Overview of Models for Automated Process Control
|
|
- Dwain McDaniel
- 6 years ago
- Views:
Transcription
1 Overview of Models for Automated Process Control James B. Rawlings Department of Chemical and Biological Engineering April 29, 29 Utilization of Process Modeling and Advanced Process Control in QbD based Drug Development and Manufacturing Workshop Baltimore, MD Rawlings Models for automated process control / 46
2 Outline Introduction and Motivation 2 Modeling Nonlinear differential equations Linear time invariant models Discrete time models Input-output models Constraints Modeling the noise 3 From Models to Automated Process Control Regulation State estimation Disturbance modeling and zero offset 4 Future Developments 5 Further Reading Rawlings Models for automated process control 2 / 46
3 The power of abstraction process dx = f (x, u) dt y = g(x, u) actuators sensors Rawlings Models for automated process control 3 / 46
4 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Rawlings Models for automated process control 4 / 46
5 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Rawlings Models for automated process control 4 / 46
6 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Achieved primarily by increased on-spec product, decreased energy use Rawlings Models for automated process control 4 / 46
7 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Achieved primarily by increased on-spec product, decreased energy use Eastman Chemical experience with MPC First MPC implemented in 996 Rawlings Models for automated process control 4 / 46
8 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Achieved primarily by increased on-spec product, decreased energy use Eastman Chemical experience with MPC First MPC implemented in 996 Currently 55-6 MPC applications of varying complexity Rawlings Models for automated process control 4 / 46
9 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Achieved primarily by increased on-spec product, decreased energy use Eastman Chemical experience with MPC First MPC implemented in 996 Currently 55-6 MPC applications of varying complexity 3-5 M$/year increased profit due to increased throughput (28) Rawlings Models for automated process control 4 / 46
10 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Achieved primarily by increased on-spec product, decreased energy use Eastman Chemical experience with MPC First MPC implemented in 996 Currently 55-6 MPC applications of varying complexity 3-5 M$/year increased profit due to increased throughput (28) Praxair experience with MPC Praxair currently has more than 5 MPC installations Rawlings Models for automated process control 4 / 46
11 Large industrial success story! Linear MPC and ethylene manufacturing Number of MPC applications in ethylene: 8 to 2 Credits 5 to 8 M$/yr (27) Achieved primarily by increased on-spec product, decreased energy use Eastman Chemical experience with MPC First MPC implemented in 996 Currently 55-6 MPC applications of varying complexity 3-5 M$/year increased profit due to increased throughput (28) Praxair experience with MPC Praxair currently has more than 5 MPC installations 6 M$/year increased profit (28) Rawlings Models for automated process control 4 / 46
12 Broader industrial impact (Qin and Badgwell, 23) Area Aspen Honeywell Adersa PCL MDC Total Technology Hi-Spec Refining Petrochemicals Chemicals Pulp and Paper Air & Gas Utility Mining/Metallurgy Food Processing Polymer Furnaces Aerospace/Defense Automotive Unclassified Total First App. DMC:985 PCT:984 IDCOM:973 PCL: SMOC: IDCOM-M:987 RMPCT:99 HIECON: OPC:987 Largest App 63x x85-3x2 - Rawlings Models for automated process control 5 / 46
13 Input, output, and state variables input output process u y Input/output description Rawlings Models for automated process control 6 / 46
14 Input, output, and state variables input output process u y Input/output description input u R m state x R n output y R p process sensor State description Rawlings Models for automated process control 6 / 46
15 Nonlinear models Nonlinear differential equations dx = f (x, u, t) dt y = h(x, u, t) x(t ) = x x is the state u is the input y is the output t is time Rawlings Models for automated process control 7 / 46
16 Linear time invariant models Linear differential equations dx = Ax + Bu dt y = Cx + Du x() = x Rawlings Models for automated process control 8 / 46
17 Continuous time and discrete time models Continuous time x dx = Ax + Bu dt u t t Rawlings Models for automated process control 9 / 46
18 Continuous time and discrete time models Continuous time Discrete time x dx = Ax + Bu dt x x(k + ) = Ax + Bu u t u sample time t = k t t = k Rawlings Models for automated process control 9 / 46
19 Discrete time models Linear difference equations x(k + ) = Ax(k) + Bu(k) y(k) = Cx(k) + Du(k) x() = x Rawlings Models for automated process control / 46
20 Discrete time models Linear difference equations x(k + ) = Ax(k) + Bu(k) y(k) = Cx(k) + Du(k) x() = x Easier notation Linear difference equations x + = Ax + Bu y = Cx + Du x() = x in which x(k) + denotes x(k + ). Rawlings Models for automated process control / 46
21 Input-output models input output process u y Input/output description input u R m state x R n output y R p process sensor State description Rawlings Models for automated process control / 46
22 Input-output to state space Current output y(t) is modeled as a time series in past inputs and outputs, and noise y(t) = a y(t ) + a 2 y(t 2) + b }{{} u(t ) + b 2 u(t 2) +v(t) }{{} past outputs past inputs Rawlings Models for automated process control 2 / 46
23 Input-output to state space Current output y(t) is modeled as a time series in past inputs and outputs, and noise y(t) = a y(t ) + a 2 y(t 2) + b }{{} u(t ) + b 2 u(t 2) +v(t) }{{} past outputs past inputs We estimate the model parameters from (u, y) data (Ljung, 2) θ = [ a a 2 b b 2 ] Rawlings Models for automated process control 2 / 46
24 Input-output to state space Current output y(t) is modeled as a time series in past inputs and outputs, and noise y(t) = a y(t ) + a 2 y(t 2) + b }{{} u(t ) + b 2 u(t 2) +v(t) }{{} past outputs past inputs We estimate the model parameters from (u, y) data (Ljung, 2) θ = [ a a 2 b b 2 ] Given θ we can obtain A, B, C Rawlings Models for automated process control 2 / 46
25 Converting input-output to state space Define x(t) to hold the past inputs and outputs. 2 x(t) = 4 y(t) 3 y(t ) 5 u(t ) Rawlings Models for automated process control 3 / 46
26 Converting input-output to state space Define x(t) to hold the past inputs and outputs. The time series model is 2 x(t) = 4 y(t) 3 y(t ) 5 u(t ) y(t + ) 4 y(t) 5 = 4 a 3 2 a 2 b y(t) 3 2 y(t ) b 3 5 u(t) u(t) u(t ) {z } {z } {z } {z } x + A x B 2 y(t) = ˆ 4 y(t) 3 y(t ) 5 {z } u(t ) C {z } x Rawlings Models for automated process control 3 / 46
27 Converting input-output to state space Define x(t) to hold the past inputs and outputs. The time series model is 2 x(t) = 4 y(t) 3 y(t ) 5 u(t ) y(t + ) 4 y(t) 5 = 4 a 3 2 a 2 b y(t) 3 2 y(t ) b 3 5 u(t) u(t) u(t ) {z } {z } {z } {z } x + A x B 2 y(t) = ˆ 4 y(t) 3 y(t ) 5 {z } u(t ) C {z } x The equivalent state space model is x + = Ax + Bu y = Cx Rawlings Models for automated process control 3 / 46
28 Constraints Physical bounds on inputs (valves full open, full closed) u 2 u u(k) u k in which E = Eu(k) e k [ ] I I e = [ ] u u x 2 u x Rawlings Models for automated process control 4 / 46
29 Constraints Physical bounds on inputs (valves full open, full closed) u 2 u u(k) u k in which E = Eu(k) e k [ ] I I e = [ ] u u x 2 u Constraints on states or outputs for reasons of safety, operability, product quality, etc. Fx(k) f k x Rawlings Models for automated process control 4 / 46
