Controllability of linear parabolic equations and systems
Franck Boyer
franck.boyer@math.univ-toulouse.fr
Université Paul Sabatier - Toulouse 3
February 7, 2020
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CONTENTS iii
Contents
I Introduction 1
I.1 What is it all about ? . . . . 1
I.2 Examples . . . . 2
I.2.1 The stupid example . . . . 2
I.2.2 The rocket . . . . 4
I.2.3 Nonlinear examples . . . . 6
I.2.4 PDE examples . . . . 7
II Controllability of linear ordinary differential equations 11 II.1 Preliminaries . . . 11
II.1.1 Exact representation formula . . . 11
II.1.2 Duality . . . 12
II.1.3 Reachable states. Control spaces . . . 12
II.2 Kalman criterion. Unique continuation . . . 13
II.3 Fattorini-Hautus test . . . 17
II.4 The moments method . . . 19
II.5 Linear-Quadratic optimal control problems . . . 21
II.5.1 Framework . . . 21
II.5.2 Main result. Adjoint state . . . 21
II.5.3 Justification of the gradient computation . . . 24
II.5.4 Ricatti equation . . . 25
II.6 The HUM control . . . 26
II.7 How much it costs ? Observability inequalities . . . 29
III Controllability of abstract parabolic PDEs 35 III.1 General setting . . . 35
III.2 Examples . . . 37
III.3 Controllability - Observability . . . 38
IV The heat equation 43 IV.1 Further spectral properties and applications . . . 45
IV.1.1 The 1D case . . . 45
IV.1.2 Biorthogonal family of exponentials . . . 53
IV.1.3 The multi-D case . . . 69
IV.2 The method of Lebeau and Robbiano . . . 71
IV.3 Global elliptic Carleman estimates and applications . . . 77
IV.3.1 The basic computation . . . 78
IV.3.2 Proof of the boundary Carleman estimate . . . 80
IV.3.3 Proof of the distributed Carleman estimate . . . 82
IV.3.4 Construction of the weight functions . . . 83
IV.3.5 A Carleman estimate for augmented elliptic operators with special boundary conditions . . . . 84
iv CONTENTS
IV.4 The Fursikov-Imanuvilov approach . . . 85
IV.4.1 Global parabolic Carleman estimates . . . 85
IV.4.2 Another proof of the null-controllability of the heat equation . . . 86
V Coupled parabolic equations 89 V.1 Systems with as many controls as components . . . 89
V.2 Boundary versus distributed controllability . . . 91
V.3 Distributed control problems . . . 91
V.3.1 Constant coefficient systems with less controls than equations . . . 91
V.3.2 Variable coefficient cascade systems - The good case . . . 95
V.3.3 Variable coefficient cascade systems - The not so good case . . . 96
V.4 Boundary controllability results for some 1D systems . . . 103
V.4.1 Approximate controllability . . . 103
V.4.2 Null-controllability . . . 104
A Appendices 111 A.1 Non-autonomous linear ODEs. Resolvant . . . 111
A.2 Linear ODEs with integrable data . . . 111
A.3 Divided differences . . . 112
A.3.1 Definition and basic properties . . . 112
A.3.2 Generalized divided differences . . . 113
A.4 Biorthogonal families in a Hilbert space . . . 113
A.4.1 Notation and basic result . . . 113
A.4.2 Gram matrices. Gram determinants . . . 114
A.4.3 Generalized Gram determinants . . . 118
A.4.4 Cauchy determinants . . . 118
A.5 Sturm comparison theorem . . . 120
A.6 Counting function and summation formulas . . . 122
A.7 Generalized Tchebychev polynomials . . . 124
A.7.1 Interpolation in Müntz spaces . . . 125
A.7.2 Best uniform approximation in Müntz spaces . . . 130
1
Chapter I
Introduction
Disclaimer : Those lecture notes were written to support a Master course given by the author at Toulouse between 2016 and 2018. Since then, they were regularly updated but are still far from being complete and many references of the literature are lacking (I promise they will be added in the next release !).
It still contains almost surely many mistakes, inaccuracies or typos. Any reader is encouraged to send me 1 any comments or suggestions.
I.1 What is it all about ?
We shall consider a very unprecise setting for the moment : consider a (differential) dynamical system
( y 0 = F (t, y, v(t)),
y(0) = y 0 , (I.1)
in which the user can act on the system through the input v. Here, y (resp. v) live in a state space E (resp. a control space U ) which are finite dimensional spaces (the ODE case) or in infinite dimensional spaces (the PDE case).
We assume (for simplicity) that the functional setting is such that (I.1) is globally well-posed for any initial data y 0 and any control v in a suitable functional space.
Definition I.1.1
Let y 0 ∈ E. We say that:
• (I.1) is exactly controllable from y 0 if : for any y T ∈ E, there exists a control v : (0, T ) → U such that the corresponding solution y v,y
0of (I.1) satisfies
y v,y
0(T ) = y T .
If this property holds for any y 0 , we simply say that the system is exactly controllable.
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