Skip to main content
Log in

Optimization of Right-Hand Sides of Nonlocal Boundary Conditions in a Controlled Dynamical System

  • LINEAR SYSTEMS
  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

We study an optimal control problem described by a system of linear ordinary differential equations with boundary conditions containing point and integral values of the state variable. The controls occurring in the differential equations and the values of the right-hand sides of the nonlocal boundary conditions are determined in this problem. Necessary conditions for the existence and uniqueness of a solution of the boundary value problem and for the convexity of the objective functional, as well as necessary conditions for the optimality of the parameters to be optimized in the control problem, are studied. The formulas obtained for the gradient of the objective functional of the problem are used in the numerical solution of an illustrative problem. The results of numerical experiments are provided.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.

Similar content being viewed by others

REFERENCES

  1. Nicoletti, O., Sulle condizioni iniziali che determiniano gli integrali della diffenziali ordinazie, Torino: Atti della R. Acc. Sc., 1897.

    Google Scholar 

  2. Tamarkin, Ya.D., O nekotorykh obshchikh zadachakh teorii obyknovennykh differentsial’nykh uravnenii i o razlozhenii proizvol’nykh funktsii v ryady (On Some General Problems of the Theory of Ordinary Differential Equations and Series Expansions of Arbitrary Functions), Petrograd: Tipograf. M.P. Frolovoi, 1917.

    Google Scholar 

  3. De la Vallee-Poussin, Ch.J., Sur l’équation différentielle linéare du second ordre. Détermination d’une integrale par deux valeurs assignées. Extension aux équations d’orde \(n \), J. Math. Pures Appl., 1929, vol. 8, no. 9.

  4. Kiguradze, I.T., Boundary-value problems for systems of ordinary differential equations, J. Sov. Math., 1987, vol. 30, pp. 3–103.

    MathSciNet  Google Scholar 

  5. Nakhushev, A.M., On nonlocal boundary value problems with shift and their relations with loaded equations, Differ. Uravn., 1995, vol. 21, no. 1, pp. 92–101.

    MathSciNet  Google Scholar 

  6. Dzhumabaev, D.S. and Imanchiev, A.E., The correct solvability of a linear multipoint boundary value problem, Math. J., 2005, vol. 5, no. 15, pp. 30–38.

    MathSciNet  MATH  Google Scholar 

  7. Assanova, A.T., Imanchiyev, A.E., and Kadirbayeva, Zh.M., Solvability of nonlocal problems for systems of Sobolev-type differential equations with a multipoint condition, Russ. Math., 2019, vol. 63, no. 12, pp. 1–12.

    Article  Google Scholar 

  8. Aida-zade, K.R. and Abdullaev, V.M., On the solution of boundary value problems with nonseparated multipoint and integral conditions, Differ. Equations, 2013, vol. 49, no. 9, pp. 1114–1125.

    Article  MathSciNet  Google Scholar 

  9. Abdullaev, V.M. and Aida-zade, K.R., Numerical method of solution to loaded nonlocal boundary value problems for ordinary differential equations, Comput. Math. Math. Phys., 2014, vol. 54, no. 7, pp. 1096–1109.

    Article  MathSciNet  Google Scholar 

  10. Assanova, A.T., Solvability of a nonlocal problem for a hyperbolic equation with integral conditions, Electron. J. Differ. Equat., 2017, vol. 170, pp. 1–12.

    MathSciNet  MATH  Google Scholar 

  11. Aida-zade, K.R. and Abdullaev, V.M., On an approach to designing control of the distributed-parameter processes, Autom. Remote Control, 2012, vol. 73, no. 9, pp. 1443–1455.

    Article  MathSciNet  Google Scholar 

  12. Aida-zade, K.R. and Abdullayev, V.M., Optimizing placement of the control points at synthesis of the heating process control, Autom. Remote Control, 2017, vol. 78, no. 9, pp. 1585–1599.

