MATLAB MODEL PREDICTIVE CONTROL TOOLBOX - S Spezifikationen

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Inhaltsverzeichnis

Seite 1 - Getting Started Guide

Model Predictive Control Toolbox™Getting Started GuideAlberto BemporadManfred MorariN. Lawrence RickerR2015a

Seite 2 - How to Contact MathWorks

1 Introduction1-2Model Predictive Control Toolbox Product DescriptionDesign and simulate model predictive controllersModel Predictive Control Toolbox™

Seite 3 - Revision History

3 Designing Controllers Using the Design Tool GUI3-50Robustness Test, Accurate Model (1) and Perturbed Model (2)Note MIMO applications are usually mor

Seite 4

Design Controller for Plant with Delays3-51Design Controller for Plant with DelaysThis example shows how to design an MPC controller for a plant with

Seite 5 - Contents

3 Designing Controllers Using the Design Tool GUI3-52It is good practice to specify the prediction and control horizons such thatP M t td max- D?,/Her

Seite 6

Design Controller for Plant with Delays3-53Specify the simulation settings as follows:• In the Duration box, enter 50.• In the Setpoints table, for t

Seite 7

3 Designing Controllers Using the Design Tool GUI3-54The plant outputs plot shows that the first output does not respond for the first minute,which co

Seite 8

Design Controller for Plant with Delays3-55The plant inputs plot shows that the initial input moves are more than five timesthe final change. Also, t

Seite 9 - Introduction

3 Designing Controllers Using the Design Tool GUI3-56In the tree, select Scenarios > Scenario1.Click Simulate.

Seite 10 - 1 Introduction

Design Controller for Plant with Delays3-57The initial input moves are much smaller and the moves are less oscillatory. The trade-off is a slower out

Seite 11 - Acknowledgments

3 Designing Controllers Using the Design Tool GUI3-58Design Controller for Nonsquare PlantA nonsquare plant has an unequal number of manipulated varia

Seite 12 - Bibliography

Design Controller for Nonsquare Plant3-59More Manipulated Variables Than OutputsIn this situation, default Model Predictive Control Toolbox settings

Seite 13 - Building Models

Acknowledgments1-3AcknowledgmentsMathWorks would like to acknowledge the following contributors to Model PredictiveControl Toolbox.Alberto BemporadPr

Seite 15 - MPC Modeling

4Designing Controllers Using theCommand Line• “Design Controller at the Command Line” on page 4-2• “Simulate Controller with Nonlinear Plant” on page

Seite 16 - Input Disturbance Model

4 Designing Controllers Using the Command Line4-2Design Controller at the Command LineIn this section...“Create a Controller Object” on page 4-2“View

Seite 17 - Output Disturbance Model

Design Controller at the Command Line4-3View and Alter Controller PropertiesOnce you've defined an MPC object, it's easy to alter its prope

Seite 18 - Measurement Noise Model

4 Designing Controllers Using the Command Line4-4 Min: -Inf Max: Inf MinECR: 0 MaxECR: 0 RateMin: -Inf R

Seite 19

Design Controller at the Command Line4-5You can also specify time-varying weights and constraints. The time-varying weights andconstraints are define

Seite 20 - Signal Types

4 Designing Controllers Using the Command Line4-6In this example, the review command found two potential issues in this design. Thefirst warning asks

Seite 21 - Transfer Function Models

Design Controller at the Command Line4-7T = 26;r = [2 0];sim(MPCobj,T,r);This simulates the closed-loop response for a duration of 26 control interva

Seite 22 - State-Space Models

4 Designing Controllers Using the Command Line4-8Save Calculated ResultsIf you'd like to save simulation results in your workspace, use the follo

Seite 23 - Ax Bu= +

Simulate Controller with Nonlinear Plant4-9Simulate Controller with Nonlinear PlantYou can use sim to simulate a closed-loop system consisting of a l

Seite 24 - 2 Building Models

1 Introduction1-4Bibliography[1] Allgower, F., and A. Zheng, Nonlinear Model Predictive Control, Springer-Verlag,2000.[2] Camacho, E. F., and C. Bordo