30 Rate of change constraints Important in some applications to limit the rate of change of the input, u(k) u(k ) Rawlings Models for automated process control 5 / 46
31 Rate of change constraints Important in some applications to limit the rate of change of the input, u(k) u(k ) Augment the state x(k) = [ ] x(k) u(k ) Rawlings Models for automated process control 5 / 46
32 Rate of change constraints Important in some applications to limit the rate of change of the input, u(k) u(k ) Augment the state x(k) = [ ] x(k) u(k ) Augmented system model [ ] [ ] [ ] [ ] x(k + ) A x(k) B = + u(k) u(k) u(k ) I }{{}}{{}}{{}}{{} ex(k+) ea ex(k) eb y(k) = [ C ] [ ] x(k) }{{} u(k ) }{{} ec ex(k) Rawlings Models for automated process control 5 / 46
33 Rate of change constraints The rate of change constraint u(k) u(k ) k Rawlings Models for automated process control 6 / 46
34 Rate of change constraints The rate of change constraint u(k) u(k ) k is stated as F x(k) + Eu(k) e F = [ ] I I E = [ ] I I e = [ ] Rawlings Models for automated process control 6 / 46
35 Rate of change constraints The rate of change constraint u(k) u(k ) k is stated as F x(k) + Eu(k) e F = [ ] I I E = [ ] I I e = [ ] General linear constraints subsumes all previous forms Fx(k) + Eu(k) e k Rawlings Models for automated process control 6 / 46
36 Nonlinear constraints For nonlinear models, linear constraints are not required u(k) U x(k) X k u 2 x 2 U u X x Rawlings Models for automated process control 7 / 46
37 Nonlinear constraints For nonlinear models, linear constraints are not required u(k) U x(k) X k Even more general (u(k), x(k)) Z k u 2 x 2 U u X x Rawlings Models for automated process control 7 / 46
38 Typical process data y time Rawlings Models for automated process control 8 / 46
39 Modeling the noise Random disturbances x + = Ax + Bu + Gw y = Cx + v w is the random variable affecting the process Rawlings Models for automated process control 9 / 46
40 Modeling the noise Random disturbances x + = Ax + Bu + Gw y = Cx + v w is the random variable affecting the process v is the random variable affecting the measurement Rawlings Models for automated process control 9 / 46
41 Modeling the noise Random disturbances x + = Ax + Bu + Gw y = Cx + v w is the random variable affecting the process v is the random variable affecting the measurement So v models measurement noise and w models process disturbance Rawlings Models for automated process control 9 / 46
42 Modeling the noise Random disturbances x + = Ax + Bu + Gw y = Cx + v w is the random variable affecting the process v is the random variable affecting the measurement So v models measurement noise and w models process disturbance The controller needs to know the relative amounts of each disturbance Rawlings Models for automated process control 9 / 46
43 The model predictive control framework Measurement MH Estimate MPC control Forecast t time Reconcile the past Forecast the future sensors y actuators u Rawlings Models for automated process control 2 / 46
44 Predictive control The future influences the present just as much as the past does. Measurement MH Estimate MPC control Forecast t time Reconcile the past Forecast the future sensors y actuators u Rawlings Models for automated process control 2 / 46
45 Predictive control Measurement MH Estimate MPC control Forecast t time Reconcile the past Forecast the future sensors y actuators u min u(t) T y sp g(x, u) 2 Q + u sp u 2 R dt ẋ = f (x, u) x() = x (given) y = g(x, u) Rawlings Models for automated process control 2 / 46
46 State estimation When I want to understand what is happening today or try to decide what will happen tomorrow, I look back. Measurement MH Estimate MPC control Forecast t time Reconcile the past Forecast the future sensors y actuators u Rawlings Models for automated process control 22 / 46
47 State estimation Measurement MH Estimate MPC control Forecast t time Reconcile the past Forecast the future sensors y actuators u min x,w(t) T y g(x, u) 2 R + ẋ f (x, u) 2 Q dt ẋ = f (x, u) + w (process noise) y = g(x, u) + v (measurement noise) Rawlings Models for automated process control 22 / 46
48 Separation of the control problem input output process u y Input/output description Rawlings Models for automated process control 23 / 46
49 Separation of the control problem input output process u y Input/output description input u R m state x R n output y R p process sensor State description Rawlings Models for automated process control 23 / 46
50 Separation of the control problem input output process u y Input/output description input u R m state x R n output y R p process sensor State description input u process state x Regulation problem regulator Rawlings Models for automated process control 23 / 46
51 Separation of the control problem input output process u y estimator Estimation problem estimate ˆx Rawlings Models for automated process control 24 / 46
52 Separation of the control problem input output process u y estimator Estimation problem estimate ˆx input u process output y regulator estimator Control problem ˆx Rawlings Models for automated process control 24 / 46
53 Controller Design for Zero Offset Often want zero tracking error and rejection of constant disturbances Rawlings Models for automated process control 25 / 46
54 Controller Design for Zero Offset Often want zero tracking error and rejection of constant disturbances Augment the model with a constant disturbances (Davison and Smith, 97, Kwakernaak and Sivan, 972, p. 278). [ ] x(k + ) = d(k + ) y(k) = [ C [ A Bd I ] [ ] x(k) + d(k) ] + v(k) C d ] [ x(k) d(k) [ ] B u(k) + w(k) Rawlings Models for automated process control 25 / 46
55 Offset free control system x + = A x + Bũ (Q, R) regulator u plant y x s u s ˆx estimator y sp, u sp, z sp (Q s, R s ) target selector ˆx ˆd [ ] + x = d [ ] [ A Bd x I d] y = [ ] [ x C C d d (Q w, R v) [ B + u + w ] MPC controller consisting of: receding horizon regulator, state estimator, and target selector. ] + v Rawlings Models for automated process control 26 / 46
56 Design example. (Pannocchia and Rawlings, 23) F, T, c Reaction A B T c T, c r F h Rawlings Models for automated process control 27 / 46
57 Design example. (Pannocchia and Rawlings, 23) F, T, c T c r T, c F h Reaction A B Controlled variables: level of the tank (h) Rawlings Models for automated process control 27 / 46
58 Design example. (Pannocchia and Rawlings, 23) F, T, c T c r T, c F h Reaction A B Controlled variables: level of the tank (h) concentration of species A (c) Rawlings Models for automated process control 27 / 46
59 Design example. (Pannocchia and Rawlings, 23) F, T, c T c r T, c F h Reaction A B Controlled variables: level of the tank (h) concentration of species A (c) State variable: reactor temp (T ) Rawlings Models for automated process control 27 / 46
60 Design example. (Pannocchia and Rawlings, 23) F, T, c T c r T, c F h Reaction A B Controlled variables: level of the tank (h) concentration of species A (c) State variable: reactor temp (T ) Manipulated variables: coolant liquid temp (T c ) Rawlings Models for automated process control 27 / 46
61 Design example. (Pannocchia and Rawlings, 23) F, T, c T c r T, c F h Reaction A B Controlled variables: level of the tank (h) concentration of species A (c) State variable: reactor temp (T ) Manipulated variables: coolant liquid temp (T c ) outlet flowrate (F ) Rawlings Models for automated process control 27 / 46
