Calculus of Variations

Transcription

1 Lecture 5 Calculus of Variations Calculus of Variations Most books cover this material well, but Kirk Chapter 4 oes a particularly nice job. x(t) x* x*+ αδx (1) x*- αδx (1) αδx (1) αδx (1) t f t Figure by MIT OCW.

2 Spr Calculus of Variations Goal: Develop alternative approach to solve general optimization problems for continuous systems variational calculus Formal approach will provie new insights for constraine solutions, an a more irect path to the solution for other problems. Main issue Cost metric is a function of the functions x(t) an u(t). min J = h(x(t f )) + g(x(t), u(t), t)) subject to ẋ = f (x, u, t) x( ), = given m(x(t f ), t f ) = 0 Call J (x(t), u(t)) a functional. Nee to investigate how to fin the optimal values of a functional. For a function, we foun the graient, an set it to zero to fin the stationary points, an then investigate the higher orer erivatives to etermine if it is a maximum or minimum. Will investigate something similar for functionals.

3 Spr Maximum an minimum: function A function f (x) has a local minimum at x if f (x) f (x ) for all amissible x in x x ɛ Minimum can occur at (i) stationary point, (ii) at a bounary, or (iii) a point of iscontinuous erivative. If we only consier stationary points of the ifferentiable function f (x), then this conition is equivalent to requiring that the ifferential of f satisfy: f f = x = 0 x for all small x, which gives the same necessary conition as before f = 0 x Note that this efinition use norms to compare two vectors. Can o the same thing with functions: istance between two functions where = x 2 (t) x 1 (t) 1. x(t) 0 for all x(t), an x(t) = 0 only if x(t) = 0 for all t in the interval of efinition. 2. ax(t) = a x(t) for all real scalars a. 3. x 1 (t) + x 2 ( t) x 1 (t) + x 2 (t) Common functional norm: x(t) 2 = ( ) 1/2 x(t) T x(t)

4 Spr Maximum an minimum: functional A functional J(x(t)) has a local minimum at x (t) if J(x(t)) J(x (t)) for all amissible x(t) in x(t) x (t) ɛ Now efine something equivalent to the ifferential of a function calle a variation of a functional. An increment of a functional ΔJ(x(t), δx(t)) = J(x(t) + δx(t)) J(x(t)) A variation of the functional is a linear approximation of this increment: ΔJ(x(t), δx(t)) = δj(x(t), δx(t)) + H.O.T. i.e. δj(x(t), δx(t)) is linear in δx(t). f(t) Slope equals f' (t 1 ) f f t t t 1 Differential f versus increment f shown for a function, but the same ifference hols for a functional. Figure by MIT OCW.

5 Spr x(t) x* x*+ αδx (1) x*- αδx(1) αδx (1) αδx (1) t f t Figure by MIT OCW. Figure 2: Visualization of perturbations to function x(t) by δx(t) it is a potential change in the value of x over the entire time perio of interest. Typically require that if x(t) is in some class (i.e., continuous), that x(t) + δx(t) is also in that class. Funamental theorem of the calculus of variations Let x be a function of t in the class Ω, an J(x) be a ifferentiable functional of x. Assume that the functions in Ω are not constraine by any bounaries. If x is an extremal function, then the variation of J must vanish on x, i.e. for all amissible δx, δj(x(t), δx(t)) = 0 Proof is in Kirk, page 121, but it is relatively straightforwar. How o we compute the variation? If J(x(t)) = f(x(t)) where f has cts first an secon erivatives with respect to x, then { } f(x(t)) δj(x(t), δx) = (t) δx + f(x(t f ))δt f f(x( ))δ x tf = f x (x(t))δx + f(x(t f ))δt f f(x( ))δ

6 Spr 2006 Variation Examples: Scalar For more general problems, consier the cost evaluate on a scalar function x(t) J(x(t)) = tf g(x(t), ẋ(t), t) with, t f an the curve enpoints fixe. Then δj(x(t), δx) = [ g x (x(t), ẋ(t), t)δx + g ẋ (x(t), ẋ(t), t)δẋ] Note that δẋ = δx so δx an δẋ are not inepenent. Integrate by parts: uv = uv vu with u = g ẋ an v = δ ẋ to get: δj(x(t), δx) t = f gx (x(t), ẋ(t), t)δx + [g ẋ (x(t), ẋ(t), t)δx] t0 gẋ(x(t), ẋ(t), t)δx t 0 t f = g x (x(t), ẋ(t), t) g ẋ (x(t), ẋ(t), t) δx(t) t + f [gẋ (x(t), ẋ(t), t)δx] t0 But since x( ) an x(t f ) are given, then δx( ) = δx(t f ) = 0, yieling δj(x(t), δx) = g x (x(t), ẋ(t), t) g ẋ (x(t), ẋ(t), t) δx(t)

