Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Control Synthesis by Sum of Squares Optimization

1,462 views

Published on

Published in: Education, Technology
  • Be the first to comment

  • Be the first to like this

Control Synthesis by Sum of Squares Optimization

  1. 1. Control Synthesis by Sum of Squares Optimization An Introduction Behzad Samadi bsamadi@encs.concordia.ca Concordia University Montreal, Canada
  2. 2. Outline ◮ Convex Optimization ▽Control Synthesis by Sum of Squares Optimization – p.1/30
  3. 3. Outline ◮ Convex Optimization ◮ Sum of Squares ▽Control Synthesis by Sum of Squares Optimization – p.1/30
  4. 4. Outline ◮ Convex Optimization ◮ Sum of Squares ◮ Control Applications ▽Control Synthesis by Sum of Squares Optimization – p.1/30
  5. 5. Outline ◮ Convex Optimization ◮ Sum of Squares ◮ Control Applications ◮ Conclusion Control Synthesis by Sum of Squares Optimization – p.1/30
  6. 6. Convex set C ⊆ Rn is convex if x, y ∈ C, θ ∈ [0, 1] =⇒ θx + (1 − θ)y ∈ C "Convex Optimization with Engineering Applications", Professor Stephan Boyd, Stanford University Control Synthesis by Sum of Squares Optimization – p.2/30
  7. 7. Convex function f : Rn −→ R is convex if x, y ∈ Rn, θ ∈ [0, 1] ⇓ f(θx + (1 − θ)y) ≤ θf(x) + (1 − θ)f(y) "Convex Optimization with Engineering Applications", Professor Stephan Boyd, Stanford University Control Synthesis by Sum of Squares Optimization – p.3/30
  8. 8. Convex optimization problem minimize f(x) subject to x ∈ C f convex, C convex "Convex Optimization with Engineering Applications", Professor Stephan Boyd, Stanford University ▽Control Synthesis by Sum of Squares Optimization – p.4/30
  9. 9. Convex optimization problem minimize f(x) subject to x ∈ C f convex, C convex Convex optimization problems ◮ can be solved numerically with great efficiency ◮ have extensive useful theory ◮ occur often in engineering problems ◮ often go unrecognized "Convex Optimization with Engineering Applications", Professor Stephan Boyd, Stanford University Control Synthesis by Sum of Squares Optimization – p.4/30
  10. 10. Linear programming minimize aT 0 x subject to aT i x ≤ bi, i = 1, . . . , m. "Convex Optimization with Engineering Applications", Professor Stephan Boyd, Stanford University Control Synthesis by Sum of Squares Optimization – p.5/30
  11. 11. Semidefinite programming minimize cT x subject to x1F1 + · · · + xnFn + G ≤ 0 Ax = b, "Convex Optimization with Engineering Applications", Professor Stephan Boyd, Stanford University Control Synthesis by Sum of Squares Optimization – p.6/30
  12. 12. In fact the great watershed in optimization isn’t between linearity and nonlinearity, but convexity and nonconvexity. Rockafellar 1993 Control Synthesis by Sum of Squares Optimization – p.7/30
  13. 13. Nonnegativity of polynomials Polynomials of degree d in n variables: p(x) p(x1, x2, . . . , xn) = k1+k2+···+kn≤d ak1k2...kn xk1 1 xk2 2 · · · xkn n ▽Control Synthesis by Sum of Squares Optimization – p.8/30
  14. 14. Nonnegativity of polynomials Polynomials of degree d in n variables: p(x) p(x1, x2, . . . , xn) = k1+k2+···+kn≤d ak1k2...kn xk1 1 xk2 2 · · · xkn n How to check if a given p(x) (of even order) is globally nonnegative? p(x) ≥ 0, ∀x ∈ Rn ▽Control Synthesis by Sum of Squares Optimization – p.8/30
  15. 15. Nonnegativity of polynomials Polynomials of degree d in n variables: p(x) p(x1, x2, . . . , xn) = k1+k2+···+kn≤d ak1k2...kn xk1 1 xk2 2 · · · xkn n How to check if a given p(x) (of even order) is globally nonnegative? p(x) ≥ 0, ∀x ∈ Rn ◮ For d = 2, easy (check eigenvalues). What happens in generel? ◮ Decidable, but NP-hard when d ≥ 4. ◮ "Low complexity" is desired at the cost of possibly being conservative. "Certificates, convex Optimization, and their applications", Pablo A. Parrilo, Swiss Federal Institute of Technology Zurich Control Synthesis by Sum of Squares Optimization – p.8/30