    Article  MathSciNet  Google Scholar 

  13. Aida-zade, K.R. and Hashimov, V.A., Optimization of measurement points positioning in a border control synthesis problem for the process of heating a rod, Autom. Remote Control, 2018, vol. 79, pp. 11–25.

    Article  Google Scholar 

  14. Mardanov, M.J., Sharifov, Y.A., and Zeynalli, F.M., Existence and uniqueness of the solutions to impulsive nonlinear integro-differential equations with nonlocal boundary conditions, Proc. Inst. Math. Mech., Natl. Acad. Sci. Azerb., 2019, vol. 45, no. 2, pp. 222–232.

    MathSciNet  MATH  Google Scholar 

  15. Sharifov, Y.A. and Mammadova, N.B., Optimal control problem described by impulsive differential equations with nonlocal boundary conditions, Differ. Equations, 2014, vol. 50, no. 3, pp. 403–411.

    Article  MathSciNet  Google Scholar 

  16. Devadze, D. and Beridze, V., Optimality conditions and solution algorithms of optimal control problems for nonlocal boundary-value problems, J. Math. Sci., 2016, vol. 218, pp. 731–736.

    Article  MathSciNet  Google Scholar 

  17. Zubova, S.P. and Raetskaya, E.V., Algorithm to solve linear multipoint problems of control by the method of cascade decomposition, Autom. Remote Control, 2017, vol. 78, no. 7, pp. 1189–1202.

    Article  MathSciNet  Google Scholar 

  18. Dmitruk, A.V. and Kaganovich, A.M., Maximum principle for optimal control problems with intermediate constraints, in Nelineinaya dinamika i upravlenie. Vyp. 6 (Nonlinear Dynamics and Control. Iss. 6), Moscow: Fizmatlit, 2008, pp. 101–136.

    Google Scholar 

  19. Bryson, A. and Ho, Yu-Chi, Applied Optimal Control. Optimization Estimation and Control, London: Blaisdell, 1968.

    Google Scholar 

  20. Ashchepkov, L.T., Optimal control of system with intermediate conditions, Prikl. Mat. Mekh., 1981, vol. 45, no. 2, pp. 215–222.

    MathSciNet  MATH  Google Scholar 

  21. Vasil’eva, O.O. and Mizukami, K., Dynamic processes described by boundary problem: necessary optimality conditions and methods of solution, J. Comput. Syst. Sci. Int. (A J. Optim. Control), 2000, vol. 1, pp. 95–100.

    Google Scholar 

  22. Abdullayev, V.M. and Aida-zade, K.R., Approach to the numerical solution of optimal control problems for loaded differential equations with nonlocal conditions, Comput. Math. Math. Phys., 2019, vol. 59, no. 5, pp. 739–751.

    Article  MathSciNet  Google Scholar 

  23. Polyak, B.T., Vvedenie v optimizatsiyu (Introduction to Optimization), Moscow: Lenand, 2019.

    MATH  Google Scholar 

  24. Vasil’ev, F.P., Metody optimizatsii (Optimization Methods), Moscow: Faktorial, 2002.

    Google Scholar 

  25. Aida-zade, K.R. and Abdullayev, V.M., Solution to a class of inverse problems for system of loaded ordinary differential equations with integral conditions, J. Inverse Ill-Posed Probl., 2016, vol. 24, no. 5, pp. 543–558.

    Article  MathSciNet  Google Scholar 

  26. Moszynski, K., A method of solving the boundary value problem for a system of linear ordinary differential equations, Algorytmy. Warszawa, 1964, vol. 11, no. 3, pp. 25–43.

    MathSciNet  MATH  Google Scholar 

  27. Abramov, A.A., A variation of the ‘dispersion’ method, USSR Comput. Math. Math. Phys., 1961, vol. 1, no. 2, pp. 368–371.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to K. R. Aida-zade or V. M. Abdullayev.

