Results 1  10
of
68
Buffer Tank Design for Acceptable Control Performance
 Ind. Eng. Chem. Res
, 2003
"... (in press) This paper provides a systematic approach for the design of buffer tanks. We consider mainly the case where the objective of the buffer tank is to dampen (“average out”) the fast (i.e., highfrequency) disturbances, which cannot be handled by the feedback control system. We consider sepa ..."
Abstract

Cited by 6 (1 self)
 Add to MetaCart
(in press) This paper provides a systematic approach for the design of buffer tanks. We consider mainly the case where the objective of the buffer tank is to dampen (“average out”) the fast (i.e., highfrequency) disturbances, which cannot be handled by the feedback control system. We consider separately design procedures for (I) mixing tanks to dampen quality disturbances and (II) surge tanks with averaging level control to handle flowrate disturbances.
PID Controller Tuning Rules for Robust step response of FirstOrderPlusDeadTime models
 IEEE Trans. on
, 2006
"... PID controller tuning rules for robust step response of firstorderplusdeadtime models ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
(Show Context)
PID controller tuning rules for robust step response of firstorderplusdeadtime models
Hiyama “Multiobjective PI/PID control design using an iterative linear matrix inequalities algorithm,” Int
 Journal of Control, Automation and Systems
, 2007
"... Abstract: Many real world control systems usually track several control objectives, simultaneously. At the moment, it is desirable to meet all specified goals using the controllers with simple structures like as proportionalintegral (PI) and proportionalintegralderivative (PID) which are very use ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
Abstract: Many real world control systems usually track several control objectives, simultaneously. At the moment, it is desirable to meet all specified goals using the controllers with simple structures like as proportionalintegral (PI) and proportionalintegralderivative (PID) which are very useful in industry applications. Since in practice, these controllers are commonly tuned based on classical or trialanderror approaches, they are incapable of obtaining good dynamical performance to capture all design objectives and specifications. This paper addresses a new method to bridge the gap between the power of optimal multiobjective control and PI/PID industrial controls. First the PI/PID control problem is reduced to a static output feedback control synthesis through the mixed H2/H ∞ control technique, and then the control parameters are easily carried out using an iterative linear matrix inequalities (ILMI) algorithm. Numerical examples on loadfrequency control (LFC) and power system stabilizer (PSS) designs are given to illustrate the proposed methodology. The results are compared with genetic algorithm (GA) based multiobjective control and LMI based full order mixed H2/H ∞ control designs.
Estimating Disturbance Covariances From Data For Improved Control Performance
"... To my parents for letting me take things apart, to my grandfather for showing me how to put them back together, to Karen for sharing the highs and lows of this journey, and to Abby for making it all worthwhileiiiii Acknowledgments It has been my pleasure to work with such a talented group of people ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
To my parents for letting me take things apart, to my grandfather for showing me how to put them back together, to Karen for sharing the highs and lows of this journey, and to Abby for making it all worthwhileiiiii Acknowledgments It has been my pleasure to work with such a talented group of people in my time here in Madison. Foremost is my advisor, Prof. James B. Rawlings. I continue to be amazed at his knowledge of the control field, especially his ability to distill complex concepts down into very simple ideas. I am grateful for the latitude he gave me to pursue my ideas, most of which didn’t pan out. I also thank him for helping to make me a better technical writer. I would like to thank those professors who took the time to serve on my thesis committee: Professors Ray, Graham, and Swaney from the department and Professor Bates from Statistics. The members of Rawlings group have made my time here especially enjoyable.