  16. 16. A sufficient condition A "simple" sufficient condition: a sum of squares (SOS) decomposition: p(x) = m i=1 f2 i (x) If p(x) can be written as above, for some polynomials fi, then p(x) ≥ 0. ▽Control Synthesis by Sum of Squares Optimization – p.9/30
  17. 17. A sufficient condition A "simple" sufficient condition: a sum of squares (SOS) decomposition: p(x) = m i=1 f2 i (x) If p(x) can be written as above, for some polynomials fi, then p(x) ≥ 0. ◮ p(x) is an SOS if and only if a positive semidefinite matrix Q exists such that p(x) = ZT (x)QZ(x) where Z(x) is the vector of monomials of degree less than or equal to deg(p)/2 "Certificates, convex Optimization, and their applications", Pablo A. Parrilo, Swiss Federal Institute of Technology Zurich Control Synthesis by Sum of Squares Optimization – p.9/30
  18. 18. Example p(x, y) = 2x4 + 5y4 − x2 y2 + 2x3 y =    x2 y2 xy    T    q11 q12 q13 q21 q22 q23 q13 q23 q33       x2 y2 xy    = q11x4 + q22y4 + (q33 + 2q12)x2 y2 + 2q13x3 y + 2q23xy3 An SDP with equality constraints. ▽Control Synthesis by Sum of Squares Optimization – p.10/30
  19. 19. Example p(x, y) = 2x4 + 5y4 − x2 y2 + 2x3 y =    x2 y2 xy    T    q11 q12 q13 q21 q22 q23 q13 q23 q33       x2 y2 xy    = q11x4 + q22y4 + (q33 + 2q12)x2 y2 + 2q13x3 y + 2q23xy3 An SDP with equality constraints. Solving, we obtain: Q =    2 −3 1 −3 5 0 1 0 5    = LT L, L = 1 √ 2 2 −3 1 0 1 3 And therefore p(x, y) = 1 2(2x2 − 3y2 + xy)2 + 1 2(y2 + 3xy)2. "Certificates, convex Optimization, and their applications", Pablo A. Parrilo, Swiss Federal Institute of Technology Zurich Control Synthesis by Sum of Squares Optimization – p.10/30
  20. 20. Sum of squares programming A sum of squares program is a convex optimization program of the following form: Minimize J j=1 wjαj subject to fi,0 + J j=1 αjfi,j(x) is SOS, for i = 1, . . . , I where the αj’s are the scalar real decision variables, the wj’s are some given real numbers, and the fi,j are some given multivariate polynomials. "Nonlinear control synthesis by sum of squares optimization: a Lyapunov-based approach", Stephen Prajna et al, ASCC 2004 Control Synthesis by Sum of Squares Optimization – p.11/30
  21. 21. SOSTOOLS: Sum of squares toolbox ◮ SOSTOOLS handles the general SOS programming. ◮ MATLAB toolbox, freely available. ◮ Requires SeDuMi (a freely available SDP solver). ◮ Natural syntax, efficient implementation ◮ Developed by S. Prajna, A. Papachristodoulou and P. Parrilio ◮ Includes customized functions for several problems Get it from: http://www.aut.ee.ethz.ch/~parrilo/sostools http://www.cds.caltech.edu/sostools "Certificates, convex Optimization, and their applications", Pablo A. Parrilo, Swiss Federal Institute of Technology Zurich Control Synthesis by Sum of Squares Optimization – p.12/30
  22. 22. Global optimization Consider for example: min x,y F(x, y) with F(x, y) = 4x2 − 21 10x4 + 1 3x6 + xy − 4y2 + 4y4 "Certificates, convex Optimization, and their applications", Pablo A. Parrilo, Swiss Federal Institute of Technology Zurich Control Synthesis by Sum of Squares Optimization – p.13/30
  23. 23. Global optimization ◮ Not convex, many local minima. NP-Hard in general. ▽Control Synthesis by Sum of Squares Optimization – p.14/30
