This document discusses higher-order linear dynamical systems and their properties. It begins by outlining how to solve higher-order systems using eigenvalues. It then presents theorems for determining asymptotic stability, including necessary and sufficient conditions. The document shows how to check if a system will produce oscillations using determinants. It also discusses how delayed feedback can be approximated and used to enforce oscillations in a two-region network model.
Hensley - Speaking feedback | 20 Jan 2012Nagasaki JALT
Hensley reported on his development of a combined conversational storytelling and learner noticing through self-transcription course in which student pairs were trained and instructed to self-transcribe their own recorded conversations.
Though only reporting on his initial implementation of such a course, Hensley indicated that, while the effects of self-transcription may be hard to measure in learners’ performance, the conversational storytelling appeared to be having a positive effect
on learners’ fluency.
Dr. Richard Hodson and Joel Hensley covered their ongoing research into the effect of feedback order on student writing. Reporting on their initial findings from a small-scale exploratory study, Hodson and Hensley discussed perceived similarities and differences in learners’ final essays depending on the order in which feedback was given: global revisions first and then local corrections, or vice versa.
2012
University of Dayton
Electrical and computer department
Nonlinear control system
ABDELBASET ELHANGARI
1011647470
Final project: Designing of Nonlinear Controllers[ ]
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Contents
1. Experiment #1:.................................................................................................................................... 2
Part A Experiment #1 (Feedback linearization): ................................................................................... 2
Part B of Experiment #1(Sliding Mode Control): .................................................................................. 8
2. Experiment #2:.................................................................................................................................. 12
3. Experiment #3:.................................................................................................................................. 14
Figures:
Figure 1: The System Phase Portrait ............................................................................................................ 4
Figure 2: The System is Open loop Stable .................................................................................................... 5
Figure 3: Regulation of the System States by the Controller ........................................................................ 6
Figure 4: Failure of the Same Controller in the Regulation Process .............................................................. 7
Figure 5: Sliding Mode Control with the Alternative Manifold ................................................................... 10
Figure 6: Sliding Mode Control with the Given Manifold ........................................................................... 11
Figure 7: The Pendulum States Being Regulated near the Zero .................................................................. 13
Figure 8: The phase Portrait of the Open Loop .......................................................................................... 15
Figure 9: Back Stepping Controller Succession........................................................................................... 18
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1. Experiment #1:
The system is given by
̇
̇
With (.
Robust Fuzzy Output Feedback Controller for Affine Nonlinear Systems via T–S ...Mostafa Shokrian Zeini
This presentation concerns the design of a robust H_∞ fuzzy output feedback controller for a class of affine nonlinear systems with disturbance via Takagi-Sugeno (T–S) fuzzy bilinear model. The parallel distributed compensation (PDC) technique is utilized to design a fuzzy controller. The stability conditions of the overall closed loop T-S fuzzy bilinear model are formulated in terms of Lyapunov function via linear matrix inequality (LMI). The control law is robustified by H_∞ sense to attenuate external disturbance. Moreover, the desired controller gains can be obtained by solving a set of LMI.
1. Higher-order linear dynamical systems
Kay Henning Brodersen
Computational Neuroeconomics Group
Department of Economics, University of Zurich
Machine Learning and Pattern Recognition Group
Department of Computer Science, ETH Zurich
http://people.inf.ethz.ch/bkay
2. Outline
1 Solving higher-order systems
2 Asymptotic stability: necessary condition
3 Asymptotic stability: necessary and sufficient condition
4 Oscillations
5 Delayed feedback
The material in these slides follows:
H R Wilson (1999). Spikes, Decisions, and Actions: The Dynamical
Foundations of Neuroscience. Oxford University Press.
2
5. Example: a network of three nodes
Theorem 4 tells us that the states 𝐸 𝑖 will all have the form:
where −8 and −3.5 + 14.7𝑖 and −3.5 − 14.7𝑖 are the three
eigenvalues of the system.
5
6. Example: solving vs. characterizing the system
Whether or not we care to solve for the constants, we can
use the eigenvalues to characterize the system:
eig([-5 -10 7; 7 -5 -10; -10 7 -5])
ans =
-3.5 + 14.72243i
-3.5 - 14.72243i
-8
From Chapter 3 we know that the eqilibrium point of this system
• must be a spiral point (because the eigenvalues are a complex conjugate pair)
• must be asymptotically stable (because the real part of the eigenvalues is negative)
http://demonstrations.wolfram.com/
TwoDimensionalLinearSystems/
spiral point node saddle point centre
6
15. Example: checking for oscillations
To evaluate whether this system will produce oscillations, we consider two determinants:
Δ1 = 𝑎1 = 15
>0
𝑎1 1 15 1
Δ2 = = = 2443
𝑎3 𝑎2 1832 285 =0
Thus, this dynamical system will not produce oscillations. (Note that we knew this already
from the fact that the system had an asymptotically stable equilibrium point.)