Seite 25

4 Designing Controllers Using the Command Line4-10Example Code for Successive LinearizationIn the following code, the simulation begins at the CSTR mo

Seite 26

Simulate Controller with Nonlinear Plant4-11 % Define MPC Toolbox controller for the latest model MPCobj = mpc(Model, Ts); MPCobj.W.Outp

Seite 27 - LTI Model Characteristics

4 Designing Controllers Using the Command Line4-12figure(2)plot(tsave,usave(:,3));title('Coolant Temperature')CSTR Results and DiscussionThe

Seite 28

Simulate Controller with Nonlinear Plant4-13• Function linearize relinearizes the plant as its state evolves. This function wasdiscussed previously i

Seite 29

4 Designing Controllers Using the Command Line4-14controller design, always have nominal zero values. As they are unmeasured, thecontroller cannot be

Seite 30

Control Based On Multiple Plant Models4-15Control Based On Multiple Plant ModelsThe “Nonlinear CSTR Application” on page 4-9 shows how updates to the

Seite 31 - CSTR Model

4 Designing Controllers Using the Command Line4-16Animation of the Multi-Model ExampleIn order to achieve its objective, the controller can adjust the

Seite 32

Control Based On Multiple Plant Models4-17variable, and a spring constant calibration signal, which is a measured disturbanceinput.A1=[0 1;-k1/M1 -b1

Seite 33 - Linearize Simulink Models

4 Designing Controllers Using the Command Line4-18Simulating Controller PerformanceBlock Diagram of the Two-Model Example shows the Simulink block dia

Seite 34

Control Based On Multiple Plant Models4-19• A simulation of a contact sensor. When the two masses have the same position, theCompare to Constant bloc

Seite 35

2Building Models• “MPC Modeling” on page 2-2• “Signal Types” on page 2-8• “Construct Linear Time Invariant (LTI) Models” on page 2-9• “Specify Multi-I

Seite 36

4 Designing Controllers Using the Command Line4-20When the switch input is 1 the block automatically activates the first controller listed(MPC1), whic

Seite 37 - Linearize Simulink Models

Control Based On Multiple Plant Models4-21In the upper plot, the cyan curve is the desired position. It starts at –5. The M1 position(yellow) starts

Seite 38

4 Designing Controllers Using the Command Line4-22When the desired position changes step-wise to 5, the two masses separate briefly (withappropriate s

Seite 39

Control Based On Multiple Plant Models4-23If we put MPC1 in charge exclusively, we instead see sluggish movements that fail tosettle at the desired p

Seite 40

4 Designing Controllers Using the Command Line4-24Compute Steady-State GainThis example shows how to analyze a Model Predictive Controller using cloff

Seite 41

Extract Controller4-25Extract ControllerThis example shows how to obtain an LTI representation of an unconstrained ModelPredictive Control Toolbox co

Seite 42

4 Designing Controllers Using the Command Line4-26Bibliography[1] Lee, J. H. and N. L. Ricker, “Extended Kalman Filter Based Nonlinear ModelPredictive

Seite 43 - Identify Plant from Data

Signal Previewing4-27Signal PreviewingBy default, a Model Predictive Controller assumes the current reference and measuredplant disturbance signals w

Seite 44

4 Designing Controllers Using the Command Line4-28Run-Time Constraint UpdatingConstraint bounds can change during controller operation. The mpcmove,mp

Seite 45

Run-Time Weight Tuning4-29Run-Time Weight TuningThere are two ways to perform tuning experiments using Model Predictive ControlToolbox software:• Mod

Seite 46

2 Building Models2-2MPC ModelingIn this section...“Plant Model” on page 2-2“Input Disturbance Model” on page 2-4“Output Disturbance Model” on page 2-5

Seite 47

4 Designing Controllers Using the Command Line4-30More About• “Signal Previewing” on page 4-27

Seite 48

5Designing and Testing Controllers inSimulink• “Design Controller in Simulink” on page 5-2• “Test an Existing Controller” on page 5-15• “Schedule Cont