  24. 24. Global optimization ◮ Not convex, many local minima. NP-Hard in general. ◮ Find the largest γ s.t. F(x, y) − γ is SOS. ▽Control Synthesis by Sum of Squares Optimization – p.14/30
  25. 25. Global optimization ◮ Not convex, many local minima. NP-Hard in general. ◮ Find the largest γ s.t. F(x, y) − γ is SOS. ◮ A semidefinite program (convex!). ▽Control Synthesis by Sum of Squares Optimization – p.14/30
  26. 26. Global optimization ◮ Not convex, many local minima. NP-Hard in general. ◮ Find the largest γ s.t. F(x, y) − γ is SOS. ◮ A semidefinite program (convex!). ◮ If exact, can recover optimal solution. ▽Control Synthesis by Sum of Squares Optimization – p.14/30
  27. 27. Global optimization ◮ Not convex, many local minima. NP-Hard in general. ◮ Find the largest γ s.t. F(x, y) − γ is SOS. ◮ A semidefinite program (convex!). ◮ If exact, can recover optimal solution. ◮ Surprisingly effective. ▽Control Synthesis by Sum of Squares Optimization – p.14/30
  28. 28. Global optimization ◮ Not convex, many local minima. NP-Hard in general. ◮ Find the largest γ s.t. F(x, y) − γ is SOS. ◮ A semidefinite program (convex!). ◮ If exact, can recover optimal solution. ◮ Surprisingly effective. Solving, the maximum value is −1.0316. Exact value. Many more details in Parrilio & Strumfels, 2001 "Certificates, convex Optimization, and their applications", Pablo A. Parrilo, Swiss Federal Institute of Technology Zurich Control Synthesis by Sum of Squares Optimization – p.14/30
  29. 29. Lyapunov stability analysis ◮ To prove asymptotic stability of ˙x = f(x), V (x) > 0, x = 0, ˙V (x) = ∂V ∂x T f(x) < 0, x = 0 ▽Control Synthesis by Sum of Squares Optimization – p.15/30
  30. 30. Lyapunov stability analysis ◮ To prove asymptotic stability of ˙x = f(x), V (x) > 0, x = 0, ˙V (x) = ∂V ∂x T f(x) < 0, x = 0 ◮ For linear systems ˙x = Ax, quadratic Lyapunov functions V (x) = xT Px P > 0, AT P + PA < 0 ▽Control Synthesis by Sum of Squares Optimization – p.15/30
  31. 31. Lyapunov stability analysis ◮ To prove asymptotic stability of ˙x = f(x), V (x) > 0, x = 0, ˙V (x) = ∂V ∂x T f(x) < 0, x = 0 ◮ For linear systems ˙x = Ax, quadratic Lyapunov functions V (x) = xT Px P > 0, AT P + PA < 0 ◮ With an affine family of candidate Lyapunov functions V , ˙V is also affine. ▽Control Synthesis by Sum of Squares Optimization – p.15/30
  32. 32. Lyapunov stability analysis ◮ To prove asymptotic stability of ˙x = f(x), V (x) > 0, x = 0, ˙V (x) = ∂V ∂x T f(x) < 0, x = 0 ◮ For linear systems ˙x = Ax, quadratic Lyapunov functions V (x) = xT Px P > 0, AT P + PA < 0 ◮ With an affine family of candidate Lyapunov functions V , ˙V is also affine. ◮ Instead of checking nonnegativity, use an SOS condition "Certificates, convex Optimization, and their applications", Pablo A. Parrilo, Swiss Federal Institute of Technology Zurich Control Synthesis by Sum of Squares Optimization – p.15/30
  33. 33. Lyapunov stability - Example A jet engine model (derived from Moore-Greitzer), with controller: ˙x = −y + 3 2 x2 − 1 2 x3 ˙y = 3x − y Try a generic 4th order polynomial Lyapunov function. ▽Control Synthesis by Sum of Squares Optimization – p.16/30
  34. 34. Lyapunov stability - Example A jet engine model (derived from Moore-Greitzer), with controller: ˙x = −y + 3 2 x2 − 1 2 x3 ˙y = 3x − y Try a generic 4th order polynomial Lyapunov function. Find a V (x, y) that satisfies the conditions: ◮ V (x, y) is SOS. ◮ − ˙V (x, y) is SOS Can easily do this using SOS/SDP techniques... "Certificates, convex Optimization, and their applications", Pablo A. Parrilo, Swiss Federal Institute of Technology Zurich Control Synthesis by Sum of Squares Optimization – p.16/30
