SlideShare a Scribd company logo
1 of 1
Download to read offline
TETRACOM: Technology Transfer in Computing Systems
FP7 Coordination and support action to fund 50 technology transfer projects (TTP) in computing systems.
This project has received funding from the European Union’s Seventh Framework Programme for research,
technological development and demonstration under grant agreement n⁰ 609491.
TETRACOM coordinator: Prof. Rainer Leupers, leupers@ice.rwth-aachen.de http://www.tetracom.eu | @TetracomProject
Self-Tuning of Predictive Controller Based on Step Response
Model in Real-Time Framework
Dejan Dovžan and Igor Škrjanc
Faculty of Electrical Engineering, Tržaška 25, Ljubljana, Slovenia
Contact: dejan.dovzan@fe.uni-lj.si; igor.skrjanc@fe.uni-lj.si
IDEA
PROS AND CONS
PPCT FRAMEWORK
COMPARISON WITH PFC AND PI
PFC principle
DMC principle
PFC based on step
response model
Original PFC control law
OVER PI PFC DMC
PROS of
PFC-step
Easier to tuneEasier to tune
Superior
handling of
dead-time
Better control
of higher order
models
No matrix
manipulations
Easier
implementation on
low level hardware
Problems with stability when a lot of
noise is present
CON of PFC-step To improve this
additional filtering of
the response must be
applied
=
−
−
−
− +
−
from
equivalence of the
process increment and
the model output
increment
process incrementmodel increment
process model
predictionreference model
prediction
∆ = ∆ ∆ = + −∆ = + −
( + ) = − −
+ = +
−
( − )
PFC based on step response
model
= + + ⋯ + + −
constant future
control
Replace with a process model based on a step response (g)
+ = ∆ ( + − ) + + ( − ) ∆ ( − )
∆ =
−
− − ( − )∆ ( − )
Simple control law for processes without dead-time
CONTROL LAWS
∆ =
−
∑
− −
∑
( − )∆ ( − ) ∆ =
−
∑
− −
∑
− ∆ − −
−
∑
− ∆ −
∆ =
−
− − − ∆ − −
−
− ∆ −
exponential
control signal
decay
assumption:
constant future
control
assumption:
Control law for processes with dead-time
= − + ∆
First order process
( ) =
+ 1
( )
= 5 = 100
Step response:
Second order process
( ) =
+ 2 +
( )
= 0.75 = 0.1
Step response:
Process examples
PFC tuning: = 5, = , model = process
PI tuning: K = , = , = ,
model = process, = 5, = , =
PFC-step tuning: = 5, = , N = 900
model = step response of the process
As expected with all three control
algorithms the practically the same results
were achieved
PFC tuning: = 4, = , = 1, = 15
PI tuning: for K and K look
= 4, = , = 1, = 15
PFC-step tuning: = 4, = 7.5, = 155
model = step response of the process
As expected the PI and PFC control is not
good as they assume the first order model.
The PFC-step algorithm controls the
process with no problem.
Add 100 s delay to the process and keep
other settings
The control with the PFC and PFC-step is
again practically the same.
Effect of nosie
First order aproximation
Bad signal to noise ratio is a
problem with PFC-step algorithm.
First order step response with
Gaussian noise (variance 0.1) The same tuning of the PFC-
step as with no noise present
Not stable control
−
−
The terms and
enhance noise
Should be
Decreasing the effect
of noise
Use control law with exponential
control signal decay assumption
Filter the output change and terms
( = 0.5 + 0.5 ( − 1))
Use higher values of H
Use oversampling to reduce the signal to noise ratio
Recorded step response
with 10x oversampling
using filtering using filtering
Tuning: = 10, = 900
= 10, = 0.999,
ProcessoutputController
output
Plug and Play Control Toolbox
Auto tuning of:
PIDPI
PD PI-D
I-PD
Relayfeedbackmethod
MPFC
PFC-step
Experiment options
Tune and test
Tune and control
Perform tuning and generate report.
The user can revise the parameters and test
the controller on a model before implementation
Direct transition from tuning to control
Connections
OPC server NI cards (DAQ) Model
Snapshot of main panel
Panel showing experiments
Panel showing
controller
parameters
Loading
parameters to
OPC
PDF report
generation
Setup new
experiment
NI-card
Experiment:
Motor generator PPCT on PC
Results (Tune and control mode):Settings:
- Sampling time 0.01 s
- Working point U0 = 2
- Oversampling 0.002 s
- Step size 0.3 V
Name of the
experiment
Singal and
experiment settings
can be expoRted for
use in other programs

