the maximum principle: continuous time main purpose: to introduce the maximum principle as a...

The Maximum Principle: Continuous TimeMain purpose: to introduce the maximum principle as

a necessary condition that must be satisfied by any optimal control.

--Necessary conditions for optimization of dynamic systems

--General derivation by Pontryagin in 1956-60

--A simple (but not completely rigorous) proof using dynamic programming

-- Examples

-- Statement of sufficiency conditions

-- Computational method

2.1 Statement of the problemOptimal control theory deals with the problem of

optimization of dynamic systems

2.1.1 The Mathematical Model

State equation

where state variables, x(t) En ,control variables,

u(t) Em , and f : En x Em x E1 En .

f is assumed to be continuously differentiable. The

path x(t), t [0,T] is called a state trajectory and u(t),

t [0,T] is called a control trajectory.

2.1.2 Constraints

Admissible control u(t), t [0,T] is piecewise continuous and satisfies, in addition,

2.1.3 The Objective FunctionObjective function is defined as follows

Where F: En x Em x E1 E1 and S: En x E1 E1 . F andS are assumed to be continuously differentiable.

2.1.4 The Optimal Control ProblemThe problem is to find an admissible control u* ,

which maximizes the objective function (2.3) subject

to (2.1) and (2.2). We restate the optimal control

problem as:

Control u* is called optimal control, x* is called the optimal trajectory under u*, J(u*) or J* will denote optimal value of J.

Case 1. The optimal control problem (2.4) is said to be

in Bolza form.

Case 2. When S0, it is said to be in Lagrange form.

Case 3. When F 0, it is said to be in Mayer form, If

F 0 and S is linear, it is in linear Mayer form, i.e.,

where c=(c1,c2,…,cn) En.

The Bolza form can be reduced to the linear Mayer

form,we define a new state vector y=(y1,y2,…,yn+1),

having n+1 components defined as follows:yi=xi for

i=1,2…,n and

so that the objective function is J=cy(T)=yn+1(T). If

we now integrate (2.6) from 0 to T, we have

which is the same as the objective function J in (2.4)

Statement of Bellman’s Optimality Principle

“An optimal policy has the property that whatever the initial state and initial decision are, the remaining decisions must constitute an optimal policy with regard to the state resulting from the first decision.”

PRINCIPLE OF OPTIMALITY

a cAssertion: If abe is the optimal path from a to e, then be is the optimal path from b to e.Proof: Suppose it is not. Then there is another path (note that existence is assumed here) bce which is optimal from b to e, i.e.

Jbce > Jbe but then

Jabe = Jab+Jbe< Jab+ Jbce= Jabce

This contradicts the hypothesis that abe is the optimal path from a to e.

JabJbe

eJbceb

A Dynamic Programming ExampleStagecoach Problem

Costs:

Solution:

Let 1-u1-u2-u3-10 be the optimal path.

Let fn(s,un) be the minimal cost path given that current

state is s and the decision taken is un .

fn*(s) = min f (s,un) = fn(s,un*)

un

This is the Recursion Equation of D.P. It can be

solved by a backward procedure – which starts at the

terminal stage and stops at the initial statge.

Note: 1-2-6-9-10 with cost=13 is a greedy path that

minimizes cost at each stage. This may not be minimal cost

solution, however, E.g. 1-4-6 is cheaper overall than 1-2-6.

2.2 Dynamic Programming and the Maximum Principle2.2.1 The Hamilton – Jacobi- Bellman Equation

where for s t ,

Principle of OptimalityAn optimal policy has the property that, whatever the initial state and initial decision are, the remaining decision must constitute an optimal policy with regardto the outcome resulting from the first decision.

Figure 2.1 An Optimal Path in the State-Time Space

The change in the objective function consists of two parts:1. The incremental changes in J from t to t+t, which is

given by the integral of F(x,u,t) from t to t+t,

2. The value function V(x+x, t+t) at time t+t.

In equation form we have

Since F is continuous, the integral in (2.9) is

approximately F(x,u,t) t so that we rewrite (2.9) as

Assume that V is a continuously differentiable. Use Taylor series expansion of V with respect to t and obtain

Substituting for x from (2.1)

canceling V(x,t) on both sides and then dividing by t

we get

Let t 0

with the boundary condition

Vx(x,t) can be interpreted as the marginal contribution

of the state variable x to the maximized objective function. Denote it by (t) En called the adjoint (row)

vector i.e.,

We introduce the so-called Hamiltonian

or

The (2.14) can rewritten as the following

(2.19) will be called the Hamilton-Jacobi-Bellman

equation or, HJB equation.

From (2.19) we can get Hamiltonian maximizing condition of the maximum principle

canceling the term Vt on both sides, we obtain

for all u (t).

Remark: H decouples the problem over time by

means of (t), which is analogous to dual variables or

shadow prices in Linear Programming.

2.2.2 Derivation of the Adjoint Equation

Let

where for a small positive .Fix t and use H-J-B equation (2.19)

LHS=0 from (2.19) since u* maximizes [H+Vt]. RHS

will be zero if u*(t) also maximizes H+Vt with x(t) as

the state. In general x(t) x*(t), thus RHS 0, But then

RHS|x(t)=x*(t) =0 RHS is maximized at x(t)=x*(t).

Since x(t) is unconstrained, we have

RHS/ x |x(t)=x*(t) = 0

or,

By definition of H

Note: (2.25) assumes V to be twice continuously

differentiable.

