A FUZZY LOGIC BASED MULTIPLE REFERENCE MODEL ADAPTIVE CONTROL (MRMAC) By Sukumar Kamalasadan , Adel A Ghandakly Khalid S Al-Olimat Dept. of Electrical Eng. and Computer Sci., Dept. of Electrical and Computer Eng., The University of Toledo, Toledo, OH Ohio Northern University, Ada, OH 16 th International Conference on Computer Applications in Industry and Engineering (CAINE- 2003) Sponsored by International Society for Computers and Their Applications(ISCA) 11 th Nov 2003
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A FUZZY LOGIC BASED MULTIPLE REFERENCE MODEL ADAPTIVE CONTROL (MRMAC) By Sukumar Kamalasadan, Adel A Ghandakly Khalid S Al-Olimat Dept. of Electrical Eng.
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A FUZZY LOGIC BASED MULTIPLE REFERENCE MODEL ADAPTIVE
CONTROL (MRMAC)
By
Sukumar Kamalasadan, Adel A Ghandakly Khalid S Al-Olimat
Dept. of Electrical Eng. and Computer Sci., Dept. of Electrical and Computer Eng., The University of Toledo, Toledo, OH Ohio Northern University, Ada, OH
16th International Conference on Computer Applications in Industry and Engineering (CAINE-2003)Sponsored by
International Society for Computers and Their Applications(ISCA)
11th Nov 2003
Abstract
• Proposing A Fuzzy Scheme for switching Multiple Reference Models within the MRAC framework
– Following a rule base, the scheme effectively monitors drastic changes in plant operating conditions
– Reference Model switching is performed every time instant based on the plant auxiliary measurements
• The proposed scheme is computationally feasible and efficient
• It can be performed online and is well suitable for multimodal systems
• It provides an interactive multiple model environment with soft switching
• The proposed scheme is applied to an example system with distributed model parameters to show its effectiveness
• Control of Multi Modal Systems (s.a Aircraft, Robotic Arm) is difficult as the system changes its operating mode arbitrarily
Controller Properties:• It should be dynamic in nature• Efficiently respond to the system changes• Keeps track of uncertainties• Accordingly, generates appropriate control
• The Classical MRAC Framework
• Our Multiple Reference Model Adaptive Controller (MRMAC) Framework– Select a set of Reference Models suitable for the system at different
modes– Modes can include normal parametric variations , external disturbance or
both – Depending on certain schemes best suitable Reference Model at each
time instant is switched and control action is carried out
Introduction
• Heuristic based Reference Model generation• Performs switching of the Reference Model at each
time instant with respect to the plant variation• Provides a smooth change in the functional relation
between the two• Process is accomplished keeping track of the plant
auxiliary measurements• Main idea is to change the Reference Model so that it
improves the overall performance in the form of perfect tracking
Our Proposed Fuzzy MRMAC Approach
The MRMAC Concept
Multi Modal Domain – Let the system be characterized by ‘n’ Reference Models – Let these Reference Models fits in the parametric space ‘S’– ‘n’ Reference Models can be thought of representation of
‘n’ subspaces in the predefined domain ‘N’– Suppose the plant change is fully captured by these
reference models pertaining to the domain ‘N’– If any one reference model can describe the ideal plant
characteristic fully at any instant - Subspace is called “Hard Partitioned”
– Real system often exhibits transition from one subspace to another
The MRMAC Concept
− Predefined subspaces or modes switching fully from one to another is not advisable
− Need arise to smoothly change the reference model defining a trajectory movement along with the plant dynamics in order to effectively control the plant
− Effective along the imaginary boundaries of the subspace where hard switching from one reference model to another can deteriorate the system performance
− Heurstic based Multiple Reference Model Adaptive Control performs better when compared to a single reference model adaptive control
• A set of predefined Reference Models with suitable structure which models the plant for a specific domain of interest
• An effective switching scheme, which smoothly provides transition between these Reference Models keeping track of the plant change
• A robust adaptive control scheme, which tunes and provides control at each reference model subspace
The main Components of
MRMAC
• Objective– Control of Complex systems which is affine , “ Multi
Modal” and shows sudden parametric ‘Jumps’
• Features– Heuristic Based Multiple Reference Model Adaptive
Controller– Mitigating the issue related to the computational
complexities inherent in existing mathematical methods.
– An interacting individual models due to soft switching unlike hard switching algorithms
The proposed SchemeObjective and Features
• Determination of the Multiple Reference Models
• Developing a switching mechanism to switch these Reference Models online based on plant auxiliary measurements.
• Use a stable Direct Model Reference Adaptive Control (MRAC) approach
The Design Approach
Overall Scheme
:
Ref. Model 1
Ref. Model 2
Ref. Model n
Command Signal
Control Signal
Aux. Measurements
yRegulator Parameters
ErrorFuzzy Logic Switching Scheme (FLSS)
Output
+
Regulator Plant
Adjustment Mechanism
-
yR
An example Investigation
• A second order Test System is used for investigation under three mode changes
• The Test System : 1/(s2+3s-10) • Mode 1 :- 1/(s2+30s-10) • Mode 2 :- 1/(s2+3s-20) • Mode 3 :- 1/(s2+9s-30) • The RM that worked best with the original Test System :-• 5/(s2+10s+25) • The RM that worked best during transition between each
• A Multiple Reference Model Adaptive Control scheme with online Fuzzy Switching is proposed for plants with multimodal changes
• The scheme is very effective and computationally efficient for reference model switching
• Proposed scheme provides ‘soft' switching of the reference models, especially at the modal boundaries
• The scheme can be used for scheduled switching in which certain auxiliary measurements are monitored to keep track of unforeseen changes in the plant operating mode
• With the help of additional learning strategy the rule based switching can be modified online thus expanding the operating range
• The scheme can be effectively used as an Intelligent Controller for highly dynamic and functionally uncertain systems such as aircraft control
Multi Modal Domain
Predefined Domain ‘N’
Subspace‘b’
Subspace‘a’
…….
::
Subspace‘n’
• Develops a Control Law looking at the Input and Output of the Plant
• Updates the Control law using an Adaptive Mechanism
• Use a reference model to effectively model the dynamics and forces the plant to follow that model