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What is the control system engineer’s favorite dance?. The unit step. Senior Project Proposal for. Non-Linear Internal Model Controller Design with Artificial Neural Networks. By Vishal Kumar Advisor: Gary L. Dempsey 12/06/07 Bradley University
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Senior Project Proposal for Non-Linear Internal Model Controller Design with Artificial Neural Networks By Vishal Kumar Advisor: Gary L. Dempsey 12/06/07 Bradley University Department of Computer and Electrical Engineering
Senior Project Proposal • Project Description • Discussion of previous work • Project Details • Functional Description and block diagrams • Functional Requirements and Specifications • Fall ‘07 Lab Work • Spring ’08 schedule
Project Description This project is centered around controlling the Quanser Consulting Plant SRV-02 with a Non Linear Internal Model Controller implemented with Artificial Neural Networks. Artificial Neural Networks with an adaptive transfer characteristic coupled with accurate disturbance detection of Internal Model Controller can help us design a controller to manage the 4th order Quanser Plant despite its' non-linearity from friction and external disturbances due to the rotary flexible joint.
Project Description • Internal Model Control
Project Description Artificial Neural Networks
Discussion of Previous Work • Virtual Control Workstation for Adaptive Controller Workstation - Joseph Faivre, Kain Osterholt, and Adam Vaccari, 2006 • Design of a Simulink based 2-DOF robot arm control workstation – Chris Edwards and Emberly Smith, 2007
Discussion of Previous Work • Using a Neural Network Model for a robot arm to design conventional and neural controllers – Thuong D. Le, 2003 • Implementation of Conventional and Neural Networks using position and velocity feedback - Christopher Spevacek, and Manfred Meissner, 2000
Prespective • What makes this project different? New Tools • Simulink/Real Time Execution Workshop • Updated WinCon Client and WinCon Server interface Implementing an advanced controller – IMC with ANNs Exploring project worth
Functional Description Individual Components • 1.46 GHz Windows Based PC • Data Acquisition and Capture Board • Power Module PAO103 • Quanser Plant SRV-02 with embedded position sensors, gripper and motor
Functional Description Acquisition Board Port Interface
Functional Description Power Module
Functional Description • Software Interface – Discuss on Previous Slide • Examples on next 2 slides
Functional Requirements • Single Loop – Proportional , Proportional–Derivative Controller • Single Loop – Feed Forward • Feed Forwards with Artificial Neural Networks • Internal Model Control with Artificial Neural Networks
Performance Specifications • Percent Overshoot 5% max • Time to Peak 50ms max • Time to settle 200ms max • Closed Loop Bandwidth 2Hz min • Closed Loop Frequency Resp. 3dB max • Gain Margin 5.0 min • Phase Margin 60 degrees min • Steady State Error 1 degree max • Controller Execution Time 1ms max
Fall ’07 Work • Proportional Controller Design without arm • Gc(s) = K = .3
Fall ’07 Work • Proportional – Derivative Controller Design without arm • Gc(s) = .61(s + 61.5)/(s+120)
Fall ’07 Work • Comparison of Results
Fall ’07 Work • System Identification without arm
Spring ’07 Schedule Week - Task • 0 - System Identification with Arm • 1 - Single Loop Feed Forward Design • 2 - Internal Model Controller with approximate Linear Model • 3 - Train Adaline with Linear model • 4 - Implement Adaline in Internal Model Control • 5-6 - Train Adaline with real plant offline • 7 - Implement Adaline in Internal Model Controller • 8 - Performance testing, comparison with conventional methods • 9-14 - Left open for finalization, additional work, presentations and reports