Seite 49

5 Designing and Testing Controllers in Simulink5-2Design Controller in SimulinkThis example shows how to design a model predictive controller in Simul

Seite 50

Design Controller in Simulink5-3• CSTR Temperature — Temperature of the limiting reactant in the product stream.• Concentration — Concentration of th

Seite 51 - Design Tool GUI

5 Designing and Testing Controllers in Simulink5-4The MPC Controller box is blank. No controller has been designed yet.Click Design. The MPC Question

Seite 52 - Start the Design Tool

Design Controller in Simulink5-5For this example, use the default values of this dialog box. Click OK. The softwareperforms multiple tasks to design

Seite 53 - Load a Plant Model

5 Designing and Testing Controllers in Simulink5-6In the MPC open loop plant 1 node of the tree, click Operating Point.The software found a steady-sta

Seite 54

Design Controller in Simulink5-7In the tree of the Control and Estimation Tools Manager, click Operating Points. Then,click the Compute Operating Poi

Seite 55

5 Designing and Testing Controllers in Simulink5-8Click Compute Operating Points.The Computation Results tab displays the operating point search resul

Seite 56 - Navigate Using the Tree View

Design Controller in Simulink5-9Confirm that Operating Point is the desired operating point. In the tree, clickOperating Point in the Operating Point

Seite 57

MPC Modeling2-3• Identified models (requires System Identification Toolbox™): idss, idtf, idproc,and idpoly.The MPC controller performs all the estim

Seite 58

5 Designing and Testing Controllers in Simulink5-10Click OK. The software calculates the linearized plant model, MPC open loop plant 2.This model is v

Seite 59

Design Controller in Simulink5-11These values correspond to the desired operating point.Update Controller to Use New Plant ModelIn the tree, click Co

Seite 60

5 Designing and Testing Controllers in Simulink5-12Alternatively, delete MPC open loop plant 1, the model computed at default operatingpoint. Deleting

Seite 61

Design Controller in Simulink5-13Tip After you export the controller, you can examine it for design errors and stabilityproblems using the review fun

Seite 62

5 Designing and Testing Controllers in Simulink5-14The decrease in feed concentration reduces heat generation. If the controller were absent,the react

Seite 63

Test an Existing Controller5-15Test an Existing ControllerIf you have already designed a model predictive controller to use with a Simulink plant,to

Seite 64

5 Designing and Testing Controllers in Simulink5-16Schedule Controllers at Multiple Operating PointsIn this section...“A Two-Model Plant” on page 5-16

Seite 65

Schedule Controllers at Multiple Operating Points5-17In the above, mass M2 is uncontrollable. It responds solely to the spring pulling it to theleft.

Seite 66

5 Designing and Testing Controllers in Simulink5-18Define the system parameters.M1 = 1; % massM2 = 5; % massk1 = 1; % spring constantk2 = 0.1; % sprin

Seite 67 - Customize Response Plots

Schedule Controllers at Multiple Operating Points5-19Ts = 0.2;p = 20;m = 1;MPC1 = mpc(sys1,Ts,p,m); % Controller for M1 detached from M2 MPC2 = mpc(s

Seite 68 - Adding a Data Marker

2 Building Models2-4• up — Dimensionless plant input variables.• up — Dimensionless plant output variables.The resulting plant model has the following

Seite 69 - Deleting Data Markers

5 Designing and Testing Controllers in Simulink5-20The lower part contains the following key elements:• A pulse generator that supplies the desired M1

Seite 70 - Viewing Selected Scenarios

Schedule Controllers at Multiple Operating Points5-21When the switch input is 1, the block automatically activates the first controllerthat is listed

Seite 71 - Revising a Scenario

5 Designing and Testing Controllers in Simulink5-22y2initial = 10;open('mpc_switching');sim('mpc_switching',Tstop);The figure belo

