LIMITATIONS TO INPUT-OUTPUT ANALYSIS OF. Rensselaer Polytechnic Institute, Howard P. Isermann Department of Chemical Engineering, Troy,

Size: px
Start display at page:

Download "LIMITATIONS TO INPUT-OUTPUT ANALYSIS OF. Rensselaer Polytechnic Institute, Howard P. Isermann Department of Chemical Engineering, Troy,"

Transcription

1 LIMITATIONS TO INPUT-OUTPUT ANALYSIS OF CASCADE CONTROL OF UNSTABLE CHEMICAL REACTORS LOUIS P. RUSSO and B. WAYNE BEQUETTE Rensselaer Polytechnic Institute, Howard P. Isermann Department of Chemical Enineerin, Troy, NY 8-9, USA. Abstract. In this paper we show that the traditional methods of analyzin cascade control (based on series or parallel input/output relationships) should not be used when the primary and secondary processes are coupled, and the process is open-loop unstable. The connection between cascade control and traditional state feedback control is studied. In addition, we develop a cascade control system methodoloy which incorporates a specied inner-loop in the outer-loop desin. This cascade control desin procedure is compared to a conventional cascade control desin procedure and to a noncascade control stratey. Key Words. Cascade control; chemical s; unstable processes; state-space analysis; input/output analysis. INTRODUCTION Input/output (transfer function) analysis is often used to desin feedback control systems for chemical processes. Cascade control systems are usually desined by rst tunin a slave control loop based on the transfer function model of the secondary process. If the slave loop can be tuned \fast" enouh, then the master loop can be tuned based on the primary process transfer function, independent of the slave loop. If the bandwidth of the slave loop is close to that of the primary loop, then the loops must be iteratively tuned, until acceptable performance of the entire closedloop process is obtained. Implicit in the typical desin process is the assumption that the secondary and primary processes are decoupled. This is illustrated by the typical series cascade control block diaram, shown in Fiure. The output of the secondary process is the input to the primary process in a series cascade control block diaram. Similarly, a parallel cascade control structure (in which the manipulated input has a direct eect on both of the outputs) is shown in Fiure. The motivatin problem is cascade control of an exothermic continuous stirred tank (CSTR) operated at an open-loop unstable operatin point. Althouh many new nonlinear control techniques have been developed (Bequette, 99; McLellan et al., 99; Kravaris and Kantor, 99), we use linear control for two reasons: Linear feedback and cascade s are still the dominant strateies used in industry. The linear techniques are well-known and easy to use for comparison purposes.. CSTR MODELING EQUATIONS The standard two-state CSTR model (Uppal et al., 97) describin an exothermic diabatic irreversible rst-order reaction (A! B) is a set of two nonlinear ordinary dierential equations obtained from dynamic material and enery balances (with the assumptions of constant volume, perfect mixin, neliible coolin dynamics, and constant physical parameters). dc a Q?Ea dt V (C af? C a )? k exp C a () RT dt Q dt V (T f? T )? U A (T? T c ) V C p?h?ea + k exp C a () C p RT where C a and T are the concentration of component A and the in the, respectively. An additional enery balance around the coolin assumin perfect mixin yields dt c dt Q c (T cf? T c ) + U A (T? T c ) () V c V c c C pc

2 where T c is the coolin. Equations - can be written in dimensionless form dx d q(x f? x )? x (x ) () dx d q(x f? x )? (x? x ) +x (x ) () dx d [ (x f? x ) + (x? x )] () where x, x, x, and are the dimensionless concentration,, coolin, and coolin owrate, respectively. These dimensionless variables and parameters (for example:, q,, etc.) are dened in Table. Representative values of the parameters can be found in Russo and Bequette (99a, 99b). Case is open-loop stable over the entire reion of operation for the two-state CSTR model but open-loop unstable in a certain operatin reion for the three-state CSTR model, while Case exhibits inition/extinction behavior for the two and three-state models. It is well-known that the exponential relationship of reaction rate with respect to is one of the major nonlinearities of the CSTR. Russo and Bequette (99a, 99b) studied both the steady-state nonlinearities (output and input multiplicities, infeasible operation reions) and the dynamic nonlinearities (Hopf bifurcation behavior) of this three-state CSTR model. Sistu and Bequette (99) determined conditions under which input multiplicity behavior results in the open-loop system havin unstable zero dynamics (nonminimum-phase behavior).. CONTROL SYSTEM DESIGN A common control conuration in chemical processes is cascade control. In a cascade control con- uration we have one manipulated variable (in this case the dimensionless coolin owrate ) and more than one measurement (the dimensionless x and dimensionless coolin x ). Cascade control can also be thouht of as multiple state feedback. The major benet of cascade control is that action is taken to reject inner-loop disturbances before they eect the outer-loop. Therefore, the innerloop of the cascade control stratey is enerally tuned very tihtly in order to obtain a fast response. In addition, intuitively we expect the performance of a cascade control system to be superior (enerally) to a noncascade control system, since more system information (outputs) is bein fed back to the control system... Transfer Function Models The transfer functions between the deviation variables x (s), x (s), and (s) are: x (s) (s)q c(s) (c s + ) k (a s + a s + a s + ) c(s) (7) q x (s) (s)x (s) k (c s + ) (b s + b s + ) x (s) (8) x (s) (s)q c (s) k (b s + b s + ) (a s + a s + a s + ) (s) (9) where the ains and coecients of the transfer functions are dened in Table (the indicates deviation variable). We can view these equations in block diaram form, as shown in Fiure. The poles of the two-state model relationship (relatin x (s) to x (s)), (s), are the zeros of the x (s) - qc(s) relationship in the three-state model, (s). When (s) is unstable at a particular operatin point, then (s) will have riht-halfplane zeros. We would think that the performance of the \inner-loop" process would be reduced, since riht-half-plane zeros cannot be \inverted" for \perfect" control. We nd that this is not the case, because the real cascade system is actually a multiple state feedback control. Since the real measures two outputs and manipulates a sinle input, there are no problems with the secondary process riht-half-plane-zeros... Series and Parallel Cascade Control The two most typical cascade control conurations are the series and parallel structures, which were shown in ures and. Luyben (97) stressed the dierences between conventional series cascade control and parallel cascade control. The three-state CSTR transfer function models have a parallel cascade control structure, since the manipulated variable (dimensionless coolin owrate) has a direct inuence on x (dimensionless ) and x (dimensionless coolin ) throuh parallel transfer functions. In this subsection we show that the traditional methods of analyzin cascade control (based on series or parallel input/output relationships) should not be used when the primary and secondary processes are coupled, and the process is

