What is the control system engineer’s favorite dance?
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Transcript of What is the control system engineer’s favorite dance?
What is the control system What is the control system engineer’s favorite dance?engineer’s favorite dance?
Non-Linear Internal Model Controller Non-Linear Internal Model Controller Design with Artificial Neural NetworksDesign with Artificial Neural Networks
By Vishal KumarBy Vishal KumarAdvisor: Gary L. DempseyAdvisor: Gary L. Dempsey
12/06/0712/06/07Bradley UniversityBradley University
Department of Computer and Electrical EngineeringDepartment of Computer and Electrical Engineering
Senior Project Proposal for
Senior Project ProposalSenior Project Proposal
1.1. Project DescriptionProject Description
2.2. Discussion of previous work Discussion of previous work
3.3. Project DetailsProject Details1.1. Functional Description and block diagramsFunctional Description and block diagrams
2.2. Functional Requirements and SpecificationsFunctional Requirements and Specifications
3.3. Fall ‘07 Lab WorkFall ‘07 Lab Work
4.4. Spring ’08 scheduleSpring ’08 schedule
Project DescriptionProject Description
This project is centered around controlling the This project is centered around controlling the Quanser Consulting Plant SRV-02Quanser Consulting Plant SRV-02 with a Non with a Non Linear Linear Internal Model ControllerInternal Model Controller implemented implemented with with Artificial Neural NetworksArtificial Neural Networks. Artificial Neural . Artificial Neural Networks with an adaptive transfer characteristic Networks with an adaptive transfer characteristic coupled with accurate disturbance detection of coupled with accurate disturbance detection of Internal Model Controller can help us design a Internal Model Controller can help us design a controller to manage the 4th order Quanser controller to manage the 4th order Quanser Plant despite its' non-linearity from friction and Plant despite its' non-linearity from friction and external disturbances due to the rotary flexible external disturbances due to the rotary flexible joint.joint.
Discussion of Previous WorkDiscussion of Previous Work
Virtual Control Workstation for Adaptive Virtual Control Workstation for Adaptive Controller Workstation - Joseph Faivre, Controller Workstation - Joseph Faivre, Kain Osterholt, and Adam Vaccari, 2006Kain Osterholt, and Adam Vaccari, 2006
Design of a Simulink based 2-DOF robot Design of a Simulink based 2-DOF robot arm control workstation – Chris Edwards arm control workstation – Chris Edwards and Emberly Smith, 2007and Emberly Smith, 2007
Discussion of Previous WorkDiscussion of Previous Work
Using a Neural Network Model for a robot Using a Neural Network Model for a robot arm to design conventional and neural arm to design conventional and neural controllers – Thuong D. Le, 2003controllers – Thuong D. Le, 2003
Implementation of Conventional and Implementation of Conventional and Neural Networks using position and Neural Networks using position and velocity feedback - Christopher Spevacek, velocity feedback - Christopher Spevacek, and Manfred Meissner, 2000and Manfred Meissner, 2000
PrespectivePrespective
What makes this project different?What makes this project different?
New ToolsNew ToolsSimulink/Real Time Execution WorkshopSimulink/Real Time Execution WorkshopUpdated WinCon Client and WinCon Server Updated WinCon Client and WinCon Server
interfaceinterface
Implementing an advanced controller – IMC Implementing an advanced controller – IMC with ANNs with ANNs
Exploring project worthExploring project worth
Functional DescriptionFunctional Description
Individual ComponentsIndividual Components1.46 GHz Windows Based PC1.46 GHz Windows Based PCData Acquisition and Capture BoardData Acquisition and Capture BoardPower Module PAO103Power Module PAO103Quanser Plant SRV-02 with embedded Quanser Plant SRV-02 with embedded
position sensors, gripper and motorposition sensors, gripper and motor
Functional DescriptionFunctional Description
Acquisition Board Port InterfaceAcquisition Board Port Interface
Functional DescriptionFunctional Description
Software Interface – Discuss on Previous Software Interface – Discuss on Previous SlideSlide
Examples on next 2 slidesExamples on next 2 slides
Functional RequirementsFunctional Requirements
1.1. Single Loop – Proportional , Single Loop – Proportional , Proportional–Derivative ControllerProportional–Derivative Controller
2.2. Single Loop – Feed ForwardSingle Loop – Feed Forward
3.3. Feed Forwards with Artificial Neural Feed Forwards with Artificial Neural NetworksNetworks
4.4. Internal Model Control with Artificial Internal Model Control with Artificial Neural NetworksNeural Networks
Performance SpecificationsPerformance Specifications
Percent Overshoot Percent Overshoot 5% max5% max Time to PeakTime to Peak 50ms max50ms max Time to settle Time to settle 200ms max200ms max Closed Loop Bandwidth Closed Loop Bandwidth 2Hz min2Hz min Closed Loop Frequency Resp.Closed Loop Frequency Resp. 3dB max 3dB max Gain Margin Gain Margin 5.0 min5.0 min Phase Margin Phase Margin 60 degrees 60 degrees
minmin Steady State Error Steady State Error 1 degree max1 degree max Controller Execution Time Controller Execution Time 1ms max1ms max
Fall ’07 WorkFall ’07 Work
Proportional Controller Design without armProportional Controller Design without armGc(s) = K = .3Gc(s) = K = .3
1.5 1.55 1.6 1.65 1.7 1.75-5
0
5
10
15
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35
Time
Deg
rees
Proportional ControlGc(s) = .3
Fall ’07 WorkFall ’07 WorkProportional – Derivative Controller Design Proportional – Derivative Controller Design
without armwithout armGc(s) = .61(s + 61.5)/(s+120)Gc(s) = .61(s + 61.5)/(s+120)
1.5 1.55 1.6 1.65 1.7 1.75 1.8 1.85 1.9 1.95 2-5
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Time
Degre
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Proportional Derivative ControllerGc(s) = .35 (s + 61.5)/(s + 120)
Fall ’07 WorkFall ’07 Work
System Identification without armSystem Identification without arm
)150/(
69)(
)35(
ss
esGp
mss
Spring ’07 ScheduleSpring ’07 Schedule
Week - TaskWeek - Task 00 - - System Identification with ArmSystem Identification with Arm 1 1 - - Single Loop Feed Forward Design Single Loop Feed Forward Design 2 2 - - Internal Model Controller with approximate Internal Model Controller with approximate
Linear ModelLinear Model 3 3 - - Train Adaline with Linear modelTrain Adaline with Linear model 4 4 - - Implement Adaline in Internal Model Control Implement Adaline in Internal Model Control 5-6 5-6 - - Train Adaline with real plant offline Train Adaline with real plant offline 7 7 - - Implement Adaline in Internal Model Implement Adaline in Internal Model
Controller Controller 8 8 - - Performance testing, comparison with Performance testing, comparison with
conventional methodsconventional methods 9-14 -9-14 - Left open for finalization, additional work, Left open for finalization, additional work,
presentations and reports presentations and reports