More Related Content

What's hot

Data structures algorithms basics
Data structures   algorithms basicsData structures   algorithms basics
Data structures algorithms basicsayeshasafdar8
 
FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMS
FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMSFPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMS
FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMSEditor IJMTER
 
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...iosrjce
 
Modern Control - Lec 06 - PID Tuning
Modern Control - Lec 06 - PID TuningModern Control - Lec 06 - PID Tuning
Modern Control - Lec 06 - PID TuningAmr E. Mohamed
 
Simulation and Comparison of P, PI, PID Controllers on MATLAB/ Simulink
Simulation and Comparison of P, PI, PID Controllers on MATLAB/ SimulinkSimulation and Comparison of P, PI, PID Controllers on MATLAB/ Simulink
Simulation and Comparison of P, PI, PID Controllers on MATLAB/ SimulinkHarshKumar649
 
Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...
Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...
Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...Brati Sundar Nanda
 
Framework for Inter-Model Analysis of Cyber-Physical Systems
Framework for Inter-Model Analysis of Cyber-Physical SystemsFramework for Inter-Model Analysis of Cyber-Physical Systems
Framework for Inter-Model Analysis of Cyber-Physical SystemsIvan Ruchkin
 
Low power vlsi implementation adaptive noise cancellor based on least means s...
Low power vlsi implementation adaptive noise cancellor based on least means s...Low power vlsi implementation adaptive noise cancellor based on least means s...
Low power vlsi implementation adaptive noise cancellor based on least means s...shaik chand basha
 
M.Tech Thesis on Simulation and Hardware Implementation of NLMS algorithm on ...
M.Tech Thesis on Simulation and Hardware Implementation of NLMS algorithm on ...M.Tech Thesis on Simulation and Hardware Implementation of NLMS algorithm on ...
M.Tech Thesis on Simulation and Hardware Implementation of NLMS algorithm on ...Raj Kumar Thenua
 
Design & Analysis of Algorithms Lecture Notes
Design & Analysis of Algorithms Lecture NotesDesign & Analysis of Algorithms Lecture Notes
Design & Analysis of Algorithms Lecture NotesFellowBuddy.com
 
Ch5 transient and steady state response analyses(control)
Ch5  transient and steady state response analyses(control)Ch5  transient and steady state response analyses(control)
Ch5 transient and steady state response analyses(control)Elaf A.Saeed
 
Control System toolbox in Matlab
Control System toolbox in MatlabControl System toolbox in Matlab
Control System toolbox in MatlabAbdul Sami
 
Hardware Implementation of Adaptive Noise Cancellation over DSP Kit TMS320C6713
Hardware Implementation of Adaptive Noise Cancellation over DSP Kit TMS320C6713Hardware Implementation of Adaptive Noise Cancellation over DSP Kit TMS320C6713
Hardware Implementation of Adaptive Noise Cancellation over DSP Kit TMS320C6713CSCJournals
 
Vlsi implementation of adaptive kalman filter for
Vlsi implementation of adaptive kalman filter forVlsi implementation of adaptive kalman filter for
Vlsi implementation of adaptive kalman filter foreSAT Publishing House
 

What's hot (20)

Data structures algorithms basics
Data structures   algorithms basicsData structures   algorithms basics
Data structures algorithms basics
 
FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMS
FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMSFPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMS
FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMS
 
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...
 
Modern Control - Lec 06 - PID Tuning
Modern Control - Lec 06 - PID TuningModern Control - Lec 06 - PID Tuning
Modern Control - Lec 06 - PID Tuning
 
Simulation and Comparison of P, PI, PID Controllers on MATLAB/ Simulink
Simulation and Comparison of P, PI, PID Controllers on MATLAB/ SimulinkSimulation and Comparison of P, PI, PID Controllers on MATLAB/ Simulink
Simulation and Comparison of P, PI, PID Controllers on MATLAB/ Simulink
 
Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...
Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...
Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...
 
ANCLMS
ANCLMSANCLMS
ANCLMS
 
Framework for Inter-Model Analysis of Cyber-Physical Systems
Framework for Inter-Model Analysis of Cyber-Physical SystemsFramework for Inter-Model Analysis of Cyber-Physical Systems
Framework for Inter-Model Analysis of Cyber-Physical Systems
 
Low power vlsi implementation adaptive noise cancellor based on least means s...
Low power vlsi implementation adaptive noise cancellor based on least means s...Low power vlsi implementation adaptive noise cancellor based on least means s...
Low power vlsi implementation adaptive noise cancellor based on least means s...
 