Seite 72

Schedule Controllers at Multiple Operating Points5-23

Seite 73 - Change Controller Settings

5 Designing and Testing Controllers in Simulink5-24In the upper plot, the cyan curve is the desired position. It starts at -5. The M1position (yellow)

Seite 74

Schedule Controllers at Multiple Operating Points5-25

Seite 75

5 Designing and Testing Controllers in Simulink5-26When the masses are disconnected, as at the start, MPC2 applies excessive force andthen over-compen

Seite 76 - ( ) [ ( ) ( )]= + - +

MPC Modeling2-5You can provide the input disturbance model as an LTI state-space (ss), transferfunction (tf), or zero-pole-gain (zpk) object. See “Co

Seite 77 - D + -u k i

2 Building Models2-6Here, Aod, Bod, Cod, and Dod are constant state space matrices and:• xod(k) — nxod ≥ 1 output disturbance model states.• yod(k) —

Seite 78 - Input Weights

MPC Modeling2-7Note: If the minimum eigenvalue of D Dn nT is less than 1x10–8, the MPC controller adds1x10–4 to each diagonal element of Dn. This adj

Seite 79 - ( ) [ ( ) ]= + -

How to Contact MathWorksLatest news:www.mathworks.comSales and services:www.mathworks.com/sales_and_servicesUser community:www.mathworks.com/matlabcen

Seite 80

2 Building Models2-8Signal TypesInputsThe plant inputs are the independent variables affecting the plant. As shown in “MPCModeling” on page 2-2, there

Seite 81

Construct Linear Time Invariant (LTI) Models2-9Construct Linear Time Invariant (LTI) ModelsIn this section...“Transfer Function Models” on page 2-9“Z

Seite 82

2 Building Models2-10Gtf1 = tf([1 2], [1 1 10], 'OutputDelay', 1.5)Control System Toolbox software builds and displays it as follows:Transfe

Seite 83

Construct Linear Time Invariant (LTI) Models2-11CSTR SchematicMeasurement of reactant concentrations is often difficult, if not impossible. Let usass

Seite 84

2 Building Models2-12C = [0 1 1 0];D = zeros(2,2);CSTR = ss(A,B,C,D);This defines a continuous-time state-space model. If you do not specify a sam

Seite 85

Construct Linear Time Invariant (LTI) Models2-13output. (See “Signal Types” on page 2-8 for definitions.) For example, the code specifiesthat input 2

Seite 86

2 Building Models2-14Input and Output TypesGeneral CaseAs mentioned in “Signal Types” on page 2-8, Model Predictive Control Toolbox softwaresupports t

Seite 87

Construct Linear Time Invariant (LTI) Models2-15Use setmpcsignals to make type definition. For exampleCSTR = setmpcsignals(CSTR, 'UD', 2, &

Seite 88

2 Building Models2-16Example Intended Resultzero(CSTR) Compute CSTR model's transmission zeros.

Seite 89

Specify Multi-Input Multi-Output (MIMO) Plants2-17Specify Multi-Input Multi-Output (MIMO) PlantsMost Model Predictive Control Toolbox applications in

Seite 90

Revision HistoryOctober 2004 First printing New for Version 2.1 (Release 14SP1)March 2005 Online only Revised for Version 2.2 (Release 14SP2)September

Seite 91

2 Building Models2-18 Distillate Purity: exp(-1*s) * ---------- 16.7 s + 1 6.6

Seite 92

CSTR Model2-19CSTR ModelThe linearized model of a continuous stirred-tank reactor (CSTR) involving anexothermic (heat-generating) reaction is represe

Seite 93

2 Building Models2-20xCTuTCyTCA cAi A=¢¢ÈÎ͢˚˙=¢¢ÈÎ͢˚˙=¢¢ÈÎ͢˚˙ ,Aa aa aBb bb bC D=ÈÎ͢˚˙=ÈÎ͢˚˙=ÈÎ͢˚˙=11 1221 2211 1221 220 11 00 000 0ÈÎ͢˚

