Integral Control via Bias Estimation

Similar documents
A Crash Course in (2 2) Matrices

f(y) signal norms system gain bounded input bounded output (BIBO) stability For what G(s) and f( ) is the closed-loop system stable?

Much that has already been said about changes of variable relates to transformations between different coordinate systems.

Quantum Mechanics I - Session 5

Notes for the standard central, single mass metric in Kruskal coordinates

Homework Set 3 Physics 319 Classical Mechanics

Physics Courseware Physics II Electric Field and Force

PH126 Exam I Solutions

Data Flow Anomaly Analysis

Session outline. Introduction to Feedback Control. The Idea of Feedback. Automatic control. Basic setting. The feedback principle

Stability of a Discrete-Time Predator-Prey System with Allee Effect

How to Obtain Desirable Transfer Functions in MIMO Systems Under Internal Stability Using Open and Closed Loop Control

Chapter 3: Theory of Modular Arithmetic 38

MODULE 5a and 5b (Stewart, Sections 12.2, 12.3) INTRO: In MATH 1114 vectors were written either as rows (a1, a2,..., an) or as columns a 1 a. ...

Chapter 7 Singular Value Decomposition

EM Boundary Value Problems

CHAPTER 25 ELECTRIC POTENTIAL

Conservation of Linear Momentum using RTT

15. SIMPLE MHD EQUILIBRIA

Simultaneous state and unknown inputs estimation with PI and PMI observers for Takagi Sugeno model with unmeasurable premise variables

556: MATHEMATICAL STATISTICS I

Physics 121 Hour Exam #5 Solution

Particle Systems. University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell

From Errors to Uncertainties in Basic Course in Electrical Measurements. Vladimir Haasz, Milos Sedlacek

Question 1: The dipole

CSCE 478/878 Lecture 4: Experimental Design and Analysis. Stephen Scott. 3 Building a tree on the training set Introduction. Outline.

On the integration of the equations of hydrodynamics

Supplementary Information for On characterizing protein spatial clusters with correlation approaches

SPH4UI 28/02/2011. Total energy = K + U is constant! Electric Potential Mr. Burns. GMm

Figure 1. We will begin by deriving a very general expression before returning to Equations 1 and 2 to determine the specifics.

2. Electrostatics. Dr. Rakhesh Singh Kshetrimayum 8/11/ Electromagnetic Field Theory by R. S. Kshetrimayum

General Relativity Homework 5

Lab 10: Newton s Second Law in Rotation

2.3. SLIDING MODE BASED OUTER CONTROL LOOP FOR INDUCTION MOTOR DRIVES WITH FORCED DYNAMICS

Lab #0. Tutorial Exercises on Work and Fields

A Note on Irreducible Polynomials and Identity Testing

PHYS 301 HOMEWORK #10 (Optional HW)

Math 301: The Erdős-Stone-Simonovitz Theorem and Extremal Numbers for Bipartite Graphs

radians). Figure 2.1 Figure 2.2 (a) quadrant I angle (b) quadrant II angle is in standard position Terminal side Terminal side Terminal side

15 Solving the Laplace equation by Fourier method

Solution to HW 3, Ma 1a Fall 2016

PROBLEM SET #1 SOLUTIONS by Robert A. DiStasio Jr.

Rigid Body Dynamics 2. CSE169: Computer Animation Instructor: Steve Rotenberg UCSD, Winter 2018

ME 210 Applied Mathematics for Mechanical Engineers

Physics 235 Chapter 5. Chapter 5 Gravitation

working pages for Paul Richards class notes; do not copy or circulate without permission from PGR 2004/11/3 10:50

Math 124B February 02, 2012

4. Compare the electric force holding the electron in orbit ( r = 0.53

7.2. Coulomb s Law. The Electric Force

Version 1.0. General Certificate of Education (A-level) June Mathematics MM04. (Specification 6360) Mechanics 4. Final.

That is, the acceleration of the electron is larger than the acceleration of the proton by the same factor the electron is lighter than the proton.

Information Retrieval (Relevance Feedback & Query Expansion)

COORDINATE TRANSFORMATIONS - THE JACOBIAN DETERMINANT

5.61 Physical Chemistry Lecture #23 page 1 MANY ELECTRON ATOMS

Pushdown Automata (PDAs)

ONE-POINT CODES USING PLACES OF HIGHER DEGREE

Spring 2001 Physics 2048 Test 3 solutions

Passivity-Based Control of Saturated Induction Motors

This lecture. Transformations in 2D. Where are we at? Why do we need transformations?