Introduction to Adaptive filters
Introduction to Adaptive filtersIntroduction to Adaptive filters
Introduction to Adaptive filters
 
M.Tech Thesis on Simulation and Hardware Implementation of NLMS algorithm on ...
M.Tech Thesis on Simulation and Hardware Implementation of NLMS algorithm on ...M.Tech Thesis on Simulation and Hardware Implementation of NLMS algorithm on ...
M.Tech Thesis on Simulation and Hardware Implementation of NLMS algorithm on ...
 
Aca11 bk2 ch9
Aca11 bk2 ch9Aca11 bk2 ch9
Aca11 bk2 ch9
 
Kalman filter demonstration
Kalman filter demonstrationKalman filter demonstration
Kalman filter demonstration
 
Design & Analysis of Algorithms Lecture Notes
Design & Analysis of Algorithms Lecture NotesDesign & Analysis of Algorithms Lecture Notes
Design & Analysis of Algorithms Lecture Notes
 
Ch5 transient and steady state response analyses(control)
Ch5  transient and steady state response analyses(control)Ch5  transient and steady state response analyses(control)
Ch5 transient and steady state response analyses(control)
 
matlab_simulink_for_control082p.pdf
matlab_simulink_for_control082p.pdfmatlab_simulink_for_control082p.pdf
matlab_simulink_for_control082p.pdf
 
Control System toolbox in Matlab
Control System toolbox in MatlabControl System toolbox in Matlab
Control System toolbox in Matlab
 
Hardware Implementation of Adaptive Noise Cancellation over DSP Kit TMS320C6713
Hardware Implementation of Adaptive Noise Cancellation over DSP Kit TMS320C6713Hardware Implementation of Adaptive Noise Cancellation over DSP Kit TMS320C6713
Hardware Implementation of Adaptive Noise Cancellation over DSP Kit TMS320C6713
 
Adaptive filter
Adaptive filterAdaptive filter
Adaptive filter
 
Vlsi implementation of adaptive kalman filter for
Vlsi implementation of adaptive kalman filter forVlsi implementation of adaptive kalman filter for
Vlsi implementation of adaptive kalman filter for
 

Similar to MPC

Project Review ppt for reference only for PG students
Project Review ppt for reference only for PG studentsProject Review ppt for reference only for PG students
Project Review ppt for reference only for PG studentsssusere28fc7
 
Eee3420 lecture01 rev2011
Eee3420 lecture01 rev2011Eee3420 lecture01 rev2011
Eee3420 lecture01 rev2011benson215
 
Design of a new PID controller using predictive functional control optimizati...
Design of a new PID controller using predictive functional control optimizati...Design of a new PID controller using predictive functional control optimizati...
Design of a new PID controller using predictive functional control optimizati...ISA Interchange
 
Disturbance Rejection with a Highly Oscillating Second-Order Process, Part I...
Disturbance Rejection with a Highly Oscillating Second-Order  Process, Part I...Disturbance Rejection with a Highly Oscillating Second-Order  Process, Part I...
Disturbance Rejection with a Highly Oscillating Second-Order Process, Part I...Scientific Review SR
 
PID Control of Runaway Processes - Greg McMillan Deminar
PID Control of Runaway Processes - Greg McMillan DeminarPID Control of Runaway Processes - Greg McMillan Deminar
PID Control of Runaway Processes - Greg McMillan DeminarJim Cahill
 
Lecture9.pdf
Lecture9.pdfLecture9.pdf
Lecture9.pdfRajKD5
 
Guidelines for Setting Filter and Module Execution Rate
Guidelines for Setting Filter and Module Execution RateGuidelines for Setting Filter and Module Execution Rate
Guidelines for Setting Filter and Module Execution RateEmerson Exchange
 
Ppt on simulink by vikas gupta
Ppt on simulink by vikas guptaPpt on simulink by vikas gupta
Ppt on simulink by vikas guptaVikas Gupta
 
Fix Point Implementation of Control Algorithms
Fix Point Implementation of Control AlgorithmsFix Point Implementation of Control Algorithms
Fix Point Implementation of Control Algorithmsostling27
 
Analysis and Design of PID controller with control parameters in MATLAB and S...
Analysis and Design of PID controller with control parameters in MATLAB and S...Analysis and Design of PID controller with control parameters in MATLAB and S...
Analysis and Design of PID controller with control parameters in MATLAB and S...MIbrar4
 
PID Tuning for Near Integrating Processes - Greg McMillan Deminar
PID Tuning for Near Integrating Processes - Greg McMillan DeminarPID Tuning for Near Integrating Processes - Greg McMillan Deminar
PID Tuning for Near Integrating Processes - Greg McMillan DeminarJim Cahill
 