  35. 35. Lyapunov stability - Example After solving the SDPs, we obtain a Lyapunov function. "Certificates, convex Optimization, and their applications", Pablo A. Parrilo, Swiss Federal Institute of Technology Zurich Control Synthesis by Sum of Squares Optimization – p.17/30
  36. 36. Lyapunov stability - Example ◮ Consider the nonlinear system ˙x1 = −x3 1 − x1x2 3 ˙x2 = −x2 − x2 1x2 ˙x3 = −x3 − 3x3 x2 3 + 1 + 3x2 1x3 ▽Control Synthesis by Sum of Squares Optimization – p.18/30
  37. 37. Lyapunov stability - Example ◮ Consider the nonlinear system ˙x1 = −x3 1 − x1x2 3 ˙x2 = −x2 − x2 1x2 ˙x3 = −x3 − 3x3 x2 3 + 1 + 3x2 1x3 ◮ Looking for a quadratic Lyapunov function s.t. V − (x2 1 + x2 2 + x2 3) is SOS, (x2 3 + 1)(− ∂V ∂x1 ˙x1 − ∂V ∂x2 ˙x2 − ∂V ∂x3 ˙x3) is SOS, we have V (x) = 5.5489x2 1 + 4.1068x2 2 + 1.7945x2 3. "SOSTOOLS: control applications and new developments", Stephen Prajna et al, CACSD 2004 Control Synthesis by Sum of Squares Optimization – p.18/30
  38. 38. Parametric robustness analysis - Example ◮ Consider the following linear system d dt    x1 x2 x3    =    −p1 1 −1 2 − p2 2 −1 3 1 −p1p2       x1 x2 x3    where p1 ∈ [p1, p1] and p2 ∈ [p2, p2] are parameters. ▽Control Synthesis by Sum of Squares Optimization – p.19/30
  39. 39. Parametric robustness analysis - Example ◮ Consider the following linear system d dt    x1 x2 x3    =    −p1 1 −1 2 − p2 2 −1 3 1 −p1p2       x1 x2 x3    where p1 ∈ [p1, p1] and p2 ∈ [p2, p2] are parameters. ◮ Parameter set can be captured by a1(p) (p1 − p1)(p1 − p1) ≤ 0 a2(p) (p2 − p2)(p2 − p2) ≤ 0 "SOSTOOLS: control applications and new developments", Stephen Prajna et al, CACSD 2004 Control Synthesis by Sum of Squares Optimization – p.19/30
  40. 40. Parametric robustness analysis - Example Find V (x; p) and qi,j(x; p), such that ◮ V (x; p) − x 2 + 2 j=1 q1,j(x; p)ai(p) is SOS, ◮ − ˙V (x; p) − x 2 + 2 j=1 q2,j(x; p)ai(p) is SOS, qi,j(x; p) is SOS, for i, j = 1, 2. "SOSTOOLS: control applications and new developments", Stephen Prajna et al, CACSD 2004 Control Synthesis by Sum of Squares Optimization – p.20/30
  41. 41. Safety verification - Example ◮ Consider the following system ˙x1 = x2 ˙x2 = −x1 + 1 3 x3 1 − x2 ◮ Initial set X0 = {x : g0(x) = (x1 − 1.5)2 + x2 2 − 0.25 ≤ 0} ◮ Unsafe set Xu = {x : gu(x) = (x1 + 1)2 + (x2 + 1)2 − 0.16 ≤ 0} "SOSTOOLS: control applications and new developments", Stephen Prajna et al, CACSD 2004 Control Synthesis by Sum of Squares Optimization – p.21/30
  42. 42. Safety verification - Example Barrier certificate B(x) ◮ B(x) < 0, ∀x ∈ X0 ◮ B(x) > 0, ∀x ∈ Xu ◮ ∂B ∂x1 ˙x1 + ∂B ∂x2 ˙x2 ≤ 0 ▽Control Synthesis by Sum of Squares Optimization – p.22/30
  43. 43. Safety verification - Example Barrier certificate B(x) ◮ B(x) < 0, ∀x ∈ X0 ◮ B(x) > 0, ∀x ∈ Xu ◮ ∂B ∂x1 ˙x1 + ∂B ∂x2 ˙x2 ≤ 0 SOS program: Find B(x) and σi(x) ◮ −B(x) − 0.1 + σ1(x)g0(x) is SOS, ◮ B(x) − 0.1 + σ2(x)gu(x) is SOS, ◮ − ∂B ∂x1 ˙x1 − ∂B ∂x2 ˙x2 is SOS ◮ σ1(x) and σ2(x) are SOS "SOSTOOLS: control applications and new developments", Stephen Prajna et al, CACSD 2004 Control Synthesis by Sum of Squares Optimization – p.22/30
  44. 44. Safety verification - Example "SOSTOOLS: control applications and new developments", Stephen Prajna et al, CACSD 2004 Control Synthesis by Sum of Squares Optimization – p.23/30
  45. 45. Nonlinear control synthesis ◮ Consider the system ˙x = f(x) + g(x)u ▽Control Synthesis by Sum of Squares Optimization – p.24/30
  46. 46. Nonlinear control synthesis ◮ Consider the system ˙x = f(x) + g(x)u ◮ State dependent linear-like representation ˙x = A(x)Z(x) + B(x)u where Z(x) = 0 ⇔ x = 0 ▽Control Synthesis by Sum of Squares Optimization – p.24/30