Seite 94 - Save Your Work

Linearize Simulink Models2-21Linearize Simulink ModelsGenerally, real systems are nonlinear. To design an MPC controller for a nonlinearsystem, you m

Seite 95

2 Building Models2-22opspec = operspec('CSTR_OpenLoop');opspec = addoutputspec(opspec,'CSTR_OpenLoop/CSTR',2);opspec.Outputs(1).Kn

Seite 96 - Load Your Saved Work

Linearize Simulink Models2-23For example, the following code specifies the coolant temperature as 305 K and initialguess values of the C_A and T_K st

Seite 97

2 Building Models2-24io(1) = linio('CSTR_OpenLoop/Feed Concentration', 1, 'input');io(2) = linio('CSTR_OpenLoop/Feed Temperat

Seite 98 - Test Controller Robustness

Linearize Simulink Models2-25For this example, the CSTR model, CSTR_OpenLoop, is linearized.Open Simulink Modelsys = 'CSTR_OpenLoop';open_s

Seite 99 - Test Controller Robustness

2 Building Models2-26To specify a signal as a:• Linearization input, right-click the signal in the Simulink model window and selectLinear Analysis Poi

Seite 100

Linearize Simulink Models2-27Create and Verify Operating PointIn the Trim the model dialog box, click Start trimming.The operating point op_trim1 dis

Seite 102

2 Building Models2-28In the Edit dialog box, select the Input tab.The coolant temperature at steady state is 299 K, as desired.Linearize ModelIn the L

Seite 103

Linearize Simulink Models2-29The step response from feed concentration to output CSTR/2 displays an interestinginverse response. An examination of th

Seite 104

2 Building Models2-30• “Design Controller in Simulink”• “Design Controller at the Command Line”

Seite 105

Identify Plant from Data2-31Identify Plant from DataThis example shows how to identify a linear plant model using measured data.When you have measure

Seite 106 - Click Simulate

2 Building Models2-32dry_data_detrended = detrend(dry_data);Estimate a linear plant model.You can use System Identification Toolbox software to estima

Seite 107 - Related Examples

Design Controller for Identified Plant2-33Design Controller for Identified PlantThis example shows how to design a model predictive controller for a

Seite 108

2 Building Models2-34To view the structure of the model predictive controller, type controller at theMATLAB command prompt.See AlsompcRelated Examples

Seite 109

Design Controller Using Identified Model with Noise Channel2-35Design Controller Using Identified Model with Noise ChannelThis example shows how to d

Seite 110

2 Building Models2-36When you use the 'augmented' input argument, ss creates two input groups,Measured and Noise, for the measured and noise

Seite 111 - Command Line

Working with Impulse-Response Models2-37Working with Impulse-Response ModelsYou can use System Identification Toolbox software to estimate finite ste

Seite 112 - Create a Controller Object

vContentsIntroduction1Model Predictive Control Toolbox Product Description . . . . 1-2Key Features . . . . . . . . . . . . . . . . . . . . . . . . . .

Seite 113

2 Building Models2-38Bibliography[1] Allgower, F., and A. Zheng, Nonlinear Model Predictive Control, Springer-Verlag,2000.[2] Camacho, E. F., and C. B

Seite 114

3Designing Controllers Using theDesign Tool GUI• “Design Controller Using the Design Tool” on page 3-2• “Test Controller Robustness” on page 3-48• “De

Seite 115 - Review Controller Design

3 Designing Controllers Using the Design Tool GUI3-2Design Controller Using the Design ToolIn this section...“Start the Design Tool” on page 3-2“Load

Seite 116 - Perform Linear Simulations

Design Controller Using the Design Tool3-3Model Predictive Control Toolbox Design Tool Initial ViewLoad a Plant ModelThe first step in the design is

Seite 117

3 Designing Controllers Using the Design Tool GUI3-4The following example uses the CSTR model described in “CSTR Model” on page 2-19.Verify that the L

Seite 118 - Save Calculated Results

Design Controller Using the Design Tool3-5Model Predictive Control Toolbox Design Tool's Signal Definition ViewSignal Property SpecificationsThe

