Sách LabVIEW Control Design User Manual

About This Manual
Conventions .xi
Related Documentation xii
Chapter 1
Introduction to Control Design
Model-Based Control Design 1-2
Developing a Plant Model .1-3
Designing a Controller 1-3
Simulating the Dynamic System .1-4
Deploying the Controller .1-4
Overview of LabVIEW Control Design 1-4
Control Design Assistant .1-4
Control Design VIs 1-5
Control Design MathScript Functions .1-5
Chapter 2
Constructing Dynamic System Models
Constructing Accurate Models 2-2
Model Representation 2-3
Model Types 2-3
Linear versus Nonlinear Models .2-3
Time-Variant versus Time-Invariant Models .2-4
Continuous versus Discrete Models 2-4
Model Forms .2-5
RLC Circuit Example 2-6
Constructing Transfer Function Models 2-6
SISO Transfer Function Models 2-7
SIMO, MISO, and MIMO Transfer Function Models 2-9
Symbolic Transfer Function Models .2-11
Constructing Zero-Pole-Gain Models 2-12
SISO Zero-Pole-Gain Models .2-13
SIMO, MISO, and MIMO Zero-Pole-Gain Models 2-14
Symbolic Zero-Pole-Gain Models .2-14
Constructing State-Space Models 2-14
SISO State-Space Models 2-16
SIMO, MISO, and MIMO State-Space Models 2-18
Symbolic State-Space Models .2-18
Obtaining Model Information 2-18

Chapter 3
Converting Models
Converting between Model Forms 3-1
Converting Models to Transfer Function Models . 3-2
Converting Models to Zero-Pole-Gain Models 3-3
Converting Models to State-Space Models . 3-4
Converting between Continuous and Discrete Models . 3-5
Converting Continuous Models to Discrete Models . 3-6
Forward Rectangular Method . 3-8
Backward Rectangular Method 3-8
Tustin’s Method 3-9
Prewarp Method . 3-10
Zero-Order-Hold and First-Order-Hold Methods . 3-11
Z-Transform Method 3-12
Matched Pole-Zero Method 3-13
Converting Discrete Models to Continuous Models . 3-13
Resampling a Discrete Model . 3-14
Chapter 4
Connecting Models
Connecting Models in Series . 4-1
Connecting SISO Systems in Series . 4-2
Creating a SIMO System in Series . 4-3
Connecting MIMO Systems in Series . 4-5
Appending Models 4-6
Connecting Models in Parallel 4-8
Placing Models in a Closed-Loop Configuration 4-12
Single Model in a Closed-Loop Configuration . 4-13
Feedback Connections Undefined 4-13
Feedback Connections Defined 4-14
Two Models in a Closed-Loop Configuration 4-14
Feedback and Output Connections Undefined . 4-15
Feedback Connections Undefined, Output Connections Defined 4-16
Feedback Connections Defined, Output Connections Undefined 4-17
Both Feedback and Output Connections Defined 4-18
Chapter 5
Time Response Analysis
Calculating the Time-Domain Solution . 5-1
Spring-Mass Damper Example 5-2
Analyzing a Step Response . 5-4

Analyzing an Impulse Response 5-7
Analyzing an Initial Response .5-8
Analyzing a General Time-Domain Simulation 5-10
Obtaining Time Response Data .5-12
Chapter 6
Working with Delay Information
Accounting for Delay Information 6-2
Setting Delay Information .6-2
Incorporating Delay Information .6-2
Delay Information in Continuous System Models 6-3
Delay Information in Discrete System Models .6-7
Representing Delay Information 6-8
Manipulating Delay Information .6-10
Accessing Total Delay Information .6-10
Distributing Delay Information .6-12
Residual Delay Information 6-13
Chapter 7
Frequency Response Analysis
Bode Frequency Analysis 7-1
Gain Margin .7-3
Phase Margin .7-3
Nichols Frequency Analysis 7-5
Nyquist Stability Analysis .7-5
Obtaining Frequency Response Data .7-7
Chapter 8
Analyzing Dynamic Characteristics
Determining Stability .8-1
Using the Root Locus Method .8-2
Chapter 9
Analyzing State-Space Characteristics
Determining Stability .9-2
Determining Controllability and Stabilizability 9-2
Determining Observability and Detectability 9-3
Analyzing Controllability and Observability Grammians .9-4
Balancing Systems .9-5