  47. 47. Nonlinear control synthesis ◮ Consider the system ˙x = f(x) + g(x)u ◮ State dependent linear-like representation ˙x = A(x)Z(x) + B(x)u where Z(x) = 0 ⇔ x = 0 ◮ Consider the following Lyapunov function and control input V (x) = ZT (x)P−1 Z(x) u(x) = K(x)P−1 Z(x) "Nonlinear control synthesis by sum of squares optimization: a Lyapunov-based approach", Stephen Prajna et al, ASCC 2004 Control Synthesis by Sum of Squares Optimization – p.24/30
  48. 48. Nonlinear control synthesis For the system ˙x = A(x)Z(x) + B(x)u, suppose there exist a constant matrix P, a polynomial matrix K(x), a constant ǫ1 and a sum of squares ǫ2(x), such that ◮ vT (P − ǫ1I)v is SOS, ◮ −vT (PAT (x)MT (x) + M(x)A(x)P + KT (x)BT (x)MT (x) + M(x)B(x)K(x) + ǫ2(x)I) is SOS, where v ∈ RN and Mij(x) = ∂Zi ∂xj (x). Then a controller that stabilizes the system is given by: u(x) = K(x)P−1 Z(x) Furthermore, if ǫ2(x) > 0 for x = 0, then the zero equilibrium is globally asymptotically stable. "Nonlinear control synthesis by sum of squares optimization: a Lyapunov-based approach", Stephen Prajna et al, ASCC 2004 Control Synthesis by Sum of Squares Optimization – p.25/30
  49. 49. Nonlinear control synthesis - Example Consider a tunnel diode circuit: ˙x1 = 0.5(−h(x1) + x2) ˙x2 = 0.2(−x1 − 1.5x2 + u) where the diode characteristic: h(x1) = 17.76x1 − 103.79x2 1 + 229.62x3 1 − 226.31x4 1 + 83.72x5 1 "Nonlinear control synthesis by sum of squares optimization: a Lyapunov-based approach", Stephen Prajna et al, ASCC 2004 Control Synthesis by Sum of Squares Optimization – p.26/30
  50. 50. Nonlinear control synthesis - Example "Nonlinear control synthesis by sum of squares optimization: a Lyapunov-based approach", Stephen Prajna et al, ASCC 2004 Control Synthesis by Sum of Squares Optimization – p.27/30
  51. 51. How conservative is SOS? ◮ It is proven by Hilbert that "nonnegativity" and "sum of squares" are equivalent in the following cases. ⊲ Univariate polynomials, any (even) degree ⊲ Quadratic polynomials, in any number of variables ⊲ Quartic polynomials in two variables ▽Control Synthesis by Sum of Squares Optimization – p.28/30
  52. 52. How conservative is SOS? ◮ It is proven by Hilbert that "nonnegativity" and "sum of squares" are equivalent in the following cases. ⊲ Univariate polynomials, any (even) degree ⊲ Quadratic polynomials, in any number of variables ⊲ Quartic polynomials in two variables ◮ When the degree is larger than two it follows that ⊲ There are signitcantly more nonnegative polynomials than sums of squares. ⊲ There are signitcantly more sums of squares than sums of even powers of linear forms. [G. Blekherman, University of Michigan, submitted for publication] Control Synthesis by Sum of Squares Optimization – p.28/30
  53. 53. Conclusion ◮ Sum of squares, conservative but much more tractable than nonnegativity ▽Control Synthesis by Sum of Squares Optimization – p.29/30
  54. 54. Conclusion ◮ Sum of squares, conservative but much more tractable than nonnegativity ◮ Many applications in control theory ▽Control Synthesis by Sum of Squares Optimization – p.29/30
  55. 55. Conclusion ◮ Sum of squares, conservative but much more tractable than nonnegativity ◮ Many applications in control theory ◮ Try your problem! Control Synthesis by Sum of Squares Optimization – p.29/30
  56. 56. References ◮ Convex optimmization by Stephen Boyd and Lieven Vandenberghe, available online http://www.stanford.edu/~boyd/cvxbook ◮ SOSTOOLS, MATLAB toolbox, freely available http://www.cds.caltech.edu/sostools Control Synthesis by Sum of Squares Optimization – p.30/30

×