# A New Optimal Homotopy Asymptotic Method for Fractional Optimal Control Problems

Authors: Oluwaseun Olumide Okundalaye; Wan Ainun Mior Othman
Journal: International Journal of Differential Equations
 (2021)

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## Abstract

Solving fractional optimal control problems (FOCPs) with an approximate analytical method has been widely studied by many authors, but to guarantee the convergence of the series solution has been a challenge. We solved this by integrating the Galerkin method of optimization technique into the whole region of the governing equations for accurate optimal values of control-convergence parametersCjs. The arbitrary-order derivative is in the conformable fractional derivative sense. We use Euler–Lagrange equation form of necessary optimality conditions for FOCPs, and the arising fractional differential equations (FDEs) are solved by optimal homotopy asymptotic method (OHAM). The OHAM technique speedily provides the convergent approximate analytical solution as the arbitrary order derivative approaches 1. The convergence of the method is discussed, and its effectiveness is verified by some illustrative test examples.


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## Body

## 1. Introduction

The general definition of an optimal control problem necessitates minimization of an objective function of the states and control inputs of the system over a set of admissible control functions. The optimal control problems emerge naturally in different areas of science, engineering, and economics. Several significant works have been done in the area of optimal control of integer order derivative dynamic systems: the optimal control design for the hyperchaotic Chen system [1], an optimal control of time-varying and state time-delay systems using shooting method [2], a CasADi software for nonlinear optimal control problem [3], direct and simultaneous approach for constrained mixed-integer optimal control [4], a tailored algorithm for mixed integer nonlinear optimal control model of hybrid electric vehicles [5], and many more.The arbitrary order of fractional derivative equations (FDEs) from fractional calculus has contributed a substantial part to mathematics, engineering, and different area of sciences. The previous studies review that arbitrary order derivatives provide more exact models of several engineering control systems and show the behaviour, performance, and mathematical representation of the dynamic systems in sciences than traditional calculus, for example, the positioning of satellite in space is a model using fractional order because traditional calculus is better equipped to capture the memory-like effect observed in the system. Fractional calculus applications are but not limited to liquid-containing gas bubbles [6], neurodynamics system [7], the FOCPs with a general derivative operator [8], a new adaptive synchronization and hyperchaos control of a biological snap oscillator [9], a new mathematical model for Zika virus transmission [10], the fractional features of a harmonic oscillator with position-dependent mass [11], electrohydrodynamic flow in a cylindrical conduit [12], fractional order of HIV infection model [13], and novel solution methods for IBVPs of arbitrary-order with conformable differentiation [14]. Many areas of FDEs have been researched by many authors: the multistage ADM for solving NLP problems over a nonlinear fractional dynamical system [15], the existence of the solution to FOCPs [16], stable analytical techniques for solving FDEs [17], Caputo–Fabrizio operator sense for brucellosis model [18], and spectral collocation method of generalized fractional operator sense for fractional calculus equations [19]. Currently, to obtain exact solutions for FOCPs is still difficult and remain a challenge. But where there is not an exact solution, appropriate approximate analytical solution (AAS) is suitable and applicable. A recent approximate analytical methods are: modified ADM for fractional optimal control problems (FOCPs) [20], optimal control of a constrained fractionally damped elastic beam [21], analyses of an optimal solutions of optimization problems from fractional gradient based system using VIM [22], conformable fractional optimal control problem of an heat conduction equations using Laplace and finite Fourier sine transforms [23], spectral Galerkin approximation [24], and direct transcription methods for FOCPs [25], but the aforementioned methods lack convergence norm that will guarantee the convergence of the series solution. In 1992, Liao proposed the homotopy analysis method (HAM) [26] for solving the nonlinear problem which was later advanced to optimal homotopy asymptotic method for noninteger order [27], new fractional homotopy method for optimal control problems (OCPs) [28], and comparisons of OHAM [29]. But OHAM has never been used to solve FOCPs, which drives this research work. We extend the work of (Alizadeh Effati, 2018a), who proposed a fractional optimal control problem with an approximate analysis solution that lacks convergence norm. We provided the solution using OHAM with an optimization technique of the Galerkin method. It is hoped that the simplicity of this formulation will initiate a new interest in the area of fractional order optimal control problems. Our focus in this paper is to widen the application of the OHAM to FOCPs. The formulation, numerical scheme, and approximate analytical results for FOCPs presented in this paper are attempts to fill this gap.We arrange the paper as follows. In Section2, a brief introduction to the fractional calculus and the necessary optimality conditions are discussed. In Section 3, we formulate OHAM with FOCPs and show the convergence analysis of the technique. We present numerical examples and results in Section 4. Finally, in Section 5, we present the conclusion.