Additional information

Translated by V. Potapchouck

APPENDIX

Proof of Theorem 1. The theorem is proved by a straightforward substitution of the Cauchy formula for system (2.1),

$$ x(t)=F(t,t^{1} )x^{1} +\int \limits _{t^{1} }^{t}F(t,\tau ) A_{2} (\tau )u(\tau )d\tau ,$$
(A.1)
into conditions (2.2). After easy transformations, by arranging the terms, we obtain the algebraic system
$$ \begin {gathered} Lx^{1} =D, \\ L=\sum _{i=1}^{l_{1} }\alpha _{i} F(\tilde {t}^{i} ,t^{1} )+\sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} }\beta _{j} (t )F(t,t^{1} )dt , \\ {D=\vartheta -\sum _{i=1}^{l_{1} }\alpha _{i}\; \int \limits _{t^{1} }^{\tilde {t}^{i} }F(\tilde {t}^{i} ,\tau )A_{2} (\tau )u(\tau )d\tau -\sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} }\beta _{j} (t )\int \limits _{t^{1} }^{t}A_{2} (\tau )u(\tau )d\tau dt} \end {gathered}$$
(A.2)
for \(x^{1}=x(t^{1}) \).

It is well known that system (A.2) has a unique solution provided that the matrix \(L\) is invertible, i.e., under condition (3.1). It is clear that \(\mathrm {rank}\,L\) is independent of the values of the vector \(D \) and hence of the vector function \(u(t) \) and the vector \(\vartheta \). At the same time, owing to the uniqueness of the representation (A.1) for the solution of the Cauchy problem for system (2.1), problem (2.1), (2.2) has a unique solution under condition (3.1). This completes the proof of Theorem 1.\(\quad \blacksquare \)

Proof of Theorem 2. Let \((u^{1} ,\vartheta ^{1} ) \) and \((u^{2} ,\vartheta ^{2}) \) be two pairs of arbitrary feasible control and parameters, and let \(x^{1} (t)\) and \(x^{2} (t) \) be the respective solutions of the boundary value problem (2.1), (2.2). Then

$$ \dot {x}^{1} (t)=A_{1} (t)x^{1} (t)+A_{2} (t)u^{1} (t),\quad t\in [t^{1} ,t^{f} ],$$
(A.3)
$$ \sum _{i=1}^{l_{1} }\alpha _{i} x^{1} (\tilde {t}_{i} )+\sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} }\beta _{j} (t )x^{1} (t)dt =\vartheta ^{1} ,$$
(A.4)
$$ \dot {x}^{2} (t)=A_{1} (t)x^{2} (t)+A_{2} (t)u^{2} (t),\quad t\in [t^{1} ,t^{f} ],$$
(A.5)
$$ \sum _{i=1}^{l_{1} }\alpha _{i} x^{2} (\tilde {t}_{i} )+\sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} }\beta _{j} (t )x^{2} (t)dt =\vartheta ^{2} .$$
(A.6)

By virtue of the convexity of the feasible sets \(U \) and \(V \), for an arbitrary \(\sigma \in [0; 1] \) one has

$$ u(t)=\sigma u^{1} (t)+(1-\sigma )u^{2} (t)\in U,\quad \vartheta =\sigma \vartheta ^{1} +(1-\sigma )\vartheta ^{2} \in V. $$
(A.7)

Set \(x(t)=\sigma x^{1} (t)+(1-\sigma )x^{2} (t) \).

We multiply both sides of (A.3) by \(\sigma \) and of (A.5) by \((1-\sigma )\), add the resulting equalities term by term, and arrange the terms to obtain

$$ \sigma \dot {x}^{1} (t)+(1-\sigma ) \dot {x}^{2} (t) =A_{1} (t)\left [\sigma x^{1} (t)+(1-\sigma )x^{2} (t)\right ]+A_{2} (t)\left [\sigma u^{1} (t)+(1-\sigma )u^{2} (t)\right ]. $$
It follows that \(x(t) \) satisfies the system of differential equations (2.1).