Chapter 10
Model Order Reduction
Obtaining the Minimal Realization of Models 10-1
Reducing the Order of Models 10-2
Selecting and Removing an Input, Output, or State 10-3
Chapter 11
Designing Classical Controllers
Root Locus Design Technique 11-1
Proportional-Integral-Derivative Controller Architecture . 11-4
Designing PID Controllers Analytically . 11-6
Chapter 12
Designing State-Space Controllers
Calculating Estimator and Controller Gain Matrices 12-1
Pole Placement Technique 12-2
Linear Quadratic Regulator Technique . 12-4
Kalman Gain . 12-5
Continuous Models . 12-6
Discrete Models 12-6
Updated State Estimate 12-6
Predicted State Estimate . 12-7
Discretized Kalman Gain 12-8
Defining Kalman Filters 12-8
Linear Quadratic Gaussian Controller 12-9
Chapter 13
Defining State Estimator Structures
Measuring and Adjusting Inputs and Outputs . 13-1
Adding a State Estimator to a General System Configuration 13-2
Configuring State Estimators 13-4
System Included Configuration 13-4
System Included with Noise Configuration 13-5
Standalone Configuration . 13-6
Example System Configurations . 13-7
Example System Included State Estimator . 13-8
Example System Included with Noise State Estimator . 13-10
Example Standalone State Estimator 13-13

Chapter 14
Defining State-Space Controller Structures
Configuring State Controllers 14-1
State Compensator .14-3
System Included Configuration 14-4
System Included with Noise Configuration 14-5
Standalone with Estimator Configuration .14-6
Standalone without Estimator Configuration 14-7
State Regulator 14-8
System Included Configuration 14-9
System Included Configuration with Noise 14-10
Standalone with Estimator Configuration .14-11
Standalone without Estimator Configuration 14-12
State Regulator with Integral Action .14-13
System Included Configuration 14-14
System Included with Noise Configuration 14-16
Standalone with Estimator Configuration .14-17
Standalone without Estimator Configuration 14-19
Example System Configurations .14-20
Example System Included State Compensator 14-22
Example System Included with Noise State Compensator .14-24
Example Standalone with Estimator State Compensator 14-25
Example Standalone without Estimator State Compensator .14-27
Chapter 15
Estimating Model States
Predictive Observer 15-2
Current Observer 15-7
Continuous Observer .15-9
Chapter 16
Using Stochastic System Models
Constructing Stochastic Models 16-1
Constructing Noise Models .16-3
Converting Stochastic Models .16-3
Converting between Continuous and Discrete Stochastic Models 16-4
Converting between Stochastic and Deterministic Models .16-4
Simulating Stochastic Models .16-4
Using a Kalman Filter to Estimate Model States .16-5

Noisy RL Circuit Example 16-7
Constructing the System Model 16-8
Constructing the Noise Model 16-9
Converting the Model . 16-11
Simulating The Model 16-12
Implementing a Kalman Filter 16-14
Chapter 17
Deploying a Controller to a Real-Time Target
Defining Controller Models 17-3
Defining a Controller Model Interactively 17-3
Defining a Controller Model Programmatically . 17-4
Writing Controller Code 17-4
Example Transfer Function Controller Code 17-5
Example State Compensator Code 17-6
Example SISO Zero-Pole-Gain Controller with Saturation Code 17-7
Example State-Space Controller with Predictive Observer Code . 17-8
Example State-Space Controller with Current Observer Code . 17-9
Example State-Space Controller with Kalman Filter for Stochastic
System Code . 17-11
Example Continuous Controller Model with Kalman Filter Code . 17-12
Finding Example NI-DAQmx I/O Code . 17-13
Chapter 18
Creating and Implementing a Model Predictive Controller
Creating the MPC Controller . 18-3
Defining the Prediction and Control Horizons . 18-3
Specifying the Cost Function 18-5
Specifying Constraints 18-7
Dual Optimization Method . 18-7
Barrier Function Method 18-8
Relationship Between Penalty, Tolerance,
and Parameter Values 18-9
Prioritizing Constraints and Cost Weightings 18-10
Specifying Input Setpoint, Output Setpoint, and Disturbance Profiles . 18-13
Implementing the MPC Controller 18-14
Providing Setpoint and Disturbance Profiles to the MPC Controller . 18-14
Updating Setpoint and Disturbance Information Dynamically . 18-16
Modifying an MPC Controller at Run Time . 18-18


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