15
16. Example: enforcing oscillations (1)
To find out under what conditions this system will produce oscillations, we require that:
Δ1 = 𝑎1 > 0
𝑎1 1
Δ2 = =0
𝑎3 𝑎2
The above coefficients are obtained from the characteristic equation:
𝐴 − 𝜆𝐼 = 0
𝜆3 + 15 𝜆2 + 21𝑔 + 75 𝜆 + 𝑔3 + 105𝑔 − 218 = 0
𝑎1 𝑎2 𝑎3
16
17. Example: enforcing oscillations (2)
Thus, we wish to find 𝑔 such that:
Δ1 = 15 > 0
15 1
Δ2 = =0
𝑔3 + 105𝑔 − 218 21𝑔 + 75
To satsify the second equation, we require that:
15 21𝑔 + 75 − 𝑔3 + 105𝑔 − 218 = 0
𝑔3 − 210𝑔 − 1343 = 0
This equation has 1 real solution: 𝑔 = 17.
17
18. Example: enforcing oscillations (3)
We can use MATLAB to do the above calculations: Thus, our system is described by:
𝐸1 −5 −17 7 𝐸1
>> A = '[-5 -g 7; 7 -5 -g; -g 7 -5]'; 𝑑
>> routh_hurwitz(A) 𝐸2 = 7 −5 −17 𝐸2
𝑑𝑡
𝐸3 −17 7 −5 𝐸3
g =
17.0000
-17
Characteristic_Eqn =
1.0e+03 *
0.0010 0.0150 0.4320 6.4800
EigenValues =
0 +20.7846i
0 -20.7846i
-15.0000
RHDeterminants =
15.0000
0
0.0000 The states have the general form:
ans =
𝐸 𝑘 = 𝐴𝑒 −15𝑡 + 𝐵 cos 20.78𝑡
Solution oscillates around equilibrium +𝐶 sin 20.78𝑡
point, which is a center.
Oscillation frequency: 3.3 Hz
18
21. Oscillations in a two-region network?
Can oscillations even occur in a simpler two-region network? We consider a simple negative
feedback loop:
𝑏/𝜏 𝐼
𝐸 𝐼
−𝐸/𝜏 𝐸 −𝑎/𝜏 𝐸 −𝐼/𝜏 𝐼
The characteristic equation is:
−1/𝜏 𝐸 −𝑎/𝜏 𝐸
− 𝜆𝐼 = 0
𝑏/𝜏 𝐼 −1/𝜏 𝐼
The equation is satisfied by a complex conjugate pair of eigenvalues:
1 1 1 𝜏 𝐸 − 𝜏 𝐼 2 − 4𝑎𝑏𝜏 𝐸 𝜏 𝐼
𝜆=− + ±
2 𝜏 𝐸 𝜏𝐼 2𝜏 𝐸 𝜏 𝐼
Since the real parts are negative, all solutions must be decaying exponential functions of time,
and so oscillations are impossible in this model. But this does not preclude oscillations in
second-order systems in general…
21
22. Delayed feedback: an approximation
Consider a two-region feedback loop with delayed inhibitory feedback:
𝐸 𝐼
Systems with true delays become exceptionally complex. Instead, we can
approximate the effect of delayed feedback by introducing an additional node:
𝐸 𝐼
Δ
22
23. Delayed feedback: enforcing oscillations
Let us specify concrete numbers for all connection strengths, except for the delay:
8/50
𝐸 𝐼
−1/10 −1/50
−1/5 1/𝛿
Δ
−1/𝛿
time [ms]
Is there a value for 𝛿 that produces oscillations?
>> routh_hurwitz('[-1/10 0 -1/5; 8/50 -1/50 0; 0 1/g -1/g]',10)
g =
7.6073
Solution oscillates around equilibrium point, which is a center.
23
24. Summary
Preparations
write down system dynamics in normal form
write down characteristic equation
turn into coefficients form
What are we interested in?
Want to fully Want to quickly check Want to check for,
solve the system for asymptotic stability or enforce, oscillations
apply theorem 4 to apply theorem 5 (necessary condition) apply theorem 7
find eigenvalues 𝜆 (necessary and sufficient
(may be condition)
computationally condition not condition
expensive) satisfied – not satisfied
stable
apply theorem 6 (sufficient condition)
combine with initial
conditions to solve
for constants 𝐴 𝑛 condition not condition satisfied –
satisfied – not stable system is stable
24