We multiply both sides of (A.4) by \(\sigma \) and of (A.6) by \((1-\sigma )\), add the resulting equalities, and arrange terms to obtain

$$ \sum _{i=1}^{l_{1} }\alpha _{i} \left [\sigma x^{1} (\tilde {t}^{i} )+(1-\sigma )x^{2} (\tilde {t}^{i} )\right ]+\sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} }\beta _{j} (t)\left [\sigma x^{1} (t)+(1-\sigma )x^{2} (t)\right ] dt =\sigma \vartheta ^{1} +(1-\sigma ) \vartheta ^{2} .$$
In view of notation (A.7), this implies that the triple \((x(t),u(t),\vartheta ) \) satisfies conditions (2.2).

By virtue of the convexity of the functions \(f^{0} (x(t),u(t),\vartheta ,t) \) and \(\Phi (\tilde {x},\hat {x},\vartheta )\) in the arguments \(x \), \(\tilde {x} \), \(\hat {x} \), \(u \), and \(\vartheta \), we have

$$ \begin {aligned} J(u,\vartheta )&=J\Big (\sigma u^{1} (t)+(1-\sigma )u^{2} (t), \sigma \vartheta ^{1} +(1-\sigma )\vartheta ^{2} \Big ) \\ &=\int \limits _{t^{1} }^{t^{f} }f^{0} \Big (\sigma x^{1} (t)+(1-\sigma )x^{2} (t), \sigma u^{1} (t)+(1-\sigma )u^{2} (t), \sigma \vartheta ^{1} +(1-\sigma )\vartheta ^{2} \Big )dt \\ &\quad {}+\Phi \Big (\sigma \tilde {x}^{1} +(1-\lambda \sigma )\tilde {x}^{2} ,\sigma \hat {x}^{1} +(1-\lambda \sigma )\hat {x}^{2} ,\sigma \vartheta ^{1} +(1-\sigma )\vartheta ^{2} \Big ) \\ &{}\le \sigma \int \limits _{t^{1} }^{t^{f} }f^{0} \Big (x^{1} (t),u^{1} (t),\vartheta ^{1} \Big )dt+(1-\sigma )\int \limits _{t^{1} }^{t^{f} }f^{0} \Big (x^{2} (t),u^{2} (t),\vartheta ^{2} \Big )dt \cr &\quad {}+\sigma \Phi \Big (\tilde {x}^{1} ,\hat {x}^{1} ,\vartheta ^{1} \Big )+(1-\sigma )\Phi \Big (\tilde {x}^{2} ,\hat {x}^{2} ,\vartheta ^{2} \Big )=\sigma J\Big (u^{1} ,\vartheta ^{1} \Big )+(1-\sigma )J\Big (u^{2} ,\vartheta ^{2} \Big ) . \end {aligned}$$
(A.8)

This implies the convexity of the functional \(J(u,\vartheta ) \). It is clear that if one of the functions \(\Phi (\tilde {x},\hat {x},\vartheta )\) and \(f^{0} (x(t),u(t),\vartheta ,t) \) is strictly convex, then the inequality in (A.8) will be strict. Consequently, the functional of problem (2.1)–(2.3) will be strictly convex. This implies the assertion in Theorem 2.\(\quad \blacksquare \)

Proof of Theorem 3. Let \(x(t)\in \mathrm {R}^{n} \) be a solution of the boundary value problem (2.1), (2.2) for some feasible control \(u(t)\in U \) and parameter vector \(\vartheta \in V \), and let \(x^{1} (t)=x(t)+\Delta x(t) \) be the solution of problem (2.1), (2.2) corresponding to incremented feasible control \({ u^{1} (t)}{=u(t)+\Delta u(t)\in U} \) and vector \(\vartheta ^{1} =\vartheta +\Delta \vartheta \in V\),

$$ \dot {x}^{1} (t)=A_{1} (t)x^{1} (t)+A_{2} (t)u^{1} (t),\quad t\in [t^{1} ,t^{f} ],$$
(A.9)
$$ \sum _{i=1}^{l_{1} }\alpha _{i} x^{1} (\tilde {t}^{i} )+\sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} }\beta _{j} (t )x^{1} (t)dt =\vartheta ^{1} .$$
(A.10)