Do Managers Do Good With Other People s Money? Online Appendix

3.1 Random variables

Electric Potential and Gauss s Law, Configuration Energy Challenge Problem Solutions

Chapter 5 Linear Equations: Basic Theory and Practice

18.06 Problem Set 4 Solution

Table of contents. Statistical analysis. Measures of statistical central tendencies. Measures of variability

Physics 201 Lecture 18

The Cross Product of Fuzzy Numbers and its Applications in Geology

DYNAMIC STABILITY STUDY AND SIMULATION OF THE SYNCHRONOUS MACHINE COUPLED WITH THE NETWORK BY A LINE AND A LOAD IN PARALLEL

Skps Media

ECE Spring Prof. David R. Jackson ECE Dept. Notes 5

4.[1pt] Two small spheres with charges -4 C and -9 C are held 9.5 m apart. Find the magnitude of the force between them.

When two numbers are written as the product of their prime factors, they are in factored form.

Chapter Eight Notes N P U1C8S4-6

A Hilbert-Type Inequality with Some Parameters and the Integral in Whole Plane

arxiv: v1 [math.oc] 2 Mar 2009

CHAPTER 2 DERIVATION OF STATE EQUATIONS AND PARAMETER DETERMINATION OF AN IPM MACHINE. 2.1 Derivation of Machine Equations

GRAVITATION. Einstein Classes, Unit No. 102, 103, Vardhman Ring Road Plaza, Vikas Puri Extn., New Delhi -18 PG 1

On the reconstruction of the coronal magnetic field from coronal Hanle / Zeeman observations

Multiple Experts with Binary Features

MAE 210B. Homework Solution #6 Winter Quarter, U 2 =r U=r 2 << 1; ) r << U : (1) The boundary conditions written in polar coordinates,

On the Quasi-inverse of a Non-square Matrix: An Infinite Solution

Estimation of the Correlation Coefficient for a Bivariate Normal Distribution with Missing Data

Absolute Specifications: A typical absolute specification of a lowpass filter is shown in figure 1 where:

Qualifying Examination Electricity and Magnetism Solutions January 12, 2006

A STRONG BOUND FOR THE NUMBER OF SOLUTIONS OF THUE EQUATIONS

EN40: Dynamics and Vibrations. Midterm Examination Thursday March

In many engineering and other applications, the. variable) will often depend on several other quantities (independent variables).

Lecture 8 - Gauss s Law

Nonlinear Control of Heartbeat Models

Lecture 16 Root Systems and Root Lattices

Gravity and isostasy

b) The array factor of a N-element uniform array can be written

B da = 0. Q E da = ε. E da = E dv

Markscheme May 2017 Calculus Higher level Paper 3

Solutions to Problems : Chapter 19 Problems appeared on the end of chapter 19 of the Textbook

Fields and Waves I Spring 2005 Homework 4. Due 8 March 2005

x 1 b 1 Consider the midpoint x 0 = 1 2

Conjugate Gradient Methods. Michael Bader. Summer term 2012

Auchmuty High School Mathematics Department Advanced Higher Notes Teacher Version

That is, the acceleration of the electron is larger than the acceleration of the proton by the same factor the electron is lighter than the proton.

Transcription:

1 Integal Contol via Bias stimation Consie the sstem ẋ = A + B +, R n, R p, R m = C +, R q whee is an nknown constant vecto. It is possible to view as a step istbance: (t) = 0 1(t). (If in fact (t) vaies with time, bt moe slowl than the othe sstem namics, then moelling the istbance as a step ma be a goo appoimation.) Let s consie as the state of a namical sstem with nknown initial conitions: ḋ = 0, 0 nknown (It is also possible to teat othe namics in this wa; I have aske o to o so in Poblems 2 an 3 of Poblem Set 3.) Sppose that we wish to foce ( t) to asmptoticall tack a constant efeence signal espite the nknown istbance. We have seen two appoaches to this poblem. In each case, we se state feeback to stabilize the sstem; this entails no loss of genealit becase we can alwas se an obseve to econstct the state. (In Poblem 1 of Poblem Set 3, o ae aske to veif that this pocee woks.) O two appoaches ae, fist, to se a constant gain pecompensato: N B ( s I A ) 1 C

