Locally Optimal Takagi-Sugeno Fuzzy Controllers Amir massoud Farahmand Mohammad javad Yazdanpanah Department of Electrical.

Slides:



Advertisements
Similar presentations
Stabilization of Multimachine Power Systems by Decentralized Feedback Control Zhi-Cheng Huang Department of Communications, Navigation and Control Engineering.
Advertisements

DETC ASME Computers and Information in Engineering Conference ONE AND TWO DIMENSIONAL DATA ANALYSIS USING BEZIER FUNCTIONS P.Venkataraman Rochester.
Numerical Analysis 1 EE, NCKU Tien-Hao Chang (Darby Chang)
Nonlinear Systems: an Introduction to Lyapunov Stability Theory A. Pascoal, Jan
State Variables.
Design Rule Generation for Interconnect Matching Andrew B. Kahng and Rasit Onur Topaloglu {abk | rtopalog University of California, San Diego.

Nonlinear Programming
Explicit Preemption Placement for Real- Time Conditional Code via Graph Grammars and Dynamic Programming Bo Peng, Nathan Fisher, and Marko Bertogna Department.
IFAC AIRTC, Budapest, October 2000 On the Dynamic Instability of a Class of Switching System Robert Noel Shorten Department of Computer Science National.
Optimization of the Pulp Mill Economical Efficiency; study on the behavior effect of the economically significant variables Alexey Zakharov, Sirkka-Liisa.
(ex) Consider a plant with
Lect.7 Steady State Error Basil Hamed
Analog Circuits for Self-organizing Neural Networks Based on Mutual Information Janusz Starzyk and Jing Liang School of Electrical Engineering and Computer.
Javad Lavaei Department of Electrical Engineering Columbia University Various Techniques for Nonlinear Energy-Related Optimizations.
Claudia Lizet Navarro Hernández PhD Student Supervisor: Professor S.P.Banks April 2004 Monash University Australia April 2004 The University of Sheffield.
Channel Assignment using Chaotic Simulated Annealing Enhanced Neural Network Channel Assignment using Chaotic Simulated Annealing Enhanced Hopfield Neural.
Direct z-Domain Digital Controller Design. OUTLINE Advantages/disadvantages. Design procedures. Direct z-design examples.
Analysis of a Pendulum Problem after Jan Jantzen
Interactive Animation of Structured Deformable Objects Mathieu Desbrun Peter Schroder Alan Barr.
Dirk Zimmer François E. Cellier Institute of Computational Science Department of Computer Science ETH Zürich A bondgraphic modeling tool and its application.
The City College of New York 1 Jizhong Xiao Department of Electrical Engineering City College of New York Manipulator Control Introduction.
Finite Difference Methods to Solve the Wave Equation To develop the governing equation, Sum the Forces The Wave Equation Equations of Motion.
1 Lavi Shpigelman, Dynamic Systems and control – – Linear Time Invariant systems  definitions,  Laplace transform,  solutions,  stability.
G. Hendeby Performance Issues in Non-Gaussian Filtering Problems NSSPW ‘06 Corpus Christi College, Cambridge Performance Issues in Non-Gaussian Filtering.
Hazırlayan NEURAL NETWORKS Radial Basis Function Networks II PROF. DR. YUSUF OYSAL.
First Order Linear Equations Integrating Factors.
Where: I T = moment of inertia of turbine rotor.  T = angular shaft speed. T E = mechanical torque necessary to turn the generator. T A = aerodynamic.
Separation Principle. Controllers & Observers Plant Controller Observer Picture.
Cheng Chen Ph.D., Assistant Professor School of Engineering San Francisco State University Probabilistic Reliability Analysis of Real-Time Hybrid Simulation.
Optimization for Operation of Power Systems with Performance Guarantee
Linear System Theory Instructor: Zhenhua Li Associate Professor Mobile : School of Control Science and Engineering, Shandong.
A CONDENSATION-BASED LOW COMMUNICATION LINEAR SYSTEMS SOLVER UTILIZING CRAMER'S RULE Ken Habgood, Itamar Arel Department of Electrical Engineering & Computer.
Computacion Inteligente Least-Square Methods for System Identification.
Time-Varying Angular Rate Sensing for a MEMS Z-Axis Gyroscope Mohammad Salah †, Michael McIntyre †, Darren Dawson †, and John Wagner ‡ Mohammad Salah †,
MESA Lab Two Interesting Papers Introduction at ICFDA 2014 Xiaobao Jia MESA (Mechatronics, Embedded Systems and Automation) Lab School of Engineering,
1 Adaptive Control Neural Networks 13(2000): Neural net based MRAC for a class of nonlinear plants M.S. Ahmed.
Introduction to ROBOTICS
Nonlinear Data Discrimination via Generalized Support Vector Machines David R. Musicant and Olvi L. Mangasarian University of Wisconsin - Madison
Reinforcement Learning Control with Robust Stability Chuck Anderson, Matt Kretchmar, Department of Computer Science, Peter Young, Department of Electrical.
Institute of Intelligent Power Electronics – IPE Page1 A Dynamical Fuzzy System with Linguistic Information Feedback Xiao-Zhi Gao and Seppo J. Ovaska Institute.
Hong-Ki Jo 1), Man-Gi Ko 2) and * In-Won Lee 3) 1) Graduate Student, Dept. of Civil Engineering, KAIST 2) Professor, Dept. of Civil Engineering, Kongju.
Nonlinear Predictive Control for Fast Constrained Systems By Ahmed Youssef.
AUTOMATIC CONTROL THEORY II Slovak University of Technology Faculty of Material Science and Technology in Trnava.
Nugget: Determining Optimal Sensor Locations for State and Parameter Estimation Juergen Hahn Artie McFerrin Department of Chemical Engineering Texas A&M.
1 ECE 1304 Introduction to Electrical and Computer Engineering Section 1.7 Linear Algebra with MATLAB.
Stochastic Optimal Control of Unknown Linear Networked Control System in the Presence of Random Delays and Packet Losses OBJECTIVES Develop a Q-learning.
Parameter estimation of forest carbon dynamics using Kalman Filter methods –Preliminary results Chao Gao, 1 Han Wang, 2 S Lakshmivarahan, 3 Ensheng Weng,
Lecture 6 - Single Variable Problems & Systems of Equations CVEN 302 June 14, 2002.
2/17/2007DSP Course-IUST-Spring Semester 1 Digital Signal Processing Electrical Engineering Department Iran University of Science & Tech.
Least Squares Estimate Additional Notes 1. Introduction The quality of an estimate can be judged using the expected value and the covariance matrix of.
September 28, 2000 Improved Simultaneous Data Reconciliation, Bias Detection and Identification Using Mixed Integer Optimization Methods Presented by:
Guaranteed Stable Projection-Based Model Reduction for Indefinite and Unstable Linear Systems Brad Bond Luca Daniel MIT Speaker: Tuck, Fang Gong.
Singular Systems Differential Algebraic Equations Introduction Systems & Control Theory Chemical Engineering & DAE Index for DAE Optimal Control Directions.
“Jožef Stefan” Institute Department of Systems and Control Modelling and Control of Nonlinear Dynamic Systems with Gaussian Process Models Juš Kocijan.
Dr. Hatem Elaydi Digital Control, EELE 4360 Dec. 16, 2014
Classification Analytical methods classical methods
SOUTHERN TAIWAN UNIVERSITY ELECTRICAL ENGINEERING DEPARTMENT
Modern Control Systems (MCS)
سمینار درس کنترل پیش بین
Department of Mechanical
Manipulator Dynamics 2 Instructor: Jacob Rosen
Intelligent Control, Its evolution, Recent Technology on Robotics
Consider Covariance Analysis Example 6.9, Spring-Mass
Comparison Functions Islamic University of Gaza Faculty of Engineering
-·.-...-· A. -.. ) ,.,.. -.,., · o# --·'1>,.. ·-·-. ·-· ;'/' : ,.,. - ' p ·-·- ·-- 'II"; -.-. t-.. p
ECE 576 POWER SYSTEM DYNAMICS AND STABILITY
ECE 576 POWER SYSTEM DYNAMICS AND STABILITY
D. Kim, B.J. Debusschere, H.N. Najm  Biophysical Journal 
Input-Output Stability
Presentation transcript:

Locally Optimal Takagi-Sugeno Fuzzy Controllers Amir massoud Farahmand Mohammad javad Yazdanpanah Department of Electrical and Computer Engineering University of Tehran Tehran, Iran

Department of Electrical and Computer Engineering University of Tehran Fuzzy Control Successful in many applications Ease of use Intuitive and interpretable Powerful nonlinear controller

Department of Electrical and Computer Engineering University of Tehran Takagi-Sugeno Plant Model, Theorem 1. The continuous uncontrolled T-S fuzzy system is globally quadratically stable if there exists a common positive definite matrix P such that

Department of Electrical and Computer Engineering University of Tehran Parallel Distributed Compensation Stability condition:

Department of Electrical and Computer Engineering University of Tehran Locally Optimal Design Linearization Locally optimal design

Department of Electrical and Computer Engineering University of Tehran Experiments: Problem description Nonlinear Mass-Spring- Damper system

Department of Electrical and Computer Engineering University of Tehran Experiments : Fuzzy Settings The dynamics of the plant is approximated using Gaussian membership function Approximation error

Department of Electrical and Computer Engineering University of Tehran Experiments: Stabilization (I) Comparison of T-S controller (bold) and linear controller (dotted) with different initial conditions Both TS and linear controller are stable in this case. However, the behavior of fuzzy controller is smoother and with lower overshoot.

Department of Electrical and Computer Engineering University of Tehran Experiments: Stabilization (II) Response of T-S controller to (10 0)' The linear controller is not stable in this case, but the fuzzy controller can handle it easily.

Department of Electrical and Computer Engineering University of Tehran Experiments: Performance Comparison LinearTS Q=I, R= Q=10I,R= Q=I, R= ,,

Department of Electrical and Computer Engineering University of Tehran Experiments: Performance Comparison Fig. 3. Performance region comparison for different performance indices: (Q=1, R=1), (Q=10, R=1), and (Q=1, R=10), from left to right, respectively (dark region means linear one has better performance).

Department of Electrical and Computer Engineering University of Tehran Conclusions and Suggestions Conclusions Stable Fuzzy Controller Local Optimality How close is it to the global optimal solution?! Suggestions Comparison with other T-S controllers Modeling error and stability (polytopic systems) Considering the effect of membership functions explicitly