It follows from (2.1), (2.2) and (A.9), (A.10) that

$$ \Delta \dot {x}(t)=A_{1} (t)\Delta x(t)+A_{2} (t)\Delta u(t),\quad t\in [t^{1} ,t^{f} ],$$
(A.11)
$$ \sum _{i=1}^{l_{1} }\alpha _{i} \Delta x(\tilde {t}^{i} )+\sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} }\beta _{j} (t )\Delta x(t)dt =\Delta \vartheta .$$
(A.12)

Then for the increment of the functional (2.3) we have

$$ \begin {aligned} &\Delta J(u,\vartheta )=J(u^{1} ,\vartheta ^{1} )-J(u,\vartheta ) \\ &=\int \limits _{t^{1} }^{t^{f} }\Big [f^{0} (x^{1} (t),u^{1} (t),\vartheta ^{1},t)-f^{0} (x(t),u(t),\vartheta ,t)\Big ] dt+\Phi (\tilde {x}^{1},\hat {x}^{1},\vartheta ^{1})-\Phi (\tilde {x},\hat {x},\vartheta ) \\ &=\int \limits _{t^{1}}^{t^{f} }\left [\frac {\partial f^{0}(x(t),u(t),\vartheta ,t)}{\partial x}\Delta x(t) + \frac {\partial f^{0}(x(t),u(t),\vartheta ,t)}{\partial u}\Delta u(t) + \frac {\partial f^{0} (x(t),u(t),\vartheta , t)}{\partial \vartheta } \Delta \vartheta \right ] dt \\ &\qquad {}+\sum _{i=1}^{l_{1} }\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}^{i}}\Delta x(\tilde {t}^{i})+\sum _{j=1}^{2l_{2} }\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \hat {x}^{j}}\Delta x(\hat {t}^{j} )+\frac {\partial \Phi (\tilde {x}, \hat {x},\vartheta )}{\partial \vartheta }\Delta \vartheta +R, \\ &\qquad \qquad \qquad \qquad \qquad R=o\left (\left \|\Delta x(t)\right \|_{C^{1,n} [t^{1},t^{f} ]},\left \|\Delta u(t)\right \|_{L_{2}^{r} [t^{1},t^{f}]},\left \|\Delta \vartheta \right \|_{\mathrm {R}^{n} }\right ). \end {aligned}$$
(A.13)

Here \(R\) is a remainder term. Under the adopted assumptions about the data of problem (2.1), (2.2), using the well-known technique in [24], we can produce an estimate of the form

$$ \left \| \Delta x(t)\right \| _{C^{1,n} [t^{1} ,t^{f} ]} \le c_{1} \left \| \Delta u(t)\right \| _{L_{2}^{r} [t^{1} ,t^{f} ]} +c_{2} \left \| \Delta \vartheta \right \| _{\mathrm {R}^{n} } , $$
where the positive constants \(c_{1} \) and \(c_{2} \) are independent of \(x(t) \). In view of (A.13), this implies the differentiability of the functional \(J(u,\vartheta )\) with respect to both \(u(t) \) and \(\vartheta \). Let us merge and order the sets of points \(\tilde {t}_{i} \), \(i=1,2,\ldots ,l_{1} \), and \(\hat {t}_{j} \), \(j=1,2,\ldots ,2l_{2} \), denoting the resulting tuple of points by \(\bar {t}_{s} \), \(s=1,2,\ldots ,(l_{1}+2l_{2}) \).