3 open-loop unstable. Consider the series cascade control case, shown in Fiure. The state-space realization is: A series C series a a a a a a a a a a a a B series b C A C A where states? represent the secondary process and states? represent the primary process transfer functions, respectively. The coecients of the A series and B series matrices come from the oriinal open-loop A and B matrices. The rst (C series ) and second outputs (C series ) correspond to the dimensionless coolin and s, respectively. The statespace realization of the two open-loop transfer functions in series results in a state-space model with a non-minimal realization, because the primary and secondary processes are actually coupled. This state-space realization is not controllable and when the primary and secondary processes are unstable it is not stabilizable (Russo, 99). Consider the case of parallel cascade control (Fiure ) of the ed exothermic CSTR. The state-space realization is also not minimal when the parallel form is used. This conuration suffers from internal stability problems (Russo, 99) when the primary and secondary processes are unstable. Hence, the unstable modes cannot be stabilized and a process so-simulated will be unstable while the actual process may be stable. These non-minimal realizations can be a serious problem if one attempts to use a commercial simulation packae such as Simulink. The actual coupled process is best represented by Fiure, where it is shown clearly that the primary and secondary outputs are coupled throuh the process model... IMC-based Control System Desin Internal model control (IMC) (Morari and Zariou, 989) provides a clear framework for desin. One advantae to the IMC procedure is that it simplies tunin because a sinle tunin parameter,, is used as opposed to the three parameters (k c, I, D ) for a PID. Linear s are often not valid over a wide rane of operatin reions due to process nonlinearities. However, since we are concerned here with operation in a small reion where the stability characteristics do not chane, linear s enerally provide acceptable performance. It is well known that for open-loop unstable responses, the IMC must be implemented in conventional feedback form since the IMC form is internally unstable (Morari and Zariou, 989). c (s) q(s)? ~(s)q(s) () Here c (s) and ~(s) are the feedback and process model transfer functions, while q(s) is the IMC. The IMC is: q(s) ~?? (s)f(s) () where ~?? (s) is the invertible part of the process transfer function and f(s) is a lter transfer function which is added to make the IMC proper. Rivera et al. (98) demonstrate that for a lare number of sinle input sinle output (SISO) linear systems the IMC desin procedure yields PID-type s. There are many possible choices for the IMC lter structure; one possible structure for open-loop unstable systems is f(s) e ks k + e k? s k? + : : : + e s + e (s + ) n () Here k is the number of distinct poles in the rihthalf-plane (RHP), n is chosen to make the IMC q(s) proper, and is the lter time constant. The IMC lter is chosen such that its steady-state ain is unity; for open-loop unstable systems, the lter must also be unity at the k unstable poles (Morari and Zariou, 989) f(s) at s p i () where p i is the i th unstable pole of (s). Rotstein and Lewin (99) showed that the IMC desin procedure for some rst and second-order unstable transfer functions (without numerator dynamics) results in PID feedback s. The choice of the IMC lter f(s) and subsequently structure for the inner and outer-loops of the cascade control scheme depends on the number of riht-half-plane (RHP) zeros and poles... Cascade Control System Desin In subsection. we saw that traditional methods of analyzin cascade control (based on series and parallel input-output relationships) should not be used when the primary and secondary processes are coupled, and the process is open-loop unsta-

4 ble. In this case input-output relationships do not adequately describe the process dynamics, hence we need to incorporate the full state-space model in our control system desin procedure. Let us consider the ubiquitous case when a PI/P structure is used for the outer and inner loops, respectively. It is well-known that interal action in the outer-loop can be attained by appendin an additional state variable to the model: _x?x () The state feedback is iven by: u?kx () where the feedback ain row vector is: h K k kc k c k ck c c I i () As one can see, cascade control can be thouht of as partial state feedback. Note that the rst feedback ain is constrained to be zero with this structure (because we are assumin that the st state can not be measured or reconstructed with an observer). Hence arbitrary relocation of the \closed-loop" poles is not possible. One potential problem with this type of desin procedure is that a stabilizin feedback ain (K) may not exist, i.e. the constrained structure may not in eneral be able to stabilize the process. Understandin the eect of structure is the focus of current work. In some industrial applications the control structure is limited to P, PI, or PID. In order to develop the desin procedure, we use the followin state-space model, which assumes proportional control on the innerloop. _x Ax + Bx sp (7) y Cx (8) where A; B; C are iven by: A A? : : : B (9) B Bk c () C () where k c is the proportional ain on the \innerloop". The desin transfer function is: y(s) C? si? A? B xsp (s) (s) x sp (s) () The IMC-desin procedure can be used on (s), which in our case turns out to be minimum-phase and is of the followin form: (s) k ( n s + ) ( s + )( s + )(? u s + ) () For a perfect linear model and process, the closedloop response will be iven by: x (s) s + (s + ) x sp(s) () where is chosen to satisfy the IMC lter f(s) unity ain condition at the unstable pole... Control Case Studies The IMC-based feedback cascade is obtained usin equation with equations and. The resultin \outer-loop" is a PI with a double lead-la. Generally, one of the poles of this is fast hence a reduced-order PI with a lead-la can be used with very little performance deradation. We compare the robustness of standard noncascade and cascade feedback control with our new cascade desin procedure, applied to the nonlinear process. The \inner-loop" proportional ain is k c?: and the IMC lter time constant is : for both sets of simulations. The rst case study considers the inuence of the cascade control system desin. The parameters are Case conditions (Russo and Bequette; 99a, 99b); the nominal is x :7 (which is open-loop unstable). A lter ( F : ) is used to remove the overshoot which results from the ISE-optimal IMC desin procedure. Fiures and demonstrate that our cascade desin procedure is superior to the normal cascade control desin where the inner-loop desin is not incorporated in the \outer-loop" desin. The second case study deals with the eect of an unmeasured disturbance on the performance of cascade and noncascade s. The parameters are Case conditions (Russo and Bequette; 99a, 99b); the nominal is x : (which is open-loop unstable). An unmeasured disturbance in the dimensionless coolin feed (x f ) occurs at (x f chanes from - to -.). Fiures 7 and 8 demonstrate that the cascade control scheme does a better job of rejectin the unmeasured disturbance in the dimensionless coolin feed. An interestin point is that if we had analyzed a two-state CSTR model (nelectin coolin dynamics) one would think that it would be possible to arbitrarily detune the system, because Case is open-loop stable for the two-state CSTR model.