62 Design example. (Pannocchia and Rawlings, 23) F, T, c T c r T, c F h Reaction A B Controlled variables: level of the tank (h) concentration of species A (c) State variable: reactor temp (T ) Manipulated variables: coolant liquid temp (T c ) outlet flowrate (F ) Disturbance: inlet flowrate (F ) Rawlings Models for automated process control 27 / 46
63 More measured outputs than inputs and zero offset Component mass and energy balances Rawlings Models for automated process control 28 / 46
64 More measured outputs than inputs and zero offset Component mass and energy balances Nonlinear differential equation model: dc dt = F ( (c c) Ur 2 k c exp E h RT dt dt = F (T T ) Ur 2 h dh dt = F F Ur 2 + H k c exp ρc p ) ( E ) + 2U (T c T ) RT rρc p Rawlings Models for automated process control 28 / 46
65 Well-stirred reactor example: parameters Parameter Nominal value Units F. m 3 /min T 35 K c kmol/m 3 r.29 m k 7.2 min E/R 875 K U kj/min m 2 K ρ kg/m 3 C p.239 kj/kg K H 5 4 kj/kmol Rawlings Models for automated process control 29 / 46
66 Well-stirred reactor example: linearized model Using = min, the linearized model is x + = Ax + Bu + B p p y = Cx in which all the states are measured c c s [ ] x = T T s Tc T u = s c c s c h h s F F s y = T T s p = F F s h h s Rawlings Models for automated process control 3 / 46
67 Well-stirred reactor example: linearized model Using = min, the linearized model is x + = Ax + Bu + B p p y = Cx in which all the states are measured c c s [ ] x = T T s Tc T u = s c c s c h h s F F s y = T T s p = F F s h h s and A = C = B = B p = Rawlings Models for automated process control 3 / 46
68 Reactor case study - two integrating disturbances We have two inputs, T c and F Rawlings Models for automated process control 3 / 46
69 Reactor case study - two integrating disturbances We have two inputs, T c and F We try to remove offset in two controlled variables, c and h Rawlings Models for automated process control 3 / 46
70 Reactor case study - two integrating disturbances We have two inputs, T c and F We try to remove offset in two controlled variables, c and h Model the disturbance with two integrating output disturbances on first and third controlled variables by choosing: [ ] C d = B d = Rawlings Models for automated process control 3 / 46
71 Reactor case study - two integrating disturbances We have two inputs, T c and F We try to remove offset in two controlled variables, c and h Model the disturbance with two integrating output disturbances on first and third controlled variables by choosing: [ ] C d = B d = Simulate the response of the controlled system after a % increase in F at time t = min using the nonlinear differential equations for the plant model Rawlings Models for automated process control 3 / 46
72 Reactor case study - two integrating disturbances We have two inputs, T c and F We try to remove offset in two controlled variables, c and h Model the disturbance with two integrating output disturbances on first and third controlled variables by choosing: [ ] C d = B d = Simulate the response of the controlled system after a % increase in F at time t = min using the nonlinear differential equations for the plant model Is there steady offset in any of the outputs? Which ones? Rawlings Models for automated process control 3 / 46
73 Two integrating disturbances: output results c (kmol/m 3 ) T (K) h (m) time (min) Three measured outputs versus time after a step change in F at t = min Rawlings Models for automated process control 32 / 46
74 Two integrating disturbances: input results Tc (K) F (m 3 /min) time (min) Two manipulated inputs versus time after a step change in F at t = min Rawlings Models for automated process control 33 / 46
75 Reactor case studies - two and three integrating disturbances Two integrating disturbances - conclusion: Despite integrators in the two controlled variables, c and h, all outputs have nonzero steady offset! Rawlings Models for automated process control 34 / 46
76 Reactor case studies - two and three integrating disturbances Two integrating disturbances - conclusion: Despite integrators in the two controlled variables, c and h, all outputs have nonzero steady offset! Next we try three integrating disturbances: two added to the two controlled variables one added to the second manipulated variable.655 C d = B d = Rawlings Models for automated process control 34 / 46
77 Three integrating disturbances: output results.88 c (kmol/m 3 ) T (K) h (m) time (min) Three measured outputs versus time after a step change in F at t = min Rawlings Models for automated process control 35 / 46
78 Three integrating disturbances: input results 3 Tc (K) F (m 3 /min) time (min) Two manipulated inputs versus time after a step change in F at t = min Rawlings Models for automated process control 36 / 46
79 Three integrating disturbances: conclusions We now have zero offset in the two controlled variables, c and h Rawlings Models for automated process control 37 / 46
80 Three integrating disturbances: conclusions We now have zero offset in the two controlled variables, c and h We have successfully forced the steady-state effect of the disturbance entirely into the second output, T Rawlings Models for automated process control 37 / 46
81 Three integrating disturbances: conclusions We now have zero offset in the two controlled variables, c and h We have successfully forced the steady-state effect of the disturbance entirely into the second output, T The dynamic behavior of all outputs is superior to that achieved with the model using two integrating disturbances Rawlings Models for automated process control 37 / 46
82 Three integrating disturbances: conclusions We now have zero offset in the two controlled variables, c and h We have successfully forced the steady-state effect of the disturbance entirely into the second output, T The dynamic behavior of all outputs is superior to that achieved with the model using two integrating disturbances The true disturbance is better represented by including the integrator in the outlet flowrate Rawlings Models for automated process control 37 / 46
83 Three integrating disturbances: conclusions We now have zero offset in the two controlled variables, c and h We have successfully forced the steady-state effect of the disturbance entirely into the second output, T The dynamic behavior of all outputs is superior to that achieved with the model using two integrating disturbances The true disturbance is better represented by including the integrator in the outlet flowrate With a more accurate disturbance model, better overall control is achieved The controller uses smaller manipulated variable action and also achieves better output variable behavior Rawlings Models for automated process control 37 / 46
84 Three integrating disturbances: conclusions We now have zero offset in the two controlled variables, c and h We have successfully forced the steady-state effect of the disturbance entirely into the second output, T The dynamic behavior of all outputs is superior to that achieved with the model using two integrating disturbances The true disturbance is better represented by including the integrator in the outlet flowrate With a more accurate disturbance model, better overall control is achieved The controller uses smaller manipulated variable action and also achieves better output variable behavior An added bonus is that steady offset is removed in the maximum possible number of outputs Rawlings Models for automated process control 37 / 46
85 Advanced process control and drug manufacturing Drug manufacturing versus petrochemicals Differences Small volume, high value-added products Rawlings Models for automated process control 38 / 46