## 2. Fractional Calculus

The definition of conformable fractional derivative (CFD) preserves many properties of classical order derivatives [30]. Some features that we will adopt are as follows.Definition 1.
The (left) FD starting from a functionz:a,∞⟶∈ of order 0<α<1 is defined by(1)Tαazψ=limε⟶0zψ+εψ−a1−α−zψε,∀ψ>0,0<α<1.
Whena=0, we write Tα. If Tαzψ exists on a,b, then Tαaza=limψ⟶a+Tαazψ. The (right) FD of order 0<α≤1 ending at b of z is defined by(2)Tαbzψ=−limε⟶0zψ+εb−ψ1−α−zψε,∀ψ>0,0<α<1.
IfTαbzψ exists on a,b, then Tαbzb=limψ⟶b−Tαbzψ.
Note that ifz is differentiable, then Tαazψ=ψ−a1−αz′ψ and αbTzψ=−b−ψ1−αz′ψ.
Notation. Iαazψ=∫atzψdαψ,a=∫atψ−aα−1zψdψ. When a=0, we write dαψ. Similarly, in the right case, we have Tαbzψ=∫tbzψdαb,ψ=∫tbb−ψα−1zψdψ. The operators Iαa and Iαb are called conformable fractional integrals of left and right order 0<α<1.Lemma 1.
Assume thatz:a,∞⟶ℜ is continuous and 0<α<1. Then, for all t>a, we have(3)TαaIαa=zψ.In the right case, we can similarly prove the following.Lemma 2.
Assume thatz:−∞,b⟶ℜ is continuous and 0<α<1. Then, ∀t<a, we have(4)TαbbIα=zψ.


### 2.1. Necessary Optimality Condition

We define optimal control problems as a minimization of a performance index (PI) subject to system dynamic constraints on the state variablezt and control variable ut. The necessary optimality condition helps reduce OCPs to a system of integer order ODE. The general formulation of Caputo and Riemann–Liouville derivatives is with OCPs [31]. Using the above definition equations (1) and (2), we present an Euler–Lagrange equation form for FOCPs under some consideration as follows.Find the optimal controlut that minimizes the performance index:(5)Ju=∫01Fz,u,tdt,subject to the system dynamic constraints(6)Tαaz=Gz,u,t,and the initial condition(7)z0=z0,where F and G are two arbitrary functions and Tαa is the conformable fractional derivative [30]. Some additional terms containing z′st may be included in equation (6) at the endpoint. When α=1, the problem above reduces to a integer OCPs [32]. Here, we take the limits of integration from 0 to 1 and consider 0.8≤α≤1. According to [33], the necessary equations are obtained through the combination of equations (5) and (6) and using a Lagrange multiplier approach. The variants of the resultant is taken, which is followed by using integration by parts to revise the equation so that it does not contain variants of a derivative term, enforce the necessary terminal conditions, and set the coefficients of δλ, δz, and δu to zero. We have(8)Tαaz=Gz,u,t,(9)αbTz=∂F∂z+λ∂G∂z,(10)∂F∂u+λ∂G∂u=0,and(11)z0=z0,λ1=0.Whereλ is the Lagrange multiplier. The details of the derivation of equations (8)–(11) are seen in [33]. The Euler–Lagrange equations for FOCP equations (6) and (7) represent the necessary conditions for the optimality of FOCP. The difference between arbitrary and integer OCP is that the arbitrary order FOCP consists of the left and the right FD. Examine that equation (8) contains left conformable derivative, whereas equation (9) contains the right conformable derivative. This demonstrates that the solution of OCP requires the insight of not only forward derivatives (FD) but also backward derivatives (BD) to account for end conditions. In integer-order OCP theories, this issue is either not addressed or not highlighted. This is broad because the BD of order 1 turns out to be negative of the FD of order 1. It can be shown that, in the limα⟶1, equations (8)–(10) goes back to those obtained using standard methods.