5 . CONCLUSION We have shown in this work that traditional methods of analyzin cascade control should not be used when the primary and secondary processes are coupled (throuh the state-space model) and the process is open-loop unstable. The parallels between traditional full state feedback control and cascade control were demonstrated. The eectiveness of the new cascade control procedure over conventional cascade and noncascade control was studied. APPENDIX Table. Dimensionless variables and parameters for the three-state CSTR model. x Ca x Caf E a RTf x Tc?Tf q Tf c T?T f Tf Qc Q (x ) exp( x + x ) (?H)C af UA C pt f C pq. REFERENCES V Q k e? q Q Q Bequette, B.W. (99). Nonlinear control of chemical processes: A review. Ind. En. Chem. Res.,, 9-. Q V t V V c Cp ccpc x f Caf Caf Kravaris, C. and J.C. Kantor (99). Geometric methods for nonlinear process control. -. Ind. En. Chem. Res., 9, 9-. Luyben, W.L (97). Parallel cascade control. Ind. En. Chem. Fund.,, -7. McLellan, P.J., T.J. Harris, and D.W. Bacon (99). Error trajectory descriptions of nonlinear desins. Chem. En. Sci,, 7-. Morari, M. and E. Zariou (989). Robust process control. Prentice Hall, Enlewood Clis, NJ. Rivera, D.E., M. Morari, and S. Skoestad (98). Internal model control.. PID Controller desin. Ind. En. Chem. Proc. Des. Dev.,, -. Rotstein, G.E. and D.R. Lewin (99). Simple PI and PID tunin for open-loop unstable systems. Ind. En. Chem. Res.,, Russo, L.P. and B.W. Bequette (99a). Impact of process desin on the multiplicity behavior of a ed exothermic CSTR. AIChE J., in press. Russo, L.P. and B.W. Bequette (99b). Eect of process desin on the open-loop behavior of a ed exothermic CSTR. Comp. Chem. En, in press. Russo, L.P. (99). Ph.D. Thesis. Rensselaer Polytechnic Institute, Troy, NY, in proress. Sistu, P.B. and B.W. Bequette (99). Model predictive control of processes with input multiplicities. Chem. En. Sci., in press. Uppal, A., W.H. Ray, and A.B. Poore (97). On the dynamic behavior of continuous stirred tank s. Chem. En. Sci., 9, x f Tf?Tf x T f f Table. Transfer function models. Tcf?Tf T f The transfer functions (s), (s), and (s) are: (s) k (c s+) (a s +a s +a s+) (s) k (c s+) (b s +b s+) (s) k (b s +b s+) (a s +a s +a s+) where k, k, and k and c - a are: k (x f? x s ) C k C k (x f? x s ) B c C b B B b B a A a A a C - are dened in terms of the CSTR parameters. B qxf x s (xf?xs) + (q + )? q (+ x s ) B (q+)qxf x s? q (x f?x s) (+ x s ) A B + ( + s ) A B + [( + s )B? ] [( + s )B? qx f x s ]

6 x sp c x sp c coolin coolant flowrate x x...8. Fi.. Series cascade control structure for a CSTR.. x sp c x sp c coolin x coolant flowrate x.. Includes inner-loop desin Doesn't include inner-loop desin Fi.. Comparison of the closed-loop dimensionless coolin owrate performance of the two cascade control strateies. Fi.. Parallel cascade control structure for a CSTR. x x Fi.. Relationship between the open-loop transfer functions. x x q sp sp c x x. x f(x,u) C + c + c - - x x C x Cascade Control Noncascade Control Fi. 7. Comparison of the closed-loop dimensionless performance of the cascade and noncascade strateies to a disturbance in the coolin feed. 7 8 Fi.. Coupled Process Formulation usin a State Space Representation. The desin is based on a linear state-space model. Actual implementation is on a nonlinear process. x Includes inner-loop desin Doesn't include inner-loop desin Fi.. Comparison of the closed-loop dimensionless performance of the two cascade control strateies Cascade Control Noncascade Control Fi. 8. Comparison of the closed-loop dimensionless coolin owrate performance of the cascade and noncascade strateies to a disturbance in the coolin feed. 7 8

Eect of Process Design on the Open-loop Behavior of a. Rensselaer Polytechnic Institute, Howard P. Isermann Department of Chemical Engineering, Troy,

Eect of Process Design on the Open-loop Behavior of a. Rensselaer Polytechnic Institute, Howard P. Isermann Department of Chemical Engineering, Troy, Eect of Process Design on the Open-loop Behavior of a Jacketed Exothermic CSTR Louis P. Russo and B. Wayne Bequette Rensselaer Polytechnic nstitute, Howard P. sermann Department of Chemical Engineering,

More information

CHAPTER 2 PROCESS DESCRIPTION

CHAPTER 2 PROCESS DESCRIPTION 15 CHAPTER 2 PROCESS DESCRIPTION 2.1 INTRODUCTION A process havin only one input variable used for controllin one output variable is known as SISO process. The problem of desinin controller for SISO systems

More information

Ch 14: Feedback Control systems

Ch 14: Feedback Control systems Ch 4: Feedback Control systems Part IV A is concerned with sinle loop control The followin topics are covered in chapter 4: The concept of feedback control Block diaram development Classical feedback controllers

More information

DESIGN OF PI CONTROLLERS FOR MIMO SYSTEM WITH DECOUPLERS

DESIGN OF PI CONTROLLERS FOR MIMO SYSTEM WITH DECOUPLERS Int. J. Chem. Sci.: 4(3), 6, 598-6 ISSN 97-768X www.sadurupublications.com DESIGN OF PI CONTROLLERS FOR MIMO SYSTEM WITH DECOUPLERS A. ILAKKIYA, S. DIVYA and M. MANNIMOZHI * School of Electrical Enineerin,

More information

ROBUST STABLE NONLINEAR CONTROL AND DESIGN OF A CSTR IN A LARGE OPERATING RANGE. Johannes Gerhard, Martin Mönnigmann, Wolfgang Marquardt

ROBUST STABLE NONLINEAR CONTROL AND DESIGN OF A CSTR IN A LARGE OPERATING RANGE. Johannes Gerhard, Martin Mönnigmann, Wolfgang Marquardt ROBUST STABLE NONLINEAR CONTROL AND DESIGN OF A CSTR IN A LARGE OPERATING RANGE Johannes Gerhard, Martin Mönnigmann, Wolfgang Marquardt Lehrstuhl für Prozesstechnik, RWTH Aachen Turmstr. 46, D-5264 Aachen,

More information

NonlinearControlofpHSystemforChangeOverTitrationCurve

NonlinearControlofpHSystemforChangeOverTitrationCurve D. SWATI et al., Nonlinear Control of ph System for Change Over Titration Curve, Chem. Biochem. Eng. Q. 19 (4) 341 349 (2005) 341 NonlinearControlofpHSystemforChangeOverTitrationCurve D. Swati, V. S. R.

More information

Feedback Control of Linear SISO systems. Process Dynamics and Control

Feedback Control of Linear SISO systems. Process Dynamics and Control Feedback Control of Linear SISO systems Process Dynamics and Control 1 Open-Loop Process The study of dynamics was limited to open-loop systems Observe process behavior as a result of specific input signals

More information

Modeling and Control of Chemical Reactor Using Model Reference Adaptive Control

Modeling and Control of Chemical Reactor Using Model Reference Adaptive Control Modeling and Control of Chemical Reactor Using Model Reference Adaptive Control Padmayoga.R, Shanthi.M 2, Yuvapriya.T 3 PG student, Dept. of Electronics and Instrumentation, Valliammai Engineering College,

More information

PROPORTIONAL-Integral-Derivative (PID) controllers

PROPORTIONAL-Integral-Derivative (PID) controllers Multiple Model and Neural based Adaptive Multi-loop PID Controller for a CSTR Process R.Vinodha S. Abraham Lincoln and J. Prakash Abstract Multi-loop (De-centralized) Proportional-Integral- Derivative

More information

On an internal multimodel control for nonlinear multivariable systems - A comparative study