86 Advanced process control and drug manufacturing Drug manufacturing versus petrochemicals Differences Small volume, high value-added products Batch and semi-batch processing Rawlings Models for automated process control 38 / 46
87 Advanced process control and drug manufacturing Drug manufacturing versus petrochemicals Differences Small volume, high value-added products Batch and semi-batch processing Batch-to-batch variability Rawlings Models for automated process control 38 / 46
88 Advanced process control and drug manufacturing Drug manufacturing versus petrochemicals Differences Small volume, high value-added products Batch and semi-batch processing Batch-to-batch variability Importance of the solid phase Rawlings Models for automated process control 38 / 46
89 Advanced process control and drug manufacturing Drug manufacturing versus petrochemicals Differences Small volume, high value-added products Batch and semi-batch processing Batch-to-batch variability Importance of the solid phase Novel solid-phase sensors (on-line video imaging) Rawlings Models for automated process control 38 / 46
90 On-line Video Imaging Figure 4. Examples of real in situ images of rod-like crystals. In situ images of rod-like crystals from our crystallization lab (Larsen and Rawlings, 29). ontinued improvements in the design of in situ video sysems will lead to images that are increasingly similar to the pling, but also effective image analysis. For example, Figure Accurate PSD estimation requires not only sufficient sam- dealized, simulated images used in this work. 6 indicates that, assuming perfect image analysis, images provides sufficient samples to achieve a reasonably good estimate of the weight PSD for the given process conditions bsolute PSD measurement Figure 7, on the other hand, shows the PSD estimated from Figures 5 and 6 show, respectively, the number- and image analysis data generated using the SHARC algorithm. 7 eight-based PSDs corresponding to the optimal cooling prole. The continuous curve shows the simulated PSD, and the the image analysis method identifies several false positives This figure indicates that towards the end of the experiment istograms show the Rawlings estimated PSD based on imaging mea- Modelsand for automated also misses process a significant control number of 39 both / 46nucleus-grown
91 Idealized data from a batch crystallization Figure 2. Examples of images generated at various times during the optimal cooling simulation. Optimal The images cooling correspondprofile. to 6-min intervals Images from h (upper correspond left) to 8 h (lower toright). 6-minute intervals from hours (upper left) to 8 hours (lower right). Rawlings Models for automated process control 4 / 46
92 Idealized data from a batch crystallization igure 3. Examples of images generated at various times during the linear cooling simulation. The images correspond to 6-min intervals from h (upper left) to 8 h (lower right). Linear cooling profile. Images correspond to 6-min intervals from h (upper left) to 8 h (lower right). DOI.2/aic Published on behalf of the AIChE April 29 Vol. 54, No. 4 AIChE Journal Rawlings Models for automated process control 4 / 46
93 Inferring size distribution from video images Figure 6. Evolution of measured and estimated weight PSD cooling and perfect image analysis. Measured and Snapshots estimated shown from t 5 minweight to t 5 48 minpsd at 6 intervals. for Bin optimal size 5 lm andcooling N 5. and perfect image analysis. Bin size = µm and bins. analysis algorithms have different tolerances for overlap, with model-based algorithms typically tolerating more overlap than other methods. On the basis of the images in Figure 2, one could reasonably expect improvements in model-based image analysis algorithms to enable effective measurement In previous studies of this system, these measurement difficulties motivated the use of scanning electron and optical microscopy to characterize the PSD qualitatively in terms of habit and maximum size. 5 Quantitative PSD measurement by microscopy could not be achieved because of sampling Rawlings Models for automated process control 42 / 46
94 Inferring size distribution from video images Figure 7. Evolution of measured and estimated weight PSD cooling and image analysis using SHARC. Measured and Snapshots estimated shown from t 5 minweight to t 5 48 at PSD 6 min intervals. for Bin optimal size 5 lm and cooling N 5. and image analysis using SHARC. Bin size = µm and Z Siþ bins. weight-based c vw (l 5l 3/l 2 4 ) can be measured effectively by imaging. General imaging considerations in which S 5 (S,..., S T) is the vector of breaks between Rawlings Models for automated process control 43 / 46 q i ¼ Si f ðlþdl; i ¼ ;...; T (7)
95 Further reading I E. J. Davison and H. W. Smith. Pole assignment in linear time-invariant multivariable systems with constant disturbances. Automatica, 7: , 97. H. Kwakernaak and R. Sivan. Linear Optimal Control Systems. John Wiley and Sons, New York, 972. P. A. Larsen and J. B. Rawlings. Assessing the reliability of particle number density measurements obtained by image analysis. Part. Part. Syst. Charact., 25(5 6):42 433, February 29. P. A. Larsen and J. B. Rawlings. The potential of current high-resolution imaging-based particle size distribution measurements for crystallization monitoring. AIChE J., (4):896 95, April 29. L. Ljung. System Identification - Theory for the User. Prentice Hall, New Jersey, 2nd edition, 2. G. Pannocchia and J. B. Rawlings. Disturbance models for offset-free MPC control. AIChE J., 49(2): , 23. Rawlings Models for automated process control 44 / 46
96 Further reading II S. J. Qin and T. A. Badgwell. A survey of industrial model predictive control technology. Control Eng. Prac., (7): , 23. J. B. Rawlings and D. Q. Mayne. Model predictive control: Theory and design. Nob Hill Publishing, 29. Rawlings Models for automated process control 45 / 46
Optimal dynamic operation of chemical processes: current opportunities
Optimal dynamic operation of chemical processes: current opportunities James B. Rawlings Department of Chemical and Biological Engineering August 6, 29 Westhollow Research Center Shell Rawlings Current
More informationOptimal dynamic operation of chemical processes: Assessment of the last 20 years and current research opportunities
Optimal dynamic operation of chemical processes: Assessment of the last 2 years and current research opportunities James B. Rawlings Department of Chemical and Biological Engineering April 3, 2 Department
More informationCourse on Model Predictive Control Part II Linear MPC design
Course on Model Predictive Control Part II Linear MPC design Gabriele Pannocchia Department of Chemical Engineering, University of Pisa, Italy Email: g.pannocchia@diccism.unipi.it Facoltà di Ingegneria,
More informationNonlinear Model Predictive Control Tools (NMPC Tools)
Nonlinear Model Predictive Control Tools (NMPC Tools) Rishi Amrit, James B. Rawlings April 5, 2008 1 Formulation We consider a control system composed of three parts([2]). Estimator Target calculator Regulator
More informationNonlinear Stochastic Modeling and State Estimation of Weakly Observable Systems: Application to Industrial Polymerization Processes
Nonlinear Stochastic Modeling and State Estimation of Weakly Observable Systems: Application to Industrial Polymerization Processes Fernando V. Lima, James B. Rawlings and Tyler A. Soderstrom Department
More informationOutline. 1 Linear Quadratic Problem. 2 Constraints. 3 Dynamic Programming Solution. 4 The Infinite Horizon LQ Problem.