Frequency Response with MATLAB Examples.pdf
Frequency Response with MATLAB Examples.pdfFrequency Response with MATLAB Examples.pdf
Frequency Response with MATLAB Examples.pdfSunil Manjani
 
Proportional integral and derivative PID controller
Proportional integral and derivative PID controller Proportional integral and derivative PID controller
Proportional integral and derivative PID controller Mostafa Ragab
 
Eee3420 lecture01 rev2011
Eee3420 lecture01 rev2011Eee3420 lecture01 rev2011
Eee3420 lecture01 rev2011benson215
 
Lec3 Computer Architecture by Hsien-Hsin Sean Lee Georgia Tech -- Performance
Lec3 Computer Architecture by Hsien-Hsin Sean Lee Georgia Tech -- PerformanceLec3 Computer Architecture by Hsien-Hsin Sean Lee Georgia Tech -- Performance
Lec3 Computer Architecture by Hsien-Hsin Sean Lee Georgia Tech -- PerformanceHsien-Hsin Sean Lee, Ph.D.
 
10 Discrete Time Controller Design.pptx
10 Discrete Time Controller Design.pptx10 Discrete Time Controller Design.pptx
10 Discrete Time Controller Design.pptxSaadAzhar15
 

Similar to MPC (20)

Project Review ppt for reference only for PG students
Project Review ppt for reference only for PG studentsProject Review ppt for reference only for PG students
Project Review ppt for reference only for PG students
 
Eee3420 lecture01 rev2011
Eee3420 lecture01 rev2011Eee3420 lecture01 rev2011
Eee3420 lecture01 rev2011
 
Design of a new PID controller using predictive functional control optimizati...
Design of a new PID controller using predictive functional control optimizati...Design of a new PID controller using predictive functional control optimizati...
Design of a new PID controller using predictive functional control optimizati...
 
Disturbance Rejection with a Highly Oscillating Second-Order Process, Part I...
Disturbance Rejection with a Highly Oscillating Second-Order  Process, Part I...Disturbance Rejection with a Highly Oscillating Second-Order  Process, Part I...
Disturbance Rejection with a Highly Oscillating Second-Order Process, Part I...
 
PID Control of Runaway Processes - Greg McMillan Deminar
PID Control of Runaway Processes - Greg McMillan DeminarPID Control of Runaway Processes - Greg McMillan Deminar
PID Control of Runaway Processes - Greg McMillan Deminar
 
Lecture9.pdf
Lecture9.pdfLecture9.pdf
Lecture9.pdf
 
Guidelines for Setting Filter and Module Execution Rate
Guidelines for Setting Filter and Module Execution RateGuidelines for Setting Filter and Module Execution Rate
Guidelines for Setting Filter and Module Execution Rate
 
Pid controller
Pid controllerPid controller
Pid controller
 
Ppt on simulink by vikas gupta
Ppt on simulink by vikas guptaPpt on simulink by vikas gupta
Ppt on simulink by vikas gupta
 
Fix Point Implementation of Control Algorithms
Fix Point Implementation of Control AlgorithmsFix Point Implementation of Control Algorithms
Fix Point Implementation of Control Algorithms
 
Analysis and Design of PID controller with control parameters in MATLAB and S...
Analysis and Design of PID controller with control parameters in MATLAB and S...Analysis and Design of PID controller with control parameters in MATLAB and S...
Analysis and Design of PID controller with control parameters in MATLAB and S...
 
PID Tuning for Near Integrating Processes - Greg McMillan Deminar
PID Tuning for Near Integrating Processes - Greg McMillan DeminarPID Tuning for Near Integrating Processes - Greg McMillan Deminar
PID Tuning for Near Integrating Processes - Greg McMillan Deminar
 
Frequency Response with MATLAB Examples.pdf
Frequency Response with MATLAB Examples.pdfFrequency Response with MATLAB Examples.pdf
Frequency Response with MATLAB Examples.pdf
 
Proportional integral and derivative PID controller
Proportional integral and derivative PID controller Proportional integral and derivative PID controller
Proportional integral and derivative PID controller
 
Acc shams
Acc shamsAcc shams
Acc shams
 
Eee3420 lecture01 rev2011
Eee3420 lecture01 rev2011Eee3420 lecture01 rev2011
Eee3420 lecture01 rev2011
 
Lec3 Computer Architecture by Hsien-Hsin Sean Lee Georgia Tech -- Performance
Lec3 Computer Architecture by Hsien-Hsin Sean Lee Georgia Tech -- PerformanceLec3 Computer Architecture by Hsien-Hsin Sean Lee Georgia Tech -- Performance
Lec3 Computer Architecture by Hsien-Hsin Sean Lee Georgia Tech -- Performance
 