7 Spr Recall that we nee δj = 0 for all amissible δx(t), which are arbitrary within (, t f ), then the (first orer) necessary conition for a maximum or minimum is that ( ) g(x(t), ẋ(t), t) g(x(t), ẋ(t), t) = 0 x ẋ which is calle the Euler equation. Example: Fin the curve that gives the shortest istance between 2 points in a plane (x 0, y 0 ) an (x f, y f ). Cost function sum of ifferential arc lengths: x f x f J = s = (x) 2 + (y) 2 x 0 x 0 x f ( ) 2 y = 1 + x x 0 x Take y as the epenent variable, an x as the inepenent one y x ẏ New form of the cost: x f x f J = 1 + ẏ 2 x g(ẏ)x x 0 x 0 Take partials: g/ y = 0, an ( ) ( ) ( ) g g ẏ ẏ ÿ = = ÿ = = 0 x ẏ ẏ ẏ x ẏ (1 + y 2 ) 1/2 (1 + y 2 ) 3/2 which implies that ÿ = 0 Most general curve with ÿ = 0 is a line y = c 1 x + c 2

8 Spr 2006 Vector Functions Can generalize the problem by incluing several (N) functions x i (t) an possibly free enpoints J (x(t)) = g(x(t), ẋ(t), t) with, t f, x( ) fixe. Then (rop the arguments for brevity) δj (x(t), δx) = [ g x δx(t) + g ẋ δẋ(t)] Integrate by parts to get: δj (x(t), δx) = g x g ẋ δx(t) + g ẋ (x(t f ), ẋ(t f ), t f )δx(t f ) The requirement then is that for t (, t f ), x(t) must satisfy g g = 0 x ẋ where x( ) = x 0 which are the given N bounary conitions, an the remaining N more BC follow from: x(t f ) = x f if x f is given as fixe, If x(t f ) are free, then g(x(t), ẋ(t), t) = 0 ẋ(t f ) Note that we coul also have a mixture, where parts of x(t f ) are given as fixe, an other parts are free just use the rules above on each component of x i (t f )

9 Spr 2006 Free Terminal Time Now consier a slight variation: the goal is to minimize J (x(t)) = g(x(t), ẋ(t), t) with, x( ) fixe, t f free, an various constraints on x(t f ) Compute the variation of the functional where we consier 2 caniate solutions: x(t), which we consier to be a perturbation of the optimal x (t) (that we nee to fin) δj (x (t), δx) = [ g x δx(t) + g ẋ δẋ(t)] + g(x (t f ), ẋ (t f ), t f )δt f Integrate by parts to get: δj (x t f (t), δx) = g x g ẋ δx(t) + g ẋ (x (t f ), ẋ (t f ), t f )δx(t f ) + g(x (t f ), ẋ (t f ), t f )δt f Looks stanar so, but we have to be careful how we hanle the terminal conitions

10 Spr x (t) δx(t f ) x f x x* δx f x 0 t f + δt f Comparison of possible changes to function at en time when t f is free. t f t By efinition, δx(t f ) is the ifference between two amissible functions at time t f (in this case the optimal solution x an another caniate x. But in this case, we must also account for possible changes to δt f. Define δx f as being the ifference between the ens of the two possible functions total possible change in the final state: δx f δx(t f ) + ẋ (t f )δt f so δx(t f ) = δx f in general. Figure by MIT OCW. Substitute to get δj (x t f (t), δx) = g x g ẋ δx(t) + g ẋ (x (t f ), ẋ (t f ), t f )δx f + [g(x (t f ), ẋ (t f ), t f ) g ẋ (x (t f ), ẋ (t f ), t f )ẋ (t f )] δt f

11 Spr Inepenent of the terminal constraint, the conitions on the solution x (t) to be an extremal for this case are that it satisfy the Euler equations gẋ (x (t), g x (x (t), ẋ (t), t) x (t), t) = 0 Now consier the aitional constraints on the iniviual elements of x (t f ) an t f to fin the other bounary conitions Type of terminal constraints etermines how we treat δx f an δt f Unrelate Relate by a simple function x(t f ) = Θ(t f ) Specifie by a more complex constraint m(x(t f ), t f ) = 0 If t f an x(t f ) are free but unrelate, then δx f an δt f are inepenent an arbitrary their coefficients must both be zero. g x (x (t), ẋ (t), t) g ẋ(x (t), ẋ (t), t) = 0 g(x (t f ), ẋ (t f ), t f ) g ẋ(x (t f ), ẋ (t f ), t f ) ẋ (t f ) = 0 g ẋ(x (t f ), ẋ (t f ), t f ) = 0 Which makes it clear that this is a two point bounary value problem, as we have conitions at both an t f