Predictive compensation for variable network delays and packet losses in networked control systems
"... This is the author’s version of a work that was submitted/accepted for publication in the following source: ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
This is the author’s version of a work that was submitted/accepted for publication in the following source:
Linear TimeVarying Systems: Theory and Identification of Model Parameters
 WSEAS TRANSACTIONS on SYSTEMS
"... Abstract: A strategy is proposed to model the complex industrial systems using linear timevarying system (LTV S). The proposed methodology is independent of model structure and the model may take any classic linear structure such as finite impulse response, inputoutput relation structures etc. To ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
Abstract: A strategy is proposed to model the complex industrial systems using linear timevarying system (LTV S). The proposed methodology is independent of model structure and the model may take any classic linear structure such as finite impulse response, inputoutput relation structures etc. To take into account the error between system and model due to model order reduction, variation of system behavior in time and perturbations, model’s parameters are considered varying but bounded variables characterized by intervals. The output of this model is characterized by a function of the piecewise linear parameters which contains all possible system’s responses taking into account modeling error as well as the perturbations. Key–Words: TimeVarying System, Identification of Model Parameters, Interval Analysis. 1
Integrated Optimization and Control
, 2003
"... iAbstract Increased competition in the process industry requires improved operation. One strategy is to use realtime optimization (RTO) based on measured disturbances and process outputs. The optimal solution is usually implemented by updating the setpoints to the control system, which then tries t ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
iAbstract Increased competition in the process industry requires improved operation. One strategy is to use realtime optimization (RTO) based on measured disturbances and process outputs. The optimal solution is usually implemented by updating the setpoints to the control system, which then tries to keep the controlled variables at their given setpoints. Thus, the selection of controlled variables integrates the optimization and the control layer. It is important to select the right controlled variables. First, there are always uncertainty with respect to the true value of the disturbances, so the optimal value of the selected controlled variables should not depend strongly on the disturbances. Second, the operation should not be sensitive to the implementation error in the controlled variables. The ideal situation is to have selfoptimizing control where we may use constant setpoint values so that no optimization layer is needed. However, even if we have an optimization layer, it is important to select the right controlled variables in order to reduce the effect of uncertainty. In the simplest case the setpoints for the controlled variables are fixed at their nominally optimal values. However, because of disturbances this may result in feasibility problems.
Stability and performance analysis of classical decentralised control of irrigation canals
 in Proceedings of the 44th IEEE CDC
, 2005
"... Abstract: Irrigation canals have a series structure which is generally used to design multivariable controllers based on the aggregation of decentralized monovariable controllers. SISO controllers are designed for each canal pool, assuming that the interactions will not destabilize the overall syste ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Abstract: Irrigation canals have a series structure which is generally used to design multivariable controllers based on the aggregation of decentralized monovariable controllers. SISO controllers are designed for each canal pool, assuming that the interactions will not destabilize the overall system. It is shown that, when the canal pools are controlled using the discharge at one boundary, the multivariable decentralized control structure is stable if and only if the SISO controllers are stable. The performance of the multivariable system is also investigated, and it is shown that the interactions decrease the overall performance of the controlled system. This loss of performance can be reduced by using a feedforward controller. Experimental results show the e®ectiveness of the method.
Computational performance of aggregated distillation models
 Computers & Chemical Engineering
, 2009
"... This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal noncommercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or sel ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal noncommercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:
The Good Gain method for simple experimental tuning of PI controllers
"... A novel experimental method – here denoted the Good Gain method – for tuning PI controllers is proposed. The method can be regarded as an alternative to the famous ZieglerNichols ’ Ultimate Gain method. The approach taken resembles the ZieglerNichols ’ method as it is based on experiments with the ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
A novel experimental method – here denoted the Good Gain method – for tuning PI controllers is proposed. The method can be regarded as an alternative to the famous ZieglerNichols ’ Ultimate Gain method. The approach taken resembles the ZieglerNichols ’ method as it is based on experiments with the closed loop system with proportional control. However, the method does not require severe process upset during the tuning like sustained oscillations. Only welldamped responses are assumed. Furthermore, in the present study it is demonstrated that the approach typically gives better stability robustness comparing with the ZieglerNichols ’ method. The method is relatively simple to use which is beneficial for the user. A theoretical rationale based on second order dynamics is given.