## 2.1. Necessary Optimality Condition

We define optimal control problems as a minimization of a performance index (PI) subject to system dynamic constraints on the state variablezt and control variable ut. The necessary optimality condition helps reduce OCPs to a system of integer order ODE. The general formulation of Caputo and Riemann–Liouville derivatives is with OCPs [31]. Using the above definition equations (1) and (2), we present an Euler–Lagrange equation form for FOCPs under some consideration as follows.Find the optimal controlut that minimizes the performance index:(5)Ju=∫01Fz,u,tdt,subject to the system dynamic constraints(6)Tαaz=Gz,u,t,and the initial condition(7)z0=z0,where F and G are two arbitrary functions and Tαa is the conformable fractional derivative [30]. Some additional terms containing z′st may be included in equation (6) at the endpoint. When α=1, the problem above reduces to a integer OCPs [32]. Here, we take the limits of integration from 0 to 1 and consider 0.8≤α≤1. According to [33], the necessary equations are obtained through the combination of equations (5) and (6) and using a Lagrange multiplier approach. The variants of the resultant is taken, which is followed by using integration by parts to revise the equation so that it does not contain variants of a derivative term, enforce the necessary terminal conditions, and set the coefficients of δλ, δz, and δu to zero. We have(8)Tαaz=Gz,u,t,(9)αbTz=∂F∂z+λ∂G∂z,(10)∂F∂u+λ∂G∂u=0,and(11)z0=z0,λ1=0.Whereλ is the Lagrange multiplier. The details of the derivation of equations (8)–(11) are seen in [33]. The Euler–Lagrange equations for FOCP equations (6) and (7) represent the necessary conditions for the optimality of FOCP. The difference between arbitrary and integer OCP is that the arbitrary order FOCP consists of the left and the right FD. Examine that equation (8) contains left conformable derivative, whereas equation (9) contains the right conformable derivative. This demonstrates that the solution of OCP requires the insight of not only forward derivatives (FD) but also backward derivatives (BD) to account for end conditions. In integer-order OCP theories, this issue is either not addressed or not highlighted. This is broad because the BD of order 1 turns out to be negative of the FD of order 1. It can be shown that, in the limα⟶1, equations (8)–(10) goes back to those obtained using standard methods.


## 3. The Formulation of OHAM with FOCPs

We exemplify the basic concept of the OHAM by considering the following general FDEs system:(12)Tαaxkt+Lkxkt+Nkxkt−gkt=0,t∈φ,k=1,2…m,with initial conditions(13)xkb=ai,where Tαa is the conformable fractional derivative, lk is a linear operator, Nk is a nonlinear operator, t is an independent variable, xkt is unknown function, φ is the problem domain, and gkt is a known function. According to OHAM, one can construct a homotopy map Hkϕit,p:φ×0,1⟶φ which satisfies that equation (12) can be constructed using OHAM as [26](14)1−ℓTαaϜkt,ℓ=HkℓTαaϜkt,ℓ+NϜkt,ℓ+LϜkt,ℓ+gkt,ℓ,where embedding parameter (ℓ) is 0≤ℓ≤1, auxiliary function Hkℓ∀ℓ≠0, unknown function (Ϝkt,ℓ), and H0=0. Definitely, when ℓ=0 and ℓ=1, it holds that Ϝkt,0=ψk,0t and Ϝkt,1=ψkt, respectively. Thus, as ℓ moves from 0 to 1, the solution Ϝkt,ℓ approaches from ψk,0t to ψkt, where initial guess ψk,0t satisfies the linear operator generated from equation (14) for ℓ=0 as(15)Tαaψk,0t=0,ψk,0b=0.Hkℓ is given as(16)Hkℓ=∑j=1nℓjCj,where Cjs can be known later. We get approximate solution by expanding Ϝkt,ℓ,Cj in Taylor’s series in terms of ℓ(17)Ϝkt,ℓ,Cj=ψk,0t+∑k≥1ψi,kt,Cjℓi,j=1,2,…,n,using equation (14) with collections of the coefficient like power of ℓ, gives the governing equations ψi,0t in a linear form in equation (15). Then, the 1st problem is given as(18)Tαaψk,1t+gkt=C1N0ψk,0t,ψk,1b=0.The general governing equations forψk,it is(19)Tαaψk,it−Tαaψk,i−1t=CiNk,0ψk,0t+∑m=1i−1Cj,mTαaψk,i−mt+Nk,i−mψk,i−1t,(20)ψk,ib=0,i=2,3,…,m,where Nk,mψ0t,ψk,1t,…,ψk,mt is the coefficient of ℓm, produced by expanding NkϜkt,ℓ,Cj in series relating to ℓ(21)NkϜkt,ℓ,Cj=Nk,0ψk,0t+∑m≥1Nk,mψ0,ψ1,…,ψmℓm.The convergence of series solution equation (21) relies on Cjs. If it is convergent at ℓ=1, it gives solution to equation (12) as(22)ψkt,Cj=ψk,0t+∑k≥1mψi,kt,Cj,j=1,2,…,n.Using equation (22) in equation (12), we have expression for the residual error as(23)Rkt,Cj=Tαψkt,Cj+Lkψkt,Cj+Nkψkt,Cj−gkt.If(24)Rkt,Cj=0,then ψ˜kt,Cj is the exact solution. Usually, such a case does not occur. We adopt Galerkin method to find the optimal values Cjs as follows:(25)ℓk=∂ψ˜kt,Cj∂Cj=0,k=1,2,…,m.Minimize the functional(26)ΔkCj=∫abℓk×Rkt,Cjdt,where the values of a and b depend on the given problem. With these known Cjs, the approximate analytical solution equation (12) is well known.We calculate the correctness of OHAM by(1)
Error normL2(27)L2=Ψexact−ΨN≈b−aN∑k=0Nψkexact−ψNk2.(2)
Error normL∞(28)L∞=Ψexact−ΨN∞≈maxkψkexact−ψNk.