Seite 119 - Nonlinear CSTR Application

3 Designing Controllers Using the Design Tool GUI3-6Note Once you leave this view, if you subsequently change a signal type, you will haveto restart t

Seite 120

Design Controller Using the Design Tool3-7Plant Models View with CSTR Model SelectedViewing Your ControllersNext, select Controllers. The view shown

Seite 121

3 Designing Controllers Using the Design Tool GUI3-8Controllers ViewThe table at the top of Controllers View lists all the controllers you've def

Seite 122 - CSTR Results and Discussion

Design Controller Using the Design Tool3-9The buttons shown in Controllers View let you do the following:• Import a controller designed previously an

Seite 123

vi ContentsCSTR Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-19Linearize Simulink Models . . . . . . . . . .

Seite 124

3 Designing Controllers Using the Design Tool GUI3-10Scenarios ViewWhenever you select the Scenarios node, you see a table summarizing your currentsce

Seite 125 - A Two-Model Plant

Design Controller Using the Design Tool3-11• “Running a Simulation” on page 3-12• “Open-Loop Simulations” on page 3-15Defining Simulation ConditionsT

Seite 126

3 Designing Controllers Using the Design Tool GUI3-12CSTR Temperature Setpoint Change ScenarioRunning a SimulationTo run a simulation, do one of the f

Seite 127 - Designing the Two Controllers

Design Controller Using the Design Tool3-13Plant Outputs for T Setpoint Scenario with Added Data Markers

Seite 128

3 Designing Controllers Using the Design Tool GUI3-14Plant Inputs for the T Setpoint ScenarioPlant Outputs for T Setpoint Scenario with Added Data Mar

Seite 129

Design Controller Using the Design Tool3-15it. Left-click in a graph's white space to erase its markers. For more information on datamarkers, se

Seite 130

3 Designing Controllers Using the Design Tool GUI3-16Using one of these, simulate the scenario (click its Simulate button). The outputresponse plot sh

Seite 131

Design Controller Using the Design Tool3-17This adds a pulse to the T output. The pulse begins at time t = 10, and lasts 20 timeunits. Its height is

Seite 132

3 Designing Controllers Using the Design Tool GUI3-18By default, each plant signal plots in its own graph area (as shown above). If thesimulation is c

Seite 133

Design Controller Using the Design Tool3-19Data Marker ContentsEach data marker provides information about the selected point, as follows:• Response

Seite 134 - Compute Steady-State Gain

viiMore Manipulated Variables Than Outputs . . . . . . . . . . . . 3-59Designing Controllers Using the Command Line4Design Controller at the Command L

Seite 135 - Extract Controller

3 Designing Controllers Using the Design Tool GUI3-20To delete a single data marker, right-click it and select the Delete option.Right-Click OptionsRi

Seite 136

Design Controller Using the Design Tool3-21Revising a ScenarioIf you modify and recalculate a scenario, its data are replotted, replacing the origina

Seite 137 - Signal Previewing

3 Designing Controllers Using the Design Tool GUI3-22Normalizing Response AmplitudesWhen you're using the Channel Grouping: All option, you might

Seite 138 - Run-Time Constraint Updating

Design Controller Using the Design Tool3-23Change Controller SettingsThe simulations shown in Plant Outputs for T Setpoint Scenario with Added DataMa

Seite 139 - Run-Time Weight Tuning

3 Designing Controllers Using the Design Tool GUI3-24• Plant model specifies the LTI model to be used for controller predictions.• Control interval se

Seite 140 - More About

Design Controller Using the Design Tool3-25• In the Output weights section, change the reactant concentration's Weight (lastentry in the second

Seite 141 - Simulink

3 Designing Controllers Using the Design Tool GUI3-26Improved Setpoint Tracking for CSTR TemperatureOn the other hand, the reactant concentration, CA,

Seite 142 - Design Controller in Simulink

Design Controller Using the Design Tool3-27the number of plant outputs, wyj is the weight for output j, and the term [rj(k + i) – yj(k +i)] is a pred