We transpose the right-hand side of (A.11) to the left and take the inner product of both sides of the resulting relation by as yet arbitrary \(n\)-dimensional vector function \(\psi (t)\) continuously differentiable on the intervals \((\bar {t}_{i},\bar {t}_{i+1})\), \(i=1,2,\ldots ,l_{1} +2l_{2} -1 \). Integrating by parts in the resulting relation and using the notation

$$ \eqalign { \psi (t_{+}^{i} )&={\mathop {\lim }\limits _{\varepsilon \to +0}} \psi (t_{i} +\varepsilon ), \cr \psi (t_{-}^{i} )&={\mathop {\lim }\limits _{\varepsilon \to +0}} \psi (t_{i}-\varepsilon ),} $$
we obtain
$$ \begin {aligned} 0&=\int \limits _{t^{1}}^{t^{f} }\psi ^{\mathrm {T}} (t)\Big [\Delta \dot {x}(t)-A_{1} (t)\Delta x(t)-A_{2} (t)\Delta u(t)\Big ]dt \\ &=\sum _{i=1}^{(l_{1} +2l_{2} -1)}\int \limits _{\bar {t}^{i}}^{\bar {t}^{i+1} } \left [\psi ^{\mathrm {T}} (t)\Delta \dot {x}(t)-\psi ^{\mathrm {T}} (t)A_{1} (t)\Delta x(t)-\psi ^{\mathrm {T}} (t)A_{2}(t)\Delta u(t)\right ]dt \\ &=\psi ^{\mathrm {T}} (t^{f} )\Delta x(t^{f} )-\psi ^{\mathrm {T}}(t^{1} )\Delta x(t^{1} )+\int \limits _{t^{1} }^{t^{f}}\left [-\dot {\psi }^{\mathrm {T}} (t)-\psi ^{\mathrm {T}} (t)A_{1}(t)\right ] \Delta x(t)dt \\ &\qquad {}+\int \limits _{t^{1} }^{t^{f} }\Big [-\psi ^{\mathrm {T}}(t)A_{2} (t)\Big ] \Delta u(t)dt+\sum _{i=2}^{l_{1} -1} \Big [\psi (\tilde {t}_{-}^{i} )-\psi (\tilde {t}_{+}^{i} )\Big ]^{\mathrm {T}} \Delta x(\tilde {t}^{i} )+\sum _{j=1}^{2l_{2} } \left [\psi (\hat {t}_{-}^{j} )-\psi (\hat {t}_{+}^{j} )\right ] ^{\mathrm {T}} \Delta x(\hat {t}^{j} ). \end {aligned} $$

Adding the resulting expression, which is zero, to (A.13), after simple transformations we obtain

$$ \begin {aligned} \Delta J(u,\vartheta )&=\int \limits _{t^{1} }^{t^{f} }\left [-\dot {\psi }^{\mathrm {T}} (t)-\psi ^{\mathrm {T}} (t)A_{1} (t)+\frac {\partial f^{0}(x(t), u(t),\vartheta , t)}{\partial x} \right ] \Delta x(t)dt \\ &\quad {}+ \int \limits _{t^{1} }^{t^{f} }\left [-\psi ^{\mathrm {T}}(t)A_{2} (t) + \frac {\partial f^{0} (x(t), u(t),\vartheta ,t)}{\partial u} \right ] \Delta u(t)dt\\ &\quad {}+ \left [\int \limits _{t^{1} }^{t^{f} }\frac {\partial f^{0} (x(t),u(t),\vartheta , t)}{\partial \vartheta } dt + \frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \vartheta } \right ] \Delta \vartheta \\ &\quad {}+\left \{\sum _{i=1}^{l_{1} }\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}^{i}}\Delta x(\tilde {t}^{i} )+\sum _{j=1}^{2l_{2} }\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \hat {x}^{j}}\Delta x(\hat {t}^{j} )+\psi ^{\mathrm {T}} (t^{f} )\Delta x(t^{f} )\right . \\ &\qquad \qquad \qquad \qquad {}-\left .\psi ^{\mathrm {T}} (t^{1} )\Delta x(t^{1} )+\sum _{i=2}^{l_{1} -1}\left [ \psi (\tilde {t}_{-}^{i} )-\psi (\tilde {t}_{+}^{i} )\right ]^{\mathrm {T}} \Delta x(\tilde {t}^{i})\right . \\ &\qquad \qquad \qquad \qquad \qquad \qquad \qquad \qquad {}+\left .\sum _{j=1}^{2l_{2} }\left [\psi (\hat {t}_{-}^{j} )-\psi (\hat {t}_{+}^{j} )\right ]^{\mathrm {T}} \Delta x(\hat {t}^{j} ) \right \}+R. \end {aligned}$$
(A.14)

Now let us deal with the terms in curly braces.