Model Predictive Control Short Course Regulation James B. Rawlings Michael J. Risbeck Nishith R. Patel Department of Chemical and Biological Engineering Copyright c 217 by James B. Rawlings Outline 1 Linear
More informationBACKSTEPPING CONTROL DESIGN FOR A CONTINUOUS-STIRRED TANK. Saleh Alshamali and Mohamed Zribi. Received July 2011; revised March 2012
International Journal of Innovative Computing, Information and Control ICIC International c 202 ISSN 349-498 Volume 8, Number, November 202 pp. 7747 7760 BACKSTEPPING CONTROL DESIGN FOR A CONTINUOUS-STIRRED
More informationOPTIMAL CONTROL WITH DISTURBANCE ESTIMATION
OPTIMAL CONTROL WITH DISTURBANCE ESTIMATION František Dušek, Daniel Honc, Rahul Sharma K. Department of Process control Faculty of Electrical Engineering and Informatics, University of Pardubice, Czech
More informationCooperation-based optimization of industrial supply chains
Cooperation-based optimization of industrial supply chains James B. Rawlings, Brett T. Stewart, Kaushik Subramanian and Christos T. Maravelias Department of Chemical and Biological Engineering May 9 2,
More informationPROPORTIONAL-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 informationOptimizing Economic Performance using Model Predictive Control
Optimizing Economic Performance using Model Predictive Control James B. Rawlings Department of Chemical and Biological Engineering Second Workshop on Computational Issues in Nonlinear Control Monterey,
More informationUse of Differential Equations In Modeling and Simulation of CSTR
Use of Differential Equations In Modeling and Simulation of CSTR JIRI VOJTESEK, PETR DOSTAL Department of Process Control, Faculty of Applied Informatics Tomas Bata University in Zlin nám. T. G. Masaryka
More informationarxiv: v1 [cs.sy] 2 Oct 2018
Non-linear Model Predictive Control of Conically Shaped Liquid Storage Tanks arxiv:1810.01119v1 [cs.sy] 2 Oct 2018 5 10 Abstract Martin Klaučo, L uboš Čirka Slovak University of Technology in Bratislava,
More informationQuis custodiet ipsos custodes?
Quis custodiet ipsos custodes? James B. Rawlings, Megan Zagrobelny, Luo Ji Dept. of Chemical and Biological Engineering, Univ. of Wisconsin-Madison, WI, USA IFAC Conference on Nonlinear Model Predictive
More informationPROCESS DESIGN AND CONTROL Offset-Free Tracking of Model Predictive Control with Model Mismatch: Experimental Results
3966 Ind. Eng. Chem. Res. 2005, 44, 3966-3972 PROCESS DESIGN AND CONTROL Offset-Free Tracking of Model Predictive Control with Model Mismatch: Experimental Results Audun Faanes and Sigurd Skogestad* Department
More informationIntermediate 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 informationModel Predictive Control
Model Predictive Control Linear time-varying and nonlinear MPC Alberto Bemporad http://cse.lab.imtlucca.it/~bemporad 2018 A. Bemporad - "Model Predictive Control" 1/30 Course structure Linear model predictive
More informationPREDICTIVE CONTROL OF NONLINEAR SYSTEMS. Received February 2008; accepted May 2008
ICIC Express Letters ICIC International c 2008 ISSN 1881-803X Volume 2, Number 3, September 2008 pp. 239 244 PREDICTIVE CONTROL OF NONLINEAR SYSTEMS Martin Janík, Eva Miklovičová and Marián Mrosko Faculty
More informationIn search of the unreachable setpoint
In search of the unreachable setpoint Adventures with Prof. Sten Bay Jørgensen James B. Rawlings Department of Chemical and Biological Engineering June 19, 2009 Seminar Honoring Prof. Sten Bay Jørgensen
More informationLinear System Theory. Wonhee Kim Lecture 1. March 7, 2018
Linear System Theory Wonhee Kim Lecture 1 March 7, 2018 1 / 22 Overview Course Information Prerequisites Course Outline What is Control Engineering? Examples of Control Systems Structure of Control Systems
More information4F3 - Predictive Control
4F3 Predictive Control - Discrete-time systems p. 1/30 4F3 - Predictive Control Discrete-time State Space Control Theory For reference only Jan Maciejowski jmm@eng.cam.ac.uk 4F3 Predictive Control - Discrete-time
More informationA tutorial overview on theory and design of offset-free MPC algorithms
A tutorial overview on theory and design of offset-free MPC algorithms Gabriele Pannocchia Dept. of Civil and Industrial Engineering University of Pisa November 24, 2015 Introduction to offset-free MPC
More informationTheory in Model Predictive Control :" Constraint Satisfaction and Stability!
Theory in Model Predictive Control :" Constraint Satisfaction and Stability Colin Jones, Melanie Zeilinger Automatic Control Laboratory, EPFL Example: Cessna Citation Aircraft Linearized continuous-time
More informationFAULT-TOLERANT CONTROL OF CHEMICAL PROCESS SYSTEMS USING COMMUNICATION NETWORKS. Nael H. El-Farra, Adiwinata Gani & Panagiotis D.