Lead-lag controller
Lead-lag controllerLead-lag controller
Lead-lag controller
 
10 Discrete Time Controller Design.pptx
10 Discrete Time Controller Design.pptx10 Discrete Time Controller Design.pptx
10 Discrete Time Controller Design.pptx
 
Opal rt e phaso rsim_2013
Opal rt e phaso rsim_2013Opal rt e phaso rsim_2013
Opal rt e phaso rsim_2013
 

MPC

  • 1. TETRACOM: Technology Transfer in Computing Systems FP7 Coordination and support action to fund 50 technology transfer projects (TTP) in computing systems. This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement n⁰ 609491. TETRACOM coordinator: Prof. Rainer Leupers, leupers@ice.rwth-aachen.de http://www.tetracom.eu | @TetracomProject Self-Tuning of Predictive Controller Based on Step Response Model in Real-Time Framework Dejan Dovžan and Igor Škrjanc Faculty of Electrical Engineering, Tržaška 25, Ljubljana, Slovenia Contact: dejan.dovzan@fe.uni-lj.si; igor.skrjanc@fe.uni-lj.si IDEA PROS AND CONS PPCT FRAMEWORK COMPARISON WITH PFC AND PI PFC principle DMC principle PFC based on step response model Original PFC control law OVER PI PFC DMC PROS of PFC-step Easier to tuneEasier to tune Superior handling of dead-time Better control of higher order models No matrix manipulations Easier implementation on low level hardware Problems with stability when a lot of noise is present CON of PFC-step To improve this additional filtering of the response must be applied = − − − − + − from equivalence of the process increment and the model output increment process incrementmodel increment process model predictionreference model prediction ∆ = ∆ ∆ = + −∆ = + − ( + ) = − − + = + − ( − ) PFC based on step response model = + + ⋯ + + − constant future control Replace with a process model based on a step response (g) + = ∆ ( + − ) + + ( − ) ∆ ( − ) ∆ = − − − ( − )∆ ( − ) Simple control law for processes without dead-time CONTROL LAWS ∆ = − ∑ − − ∑ ( − )∆ ( − ) ∆ = − ∑ − − ∑ − ∆ − − − ∑ − ∆ − ∆ = − − − − ∆ − − − − ∆ − exponential control signal decay assumption: constant future control assumption: Control law for processes with dead-time = − + ∆ First order process ( ) = + 1 ( ) = 5 = 100 Step response: Second order process ( ) = + 2 + ( ) = 0.75 = 0.1 Step response: Process examples PFC tuning: = 5, = , model = process PI tuning: K = , = , = , model = process, = 5, = , = PFC-step tuning: = 5, = , N = 900 model = step response of the process As expected with all three control algorithms the practically the same results were achieved PFC tuning: = 4, = , = 1, = 15 PI tuning: for K and K look = 4, = , = 1, = 15 PFC-step tuning: = 4, = 7.5, = 155 model = step response of the process As expected the PI and PFC control is not good as they assume the first order model. The PFC-step algorithm controls the process with no problem. Add 100 s delay to the process and keep other settings The control with the PFC and PFC-step is again practically the same. Effect of nosie First order aproximation Bad signal to noise ratio is a problem with PFC-step algorithm. First order step response with Gaussian noise (variance 0.1) The same tuning of the PFC- step as with no noise present Not stable control − − The terms and enhance noise Should be Decreasing the effect of noise Use control law with exponential control signal decay assumption Filter the output change and terms ( = 0.5 + 0.5 ( − 1)) Use higher values of H Use oversampling to reduce the signal to noise ratio Recorded step response with 10x oversampling using filtering using filtering Tuning: = 10, = 900 = 10, = 0.999, ProcessoutputController output Plug and Play Control Toolbox Auto tuning of: PIDPI PD PI-D I-PD Relayfeedbackmethod MPFC PFC-step Experiment options Tune and test Tune and control Perform tuning and generate report. The user can revise the parameters and test the controller on a model before implementation Direct transition from tuning to control Connections OPC server NI cards (DAQ) Model Snapshot of main panel Panel showing experiments Panel showing controller parameters Loading parameters to OPC PDF report generation Setup new experiment NI-card Experiment: Motor generator PPCT on PC Results (Tune and control mode):Settings: - Sampling time 0.01 s - Working point U0 = 2 - Oversampling 0.002 s - Step size 0.3 V Name of the experiment Singal and experiment settings can be expoRted for use in other programs