Chapter One
Introduction
1.1 OPTIMAL CONTROL PROBLEM
We begin by describing, very informally and in general terms, the class of optimal control problems that we want to eventually be able to solve. The goal of this brief motivational discussion is to ļ¬x the basic concepts and terminology without worrying about technical details.
The ļ¬rst basic ingredient of an optimal control problem is a control system. It generates possible behaviors. In this book, control systems will be described by ordinary differential equations (ODEs) of the form
(1.1)
where
x is the
state taking values in
Rn, u is the
control input taking values in some
control set ,
t is
time,
t0 is the
initial time, and
x0 is the
initial state. Both
x and
u are functions of
t, but we will often suppress their time arguments.
The second basic ingredient is the cost functional. It associates a cost with each possible behavior. For a given initial data (t0,x0), the behaviors are parameterized by control functions u. Thus, the cost functional assigns a cost value to each admissible control. In this book, cost functionals will be denoted by J and will be of the form
(1.2)
where L and K are given functions (running cost and terminal cost, respectively), tf is the ļ¬nal (or terminal) time which is either free or ļ¬xed, and xf := x(tf ) is the ļ¬nal (or terminal) state which is either free or ļ¬xed or belongs to some given target set. Note again that u itself is a function of time; this is why we say that J is a functional (a real-valued function on a space of functions).
The optimal control problem can then be posed as follows: Find a control u that minimizes J(u) over all admissible controls (or at least over nearby controls). Later we will need to come back to this problem formulation and ļ¬ll in some technical details. In particular, we will need to specify what regularity properties should be imposed on the function f and on the admissible controls u to ensure that state trajectories of the control system are well deļ¬ned. Several versions of the above problem (depending, for example, on the role of the ļ¬nal time and the ļ¬nal state) will be stated more precisely when we are ready to study them. The reader who wishes to preview this material can ļ¬nd it in Section 3.3.
It can be argued that optimality is a universal principle of life, in the sense that manyāif not mostāprocesses in nature are governed by solutions to some optimization problems (although we may never know exactly what is being optimized). We will soon see that fundamental laws of mechanics can be cast in an optimization context. From an engineering point of view, optimality provides a very useful design principle, and the cost to be minimized (or the proļ¬t to be maximized) is often naturally contained in the problem itself. Some examples of optimal control problems arising in applications include the following:
ā¢ Send a rocket to the moon with minimal fuel consumption.
ā¢ Produce a given amount of chemical in minimal time and/or with minimal amount of catalyst used (or maximize the amount produced in given time).
ā¢ Bring sales of a new product to a desired level while minimizing the amount of money spent on the advertising campaign.
ā¢ Maximize throughput or accuracy of information transmission over a communication channel with a given bandwidth/capacity.
The reader will easily think of other examples. Several speciļ¬c optimal control problems will be examined in detail later in the book. We brieļ¬y discuss one simple example here to better illustrate the general problem formulation.
Example 1.1 Consider a simple model of a car moving on a horizontal line. Let be the carās position and let u be the acceleration which acts as the control input. We put a bound on the maximal allowable acceleration by letting the control set U be the bounded int...