On an internal multimodel control for nonlinear multivariable systems - A comparative study On an internal multimodel control for nonlinear multivariable systems A comparative study Nahla Touati Karmani Dhaou Soudani Mongi Naceur Mohamed Benrejeb Abstract An internal multimodel control designed

More information

H-Infinity Controller Design for a Continuous Stirred Tank Reactor

H-Infinity Controller Design for a Continuous Stirred Tank Reactor International Journal of Electronic and Electrical Engineering. ISSN 974-2174 Volume 7, Number 8 (214), pp. 767-772 International Research Publication House http://www.irphouse.com H-Infinity Controller

More information

ANFIS Gain Scheduled Johnson s Algorithm based State Feedback Control of CSTR

ANFIS Gain Scheduled Johnson s Algorithm based State Feedback Control of CSTR International Journal of Computer Applications (975 8887) ANFIS Gain Scheduled Johnsons Algorithm based State Feedback Control of CSTR U. Sabura Banu Professor, EIE Department BS Abdur Rahman University,

More information

Comparative study of three practical IMC algorithms with inner controller of first and second order

Comparative study of three practical IMC algorithms with inner controller of first and second order Journal of Electrical Engineering, Electronics, Control and Computer Science JEEECCS, Volume 2, Issue 4, pages 2-28, 206 Comparative study of three practical IMC algorithms with inner controller of first

More information

ChE 6303 Advanced Process Control

ChE 6303 Advanced Process Control ChE 6303 Advanced Process Control Teacher: Dr. M. A. A. Shoukat Choudhury, Email: shoukat@buet.ac.bd Syllabus: 1. SISO control systems: Review of the concepts of process dynamics and control, process models,

More information

Ian G. Horn, Jeffery R. Arulandu, Christopher J. Gombas, Jeremy G. VanAntwerp, and Richard D. Braatz*

Ian G. Horn, Jeffery R. Arulandu, Christopher J. Gombas, Jeremy G. VanAntwerp, and Richard D. Braatz* Ind. Eng. Chem. Res. 996, 35, 3437-344 3437 PROCESS DESIGN AND CONTROL Improved Filter Design in Internal Model Control Ian G. Horn, Jeffery R. Arulandu, Christopher J. Gombas, Jeremy G. VanAntwerp, and

More information

USE OF FILTERED SMITH PREDICTOR IN DMC

USE OF FILTERED SMITH PREDICTOR IN DMC Proceedins of the th Mediterranean Conference on Control and Automation - MED22 Lisbon, Portual, July 9-2, 22. USE OF FILTERED SMITH PREDICTOR IN DMC C. Ramos, M. Martínez, X. Blasco, J.M. Herrero Predictive

More information

MULTILOOP CONTROL APPLIED TO INTEGRATOR MIMO. PROCESSES. A Preliminary Study

MULTILOOP CONTROL APPLIED TO INTEGRATOR MIMO. PROCESSES. A Preliminary Study MULTILOOP CONTROL APPLIED TO INTEGRATOR MIMO PROCESSES. A Preliminary Study Eduardo J. Adam 1,2*, Carlos J. Valsecchi 2 1 Instituto de Desarrollo Tecnológico para la Industria Química (INTEC) (Universidad

More information

A Method for PID Controller Tuning Using Nonlinear Control Techniques*

A Method for PID Controller Tuning Using Nonlinear Control Techniques* A Method for PID Controller Tuning Using Nonlinear Control Techniques* Prashant Mhaskar, Nael H. El-Farra and Panagiotis D. Christofides Department of Chemical Engineering University of California, Los

More information

V DD. M 1 M 2 V i2. V o2 R 1 R 2 C C

V DD. M 1 M 2 V i2. V o2 R 1 R 2 C C UNVERSTY OF CALFORNA Collee of Enineerin Department of Electrical Enineerin and Computer Sciences E. Alon Homework #3 Solutions EECS 40 P. Nuzzo Use the EECS40 90nm CMOS process in all home works and projects

More information

Index. INDEX_p /15/02 3:08 PM Page 765

Index. INDEX_p /15/02 3:08 PM Page 765 INDEX_p.765-770 11/15/02 3:08 PM Page 765 Index N A Adaptive control, 144 Adiabatic reactors, 465 Algorithm, control, 5 All-pass factorization, 257 All-pass, frequency response, 225 Amplitude, 216 Amplitude

More information

UCLA Chemical Engineering. Process & Control Systems Engineering Laboratory

UCLA Chemical Engineering. Process & Control Systems Engineering Laboratory Constrained Innite-time Optimal Control Donald J. Chmielewski Chemical Engineering Department University of California Los Angeles February 23, 2000 Stochastic Formulation - Min Max Formulation - UCLA

More information

Linearized optimal power flow

Linearized optimal power flow Linearized optimal power flow. Some introductory comments The advantae of the economic dispatch formulation to obtain minimum cost allocation of demand to the eneration units is that it is computationally

More information

ECE Introduction to Artificial Neural Network and Fuzzy Systems

ECE Introduction to Artificial Neural Network and Fuzzy Systems ECE 39 - Introduction to Artificial Neural Network and Fuzzy Systems Wavelet Neural Network control of two Continuous Stirred Tank Reactors in Series using MATLAB Tariq Ahamed Abstract. With the rapid

More information

MODELING A METAL HYDRIDE HYDROGEN STORAGE SYSTEM. Apurba Sakti EGEE 520, Mathematical Modeling of EGEE systems Spring 2007

MODELING A METAL HYDRIDE HYDROGEN STORAGE SYSTEM. Apurba Sakti EGEE 520, Mathematical Modeling of EGEE systems Spring 2007 MODELING A METAL HYDRIDE HYDROGEN STORAGE SYSTEM Apurba Sakti EGEE 520, Mathematical Modelin of EGEE systems Sprin 2007 Table of Contents Abstract Introduction Governin Equations Enery balance Momentum

More information

CHAPTER 10: STABILITY &TUNING

CHAPTER 10: STABILITY &TUNING When I complete this chapter, I want to be able to do the following. Determine the stability of a process without control Determine the stability of a closed-loop feedback control system Use these approaches

More information

Nonlinear adaptive control for multivariable chemical processes

Nonlinear adaptive control for multivariable chemical processes Chemical Engineering Science 56 (1) 6781 6791 www.elsevier.com/locate/ces Nonlinear adaptive control for multivariable chemical processes Bingjun Guo, Aiping Jiang, Xiangming Hua, Arthur Jutan Department

More information

Chapter 7 Interconnected Systems and Feedback: Well-Posedness, Stability, and Performance 7. Introduction Feedback control is a powerful approach to o

Chapter 7 Interconnected Systems and Feedback: Well-Posedness, Stability, and Performance 7. Introduction Feedback control is a powerful approach to o Lectures on Dynamic Systems and Control Mohammed Dahleh Munther A. Dahleh George Verghese Department of Electrical Engineering and Computer Science Massachuasetts Institute of Technology c Chapter 7 Interconnected

More information

Lecture 12. Upcoming labs: Final Exam on 12/21/2015 (Monday)10:30-12:30

Lecture 12. Upcoming labs: Final Exam on 12/21/2015 (Monday)10:30-12:30 289 Upcoming labs: Lecture 12 Lab 20: Internal model control (finish up) Lab 22: Force or Torque control experiments [Integrative] (2-3 sessions) Final Exam on 12/21/2015 (Monday)10:30-12:30 Today: Recap

More information

University of Science and Technology, Sudan Department of Chemical Engineering.