By definition (2.16) of (t)

Using (2.25) and (2.26) we have

Using (2.16), we have

From (2.18), we have

Terminal boundary condition or transversality condition:

(2.28) and (2.29) can determine the adjoint variables.From (2.28), we can rewrite the state equation as

From (2.28), (2.29), (2.30) and (2.1), we get

(2.31) is called a canonical system of equation or canonical adjoints.

Free and Fixed End Point Problems

S 0 (T) = 0

no salvage value

x(T) fixed (T) = some constant

(given)

2.2.3 The Maximum Principle

The necessary conditions for u* to be an optimal

control are:

2.2.4 Economic Interpretation of the Maximum Principles

where F is considered to be the instantaneous profit rate measured in dollars per unit of time, and S[x,T] is the salvage value, in dollars.Multiplying (2.18) formally by dt and from (2.1), we have

F(x,u,t)dt: direct contribution to J in $ from t to t+dt. dx: indirect contribution to J in dollars.Hdt: total contribution to J from time t to t+dt whenx(t)=x and u(t) =u in the interval [t,t+dt].

By (2.28) and (2.29) we have

Rewriting the first equation as

-d: marginal cost of holding capital from t to t+dt

Hxdt: marginal revenue of investing the capital

Fxdt: direct marginal contribution

fxdt: indirect marginal contribution

Thus, adjoint equation implies MC = MR

Example 2.1 Consider the problem

subject to the state equation

and the control constraint

Note that T=1, F=-x, S=0, and f =u. Because F=-x, we

can interpret the problem as one of minimizing the

(signed) area under the curve x(t) for 0t 1.

Solution.First we form the Hamiltonian

and note that, because the Hamiltonian is linear in u,

the form of the optimal control, i.e., the one that would

maximize the Hamiltonian, is

or referring to the notation in Section 1.4,

To find , we write the adjoint equation

Because this equation does not involve x and u, we

can easily solve it as

It follows that (t) = t-1 0 for all t[0,1], and since we

can set u*(1)=-1, which defines u at the single point

t=1, we have the optimal control

Substituting this into the state equation(2.34) we have

whose solution is

The graphs of the optimal state and adjoint trajectories

appear in Figure 2.2. Note that the optimal value of the

objective function is J*= -1/2.

Figure 2.2 Optimal State and Adjoint Trajectories for Example 2.1

Example 2.2 Let us solve the same problem as in example 2.1 over the interval [0,2] so that the objective is to

The dynamics and constraints are (2.33) and (2.34),

respectively, as before. Here we want to minimize the

signed area between the horizontal axis and the

trajectory of x(t) for 0t2.

Solution. As before the Hamiltonian is defined by (2.36) and the optimal control is as in (2.38). The adjoint equation

is the same as (2.39) except that now T=2 instead of

T=1. The solution of (2.44) is easily found to be

Hence the state equation (2.41) and its solution (2.42)

are exactly the same. The graphs of the optimal state

and adjoint trajectories appear in Figure 2.3. Note

that the optimal value of the objective function here is

J*=0.

Figure 2.3 Optimal State and Adjoint Trajectories for Example 2.2

Example 2.3 The next example is:

subject to the same constraints as in Example 2.1, namely,

Here F= - (1/2)x2 so that the interpretation of the objective function (2.46) is that we are trying to find the trajectory x(t) in order that the area under the curve (1/2)x2 is minimized.

Solution. The Hamiltonian is

which is linear in u so that the optimal policy is

The adjoint equation is

Here the adjoint equation involves x so that we cannot solve it directly. Because the state equation (2.47) involves u, which depends on , we also cannot integrate it independently without knowing .

The way out of this dilemma is to use some intuition.

Since we want to minimize the area under (1/2)x2 and

since x(0)=1, it is clear that we want x to decrease as

quickly as possible. Let us therefore temporaily

assume that is nonpositive in the interval [0,1] so

that from (2.49) we have u=-1 throughout the interval. (In Exercise 2.5, you will be asked to show that this

assumption is correct.) With this assumption, we can

solve (2.47) as

Substituting this into (2.50) gives

Integrating both sides of this equation from t to1 gives

or

which, using (1)=0, yields

The reader may now verify that (t) is nonpositive in

the interval [0,1], verifying our original assumption.

Hence (2.51) and (2.52) satisfy the necessary

conditions. In Exercise 2.6, you will be asked to show

that they satisfy sufficient conditions derived in Section

2.4 as well, so that they are indeed optimal. Figure 2.4

shows the graphs of the optimal trajectories. It would be clearly optimal if we could keep x*(t)=0, t

≥1. This is possible by setting

Figure 2.4 Optimal Trajectories for Example 2.3 and Example 2.4

Example 2.4 Let us rework Example 2.3 with T=2, i.e,

Subject to the constraints:

It would be clearly optimal if we could keep x*(t)=0,

t ≥1. This is possible by setting: 1 t ≤1

u*(t)=

-1 t ≥1.

Note u*(t)=0, t ≥1 is singular control.

Example 2.5 The problem is:

Solution:

Figure 2.5 Optimal control for Example 2.5

2.4 Sufficient Conditions

Since either or or both.

Theorem 2.1 (Sufficiency Conditions). Let u*(t), and the corresponding x*(t) and (t) satisfy the maximum principle necessary condition (2.32) for all t[0,T]. Then, u* is an optimal control if H0(x,(t),t) is concavein x for each t and S(x,T) is concave in x.

Example 2.6: Examples 2.1 and 2.2 satisfy the sufficient conditions.Fixed-end-point problem: Transversality condition is:

2.5 Solving a TPBVP by Using Spreadsheet Software

Example 2.7 Consider the problem:

Let: t=0.01, initial value (0)=-0.2, x(0)=5.