Seite 143

3 Designing Controllers Using the Design Tool GUI3-28than the cumulative value. Increasing this weight forces the controller to make smaller,more caut

Seite 144

Design Controller Using the Design Tool3-29S k w u k i uu juj jjniMmv( ) [ ( ) ]= + - -{ }==ÂÂ 1211where wuj is the input weight and uj is the nomin

Seite 145

viii ContentsDesigning and Testing Controllers in Simulink5Design Controller in Simulink . . . . . . . . . . . . . . . . . . . . . . . . . 5-2Test an

Seite 146

3 Designing Controllers Using the Design Tool GUI3-30Set Number of moves computed per step to 2. Verify that Blocking allocationwithin prediction hori

Seite 147

Design Controller Using the Design Tool3-31Simulate each of the three scenarios. When you run the first, new plot windows open.Leave them open when y

Seite 148

3 Designing Controllers Using the Design Tool GUI3-32Results for T Setpoint 3 are very similar to those shown in Improved Setpoint Trackingfor CSTR Te

Seite 149

Design Controller Using the Design Tool3-33Entering CSTR Manipulated Variable ConstraintsIf any simulation plot windows are open, close them (to forc

Seite 150

3 Designing Controllers Using the Design Tool GUI3-34CSTR Outputs, Unconstrained (1) and MVconstraints (2)CSTR Manipulated Variable, Unconstrained (1)

Seite 151

Design Controller Using the Design Tool3-35Copy this controller. Rename the copy OutputSteps. Click its Estimation tab. Theinitial view should be as

Seite 152

3 Designing Controllers Using the Design Tool GUI3-36Default Input Disturbance Settings for CSTRIn this case the disturbance magnitude is nonzero, and

Seite 153

Design Controller Using the Design Tool3-37In general, if your plant model includes unmeasured disturbance inputs, the toolboxdefault strategy will a

Seite 154 - See Also

3 Designing Controllers Using the Design Tool GUI3-38CSTR Disturbance 1 ScenarioCopy Disturbance 1. Rename the copy Disturbance 2, and set its Control

Seite 155 - Test an Existing Controller

Design Controller Using the Design Tool3-39The default controller expects unmeasured disturbances to enter as defined in thescenarios, so it's n

Seite 156

1Introduction• “Model Predictive Control Toolbox Product Description” on page 1-2• “Acknowledgments” on page 1-3• “Bibliography” on page 1-4

Seite 157

3 Designing Controllers Using the Design Tool GUI3-40For comparison, reset the two scenarios so that the only disturbance is a one-degree stepincrease

Seite 158

Design Controller Using the Design Tool3-41Similarly, start with a single scenario identical to CSTR Disturbance 1 Scenario, exceptthat its Controlle

Seite 159

3 Designing Controllers Using the Design Tool GUI3-42Constraint Softening Dialog BoxThe Output constraints section lists the output limits and their r

Seite 160

Design Controller Using the Design Tool3-43Constraint Softening Scenarios: 1 = None, 2 = Hard, 3 = SoftCurve 1 is without output constraints, which i

Seite 161

3 Designing Controllers Using the Design Tool GUI3-44In general, you'll have to experiment to determine the settings that provide appropriatetrad

Seite 162

Design Controller Using the Design Tool3-45The default behavior is to export the selected controller to the workspace. Click Exportto confirm. You ca

Seite 163

3 Designing Controllers Using the Design Tool GUI3-46Dialog Box for Saving a Controller Design ProjectThe default behavior saves the current project (

Seite 164

Design Controller Using the Design Tool3-47You could define the required MPC2 object in one of the following ways:• Import MPC2 from a MAT-file (assu

Seite 165

3 Designing Controllers Using the Design Tool GUI3-48Test Controller RobustnessIt's good practice to test your controller's sensitivity to p

Seite 166

Test Controller Robustness3-49Copy Accurate Model. Rename the copy Perturbed Model, and set its Plant option toCSTRp. Thus, both scenarios use the sa

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