Abstract and Applied Analysis

Volume 2014 (2014), Article ID 946026, 8 pages

http://dx.doi.org/10.1155/2014/946026

## The Constants in A Posteriori Error Indicator for State-Constrained Optimal Control Problems with Spectral Methods

Department of Mathematics, Linyi University, Shandong, China

Received 15 January 2014; Revised 7 April 2014; Accepted 26 April 2014; Published 19 May 2014

Academic Editor: Elena Berdysheva

Copyright © 2014 Jianwei Zhou. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

We employ Legendre-Galerkin spectral methods to solve state-constrained optimal control problems. The constraint on the state variable is an integration form. We choose one-dimensional case to illustrate the techniques. Meanwhile, we investigate the explicit formulae of constants within a posteriori error indicator.

#### 1. Introduction

Spectral methods provide higher accurate approximations with a relatively small number of unknowns and play increasingly important roles in design optimization, engineering design, and other scientific and engineering computations. Gottlieb and Orszag [1] summarized the theories and applications of spectral methods. There have been extensive researches on finite element methods for optimal control problems, most of which focus on control-constrained problems; see [2–5]. The authors studied the optimal control problems with the control constraint with spectral methods in [6]. In applications of engineering, one cares more about how to control the average value or -norms of the state variable. The authors [7] discussed state-constrained optimal control problems with finite element methods. However, there are few work on the state-constrained optimal control problems with spectral methods.

In order to get a numerical solution with acceptable accuracy, spectral methods only increase the degree of basis when the error indicator is larger than the a posteriori error indicator, while the finite element methods refine meshes (see [8, 9]). There have been lots of papers on the a posteriori error estimates for h-version finite element methods but not for spectral methods. Guo [10] got a reliable and efficient error indicator for -version finite element method in one dimension with a certain weight. The authors [11] deduced a simple error indicator for spectral Galerkin methods. In [12], the authors investigated Legendre-Galerkin spectral method for optimal control problems with integral constraint on state. It is difficult to obtain optimal a posteriori error indicators. Thus, if one gets the constants within upper bound a posteriori error estimates, it is easy to ensure the degree of polynomials to get an acceptable accuracy.

In this paper, we employ Legendre-Galerkin spectral methods to solve optimal control problems with state-constrained case and calculate constants in upper bound of the a posteriori error indicator, which can be used to decide the least unknowns for acceptable accuracy. With the help of auxiliary systems, we investigate explicit formulae of the constants in the a posteriori error indicator.

The outline of this paper is as follows. In Section 2, the model problem and its Legendre-Galerkin spectral approximations are listed. In Section 3, the constants within the a posteriori error indicator are investigated in detail and the explicit formulae are obtained. The conclusions are given in Section 4.

#### 2. A Model Problem and Its Legendre-Galerkin Spectral Approximations

Throughout this paper we adopt the standard notations of Sobolev spaces [13]. Let be a Sobolev space on , and , and the corresponding norms are denoted by , and , respectively. This work focuses on the Legendre polynomials, which are orthogonal polynomials on [−1,1].

We concern the following distributed convex optimal control problems with integral constraint on state: where is the control variable, is the state, and is the observation.

In order to assure the existence and regularity of the solution, we assume that is a given positive constant and is an infinitely smooth function. It is well known that the problem (OCP) has a unique solution (see [3]).

We give some basic notations which will be used in the sequel. Let Hence, the state equation reduces to Then can be rewritten as finding such that We recall the following optimal conditions of (for details please refer to [7]).

Lemma 1. *The pair is the optimal solution of if and only if there exists a unique pair such that
*

*Let and let . One prefers to choose appropriate bases of such that the resulting linear system is as simple as possible. We denote by the Legendre polynomial and employ the following basis functions (see [14]):
where
For , we denote and . By simple calculations, these coefficients satisfy
Then Legendre-Galerkin spectral approximations of can be read as finding such that
The Legendre-Galerkin spectral approximations of (5) can be read as follows.*

*Theorem 2. The pair is the optimal solution of if and only if there exists a unique pair such that
*

*3. Constants within the A Posteriori Error Estimates*

*3. Constants within the A Posteriori Error Estimates*

*In this section, we calculate all constants within the a posteriori error estimates. Here, we analyze the constant in the Poincaré inequality.*

*For all , , we recall the Poincaré inequality with -norm as (see [15])
*

*Now, we are at the point to investigate all constants in detail. We introduce an auxiliary state , which satisfies
Subtracting (12) from (3), we get
Let . It is clear that
Then
which means
Hence
Then
We denote by the constant in (18); that is,
*

*Similarly, we introduce an auxiliary state , which satisfies
Subtracting (20) from the continuous systems (5), we get
*

*We select which satisfies , where denotes the integral average on of the function and . Obviously, . In fact, . Then there hold
where we used the generalized Schwarz inequality, continuous systems (5), and auxiliary equation (20). Let
Then
It is clear that (22) reduces to
Then
where we used .*

*Hence
which means that
Denote by the constant in (28). With simple calculation, we have
*

*We select which satisfies and . For instance, , which satisfies
Meanwhile,
Hence
where .*

*Thus
With the constant , we infer that
Then
We denote by the constant in (35); that is,
*

*We calculate the error of in -norm as follows:
Provided that , we get
Considering the item with the constraint , we get
In fact, for , the derivation of the following function
is
Then we have and
Now, we are at the point to investigate
If , we get
If , , we obtain
Then
If , , we infer that
Then there hold
Combining the above discussions, we deduce that
Obviously,
We denote by the constant in (50); that is,
*

*For any , we define a projection operator , which satisfies
*

*Lemma 3. For all , one has
where .*

*Proof. *Firstly, assuming that is integer, we define a differential operator as
From the fact that
it is easy to get
By iterations, we obtain
Secondly, for all , we note that and
Hence
Finally, there hold
which means that
Let . It is clear that
This completes the proof.

*Now, we are at the point to calculate the constant for . Similarly, let and let . Then
which means that
Likewise, let and let . Then
which is equivalent to
Hence
where
Combining the above analyses, we get that
which means that
where
*

*4. Conclusions*

*4. Conclusions*

*This paper discusses the explicit formulae of constants within upper bound of the a posteriori error estimate for optimal control problems with Legendre-Galerkin spectral methods in one dimension. Thus, with those formulae, it is easy to choose a suitable degree of polynomials to obtain an acceptable accuracy. In the future, we will study the corresponding constants in lower bound of the a posteriori error indicator. Meanwhile, the corresponding constants in a two-dimensional domain will be investigated.*

*Conflict of Interests*

*Conflict of Interests*

*The author declares that there is no conflict of interests regarding the publication of this paper.*

*Acknowledgments*

*Acknowledgments*

*This work is partially supported by National Natural Science Foundation of China (no. 11201212), Promotive Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province (no. BS2012DX004), AMEP, and the Special Funds for Doctoral Authorities of Linyi University.*

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