In (A.14), we use conditions (A.12) to express some \(n \) components of the \(nl_{1} \)-dimensional vector

$$ \Delta x(\tilde {t})=\Delta \tilde {x}=\left (\Delta x_{1} (\tilde {t}^{1} ), \Delta x_{2} (\tilde {t}^{1} ),\ldots , \Delta x_{n} (\tilde {t}^{1} ), \ldots , \Delta x_{i} (\tilde {t}^{j} ), \ldots , \Delta x_{n} (\tilde {t}^{l_{1} } )\right ), $$
via the other \(nl_{1}-1 \) components.

In what follows, to simplify the exposition of technical details, we use componentwise notation of formulas jointly with the matrix operations.

Then relation (A.12) can be written as

$$ {\overset{{}_\frown}{\alpha}{}} \Delta \, {\overset{{}_\frown}{x}{}} +\breve {\alpha }\Delta \breve {x}+\sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} }\beta _{j} (t )\Delta x(t)dt =\Delta \vartheta .$$
Based on this, in view of (3.2), we have
$$ \Delta \, {\overset{{}_\frown}{x}{}} = {\overset{{}_\frown}{\alpha}{}} ^{-1} \Delta \vartheta - {\overset{{}_\frown}{\alpha}{}} ^{-1} \breve {\alpha }\Delta \breve {x}-\sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} } {\overset{{}_\frown}{\alpha}{}} ^{-1} \beta _{j} (t )\Delta x(t)dt . $$
(A.15)

With the adopted notation \(C=-{\overset{{}_\frown}{\alpha}{}} ^{-1}\) and \(B=-{\overset{{}_\frown}{\alpha}{}} ^{-1}\breve {\alpha }\), relation (A.15) acquires the form

$$ \Delta \, {\overset{{}_\frown}{x}{}} =C \Delta \vartheta +B\Delta \breve {x}-\sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} } {\overset{{}_\frown}{\alpha}{}} ^{-1} \beta _{j} (t)\Delta x(t)dt,$$
(A.16)
or, in componentwise form,
$$ \eqalign { \Delta {\overset{{}_\frown}{x}{}} _{i} &=\Delta x_{k_{i} } (\tilde {t}^{s_{i} } )=\sum _{k=1}^{n}c_{ik} \Delta \vartheta _{k} +\sum _{\nu =1}^{l_{1} n}b_{i\nu } \Delta x_{g_{\nu } } (\tilde {t}^{q_{\nu } } ) \cr &\qquad {}-\sum _{j=1}^{l_{2} }\sum _{k=1}^{n}\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} } {\overset{{}_\frown}{\alpha}{}} ^{-1} \beta _{ik}^{j} (t) \Delta x_{k} (t)dt , \quad i=1,2,\ldots ,n ,\quad 1\le g_{\nu } \le n.}$$
(A.17)
We write the last 4–7 terms in (A.14) as
$$ \eqalign { \psi ^{\mathrm {T}} (t^{f} )\Delta x(t^{f} )&=\sum _{j=1}^{n}\psi _{j} (t^{f} )\Delta x_{j} (t^{f} ) , \cr \psi ^{\mathrm {T}} (t^{1} )\Delta x(t^{1} )&=\sum _{j=1}^{n}\psi _{j} (t^{1} )\Delta x_{j} (t^{1} ) .}$$