### 3.1. Convergence Analysis of the Method

Theorem 3.1.
If the seriesψkt,Cj=ψk,0t+∑i=1mψk,it,Cj,forj=1,2,…,n, converges where ψkt,Cj is governed by equation (22) under the definitions equations (19) and (20), it achieves the solution of equations (12) and (13).Proof.
If we assume∑m=1∞ψk,mt,Cj, for k=1,2,…,n, converges to ψkt,Cj, then(29)limm⟶∞ψk,mt,Cj=0,∀k=1,2,…,n.
From equation (19), we can write(30)∑i=1∞CiNk,0xk,0t+∑m=1i−1Cj,mTαaψk,i−mt+Nk,i−mψk,i−1t=∑k=1∞Tαaψi,kt−Tαaψi,k−1tt,(31)=limn⟶∞∑k=1nTαaψi,kt−Tαaψi,k−1t,(32)=Tαaψ11t+Tαaψ22t−Tαaψ21t+⋯+Tαaψnnt−Tαaxnn−1t,(33)=Tαalimn⟶∞∑m=1nψnnt=Tαalimn⟶∞ψnnt=0.
Equate the RHS of equation (33) with the following equation:(34)0=∑m=1∞Tαaψkm−1+Nψkm−1Lψkm−1+Nψkm−1−gkt,(35)∑m=1∞Tαaψkm−1+Lψkm−1+Nψkm−1=gkt,(36)Tαaψkt,Cj+Lψkt,Cj+Nψkt,Cj−gkt=0,∀k=1,2,…,n.
If theCj is chosen properly, then equation (36) leads to the solution of equations (12) and (13)