University of Science and Technology, Sudan Department of Chemical Engineering. ISO 91:28 Certified Volume 3, Issue 6, November 214 Design and Decoupling of Control System for a Continuous Stirred Tank Reactor (CSTR) Georgeous, N.B *1 and Gasmalseed, G.A, Abdalla, B.K (1-2) University

More information

Model-based PID tuning for high-order processes: when to approximate

Model-based PID tuning for high-order processes: when to approximate Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 25 Seville, Spain, December 2-5, 25 ThB5. Model-based PID tuning for high-order processes: when to approximate

More information

Chaos Control via Non-singular Terminal Sliding Mode Controller in Auto Gauge Control System Wei-wei ZHANG, Jun-tao PAN and Hong-tao SHI

Chaos Control via Non-singular Terminal Sliding Mode Controller in Auto Gauge Control System Wei-wei ZHANG, Jun-tao PAN and Hong-tao SHI 7 International Conference on Computer, Electronics and Communication Enineerin (CECE 7) ISBN: 978--6595-476-9 Chaos Control via Non-sinular Terminal Slidin Mode Controller in Auto Gaue Control System

More information

The parameterization of all. of all two-degree-of-freedom strongly stabilizing controllers

The parameterization of all. of all two-degree-of-freedom strongly stabilizing controllers The parameterization stabilizing controllers 89 The parameterization of all two-degree-of-freedom strongly stabilizing controllers Tatsuya Hoshikawa, Kou Yamada 2, Yuko Tatsumi 3, Non-members ABSTRACT

More information

CONTROL OF MULTIVARIABLE PROCESSES

CONTROL OF MULTIVARIABLE PROCESSES Process plants ( or complex experiments) have many variables that must be controlled. The engineer must. Provide the needed sensors 2. Provide adequate manipulated variables 3. Decide how the CVs and MVs

More information

Chemical Process Operating Region

Chemical Process Operating Region Inherently Safer Design Oriented Segregation of Chemical Process Operating Region Wang Hangzhou, Yuan Zhihong, Chen Bingzhen * Zhao Jinsong, Qiu Tong, He Xiaorong, Institute of Process System Engineering,

More information

CHAPTER 2 CONTINUOUS STIRRED TANK REACTOR PROCESS DESCRIPTION

CHAPTER 2 CONTINUOUS STIRRED TANK REACTOR PROCESS DESCRIPTION 11 CHAPTER 2 CONTINUOUS STIRRED TANK REACTOR PROCESS DESCRIPTION 2.1 INTRODUCTION This chapter deals with the process description and analysis of CSTR. The process inputs, states and outputs are identified

More information

The Computational Complexity Analysis of a MINLP-Based Chemical Process Control Design

The Computational Complexity Analysis of a MINLP-Based Chemical Process Control Design J.Oto.Ktrl.Inst (J.Auto.Ctrl.Inst Vol (, 00 ISSN : 085-57 Abstract The Computational Complexity Analysis of a MINLP-Based Chemical Process Control Desin E. Ekawati, S. Yuliar Center for Instrumentation

More information

Incorporating Feedforward Action into Self-optimizing Control Policies

Incorporating Feedforward Action into Self-optimizing Control Policies Incorporating Feedforward Action into Self-optimizing Control Policies Lia Maisarah Umar, Yi Cao and Vinay Kariwala School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore

More information

Constrained Output Feedback Control of a Multivariable Polymerization Reactor

Constrained Output Feedback Control of a Multivariable Polymerization Reactor IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 8, NO. 1, JANUARY 2000 87 Constrained Output Feedback Control of a Multivariable Polymerization Reactor Michael J. Kurtz, Guang.-Yan Zhu, and Michael

More information

Linear Parameter Varying and Time-Varying Model Predictive Control

Linear Parameter Varying and Time-Varying Model Predictive Control Linear Parameter Varying and Time-Varying Model Predictive Control Alberto Bemporad - Model Predictive Control course - Academic year 016/17 0-1 Linear Parameter-Varying (LPV) MPC LTI prediction model

More information

Active Disturbance Rejection Control of Chemical Processes

Active Disturbance Rejection Control of Chemical Processes 6th I International Conference on Control Applications Part of I Multi-conference on Systems and Control Singapore, -3 October 7 TuC.4 Active Disturbance Rejection Control of Chemical Processes Zhongzhou

More information

= 1 τ (C Af C A ) kc A. τ = V q. k = k(t )=k. T0 e E 1. γ = H ρc p. β = (ρc p) c Vρc p. Ua (ρc p ) c. α =

= 1 τ (C Af C A ) kc A. τ = V q. k = k(t )=k. T0 e E 1. γ = H ρc p. β = (ρc p) c Vρc p. Ua (ρc p ) c. α = TTK485 Robust Control Department of Engineering Cybernetics Spring 27 Solution - Assignment Exercise : Unstable chemical reactor a) A CSTR-reactor with heat exchange is shown in figure. Mass- and energy

More information

Dynamics and Control of Reactor - Feed Effluent Heat Exchanger Networks

Dynamics and Control of Reactor - Feed Effluent Heat Exchanger Networks 2008 American Control Conference Westin Seattle Hotel Seattle Washington USA June 11-13 2008 WeC08.2 Dynamics and Control of Reactor - Feed Effluent Heat Exchanger Networks Sujit S. Jogwar Michael Baldea

More information

Temperature Control of CSTR Using Fuzzy Logic Control and IMC Control

Temperature Control of CSTR Using Fuzzy Logic Control and IMC Control Vo1ume 1, No. 04, December 2014 936 Temperature Control of CSTR Using Fuzzy Logic Control and Control Aravind R Varma and Dr.V.O. Rejini Abstract--- Fuzzy logic controllers are useful in chemical processes

More information

CHAPTER 3 TUNING METHODS OF CONTROLLER

CHAPTER 3 TUNING METHODS OF CONTROLLER 57 CHAPTER 3 TUNING METHODS OF CONTROLLER 3.1 INTRODUCTION This chapter deals with a simple method of designing PI and PID controllers for first order plus time delay with integrator systems (FOPTDI).