FAULT-TOLERANT CONTROL OF CHEMICAL PROCESS SYSTEMS USING COMMUNICATION NETWORKS Nael H. El-Farra, Adiwinata Gani & Panagiotis D. Christofides Department of Chemical Engineering University of California,
More informationState Estimation using Moving Horizon Estimation and Particle Filtering
State Estimation using Moving Horizon Estimation and Particle Filtering James B. Rawlings Department of Chemical and Biological Engineering UW Math Probability Seminar Spring 2009 Rawlings MHE & PF 1 /
More informationAn Introduction to Model-based Predictive Control (MPC) by
ECE 680 Fall 2017 An Introduction to Model-based Predictive Control (MPC) by Stanislaw H Żak 1 Introduction The model-based predictive control (MPC) methodology is also referred to as the moving horizon
More informationModel Predictive Controller of Boost Converter with RLE Load
Model Predictive Controller of Boost Converter with RLE Load N. Murali K.V.Shriram S.Muthukumar Nizwa College of Vellore Institute of Nizwa College of Technology Technology University Technology Ministry
More informationModel 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 informationLinear 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 informationOptimizing Control of Hot Blast Stoves in Staggered Parallel Operation
Proceedings of the 17th World Congress The International Federation of Automatic Control Optimizing Control of Hot Blast Stoves in Staggered Parallel Operation Akın Şahin and Manfred Morari Automatic Control
More informationModel Predictive Control Short Course Regulation
Model Predictive Control Short Course Regulation James B. Rawlings Michael J. Risbeck Nishith R. Patel Department of Chemical and Biological Engineering Copyright c 2017 by James B. Rawlings Milwaukee,
More informationOutline. Linear regulation and state estimation (LQR and LQE) Linear differential equations. Discrete time linear difference equations
Outline Linear regulation and state estimation (LQR and LQE) James B. Rawlings Department of Chemical and Biological Engineering 1 Linear Quadratic Regulator Constraints The Infinite Horizon LQ Problem
More informationOnline monitoring of MPC disturbance models using closed-loop data
Online monitoring of MPC disturbance models using closed-loop data Brian J. Odelson and James B. Rawlings Department of Chemical Engineering University of Wisconsin-Madison Online Optimization Based Identification
More informationApplication of Modified Multi Model Predictive Control Algorithm to Fluid Catalytic Cracking Unit
Application of Modified Multi Model Predictive Control Algorithm to Fluid Catalytic Cracking Unit Nafay H. Rehman 1, Neelam Verma 2 Student 1, Asst. Professor 2 Department of Electrical and Electronics
More informationData Driven Discrete Time Modeling of Continuous Time Nonlinear Systems. Problems, Challenges, Success Stories. Johan Schoukens
1/51 Data Driven Discrete Time Modeling of Continuous Time Nonlinear Systems Problems, Challenges, Success Stories Johan Schoukens fyuq (,, ) 4/51 System Identification Data Distance Model 5/51 System
More informationState Estimation of Linear and Nonlinear Dynamic Systems
State Estimation of Linear and Nonlinear Dynamic Systems Part I: Linear Systems with Gaussian Noise James B. Rawlings and Fernando V. Lima Department of Chemical and Biological Engineering University of
More informationAalborg Universitet. Published in: Proceedings of European Control Conference ECC' 07. Publication date: 2007
Aalborg Universitet Model Predictive Control of Thermal Comfort and Indoor Air Quality in Livestock Stable Wu, Zhuang; Rajamani, Murali R; Rawlings, James B; Stoustrup, Jakob Published in: Proceedings
More informationComparison of four state observer design algorithms for MIMO system
Archives of Control Sciences Volume 23(LIX), 2013 No. 2, pages 131 144 Comparison of four state observer design algorithms for MIMO system VINODH KUMAR. E, JOVITHA JEROME and S. AYYAPPAN A state observer
More informationMODEL PREDICTIVE CONTROL FUNDAMENTALS
Nigerian Journal of Technology (NIJOTECH) Vol 31, No 2, July, 2012, pp 139 148 Copyright 2012 Faculty of Engineering, University of Nigeria ISSN 1115-8443 MODEL PREDICTIVE CONTROL FUNDAMENTALS PE Orukpe
More informationPerformance of an Adaptive Algorithm for Sinusoidal Disturbance Rejection in High Noise
Performance of an Adaptive Algorithm for Sinusoidal Disturbance Rejection in High Noise MarcBodson Department of Electrical Engineering University of Utah Salt Lake City, UT 842, U.S.A. (8) 58 859 bodson@ee.utah.edu
More informationControlling Large-Scale Systems with Distributed Model Predictive Control
Controlling Large-Scale Systems with Distributed Model Predictive Control James B. Rawlings Department of Chemical and Biological Engineering November 8, 2010 Annual AIChE Meeting Salt Lake City, UT Rawlings
More informationSystem Identification for Model Predictive Control of Building Region Temperature
Purdue University Purdue e-pubs International High Performance Buildings Conference School of Mechanical Engineering 16 System Identification for Model Predictive Control of Building Region Temperature
More informationIntegral action in state feedback control
Automatic Control 1 in state feedback control Prof. Alberto Bemporad University of Trento Academic year 21-211 Prof. Alberto Bemporad (University of Trento) Automatic Control 1 Academic year 21-211 1 /
More informationMATH4406 (Control Theory) Unit 6: The Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) Prepared by Yoni Nazarathy, Artem
MATH4406 (Control Theory) Unit 6: The Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) Prepared by Yoni Nazarathy, Artem Pulemotov, September 12, 2012 Unit Outline Goal 1: Outline linear
More informationD(s) G(s) A control system design definition
R E Compensation D(s) U Plant G(s) Y Figure 7. A control system design definition x x x 2 x 2 U 2 s s 7 2 Y Figure 7.2 A block diagram representing Eq. (7.) in control form z U 2 s z Y 4 z 2 s z 2 3 Figure
More informationExam. 135 minutes, 15 minutes reading time
Exam August 6, 208 Control Systems II (5-0590-00) Dr. Jacopo Tani Exam Exam Duration: 35 minutes, 5 minutes reading time Number of Problems: 35 Number of Points: 47 Permitted aids: 0 pages (5 sheets) A4.