2 If the close loop sstem is stable, then we can calclate that the esponse to a step comman (t) = 0 1(t) an step istbance (t) = 0 1(t) satisfies (t) ss, whee ss := C( A + B) 1 BN 0 + [C( A + B) 1 + ] 0 If C( A + B) 1 B is ight invetible, then setting N = [C( A + B) 1 B] # iels ss := 0 + [C( A + B) 1 + ] 0 The isavantages of this scheme ae that it cannot fo the effects of istbances an small paamete vaiations. An altenate appoach that oes accont fo istbances an paamete vaiations is to se integal contol: B ( si A ) 1 C I w I / s e We have seen in class that if the close loop sstem is stable, then the stea state esponse to a step comman an a step istbance satisfies (t) ss, whee ss := 0. Hence, pefect comman tacking an istbance ejection ae possible; moeove, the sstem is insensitive to small moelling eos. Of cose, in pactice we won't sall be able to mease the states of the plant. Hence, we mst se an obseve to estimate these states,

3 as epicte in the following iagam. In Poblem 1 of Poblem Set 3, o ae aske to show that this sstem is stable if the state feeback sstem in the pevios iagam is stable, an if the obseve is stable. B ( si A ) 1 C ^ Obseve I w I / s e Note that the contol inpt in this configation oes not epen eplicitl pon the comman signal. It is often possible to mease, an hence we can consie sing this measement in a feefowa contol scheme as we i in Poblem 3 of Poblem Set 2. The avantage of the eslting Two Degee of eeom (2DO) contol topolog is that it affos geate feeom in achieving esign taeoffs.

4 Altenatel, let s sppose fo the sake of agment that we can mease the state of the istbance. We can then consie sing this measement in a feefowa/feeback contol law = ˆ I w + G as shown below: G B ( si A ) 1 C ^ Obseve I w I / s e Pesmabl, b sing feefowa fom the istbance, we can obtain a faste esponse to the istbance than othewise. Thee is one poblem: we will geneall not be able to mease the istbance! Recall, howeve, that a step istbance ma be viewe as the otpt of a namical sstem consisting solel of integatos: ḋ = 0, 0 nknown

5 Since we nee to se an obseve anwa, sppose that we esign the obseve, if possible, to estimate the state of the istbance as well as that of the plant, an se a contol law = ˆ I w + G ˆ B ( si A ) 1 C G ^ ^ Obseve I w I / s e Becase a step istbance ma be viewe as an nknown bias, the obseve in the above iagam is sometimes teme an "nknown bias estimato". A simila pocee ma be se to estimate the state of othe sots of istbances. We shall now consie the poblem of esigning an obseve to estimate the istbance. Becase the concepts we nee fo this o not epen pon the se of integal contol in the above iagam, we will instea eplain the pocee base pon the simple contol scheme with constant gain pecompensato. Once we know how to esign an obseve fo the istbance, the eslt can be applie to the poblem pose above. The following iscssion is aapte fom W. J. Rgh, Linea Sstem Theo, PenticeHall, 1993

6 Consie the sstem epicte below: N B ( s I A ) 1 C G ^ ^ Obseve The state eqations fo this configation ae: Plant: ẋ = A + B + = C + Obseve: ˆẋ ˆ = A ˆ 0 0 ˆ + B 0 + L ŷ ŷ = [ C ] ˆˆ L 2 ( ), L = L 1 Contol: = ˆ + G ˆ + N We mst answe seveal qestions: (1) When can a stable obseve fo an w be esigne? (2) When is the close loop sstem stable? (3) Does (t) 0 as esie? We also nee to fin vales fo, L, N, an G.

7 Theoem: Given the sstem escibe above, efine P (s) = C( si A) 1 B an P (s) = C(sI A) 1 + Assme that an (i) ( A, B) is contollable, (ii) ( A,C) is obsevable, (iii) nomal ank P (s) = q (iv) ( A, B,C) has no zeos at s = 0 (v) (vi) (vii) ( A, ) is contollable, nomal ank P (s) = m ( A,,C, ) has no zeos at s = 0 Then an L ma be chosen to stabilize the eslting close loop sstem, an N an G ma be chosen so that the stea state esponse to a step comman (t) = 0 1(t) an a step istbance (t) = 0 1(t) satisfies (t) ss = 0. Notes: (1) If A has no eigenvales at s = 0, then conitions (iv) an (vi) ma be eplace b ank P (0) = q an ank P w (0) = m, espectivel. (2) Conition (iii) implies that thee ae at least as man contol inpts as contolle otpts, an that these otpts ae inepenent. (3) Conitions (v)(vii) essentiall inse that all the istbance states affect the stea state otpts of the sstem. These conitions will gaantee that we can constct an obseve fo the istbance states.