12 Spr If t f an x(t f ) are free but relate as x(t f ) = Θ(t f ), then Θ δx f = (t f )δt f Substitute an collect terms gives [ Θ δj = g g ẋ (x (t f ), x g ẋ δx + x (t f ), t f ) (t f ) ] + g(x (t f ), ẋ (t f ), t f ) g ẋ (x (t f ), ẋ (t f ), t f )ẋ (t f ) δt f Set coefficient of δt f to zero (it is arbitrary) full conitions g ẋ(x (t f ), ẋ (t f ), t f ) g x (x (t), ẋ (t), t) g ẋ(x (t), ẋ (t), t) = 0 Θ (t f ) ẋ (t f ) + g(x (t f ), ẋ (t f ), t f ) = 0 Last conition calle the transversality conition

13 DETERMINATION OF BOUNDARY-VALUE RELATIONSHIPS Problem Description Substitution Bounary Conitions Remarks 1 x(t f ), t f both specifie (Problem 1) δx f = δx(t f ) = 0 δt f = 0 x*( ) = x 0 x*(t f ) = x f 2n equations to etermine 2n constants of integration 2 x(t f ) free; t f specifie (Problem 2) δx f = δx(t f ) δt f = 0 x*( ) = x 0 g x (x*(t f ), x*(t.. f ),t f ) = 0 2n equations to etermine 2n constants of integration 3 t f free; x(t f ) specifie (Problem 3) δx f = 0 x*( ) = x 0 x*(t f ) = x f. g(x*(t f ), x*(t f ),t f ) Ṭ g x (x*(t.. f), x*(t f ),t f ) x*(t f ) = 0 (2n + 1) equations to etermine 2n constants of integration an t f 4 t f, x(t f ) free an inepenent (Problem 4) x*( ) = x 0 g x (x*(t.. f), x*(t f ),t f ) = 0. g(x*(t f ), x*(t f ),t f ) = 0 (2n + 1) equations to etermine 2n constants of integration an t f 5 t f, x(t f ) free but relate by x(t f ) = θ(t f ) (Problem 4) θ δx f = δt (t f) f x*( ) = x 0 x*(t f ) = θ(t f ). g(x*(t f ), x*(t f ),t f ) T g x (x*(t.. f), x*(t f ),t f ) θ (t f) - x*(t f ) = 0 (2n + 1) equations to etermine 2n constants of integration an t f θ θ 1 θ enotes the n x 1 column vector 2... θ 7 T Figure by MIT OCW. Aapte from: figure 4 in Kirk, Donal E. Optimal Control Theory: An Introuction. New York, NY: Dover, ISBN: Figure 4: Possible terminal constraints To hanle the thir type of terminal conition, we must aress the solution of constraine problems.

14 Spr 2006 Example: Fin the shortest curve from the origin to a specifie line. Goal: minimize the cost functional (See page 5 6) J = 1 + ẋ 2 (t) Since g(x, ẋ, t) is only a function of ẋ, the Euler equation reuces to ẋ (t) = 0 [1 + ẋ (t) 2 ] 1/2 given that = 0, x(0) = 0, an t f an x(t f ) are free, but x(t f ) must line on the line θ(t) = 5t + 15 which after ifferentiating an simplifying, gives ẍ (t) = 0 from which we know that the answer is a straight line but since x(0) = 0, then c 0 = 0 x (t) = c 1 t + c 0 The transversality conition gives ẋ (t f ) 2 [ 5 ẋ (t f )] + [1 + ẋ (t f ) ] 1/2 = 0 [1 + ẋ (t f ) 2 ] 1/2 that simplifies to [ẋ (t f )] [ 5 ẋ (t f )] + [1 + ẋ (t f ) 2 ] = 5ẋ (t f ) + 1 = 0 so that ẋ (t f ) = c 1 = 1/5 Not a surprise, as this gives the slope of a line orthogonal to the constraint line. To fin final time: x(t f ) = 5t f + 15 = t f /5 which gives t f 2.88

15 Spr 2006 Example: Ha the terminal constraint been a bit more challenging, such as 1 Θ Θ(t) = ([t 5] 2 1) = t 5 2 Then the transversality conition gives ẋ (t f ) [t f 5 ẋ (t f )] + [1 + ẋ (t f ) 2 ] 1/2 = 0 [1 + ẋ (t f ) 2 ] 1/2 [ẋ (t f )] [t f 5 ẋ (t f )] + [1 + ẋ (t f ) 2 ] = 0 c 1 [t f 5] + 1 = 0 Now look at x (t) an Θ(t) at t f t f 1 x (t = ([t f 5] 2 f ) = 1) (tf 5) 2 which gives t f = 3, c 1 = 1/2 an x (t f ) = t/ x t Figure 5: Quaratic terminal constraint.

