Digital Computer Process Control
Lecture 1
Dr. Jie Yu
Department of Chemical Engineering
McMaster University
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Digital Computer Process Control
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Course Outline
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Introduction
Section I: Dynamic Models – Continuous Systems
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Fundamental models
Linearization
Transfer-function representation
State-space representation
Conversion between transfer-function and state-space models
Section II: Dynamic Models – Discrete Systems
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Model discretization
Z-transforms
Pulse transfer function
Conversion between transfer-function and state-space models
Dynamic Responses
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Course Outline
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Section III: Systems Identification
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Section IV: Closed-Loop Analysis – Continuous Systems
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Least-square formulation and solution to first-order models
First-order plus dead-time model
General ARMA models
Model Diagnostics
Input test signal and data pre-processing
Closed-loop transfer function
Stability analysis
Controller design using direct synthesis
Section V: Closed-Loop Analysis – Discrete Systems
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Closed-loop transfer function of sampled-data systems
Stability analysis
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Course Outline
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Section VI: Digital Control Design
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Discretization of continuous-time control laws
Direct Approach
Section VII: Model Predictive Control
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Unconstrained MPC – SISO systems
Unconstrained MPC – MIMO systems
Closed-loop properties
Constrained MPC
MPC implementation issues
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What is control system?
Set-point
Controlled Variable
Feedback
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Manipulated Variable
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What is control system?
Manipulated Variable
- Insulin Injection
Controlled Variable
- Blood Glucose
Set-point
Controller
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pump
patient
Digital Computer Process Control
sensor
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What is digital control?
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Process parameters and variables sampled at discrete time
instants
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Discrete sample systems
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Controller sees output responses only at discrete sample points
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However, the process being controlled may still be in continuous
operation
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Control law computed discretely by digital computer
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Distributed computer control network used in a plant
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Digital Computer Control Diagram
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Pros/Cons of Digital Computer Control
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Pros
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Easy data acquisition
High speed computation
Data storage and retrieval
Flexible calculation of control law
Cons
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Potential failure of control systems due to computer malfunction
Inability for handle unexpected operation event
Security issue on process control computer network may cause
plant operation upset or even serious incident
Software cost for control systems
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What you are expected to learn?
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How to discretize continuous-time control law?
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How to analyze closed-loop control systems?
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What is the effect of sampling time on closed-loop stability?
How to design discrete-time control law?
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Regulatory control
Model predictive control
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How to run discrete control law on continuous systems?
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How to achieve desirable closed-loop behavior?
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Major Steps of Control System Design
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Remarks on Digital Control Design
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Identification of discrete-time dynamic models from plant data
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Digital signal processing and analysis
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Control design strategy specifically for discrete-time system
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Simulation of digital control system
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Matlab
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Simulink
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Performance analysis of digital control system
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Process Control Basics
Specific control objectives
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Increase product throughput
Increase yield of higher valued products
Reduce energy consumption
Reduce environmental pollution
Improve product quality
Improve process safety
Extend equipment life
Improved economic profit
Reduce work labor
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Process Control Basics
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Controlled Variable (CV)
- Variables used to quantify control objectives such as product
quality and profit
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Manipulated Variable (MV)
- Variables being adjusted dynamically to keep the controlled
variables at set-points
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Disturbance Variable (DV)
- Variables that can cause controlled variables to deviate from
set-points
DV
MV
Controller
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Plant
Digital Computer Process Control
CV
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Feedback Control
Feedback – Boiler Level
DV – Steam Flow
MV – Feedwater Flow
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Feedforward Control
Feedforward – Steam Flow
MV – Feedwater Flow
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Feedforward-feedback Control
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Key Items to Control Design
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Closed-loop Stability
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Controller Performance
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Bounded Inputs Bounded Outputs
Setpoint Tracking
Disturbance Rejection
Process Output Variability
Valve Moves
System Robustness
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Model Uncertainty
Operation Condition Change
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