## 3.1. Convergence Analysis of the Method

Theorem 3.1.
If the seriesψkt,Cj=ψk,0t+∑i=1mψk,it,Cj,forj=1,2,…,n, converges where ψkt,Cj is governed by equation (22) under the definitions equations (19) and (20), it achieves the solution of equations (12) and (13).Proof.
If we assume∑m=1∞ψk,mt,Cj, for k=1,2,…,n, converges to ψkt,Cj, then(29)limm⟶∞ψk,mt,Cj=0,∀k=1,2,…,n.
From equation (19), we can write(30)∑i=1∞CiNk,0xk,0t+∑m=1i−1Cj,mTαaψk,i−mt+Nk,i−mψk,i−1t=∑k=1∞Tαaψi,kt−Tαaψi,k−1tt,(31)=limn⟶∞∑k=1nTαaψi,kt−Tαaψi,k−1t,(32)=Tαaψ11t+Tαaψ22t−Tαaψ21t+⋯+Tαaψnnt−Tαaxnn−1t,(33)=Tαalimn⟶∞∑m=1nψnnt=Tαalimn⟶∞ψnnt=0.
Equate the RHS of equation (33) with the following equation:(34)0=∑m=1∞Tαaψkm−1+Nψkm−1Lψkm−1+Nψkm−1−gkt,(35)∑m=1∞Tαaψkm−1+Lψkm−1+Nψkm−1=gkt,(36)Tαaψkt,Cj+Lψkt,Cj+Nψkt,Cj−gkt=0,∀k=1,2,…,n.
If theCj is chosen properly, then equation (36) leads to the solution of equations (12) and (13)


