scrubbing for coal and natural gas-fired power plants represents a key technology to reduce CO 2 emissions. Among the strategies required to maximize CO 2 capture during plant operations is ...
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The development of amine scrubbing for coal and natural gas-fired power plants represents a key technology to reduce CO 2 emissions. Among the strategies required to maximize CO 2 capture during plant operations is the design of tailor-made dynamic models for optimal control. This paper presents a novel application of subspace system identification to a CO 2 recovery plant, where major decision variables are considered to develop a simple state space model that can estimate more than sixty process outputs. This model demonstrates to have a great predictive potential, which opens opportunities for the implementation of robust predictive controllers that can quickly adjust to power plant load changes.
Using an extension of the GarSD-toolbox for MATLAB ® , the paper considers the design of a digital control algorithm for a continuous-time process under external stochastic disturbance, which could possess non-ze...
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Using an extension of the GarSD-toolbox for MATLAB ® , the paper considers the design of a digital control algorithm for a continuous-time process under external stochastic disturbance, which could possess non-zero mean and its distribution is not exactly known. The problem consists in finding a controller which guarantees a certain performance of the controlsystem in the sense of a given cost functional for all admissible disturbances, i.e. a controller, which is robust with respect to the distribution of the disturbance. In addition, the provided method permits to reduce the static error to zero, and pure delays in the loop are allowed. The method is based on the parametric transfer function concept and design methods for control with guaranteed performance. An example demonstrates its application.
The numerical solution of optimal control problems is important in a variety of industries. One of the popular methods for complex problems is direct transcription. Direct transcription does not rely on the necessary ...
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The numerical solution of optimal control problems is important in a variety of industries. One of the popular methods for complex problems is direct transcription. Direct transcription does not rely on the necessary conditions and it is easy to incorporate a wide variety of constraints on both controls and states. Optimal control problems can often involve delays in the state or the control or both. While some numerical methods exist for some optimal control problems with delays they are not direct transcription methods. This paper reports on progress in developing a general purpose industrial grade software package to numerically solve complex optimal control problems with delays and state and control constraints using direct transcription.
computer-aidedcontrolsystemdesign software packages offer several numerical methods to simulate continuous-time (CT) deterministic dynamical systems. However, simulating stochastic dynamical systems (SDSs) has not ...
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computer-aidedcontrolsystemdesign software packages offer several numerical methods to simulate continuous-time (CT) deterministic dynamical systems. However, simulating stochastic dynamical systems (SDSs) has not received as much attention. This paper presents a self-contained treatment of how to simulate linear time-invariant CT SDSs with noisy measurements. The system model considered is quite general and avoids many simplifying assumptions generally made in the literature. An analytical solution to a state-space representation of the CT SDS is presented, and numerical approximations to such solution are derived. Moreover, the connection between the CT SDS and a discrete-time (DT) statistically equivalent system is established. Numerical methods for computing the various DT system and noise statistics matrices are discussed. An example is presented to illustrate the application of the various methods discussed in this paper.
This paper deals with a computational optimization approach to the problem of state-feedback controldesign for a wind turbine system. The first step of the study is to develop a reduced order model for the system by ...
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This paper deals with a computational optimization approach to the problem of state-feedback controldesign for a wind turbine system. The first step of the study is to develop a reduced order model for the system by considering the most important physical phenomena of aerodynamics and structural dynamics. Moreover, the behavior of the system can be influenced by the coupled dynamics between the tower motions and the blade pitch and turbine speed which can cause instabilities in the control loops in the worst case. By using a suitable wavelet function, called Haar functions, a recursive computational procedure is established for finding the system dynamics approximately by solving only algebraic equations instead of solving the Riccati differential. Simulation results are given to illustrate the usefulness of the proposed control methodology.
Offshore wind energy capitalizes on the higher and less turbulent wind speeds at sea. The use of floating structures for deeper waters is being explored. The control objective is a tradeoff between power capture and f...
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Offshore wind energy capitalizes on the higher and less turbulent wind speeds at sea. The use of floating structures for deeper waters is being explored. The control objective is a tradeoff between power capture and fatigue, especially that produced by the oscillations caused by the reduced structural stiffness of a floating installation in combination with a coupling between the fore-aft motion of the tower and the blade pitch. To address this problem, the present paper models a ballast-stabilized floating wind turbine, and suggests a linear quadratic regulator (LQR) in combination with a wind estimator and a state observer. The results are simulated using aero elastic code and analysed in terms of damage equivalent loads. When compared to a baseline controller, this controller clearly demonstrates better generator speed and power tracking while reducing fatigue loads.
Daily traffic congestion on freeway networks around the world continues to increase, with detrimental effects on travel times, traffic safety, fuel consumption and environmental pollution. The annual cost of traffic c...
Daily traffic congestion on freeway networks around the world continues to increase, with detrimental effects on travel times, traffic safety, fuel consumption and environmental pollution. The annual cost of traffic congestion on European roads is estimated to approach 100 b€ or 1% of the GDP. Traffic congestion is only partly due to high demand, since the appearing congestion degrades the expensive infrastructure capacity essentially at the only times it is actually needed, i.e. during the daily peak periods. Traffic control measures, if properly designed and deployed, may lead to substantial savings of travel time, fuel consumption and environmental impact, along with an improvement of traffic safety. The presentation will outline the related traffic control problems and methods, with a focus on optimal control and feedback approaches. More specifically, the areas of macroscopic traffic flow modeling, local and coordinated ramp metering, variable speed limit control, mainstream traffic flow control, merging traffic control and route information and guidance will be addressed, along with the presentation of some selected field results. The 2011 Transition to Practice Award goes to Dr. Richard Braatz (MIT) “For significant contribution to the control of crystallization processes in the pharmaceutical industry”.
The production and loading of wind farms are significantly influenced by the turbulence of the flowing wind field. Estimation of turbulence allows us to optimize the performance of the wind farm. Turbulence estimation...
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The production and loading of wind farms are significantly influenced by the turbulence of the flowing wind field. Estimation of turbulence allows us to optimize the performance of the wind farm. Turbulence estimation is; however, highly challenging due to the chaotic behavior of the wind. In this paper, a method is presented for estimation of the turbulence. The spectral model of the wind is used in order to provide the estimations. The suggested estimation approach is applied to a case study in which the objective is to estimate wind turbulence at desired points using the measurements of wind speed outside the wind field. The results show that the method is able to provide estimations which explain more than 50% of the wind turbulence from the distance of about 300 meters.
An anti-windup scheme is said to be robustness preserving if it inherits the robustness properties of the corresponding unsaturated loop. It has recently been shown in the literature that anti-windup based on internal...
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An anti-windup scheme is said to be robustness preserving if it inherits the robustness properties of the corresponding unsaturated loop. It has recently been shown in the literature that anti-windup based on internal model control preserves robustness against additive uncertainty. In this paper we illustrate examples where other anti-windup schemes are also robustness preserving. We also demonstrate by counterexample that internal model control need not preserve robustness against multiplicative uncertainty. We consider both single-input single-output and multivariable control loops.
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