More information

BITS-Pilani Dubai, International Academic City, Dubai Second Semester. Academic Year

BITS-Pilani Dubai, International Academic City, Dubai Second Semester. Academic Year BITS-Pilani Dubai, International Academic City, Dubai Second Semester. Academic Year 2007-2008 Evaluation Com anent: Com rehensive Examination Closed Book CHE UC441/11NSTR UC 45'1 PROCESS CONTROL Date:

More information

Design of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process

Design of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process Design of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process D.Angeline Vijula #, Dr.N.Devarajan * # Electronics and Instrumentation Engineering Sri Ramakrishna

More information

Chapter 9 Observers, Model-based Controllers 9. Introduction In here we deal with the general case where only a subset of the states, or linear combin

Chapter 9 Observers, Model-based Controllers 9. Introduction In here we deal with the general case where only a subset of the states, or linear combin Lectures on Dynamic Systems and Control Mohammed Dahleh Munther A. Dahleh George Verghese Department of Electrical Engineering and Computer Science Massachuasetts Institute of Technology c Chapter 9 Observers,

More information

EE 435. Lecture 18. Two-Stage Op Amp with LHP Zero Loop Gain - Breaking the Loop

EE 435. Lecture 18. Two-Stage Op Amp with LHP Zero Loop Gain - Breaking the Loop EE 435 Lecture 8 Two-Stae Op Amp with LHP Zero Loop Gain - Breakin the Loop Review from last lecture Nyquist and Gain-Phase Plots Nyquist and Gain-Phase Plots convey identical information but ain-phase

More information

R. Antonelli. A. Astol * and. Milano, ITALY

R. Antonelli. A. Astol * and. Milano, ITALY Continuous Stirred Tank Reactors: easy to stabilise? R. Antonelli Centre for Process Systems Engineering Imperial College of Science, Technology and Medicine London, SW7 BY, UK A. Astol * Dept. of Electrical

More information

Input /Output Linearization: A Nonlinear Analog of Placing Poles at Process Zeros

Input /Output Linearization: A Nonlinear Analog of Placing Poles at Process Zeros Input /Output Linearization: A Nonlinear Analog of Placing Poles at Process Zeros This paper deals with SlSO nonlinear processes and their control with nonlinear static state feedback. The Byrnes-lsidori

More information

A unified double-loop multi-scale control strategy for NMP integrating-unstable systems

A unified double-loop multi-scale control strategy for NMP integrating-unstable systems Home Search Collections Journals About Contact us My IOPscience A unified double-loop multi-scale control strategy for NMP integrating-unstable systems This content has been downloaded from IOPscience.

More information

Guide to Selected Process Examples :ili3g eil;]iil

Guide to Selected Process Examples :ili3g eil;]iil Guide to Selected Process Examples :ili3g eil;]iil Because of the strong interplay between process dynamics and control perfor mance, examples should begin with process equipment and operating conditions.

More information

Multi-Loop Control. Department of Chemical Engineering,

Multi-Loop Control. Department of Chemical Engineering, Interaction ti Analysis and Multi-Loop Control Sachin C. Patawardhan Department of Chemical Engineering, I.I.T. Bombay Outline Motivation Interactions in Multi-loop control Loop pairing using Relative

More information

Control of integral processes with dead time Part IV: various issues about PI controllers

Control of integral processes with dead time Part IV: various issues about PI controllers Control of integral processes with dead time Part IV: various issues about PI controllers B. Wang, D. Rees and Q.-C. Zhong Abstract: Various issues about integral processes with dead time controlled by

More information

Chapter Stability Robustness Introduction Last chapter showed how the Nyquist stability criterion provides conditions for the stability robustness of

Chapter Stability Robustness Introduction Last chapter showed how the Nyquist stability criterion provides conditions for the stability robustness of Lectures on Dynamic Systems and Control Mohammed Dahleh Munther A Dahleh George Verghese Department of Electrical Engineering and Computer Science Massachuasetts Institute of Technology c Chapter Stability

More information

Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science

Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science Massachusetts nstitute of Technoloy Department of Electrical Enineerin and Computer Science 6.685 Electric Machines Class Notes 2 Manetic Circuit Basics September 5, 2005 c 2003 James L. Kirtley Jr. 1

More information

Control of MIMO processes. 1. Introduction. Control of MIMO processes. Control of Multiple-Input, Multiple Output (MIMO) Processes

Control of MIMO processes. 1. Introduction. Control of MIMO processes. Control of Multiple-Input, Multiple Output (MIMO) Processes Control of MIMO processes Control of Multiple-Input, Multiple Output (MIMO) Processes Statistical Process Control Feedforward and ratio control Cascade control Split range and selective control Control

More information

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science : MULTIVARIABLE CONTROL SYSTEMS by A.

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science : MULTIVARIABLE CONTROL SYSTEMS by A. Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.245: MULTIVARIABLE CONTROL SYSTEMS by A. Megretski Q-Parameterization 1 This lecture introduces the so-called

More information

FEEDFORWARD CONTROLLER DESIGN BASED ON H ANALYSIS

FEEDFORWARD CONTROLLER DESIGN BASED ON H ANALYSIS 271 FEEDFORWARD CONTROLLER DESIGN BASED ON H ANALYSIS Eduardo J. Adam * and Jacinto L. Marchetti Instituto de Desarrollo Tecnológico para la Industria Química (Universidad Nacional del Litoral - CONICET)

More information

SELECTION OF VARIABLES FOR REGULATORY CONTROL USING POLE VECTORS. Kjetil Havre 1 Sigurd Skogestad 2

SELECTION OF VARIABLES FOR REGULATORY CONTROL USING POLE VECTORS. Kjetil Havre 1 Sigurd Skogestad 2 SELECTION OF VARIABLES FOR REGULATORY CONTROL USING POLE VECTORS Kjetil Havre 1 Sigurd Skogestad 2 Chemical Engineering, Norwegian University of Science and Technology N-734 Trondheim, Norway. Abstract:

More information

Altitude measurement for model rocketry

Altitude measurement for model rocketry Altitude measurement for model rocketry David A. Cauhey Sibley School of Mechanical Aerospace Enineerin, Cornell University, Ithaca, New York 14853 I. INTRODUCTION In his book, Rocket Boys, 1 Homer Hickam

More information

CDS 101/110a: Lecture 8-1 Frequency Domain Design

CDS 101/110a: Lecture 8-1 Frequency Domain Design CDS 11/11a: Lecture 8-1 Frequency Domain Design Richard M. Murray 17 November 28 Goals: Describe canonical control design problem and standard performance measures Show how to use loop shaping to achieve

More information

IMC based automatic tuning method for PID controllers in a Smith predictor configuration

IMC based automatic tuning method for PID controllers in a Smith predictor configuration Computers and Chemical Engineering 28 (2004) 281 290 IMC based automatic tuning method for PID controllers in a Smith predictor configuration Ibrahim Kaya Department of Electrical and Electronics Engineering,

More information

YOULA KUČERA PARAMETRISATION IN SELF TUNING LQ CONTROL OF A CHEMICAL REACTOR

YOULA KUČERA PARAMETRISATION IN SELF TUNING LQ CONTROL OF A CHEMICAL REACTOR YOULA KUČERA PARAMETRISATION IN SELF TUNING LQ CONTROL OF A CHEMICAL REACTOR Ján Mikleš, L uboš Čirka, and Miroslav Fikar Slovak University of Technology in Bratislava Radlinského 9, 812 37 Bratislava,