More informationOutline. 1 Full information estimation. 2 Moving horizon estimation - zero prior weighting. 3 Moving horizon estimation - nonzero prior weighting
Outline Moving Horizon Estimation MHE James B. Rawlings Department of Chemical and Biological Engineering University of Wisconsin Madison SADCO Summer School and Workshop on Optimal and Model Predictive
More informationBasic Procedures for Common Problems
Basic Procedures for Common Problems ECHE 550, Fall 2002 Steady State Multivariable Modeling and Control 1 Determine what variables are available to manipulate (inputs, u) and what variables are available
More informationModelling and Control of Dynamic Systems. Stability of Linear Systems. Sven Laur University of Tartu
Modelling and Control of Dynamic Systems Stability of Linear Systems Sven Laur University of Tartu Motivating Example Naive open-loop control r[k] Controller Ĉ[z] u[k] ε 1 [k] System Ĝ[z] y[k] ε 2 [k]
More informationModel Predictive Control Design for Nonlinear Process Control Reactor Case Study: CSTR (Continuous Stirred Tank Reactor)
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-issn: 2278-1676,p-ISSN: 2320-3331, Volume 7, Issue 1 (Jul. - Aug. 2013), PP 88-94 Model Predictive Control Design for Nonlinear Process
More informationIndustrial Model Predictive Control
Industrial Model Predictive Control Emil Schultz Christensen Kongens Lyngby 2013 DTU Compute-M.Sc.-2013-49 Technical University of Denmark DTU Compute Matematiktovet, Building 303B, DK-2800 Kongens Lyngby,
More informationDynamic Operability for the Calculation of Transient Output Constraints for Non-Square Linear Model Predictive Controllers
Dynamic Operability for the Calculation of Transient Output Constraints for Non-Square Linear Model Predictive Controllers Fernando V. Lima and Christos Georgakis* Department of Chemical and Biological
More informationClick to edit Master title style
Click to edit Master title style Fast Model Predictive Control Trevor Slade Reza Asgharzadeh John Hedengren Brigham Young University 3 Apr 2012 Discussion Overview Models for Fast Model Predictive Control
More informationTheoretical Models of Chemical Processes
Theoretical Models of Chemical Processes Dr. M. A. A. Shoukat Choudhury 1 Rationale for Dynamic Models 1. Improve understanding of the process 2. Train Plant operating personnel 3. Develop control strategy
More informationProcess Unit Control System Design
Process Unit Control System Design 1. Introduction 2. Influence of process design 3. Control degrees of freedom 4. Selection of control system variables 5. Process safety Introduction Control system requirements»
More informationPractical Implementations of Advanced Process Control for Linear Systems
Downloaded from orbitdtudk on: Jul 01, 2018 Practical Implementations of Advanced Process Control for Linear Systems Knudsen, Jørgen K H ; Huusom, Jakob Kjøbsted; Jørgensen, John Bagterp Published in:
More informationMPC for tracking periodic reference signals
MPC for tracking periodic reference signals D. Limon T. Alamo D.Muñoz de la Peña M.N. Zeilinger C.N. Jones M. Pereira Departamento de Ingeniería de Sistemas y Automática, Escuela Superior de Ingenieros,
More informationDisturbance Modeling and State Estimation for Predictive Control with Different State-Space Process Models
Milano (Italy) August 28 - September 2, 2 Disturbance Modeling and State Estimation for Predictive Control with Different State-Space Process Models Piotr Tatjewski Institute of Control and Computation
More informationDESIGN OF AN ON-LINE TITRATOR FOR NONLINEAR ph CONTROL
DESIGN OF AN ON-LINE TITRATOR FOR NONLINEAR CONTROL Alex D. Kalafatis Liuping Wang William R. Cluett AspenTech, Toronto, Canada School of Electrical & Computer Engineering, RMIT University, Melbourne,
More informationRobust Model Predictive Control
Robust Model Predictive Control Motivation: An industrial C3/C4 splitter: MPC assuming ideal model: MPC considering model uncertainty Robust Model Predictive Control Nominal model of the plant: 1 = G ()
More informationPostface to Model Predictive Control: Theory and Design
Postface to Model Predictive Control: Theory and Design J. B. Rawlings and D. Q. Mayne August 19, 2012 The goal of this postface is to point out and comment upon recent MPC papers and issues pertaining
More informationModel Predictive Control
Model Predictive Control Davide Manca Lecture 6 of Dynamics and Control of Chemical Processes Master Degree in Chemical Engineering Davide Manca Dynamics and Control of Chemical Processes Master Degree
More informationAPPLICATION OF ADAPTIVE CONTROLLER TO WATER HYDRAULIC SERVO CYLINDER
APPLICAION OF ADAPIVE CONROLLER O WAER HYDRAULIC SERVO CYLINDER Hidekazu AKAHASHI*, Kazuhisa IO** and Shigeru IKEO** * Division of Science and echnology, Graduate school of SOPHIA University 7- Kioicho,
More informationFundamental Principles of Process Control
Fundamental Principles of Process Control Motivation for Process Control Safety First: people, environment, equipment The Profit Motive: meeting final product specs minimizing waste production minimizing
More informationChE 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 informationCSTR CONTROL USING MULTIPLE MODELS
CSTR CONTROL USING MULTIPLE MODELS J. Novák, V. Bobál Univerzita Tomáše Bati, Fakulta aplikované informatiky Mostní 39, Zlín INTRODUCTION Almost every real process exhibits nonlinear behavior in a full
More informationMoving Horizon Estimation (MHE)
Moving Horizon Estimation (MHE) James B. Rawlings Department of Chemical and Biological Engineering University of Wisconsin Madison Insitut für Systemtheorie und Regelungstechnik Universität Stuttgart
More informationLQ and Model Predictive Control (MPC) of a tank process
Uppsala University Information Technology Systems and Control Susanne Svedberg 2000 Rev. 2009 by BPGH, 2010 by TS, 2011 by HN Last rev. September 22, 2014 by RC Automatic control II Computer Exercise 4
More informationApplication of Autocovariance Least-Squares Methods to Laboratory Data
2 TWMCC Texas-Wisconsin Modeling and Control Consortium 1 Technical report number 23-3 Application of Autocovariance Least-Squares Methods to Laboratory Data Brian J. Odelson, Alexander Lutz, and James
More informationFall 線性系統 Linear Systems. Chapter 08 State Feedback & State Estimators (SISO) Feng-Li Lian. NTU-EE Sep07 Jan08
Fall 2007 線性系統 Linear Systems Chapter 08 State Feedback & State Estimators (SISO) Feng-Li Lian NTU-EE Sep07 Jan08 Materials used in these lecture notes are adopted from Linear System Theory & Design, 3rd.
More informationSubject: Introduction to Process Control. Week 01, Lectures 01 02, Spring Content
v CHEG 461 : Process Dynamics and Control Subject: Introduction to Process Control Week 01, Lectures 01 02, Spring 2014 Dr. Costas Kiparissides Content 1. Introduction to Process Dynamics and Control 2.