## 4. Numerical Example and Results

Example 1.
Find the controlut that minimizes the quadratic performance index(37)Ju=12∫01z2t+u2dt,subject to system dynamics(38)Tαa=−z+u,and the initial condition(39)z0=1.
The exact solution for the system forα=1 is given as(40)zt=cosh2t+βsinh2t,ut=1+2βcosh2t+2+βsinh2t,where(41)β=cosh2+2sinh22cosh2+sinh2≈−0.98.
Following equations (8)–(11) procedure, we have(42)Tαaz=−z+u,Tαabu=−z−u,z0=1,u1=0.
Using OHAM, the solutions for fractional-order are acquired. We choose the linear and nonlinear operators in the following forms:(43)L1Ϝ1t,ℓ=TαaϜ1t,ℓ,(44)L2Ϝ2t,ℓ=bTαϜ2t,ℓ,(45)N1Ϝ1t,ℓ=TαaϜ1t,ℓ+Ϝ1t,ℓ+Ϝ2t,ℓ,(46)N2Ϝ2t,ℓ=bTαϜ2t,ℓ−Ϝ1t,ℓ−Ϝ2t,ℓ.
Using homotopy in equation (14),(47)1−ℓTαaϜ1t,ℓ=HkℓTαaϜ1t,ℓ+Ϝ1t,ℓ+Ϝ2t,ℓ,(48)1−ℓbTαϜ2t,ℓ=HkℓTαbϜ2t,ℓ−Ϝ1t,ℓ−Ϝ2t,ℓ,where(49)Ϝ1t,ℓ=z0t+∑j≤1z1,jtℓj,(50)Ϝ2t,ℓ=u0t+∑j≤1u1,jtℓj,(51)Hkℓ=ℓC1+ℓ2C2+ℓ3C3+⋯ℓmCm,k=1,2,…,m.
SubstitutingϜ1t,ℓ,Ϝ2t,ℓ, and Hkℓ into equations (47) and (48), and equating the coefficient of likes power of ℓ, it gives linear FDEs as follows:(52)ℓ0:Tαaz0t=0,(53)ℓ0:Tαbu0t=0,(54)ℓ1:Tαaz1t=Tαaz0tC1−Tαaz0t+z0tC1+u0tC1=0,(55)ℓ1:Tαbu1t=Tαbu0tC1−Tαau0t+z0tC1+u0tC1=0,(56)ℓ2:Tαaz2t=Tαaz0tC2+Tαaz1tC1−Tαaz1t+z0tC2+z1tC1+u0tC2+u1tC1=0,(57)ℓ2:Tαbu2t=Tαbu0tC2+Tαbu1tC1−Tαbu1t−u0tC2−u1tC1+z0tC2+z1tC1=0.
Using the operator Lemmas1 and 2 on the above equations with initial condition gives(58)z0t=0,(59)u0t=0,(60)z1t,C1=−5t1/5C1+1,(61)u1t,C1=5t1/5C1,(62)z2t,C1,C2=5t1/5C12−2C1+C2+1,(63)u2t,C1,C2=−25t2/5C12−5t1/5C12+10t1/5C1+5C2t1/5….
Summing up the solution from equations (58)–(63), the 3rd-order approximate analytical method generated by OHAM, for ℓ=1, is(64)zt,C1,C2=5C12−15C1−5C2t1/5+3,(65)ut,C1,C2=15C1−5C12+5C2t1/5−252/5C12.
We determineC1 and C2 by using the procedure mentioned in equations (24)–(26).
Forzt,(66)C1=−0.2553704121,C2=1.408561895.
And, forut,(67)C1=−0.1925118854,C2=1.0134328546.
Using the3rd-order approximate analytical solution given in equations (64) and (65), we have(68)zt=−4.591553468t1/5+3,(69)ut=−0.9265206505t2/5+1.994181864t1/5.
Figure1 shows the approximate analytical solutions of the state variable zt and control variable ut at different values of α using OHAM after the 3rd order of approximate solution. Table 1 shows the control-convergence parameter at different values of fractional-order for both state and control variables, Table 2 shows comparisons and absolute errors between the proposed method and the exact solution at integer order α=1, and Table 3 shows the optimal values of the cost function and the comparisons between OHAM and VIM. It is observed that OHAM converges to optimal values faster in CPU times and iterations.Figure 1
(a, b) For different values ofα (exact = dot, α=1: long dash, α=0.9: solid, and α=0.8: dash).
(a)(b)Table 1
Control-convergence parametersCjs at different values of α.
Variablez (t)z (t)u (t)u (t)αC1C2C1C21−0.25537041211.408561895−0.19251188541.01343285460.9−0.2823113851.354541654−0.21362175530.91343285460.8−0.3120125131.319562884−0.24142176240.8134328546Table 2
Comparisons and absolute error between (OHAM and exact,α=1).
tkOHAMz (t)OHAMu (t)Exactz (t)Exactu (t)Errorz (t)Erroru (t)0.01.000000000.000000001.000000000.000000000.000000000.000000000.10.6676064640.681141710.6671441950.6816039870.0004622770.0004622690.20.5724900441.150820420.5718145751.1514958910.0006754690.0006754710.30.5130096071.537176220.5121551961.5380306340.0008544110.0008544140.40.4709737691.886722530.4699552261.8877410760.0010185430.0010185460.50.4514731342.216980460.4439451012.2181556580.0075280330.0011751980.60.4395674032.536737060.4383922052.5380651260.0011751980.0013280660.70.4154031192.851123160.4140750622.8526024420.0013280570.0014792820.80.3965289463.163442660.3950496693.1650729170.0014792770.0016302570.90.381710583.475977030.380080333.4777589810.001630250.00178195110.368415753.791388120.3683338133.7923231888.1937E-050.000935068Table 3
Optimal values ofJ at different choices of α for example 1.
αVIM Ali et al. (2018)OHAM1.00.19290930.19290900.90.179530.171520.80.167110.16709Example 2.
Considering the following problem of minimising the functional(70)Ju=12∫01z1t2+z2t2+ut2dt,subjected to the dynamic constraints(71)Tαaz1t=−z1t+z2t+ut,(72)Tαbz2t=−2z2t,and the initial conditions(73)z10=z20=1.
The exact solution for this system forα=1 is(74)z1t=−32e−2t+2.48164e−2t+0.018352e2t,(75)z2t=e−2t,(76)ut=12e−2t−1.027934e−2t+0.0443056e2t.
For this example, the necessary optimality conditions are as follows:(77)Tαaz1t−z1t+z2t+ut=0,(78)Tαbz2t+2z2t=0,(79)ut+p1t=0,(80)Tαap1t+pt−z1t=0,(81)Tαbp2t−2p2t+p1t+z2t=0,and(82)z10=z20=1,p11=p21=0.