More information

Nonlinear Model Reduction of Differential Algebraic Equation (DAE) Systems

Nonlinear Model Reduction of Differential Algebraic Equation (DAE) Systems Nonlinear Model Reduction of Differential Alebraic Equation DAE Systems Chuili Sun and Jueren Hahn Department of Chemical Enineerin eas A&M University Collee Station X 77843-3 hahn@tamu.edu repared for

More information

Improved cascade control structure for enhanced performance

Improved cascade control structure for enhanced performance Improved cascade control structure for enhanced performance Article (Unspecified) Kaya, İbrahim, Tan, Nusret and Atherton, Derek P. (7) Improved cascade control structure for enhanced performance. Journal

More information

Multi-Input Multi-output (MIMO) Processes CBE495 LECTURE III CONTROL OF MULTI INPUT MULTI OUTPUT PROCESSES. Professor Dae Ryook Yang

Multi-Input Multi-output (MIMO) Processes CBE495 LECTURE III CONTROL OF MULTI INPUT MULTI OUTPUT PROCESSES. Professor Dae Ryook Yang Multi-Input Multi-output (MIMO) Processes CBE495 LECTURE III CONTROL OF MULTI INPUT MULTI OUTPUT PROCESSES Professor Dae Ryook Yang Fall 2013 Dept. of Chemical and Biological Engineering Korea University

More information

Control System Design

Control System Design ELEC ENG 4CL4: Control System Design Notes for Lecture #36 Dr. Ian C. Bruce Room: CRL-229 Phone ext.: 26984 Email: ibruce@mail.ece.mcmaster.ca Friday, April 4, 2003 3. Cascade Control Next we turn to an

More information

AADECA 2006 XXº Congreso Argentino de Control Automático ITERATIVE LEARNING CONTROL APPLIED TO NONLINEAR BATCH REACTOR E.J.

AADECA 2006 XXº Congreso Argentino de Control Automático ITERATIVE LEARNING CONTROL APPLIED TO NONLINEAR BATCH REACTOR E.J. ITERATIVE LEARNING CONTROL APPLIED TO NONLINEAR BATCH REACTOR E.J. ADAM (1) Institute of Technological Development for the Chemical Industry (INTEC), CONICET Universidad Nacional del Litoral (UNL). Güemes

More information

Closed-loop system 2/1/2016. Generally MIMO case. Two-degrees-of-freedom (2 DOF) control structure. (2 DOF structure) The closed loop equations become

Closed-loop system 2/1/2016. Generally MIMO case. Two-degrees-of-freedom (2 DOF) control structure. (2 DOF structure) The closed loop equations become Closed-loop system enerally MIMO case Two-degrees-of-freedom (2 DOF) control structure (2 DOF structure) 2 The closed loop equations become solving for z gives where is the closed loop transfer function

More information

Enhanced Single-Loop Control Strategies (Advanced Control) Cascade Control Time-Delay Compensation Inferential Control Selective and Override Control

Enhanced Single-Loop Control Strategies (Advanced Control) Cascade Control Time-Delay Compensation Inferential Control Selective and Override Control Enhanced Single-Loop Control Strategies (Advanced Control) Cascade Control Time-Delay Compensation Inferential Control Selective and Override Control 1 Cascade Control A disadvantage of conventional feedback

More information

Model Predictive Control For Interactive Thermal Process

Model Predictive Control For Interactive Thermal Process Model Predictive Control For Interactive Thermal Process M.Saravana Balaji #1, D.Arun Nehru #2, E.Muthuramalingam #3 #1 Assistant professor, Department of Electronics and instrumentation Engineering, Kumaraguru

More information

Process Control, 3P4 Assignment 6

Process Control, 3P4 Assignment 6 Process Control, 3P4 Assignment 6 Kevin Dunn, kevin.dunn@mcmaster.ca Due date: 28 March 204 This assignment gives you practice with cascade control and feedforward control. Question [0 = 6 + 4] The outlet

More information

Analysis and Synthesis of Single-Input Single-Output Control Systems

Analysis and Synthesis of Single-Input Single-Output Control Systems Lino Guzzella Analysis and Synthesis of Single-Input Single-Output Control Systems l+kja» \Uja>)W2(ja»\ um Contents 1 Definitions and Problem Formulations 1 1.1 Introduction 1 1.2 Definitions 1 1.2.1 Systems

More information

Hybrid Direct Neural Network Controller With Linear Feedback Compensator

Hybrid Direct Neural Network Controller With Linear Feedback Compensator Hybrid Direct Neural Network Controller With Linear Feedback Compensator Dr.Sadhana K. Chidrawar 1, Dr. Balasaheb M. Patre 2 1 Dean, Matoshree Engineering, Nanded (MS) 431 602 E-mail: sadhana_kc@rediff.com

More information

Input-output Controllability Analysis

Input-output Controllability Analysis Input-output Controllability Analysis Idea: Find out how well the process can be controlled - without having to design a specific controller Note: Some processes are impossible to control Reference: S.

More information

Anisochronic First Order Model and Its Application to Internal Model Control

Anisochronic First Order Model and Its Application to Internal Model Control Anisochronic First Order Model and Its Application to Internal Model Control Vyhlídal, Tomáš Ing., Czech Technical University, Inst. of Instrumentation and Control Eng., Technická 4, Praha 6, 66 7, vyhlidal@student.fsid.cvut.cz

More information

MEM 355 Performance Enhancement of Dynamical Systems

MEM 355 Performance Enhancement of Dynamical Systems MEM 355 Performance Enhancement of Dynamical Systems Frequency Domain Design Intro Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University /5/27 Outline Closed Loop Transfer

More information

ULOF Accident Analysis for 300 MWt Pb-Bi Coolled MOX Fuelled SPINNOR Reactor

ULOF Accident Analysis for 300 MWt Pb-Bi Coolled MOX Fuelled SPINNOR Reactor ULOF Accident Analysis for 300 MWt Pb-Bi Coolled MOX Fuelled SPINNOR Reactor Ade afar Abdullah Electrical Enineerin Department, Faculty of Technoloy and Vocational Education Indonesia University of Education

More information

Control Systems Design

Control Systems Design ELEC4410 Control Systems Design Lecture 3, Part 2: Introduction to Affine Parametrisation School of Electrical Engineering and Computer Science Lecture 3, Part 2: Affine Parametrisation p. 1/29 Outline

More information

Model based control design

Model based control design Model based control design Alf Isaksson September, 999 Supplied as supplement to course book in Automatic Control Basic course (Reglerteknik AK) Objective: To introduce some general approaches to model

More information

Renormalization Group Theory

Renormalization Group Theory Chapter 16 Renormalization Group Theory In the previous chapter a procedure was developed where hiher order 2 n cycles were related to lower order cycles throuh a functional composition and rescalin procedure.