More informationIntroduction to Model Predictive Control. Dipartimento di Elettronica e Informazione
Introduction to Model Predictive Control Riccardo Scattolini Riccardo Scattolini Dipartimento di Elettronica e Informazione Finite horizon optimal control 2 Consider the system At time k we want to compute
More informationOptimal Polynomial Control for Discrete-Time Systems
1 Optimal Polynomial Control for Discrete-Time Systems Prof Guy Beale Electrical and Computer Engineering Department George Mason University Fairfax, Virginia Correspondence concerning this paper should
More informationState Estimation of Linear and Nonlinear Dynamic Systems
State Estimation of Linear and Nonlinear Dynamic Systems Part II: Observability and Stability James B. Rawlings and Fernando V. Lima Department of Chemical and Biological Engineering University of Wisconsin
More informationCBE495 LECTURE IV MODEL PREDICTIVE CONTROL
What is Model Predictive Control (MPC)? CBE495 LECTURE IV MODEL PREDICTIVE CONTROL Professor Dae Ryook Yang Fall 2013 Dept. of Chemical and Biological Engineering Korea University * Some parts are from
More informationTemperature 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 informationJUSTIFICATION OF INPUT AND OUTPUT CONSTRAINTS INCORPORATION INTO PREDICTIVE CONTROL DESIGN
JUSTIFICATION OF INPUT AND OUTPUT CONSTRAINTS INCORPORATION INTO PREDICTIVE CONTROL DESIGN J. Škultéty, E. Miklovičová, M. Mrosko Slovak University of Technology, Faculty of Electrical Engineering and
More informationLinear Discrete-time State Space Realization of a Modified Quadruple Tank System with State Estimation using Kalman Filter
Journal of Physics: Conference Series PAPER OPEN ACCESS Linear Discrete-time State Space Realization of a Modified Quadruple Tank System with State Estimation using Kalman Filter To cite this article:
More informationOn the Inherent Robustness of Suboptimal Model Predictive Control
On the Inherent Robustness of Suboptimal Model Predictive Control James B. Rawlings, Gabriele Pannocchia, Stephen J. Wright, and Cuyler N. Bates Department of Chemical and Biological Engineering and Computer
More informationMoving Horizon Control and Estimation of Livestock Ventilation Systems and Indoor Climate
Proceedings of the 17th World Congress The International Federation of Automatic Control Seoul, Korea, July -11, Moving Horizon Control and Estimation of Livestock Ventilation Systems and Indoor Climate
More informationIdentification of a Chemical Process for Fault Detection Application
Identification of a Chemical Process for Fault Detection Application Silvio Simani Abstract The paper presents the application results concerning the fault detection of a dynamic process using linear system
More informationRecovering from a Gradual Degradation in MPC Performance
Preprints of the The International Federation of Automatic Control Furama Riverfront, Singapore, July 1-13, 212 Recovering from a Gradual Degradation in MPC Performance Mohammed Jimoh*, John Howell** *School
More informationMODEL PREDICTIVE CONTROL
Process Control in the Chemical Industries 115 1. Introduction MODEL PREDICTIVE CONTROL An Introduction Model predictive controller (MPC) is traced back to the 1970s. It started to emerge industrially
More informationReal-Time Feasibility of Nonlinear Predictive Control for Semi-batch Reactors
European Symposium on Computer Arded Aided Process Engineering 15 L. Puigjaner and A. Espuña (Editors) 2005 Elsevier Science B.V. All rights reserved. Real-Time Feasibility of Nonlinear Predictive Control
More informationInternational Journal of ChemTech Research CODEN (USA): IJCRGG ISSN: Vol.8, No.4, pp , 2015
International Journal of ChemTech Research CODEN (USA): IJCRGG ISSN: 0974-4290 Vol.8, No.4, pp 1742-1748, 2015 Block-Box Modelling and Control a Temperature of the Shell and Tube Heatexchanger using Dynamic
More informationH-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 informationParameter Estimation in a Moving Horizon Perspective
Parameter Estimation in a Moving Horizon Perspective State and Parameter Estimation in Dynamical Systems Reglerteknik, ISY, Linköpings Universitet State and Parameter Estimation in Dynamical Systems OUTLINE
More informationOnline Support Vector Regression for Non-Linear Control
Online Support Vector Regression for Non-Linear Control Gaurav Vishwakarma, Imran Rahman Chemical Engineering and Process Development Division, National Chemical Laboratory, Pune (MH), India-411008 ---------------------------------------------------------------------------------------------------------------------------------------
More informationLecture 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 informationControl Systems Lab - SC4070 System Identification and Linearization
Control Systems Lab - SC4070 System Identification and Linearization Dr. Manuel Mazo Jr. Delft Center for Systems and Control (TU Delft) m.mazo@tudelft.nl Tel.:015-2788131 TU Delft, February 13, 2015 (slides
More informationControl Design. Lecture 9: State Feedback and Observers. Two Classes of Control Problems. State Feedback: Problem Formulation
Lecture 9: State Feedback and s [IFAC PB Ch 9] State Feedback s Disturbance Estimation & Integral Action Control Design Many factors to consider, for example: Attenuation of load disturbances Reduction
More informationAN APPROACH TO TOTAL STATE CONTROL IN BATCH PROCESSES
AN APPROACH TO TOTAL STATE CONTROL IN BATCH PROCESSES H. Marcela Moscoso-Vasquez, Gloria M. Monsalve-Bravo and Hernan Alvarez Departamento de Procesos y Energía, Facultad de Minas Universidad Nacional
More informationClass 27: Block Diagrams
Class 7: Block Diagrams Dynamic Behavior and Stability of Closed-Loop Control Systems We no ant to consider the dynamic behavior of processes that are operated using feedback control. The combination of
More informationDYNAMIC MATRIX CONTROL
DYNAMIC MATRIX CONTROL A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF BACHELOR OF TECHNOLOGY IN ELECTRONICS AND INSTRUMENTATION ENGINEERING BY RASHMI RANJAN KAR (10607030)
More informationCBE507 LECTURE III Controller Design Using State-space Methods. Professor Dae Ryook Yang
CBE507 LECTURE III Controller Design Using State-space Methods Professor Dae Ryook Yang Fall 2013 Dept. of Chemical and Biological Engineering Korea University Korea University III -1 Overview States What
More informationFINITE HORIZON ROBUST MODEL PREDICTIVE CONTROL USING LINEAR MATRIX INEQUALITIES. Danlei Chu, Tongwen Chen, Horacio J. Marquez
FINITE HORIZON ROBUST MODEL PREDICTIVE CONTROL USING LINEAR MATRIX INEQUALITIES Danlei Chu Tongwen Chen Horacio J Marquez Department of Electrical and Computer Engineering University of Alberta Edmonton
More informationActive Fault Diagnosis for Uncertain Systems
Active Fault Diagnosis for Uncertain Systems Davide M. Raimondo 1 Joseph K. Scott 2, Richard D. Braatz 2, Roberto Marseglia 1, Lalo Magni 1, Rolf Findeisen 3 1 Identification and Control of Dynamic Systems
More informationA FAST, EASILY TUNED, SISO, MODEL PREDICTIVE CONTROLLER. Gabriele Pannocchia,1 Nabil Laachi James B. Rawlings
A FAST, EASILY TUNED, SISO, MODEL PREDICTIVE CONTROLLER Gabriele Pannocchia, Nabil Laachi James B. Rawlings Department of Chemical Engineering Univ. of Pisa Via Diotisalvi 2, 5626 Pisa (Italy) Department
More informationOn the Inherent Robustness of Suboptimal Model Predictive Control
On the Inherent Robustness of Suboptimal Model Predictive Control James B. Rawlings, Gabriele Pannocchia, Stephen J. Wright, and Cuyler N. Bates Department of Chemical & Biological Engineering Computer
More informationChapter 3 - Solved Problems
Chapter 3 - Solved Problems Solved Problem 3.. A nonlinear system has an input-output model given by dy(t) + ( + 0.2y(t))y(t) = u(t) + 0.2u(t) 3 () 3.. Compute the operating point(s) for u Q = 2. (assume
More information