16 Spr 2006 Constraine Solutions Now consier variations of the basic problem that inclue constraints. For example, if the goal is to fin the extremal function x that minimizes tf J(x(t), t) = g(x(t), ẋ(t), t) subject to the constraint that a given set of n ifferential equations be satisfie f (x(t), ẋ(t), t) = 0 where we assume that x R n+m (take t f an x(t f ) to be fixe) As with the basic optimization problems in Lecture 2, we procee by augmenting the cost with the constraints using Lagrange multipliers Since the constraints must be satisfie at all time, these multipliers are also assume to be functions of time. { } J a (x(t), t) = g(x, ẋ, t) + p(t) T f (x, ẋ, t) Which oes not change the cost if the constraints are satisfie. Take variation of augmente functional consiering perturbations to both x(t) an p(t) δj(x(t), δx(t), p(t), δp(t)) { } = g x + p(t) T f x δx(t) + g ẋ + p(t) T f ẋ δẋ(t) + f T δp(t) As before, integrate by parts to get: δj(x(t), δx(t), p(t), δp(t)) ({ } ) = g x + p(t) T f x g ẋ + p(t) T f ẋ δx(t) + f T δp(t)

17 Spr To simplify things a bit, efine g a (x(t), ẋ(t), t) g(x(t), ẋ(t), t) + p(t) T f (x(t), ẋ(t), t) On the extremal, the variation must be zero, but since δx(t) an δp(t) can be arbitrary, can only occur if g a (x(t), ẋ(t), t) x g a (x(t), ẋ(t), t) ẋ = 0 f (x(t), ẋ(t), t) = 0 which are obviously a generalize version of the Euler equations that we saw before.

18 Spr General Terminal Conitions Now re consier the case on 5 10 with very general terminal constraints, with t f free, an x(t f ) given by the conition: m(x(t f ), t f ) = 0 Constraine optimization, so as before, augment the cost functional J (x(t), t) = h(x(t f ), t f ) + g(x(t), ẋ(t), t) with the constraint using Lagrange multipliers: J a (x(t), ν, t) = h(x(t f ), t f )+ν T m(x(t f ), t f )+ g(x(t), ẋ(t), t) Consiering changes to x(t), t f, x(t f ) an ν, the variation for J a is δj a = h x (t f )δx f + h tf δt f + m T (t f )δν + ν T m x (t f )δx f +ν T m tf (t f )δt f + [g x δx + g ẋ δẋ] + g(t f )δt f = h x (t f ) + ν T m x (t f ) δx f + h tf + ν T m tf (t f ) + g(t f ) δt f T +m (t f )δν + g x g ẋ δx + g ẋ (t f )δx(t f ) Now use that δx f = δx(t f ) + ẋ(t f )δt f as before to get δj a = h [ x (t f ) + ν T m x (t f ) + g ẋ (t f ) δx f ] + h tf + ν T m tf (t f ) + g(t f ) g ẋ (t f )ẋ(t f ) δt f + m T (t f )δν + g x g ẋ δx

19 Spr Looks like a bit of a mess, but we can clean it up a bit using to get w(x(t f ), ν, t f ) = h(x(t f ), t f ) + ν T m(x(t f ), t f ) δj a = [w [ x (t f ) + g ẋ (t f )] δx f ] + w tf + g(t f ) g ẋ (t f )ẋ(t f ) δt f + m T (t f )δν + g x g ẋ δx Given the extra egrees of freeom in the multipliers, we can treat all of the variations as inepenent all coefficients must be zero to achieve δj a = 0 So the necessary conitions are g x g ẋ = 0 (im n) (2) l x (t f ) + g ẋ (t f ) = 0 (im n) (3) l tf + g(t f ) g ẋ (t f )ẋ(t f ) = 0 (im 1) (4) with x( ) = x 0 (there are n of these) an m(x(t f ), t f ) = 0 (m of these), (3) an (4) From the solution of Euler s equation, there are 2n constants of integration to fin for x(t), we must also fin ν (m of those), an 2n + m + 1 variables to fin t f Some special cases: If t f is fixe, h = h(x(t f )), m m(x(t f )) an we lose the last conition (which is the transversality conition (4)) others remain unchange If t f is fixe, x(t f ) free, then there is no m, no ν an w reuces to h. Kirk consiers several other type of constraints.