More information

ISA-PID Controller Tuning: A combined min-max / ISE approach

ISA-PID Controller Tuning: A combined min-max / ISE approach Proceedings of the 26 IEEE International Conference on Control Applications Munich, Germany, October 4-6, 26 FrB11.2 ISA-PID Controller Tuning: A combined min-max / ISE approach Ramon Vilanova, Pedro Balaguer

More information

Cascade Control of a Continuous Stirred Tank Reactor (CSTR)

Cascade Control of a Continuous Stirred Tank Reactor (CSTR) Journal of Applied and Industrial Sciences, 213, 1 (4): 16-23, ISSN: 2328-4595 (PRINT), ISSN: 2328-469 (ONLINE) Research Article Cascade Control of a Continuous Stirred Tank Reactor (CSTR) 16 A. O. Ahmed

More information

Chapter 9 Robust Stability in SISO Systems 9. Introduction There are many reasons to use feedback control. As we have seen earlier, with the help of a

Chapter 9 Robust Stability in SISO Systems 9. Introduction There are many reasons to use feedback control. As we have seen earlier, with the help of a Lectures on Dynamic Systems and Control Mohammed Dahleh Munther A. Dahleh George Verghese Department of Electrical Engineering and Computer Science Massachuasetts Institute of Technology c Chapter 9 Robust

More information

and reconizin that we obtain the followin equation for ~x (s): ~q(s) = ~x = dt e?st q(t) dt e?st x (t) dt e?st x (t) = s ~x (s)? sx ()? _x () or (s +!

and reconizin that we obtain the followin equation for ~x (s): ~q(s) = ~x = dt e?st q(t) dt e?st x (t) dt e?st x (t) = s ~x (s)? sx ()? _x () or (s +! G5.65: Statistical Mechanics Notes for Lecture 4 I. THE HARMONIC BATH HAMILTONIAN In the theory of chemical reactions, it is often possible to isolate a small number or even a sinle deree of freedom in

More information

Intro to Frequency Domain Design

Intro to Frequency Domain Design Intro to Frequency Domain Design MEM 355 Performance Enhancement of Dynamical Systems Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University Outline Closed Loop Transfer Functions

More information

3.1 Overview 3.2 Process and control-loop interactions

3.1 Overview 3.2 Process and control-loop interactions 3. Multivariable 3.1 Overview 3.2 and control-loop interactions 3.2.1 Interaction analysis 3.2.2 Closed-loop stability 3.3 Decoupling control 3.3.1 Basic design principle 3.3.2 Complete decoupling 3.3.3

More information

Internal Model Control of A Class of Continuous Linear Underactuated Systems

Internal Model Control of A Class of Continuous Linear Underactuated Systems Internal Model Control of A Class of Continuous Linear Underactuated Systems Asma Mezzi Tunis El Manar University, Automatic Control Research Laboratory, LA.R.A, National Engineering School of Tunis (ENIT),

More information

CHAPTER 19: Single-Loop IMC Control

CHAPTER 19: Single-Loop IMC Control When I coplete this chapter, I want to be able to do the following. Recognize that other feedback algoriths are possible Understand the IMC structure and how it provides the essential control features

More information

Energizing Math with Engineering Applications

Energizing Math with Engineering Applications Enerizin Math with Enineerin Applications Understandin the Math behind Launchin a Straw-Rocket throuh the use of Simulations. Activity created by Ira Rosenthal (rosenthi@palmbeachstate.edu) as part of

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MATERIALS SCIENCE AND ENGINEERING CAMBRIDGE, MASSACHUSETTS 02139

MASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MATERIALS SCIENCE AND ENGINEERING CAMBRIDGE, MASSACHUSETTS 02139 MASSACHUSES INSIUE OF ECHNOLOGY DEPARMEN OF MAERIALS SCIENCE AND ENGINEERING CAMBRIDGE, MASSACHUSES 02139 3.22 MECHANICAL PROPERIES OF MAERIALS PROBLEM SE 5 SOLUIONS 1. (Hertzber 6.2) If it takes 300 seconds

More information

IMPROVED CONSTRAINED CASCADE CONTROL FOR PARALLEL PROCESSES. Richard Lestage, André Pomerleau and André Desbiens

IMPROVED CONSTRAINED CASCADE CONTROL FOR PARALLEL PROCESSES. Richard Lestage, André Pomerleau and André Desbiens IMPROVED CONSTRAINED CASCADE CONTROL FOR PARALLEL PROCESSES Richard Lestage, André Pomerleau and André Desbiens Groupe de Recherche sur les Applications de l Informatique à l Industrie Minérale(GRAIIM),

More information

CHAPTER 5 ROBUSTNESS ANALYSIS OF THE CONTROLLER

CHAPTER 5 ROBUSTNESS ANALYSIS OF THE CONTROLLER 114 CHAPTER 5 ROBUSTNESS ANALYSIS OF THE CONTROLLER 5.1 INTRODUCTION Robust control is a branch of control theory that explicitly deals with uncertainty in its approach to controller design. It also refers

More information

DESIGN, IMPLEMENTATION, AND REAL- TIME SIMULATION OF A CONTROLLER- BASED DECOUPLED CSTR MIMO CLOSED LOOP SYSTEM

DESIGN, IMPLEMENTATION, AND REAL- TIME SIMULATION OF A CONTROLLER- BASED DECOUPLED CSTR MIMO CLOSED LOOP SYSTEM International Journal of Electrical Engineering & Technology (IJEET) Volume 7, Issue 3, May June, 2016, pp.126 144, Article ID: IJEET_07_03_011 Available online at http://www.iaeme.com/ijeet/issues.asp?jtype=ijeet&vtype=7&itype=3

More information

Linear State Feedback Controller Design

Linear State Feedback Controller Design Assignment For EE5101 - Linear Systems Sem I AY2010/2011 Linear State Feedback Controller Design Phang Swee King A0033585A Email: king@nus.edu.sg NGS/ECE Dept. Faculty of Engineering National University

More information

Type-2 Fuzzy Logic Control of Continuous Stirred Tank Reactor

Type-2 Fuzzy Logic Control of Continuous Stirred Tank Reactor dvance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 3, Number 2 (2013), pp. 169-178 Research India Publications http://www.ripublication.com/aeee.htm Type-2 Fuzzy Logic Control of Continuous

More information

Intermediate Process Control CHE576 Lecture Notes # 2

Intermediate Process Control CHE576 Lecture Notes # 2 Intermediate Process Control CHE576 Lecture Notes # 2 B. Huang Department of Chemical & Materials Engineering University of Alberta, Edmonton, Alberta, Canada February 4, 2008 2 Chapter 2 Introduction

More information

Sensors & Transducers 2015 by IFSA Publishing, S. L.

Sensors & Transducers 2015 by IFSA Publishing, S. L. Sensors & Transducers 2015 by IFSA Publishing, S. L. http://www.sensorsportal.com Multi-Model Adaptive Fuzzy Controller for a CSTR Process * Shubham Gogoria, Tanvir Parhar, Jaganatha Pandian B. Electronics

More information

MRAGPC Control of MIMO Processes with Input Constraints and Disturbance

MRAGPC Control of MIMO Processes with Input Constraints and Disturbance Proceedings of the World Congress on Engineering and Computer Science 9 Vol II WCECS 9, October -, 9, San Francisco, USA MRAGPC Control of MIMO Processes with Input Constraints and Disturbance A. S. Osunleke,

More information