1. MULTIPROCESSOR SYSTEM ON CHIP (MPSOC) FPGA BASED INTELLIGENT CONTROLLER DESIGN FOR SHELL AND TUBE HEAT EXCHANGER
Prepared By : RAJARSHI PAUL (Regd. No , Ph.D Student MIT, Manipal)
Under the Guidance of : Dr. SHREESHA. C (HOD and Professor Dept. of ICE, MIT, Manipal)
INTRODUCTION
METHODOLOGY
In any of the control application, controller design is the most important part.
After mathematical modeling of the plant is completed, the control objective was set.
Conventional controllers due to their steady transient disadvantages and more tuning parameters, often give tedious task.
But to embed some kind of intelligence fuzzy logic and neural network based controller was designed.
Next the proposed soft-computing based algorithm will be synthesized on Multi-Processor System on Chip (MPSOC) based FPGA.
The experiments will be carried out in a heat exchanger test facility and on the proposed System on chip (SOC). M E T H O D L G Y
Validating and Documenting the Research
(6months)
Phase 5: Synthesizing the model on FPGA
(6 months)
Phase 4: Designing and Simulation of control algorithm (6months)
Phase 3: Developing mathematical model and study the dynamics of the plant (6months)
Phase 1&2: Course work and Literature survey (1st year)
COMPLETED
RESULTS ANALYSIS
(a)
OBJECTIVES
Our main aim is to design a control system with the outcome to support the shell and tube heat exchanger.
The following objectives while performing the design are to be followed:
To propose an appropriate control algorithm for shell and tube heat exchanger.
To implement the proposed algorithm on Multiprocessor System On Chip (MPSOC) based FPGA.
To evaluate the performance of the proposed algorithm.
(b)
Fig 2 : Real-Time Photographic view of the Plant
(c)
Fig 3: RTL Synthesis for FPGA Configuration – Quartus II
Table I : Performance Comparison of different controllers
PID CONTROLLER (CONVENTIONAL CONTROLLER)
Control action
Mp(%)
[Peak-overshoot]
Rise-time instants
Settling time instants
Nominal(35 ºC) 7
1600
3300
Step change(5 ºC)
1000
1500
Load (-2 ºC) -
1200
FUZZY LOGIC AND NARMA-L2 NEURO CONTROLLER
Nominal(40 ºC)
700
Step change (5 ºC)
200
370 70
(d)
Fig 4 (a,b) : Fuzzy logic Simulation with Rule Viewer
Fig 4 (c,d): NARMA-L2 training and Real-Time Simulation using Matlab/Simulink
SCOPE OF RESEARCH
The issues which can be addressed clearly by the usage of FPGA in Control fields are as follows:
Programmable System Integration
Increased System Performance By Parallel Processing
Total Power Reduction because FPGA consumes very less power.
Reduction of Cost due to On-board-Processor.
BACKEND DESIGN
Software Programming platforms used
Fuzzy logic Code in
Matlab
LabVIEW Program
NEURAL NETWORK DESIGN IN Matlab/Simulink
RESEARCH OUTPUT
Presented research paper in International Conference ICCMEH-2014 “Statistical Analysis and implementation of Intelligent Controller for heat exchanger temperature process”, Rajarshi Paul, Dr,Shreesha.C and Dr.Shrikanth Prabhu.
Fig 1 : Work flow schematics