Following the same procedure above, we have(83)z1t,C1,C2−30C1+10C12−10C2t1/10+50t1/5C12+3,(84)z2t,C1,C2=30C1+90C12+10C2t1/10−30C1−50×t1/5C12−40C12−10C2,(85)ut,C1,C2=30C1+110C12+10C2t1/10−30C1−100×t1/5C12−10C12−10C2.
We determineC1 and C2 by using the procedure mentioned in equations (24)–(26).For z1t,(86)C1=1.143999690×10−9,C2=0.2950819638,
Forz2t,(87)C1=−0.1820636120,C2=0.2099839413,and ut(88)C1=−6.577999908×10−10,C2=0.3913043497.
Using the3rd-order approximate analytical solution given in equations (83)–(85), we have(89)z1t=−2.950819672×t1/10+3.01234+6.543676455×10−17×t1/5,(90)z2t=−0.378824654×t1/10+2.036182595−1.657357940×t1/5,(91)ut=3.913043477×t1/10−3.913043477−4.327008279×10−17×t1/5.
Figure2 shows the approximate analytical solutions of the states variables z1t and z2t and control variable ut at different values of α using OHAM after the 3rd order of approximate solution. Tables 4 and 5 show the control-convergence parameter at different values of fractional-order for both states and control variables, Table 6 shows comparisons between OHAM and the exact solution at integer order α=1, Table 7 shows absolute errors between the proposed method and the exact solution at integer order at α=1, and Table 8 shows the optimal values of the cost function and the comparisons between OHAM and VIM. It is observed that OHAM converges to optimal values faster in CPU times and iterations.Figure 2
(a–c) For different values ofα (exact = dot, α=1: long dash, α=0.9: solid, and α=0.8: dash).
(a)(b)(c)Table 4
Control-convergence parametersCjs at different values of α.
Variablez1 (t)z1 (t)z2 (t)z2 (t)αC1C2C1C211.143999690 x 10−90.2950819638−0.18206361200.20998394130.91.113328281 x 10−90.2536726322−0.21215472310.18456232250.81.091424280 x 10−90.1963215213−0.24315453430.159436311Table 5
Control-convergence parametersCjs at different values of α.
Variableu (t)u (t)αC1C21−6.577999908 x 10−100.39130434970.9−6.661323216 x 10−100.36121344210.8−6.755673906 x 10−100.3125152370Table 6
Comparisons of (OHAM and exact,α=1).
tkOHAMz1 (t)OHAMz2 (t)OHAMu (t)Exactz1 (t)Exactz2 (t)Exactu (t)0.01.00000000.000000001.00000000.000000000.00000000.000000000.10.3880253920.852996359−0.1782001690.3873859840.852143789−0.1786097390.20.3480555080.727602788−0.1269012190.3475242210.726149037−0.1275722090.30.3608263870.620642529−0.1024025160.3603654960.618783392−0.103226970.40.3859033340.529405818−8.63E−020.3854944920.527292424−8.72E−020.50.3859033340.451581235−7.29E−020.3854944920.449328964−7.38E−020.60.4113811370.385197149−5.97E−020.4110132570.382892886−6.06E−020.70.4332645310.328571766−4.68E−020.4329301390.326279795−4.69E−020.80.4504363930.28027052−3.21E−020.45013010.278037301−3.22E−020.90.462935190.239069733−1.65E−020.4626529250.236927759−1.66E−0210.470999840.207193943−1.69E−030.4709971670.205152843−1.70E−03Table 7
Absolute error between (OHAM and exact,α=1).
Errorz1 (t)Errorz2 (t)Erroru (t)0.0000000.0000000.0000000.0006394080.000852570.000409570.0005312870.0014537510.000670990.0004608910.0018591370.0008244540.0004088420.0021133940.00090.0004088420.0022522710.00090.000367880.0023042630.00090.0003343920.0022919711E−040.0003062930.0022332190.00010.0002822650.0021419741E−042.673E−060.00204111E−05Table 8
Optimal values ofJ at different choices of α.
αVIM Ali et al, (2018)OHAM1.00.43190890.43190880.90.40308970.40308930.80.37601240.3760122


## 5. Conclusion

In this research article, we examine the effectiveness of the OHAM to generate an accurate approximate analytical solution to FOCPs. We derive the necessary optimal condition and then solve the system of nonlinear FDEs with OHAM. The technique helps provide an accurate approximate analytical solution to the problems. The fractional derivative operator is in the conformable fractional derivative operator sense. We show that Galerkin method helps to determine the optimal values of the control-convergence parameter. It can be seen that the OHAM results corresponded with the exact solution atα=1, which shows the effectiveness of the proposed method to provide an accurate approximate analytical solution. OHAM is used to solve this type of problems for the first time and gave optimal values to the convergence norm challenge for accurate series solution. No existing approximate solution is known to have considered such a nonlinear constrained programming problem with OHAM ahead of this one. OHAM technique is very easy to implement and efficient for solving FOCPs.



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*Source: 6633130-2021-05-15.xml*