8 Poof: ist sbstitte the eqation fo ŷ into the obseve; this iels: ˆẋ ˆ = ( à L C ) ˆˆ + B 0 + L = A L 1 C L 1 ˆ L 2 C L 2 ˆ + B 0 + L 1 L 2 whee à = A 0 0, C = [ C ]. Define the estimation eo states := ˆ an := ˆ. Then the estimation eo namics ae given b: ẋ ḋ = A L1C L 1 L 2 C L 2. It follows that we can esign a stable obseve if the matices constitte an obsevable pai. Using the feeback contol ( Ã, C ) = ˆ + G ˆ + N iels the following state space esciption fo the close loop sstem: ẋ = A + B( ˆ + Gŵ + N) + w ˆẋ ˆ = A L1C L 2 C ( ) + L 1 L 1 ˆ L 2 ˆ + B 0 ˆ + G ˆ + N L C + 2 ( ) Combining these eqations iels: ẋ A B BG BN ˆẋ = L 1 C A B L 1 C + BG L 1 L 2 ˆ + BN + L 1 ˆ L 2 C L 2 C L 2 ˆ 0 L 2

9 = [ C 0 0 ] ˆ + ˆ As sal, it is ifficlt to etemine an close loop popeties in these cooinates. Let s instea change cooinates to. Using the fact that ḋ = ˆḋ (becase is a constant) iels (afte some algeba) that: ẋ A B B BG BN + BG ẋ = 0 A L 1 C L 1 + 0 + 0 ḋ 0 L 2 C L 2 0 0 = [ C 0 0 ] + It is clea fom the above eqations that (a) Thee is a sepaation pinciple: the close loop eigenvales ae the eigenvales of the state feeback, A B, pls those of the obseve fo ˆ an ˆ. It follows that if ( A, B) is contollable, an Ã, C ( ) is obsevable, then we ma alwas obtain a stable close loop sstem. (b) If the close loop sstem is stable, then the stea state esponse to a step comman (t) = 0 1(t) an a step istbance (t) = 0 1(t) appoaches a constant vale. Hence, the eivatives of the state vaiables asmptoticall appoach zeo. Setting the left han sie of the above eqation eqal to zeo ths iels that an ss = 0 ss = 0 ( ) 1 BN 0 + C( A + B) 1 + + C( A + B) 1 BG ss := C A + B [ ] 0

Sppose that C( A + B) 1 B is ight invetible. (Right invetibilit is gaantee b assmptions (iii) an (iv).) Then setting 10 an N = [ C( A + B) 1 B] # G = N[ C( A + B) 1 + ] iels ss = 1. The onl thing that emains to be poven is that o assmptions gaantee that Ã, C ( ) is obsevable. We o this b showing that ank λi à λi A = ank 0 λi = n + m, C C whee λ is eithe eqal to an eigenvale of A, o λ = 0. Case 1: Sppose that λ 0 is an eigenvale of A. Then λi A ank 0 λi = n + m C if an onl if this mati has n + m lineal inepenent colmns. Becase λ 0, it is clea that the last m colmns ae lineal inepenent among themselves an ae also lineal inepenent fom the fist n colmns. Hence λi A ank 0 λi = n + m ank λi A C = n C if an onl if λ is an obsevable eigenvale of A.

11 Case 2: Consie λ = 0. The ank test eces to eqiing that ank A C = n + m This conition is implie b assmptions (ii) an (v)(vii). ### To smmaize: Choose so that A B is stable Choose L 1 an L 2 so that A 0 0 L 1 L [ 2 C ] is stable Set N = [ C( A + B) 1 B] # an G = N[ C( A + B) 1 + ] Then ( t) 0, 0, 0. It is inteesting to note that this contol scheme is also a fom of integal contol; it is jst that the integatos ae bie in the obseve. Inee, we have ˆ = L 2 ŷ ( ) o, in block iagam fom ^ L 2. ^ I / s ^