Combining the fourth and eighth terms in (A.14) and taking into account (A.17), we obtain

$$ \eqalign { \sum _{i=1}^{l_{1} }\sum _{j=1}^{n}&{}\left [\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{i}^{j} } + \Delta \psi _{j} (\tilde {t}^{i} )\right ] \Delta x_{j} (\tilde {t}^{i} )\cr &\qquad {}=\sum _{i=1}^{n}\left [\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{k_{i} }^{s_{i} } } + \Delta \psi _{k_{i} } (\tilde {t}^{s_{i} } )\right ]\Delta x_{k_{i} } (\tilde {t}^{s_{i} } )\cr &\qquad \qquad {}+\sum _{\nu =1}^{l_{1} n} \left [\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{g_{\nu } }^{q_{\nu } } } +\Delta \psi _{g_{\nu } } (\tilde {t}^{q_{\nu } } )\right ] \Delta x_{g_{\nu } } (\tilde {t}^{q_{\nu } } ) \cr &\qquad {}=\sum _{i=1}^{n}\left [\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{k_{i} }^{s_{i} } } + \Delta \psi _{k_{i} } (\tilde {t}^{s_{i} } )\right ] \cr &\qquad \qquad \qquad {}\times \left [\sum _{k=1}^{n}c_{ik} \Delta \vartheta _{k} + \sum _{\nu =1}^{l_{1} n}b_{i\nu } \Delta x_{g_{\nu } } (\tilde {t}^{q_{\nu } } ) - \sum _{j=1}^{l_{2} }\sum _{k=1}^{n}\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} } {\overset{{}_\frown}{\alpha}{}} ^{-1} \beta _{ik}^{j} (t) \Delta x_{k} (t)dt \right ] \cr &\qquad \qquad {}+\sum _{\nu =1}^{l_{1} n} \left [\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{g_{\nu } }^{q_{\nu } } } +\Delta \psi _{g_{\nu } } (\tilde {t}^{q_{\nu } } )\right ] \Delta x_{g_{\nu } } (\tilde {t}^{q_{\nu } } ).}$$

From (A.14), considering the resulting relation, we have

$$ \begin {aligned} \Delta J(u,\vartheta )&=\int \limits _{t^{1} }^{t^{f} }\left [\vphantom {\sum _{j=1}^{l_{2} }}-\dot {\psi }^{\mathrm {T}} (t)-\psi ^{\mathrm {T}} (t)A_{1} (t)+\frac {\partial f^{0} (x(t), u(t),\vartheta , t)}{\partial x} \right . \\ &\qquad \qquad {}-\left .\sum _{i=1}^{n}\left (\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{k_{i} }^{s_{i} } } +\Delta \psi _{k_{i} } (\tilde {t}^{s_{i} } )\right ) \sum _{j=1}^{l_{2} }\chi _{[\hat {t}^{2j-1} , \hat {t}^{2j}]} (t){\overset{{}_\frown}{\alpha}{}} ^{-1} \beta _{j} (t) \right ]\Delta x(t)dt \\ &\qquad {}+\int \limits _{t^{1} }^{t^{f} }\left [-\psi ^{\mathrm {T}} (t)A_{2} (t)+\frac {\partial f^{0} (x(t), u(t),\vartheta , t)}{\partial u} \right ]\Delta u(t)dt \\ &\qquad {}+\sum _{k=1}^{n}\left \{\vphantom {\int \limits _{t^{1} }^{t^{f} }}\sum _{i=1}^{n}\left (\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{k_{i} }^{s_{i} } } +\Delta \psi _{k_{i} } (\tilde {t}^{s_{i} } )\right ) c_{ik}\right . \\ &\qquad \qquad \qquad \qquad {}+\left . \frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \vartheta _{k} } \right . +\left .\int \limits _{t^{1} }^{t^{f} }\frac {\partial f^{0} (x(t),u(t),\vartheta ,t)}{\partial \vartheta _{k} } dt\right \} \Delta \vartheta _{k} \\ &\qquad {}+\sum _{\nu =1}^{l_{1} n} \Bigg [\sum _{i=1}^{n}b_{i\nu } \left (\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{k_{i} }^{s_{i} } } +\Delta \psi _{k_{i} } (\tilde {t}^{s_{i} } )\right ) \\ &\qquad \qquad \qquad \qquad {}+\left (\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{g_{\nu } }^{q_{\nu } } } +\Delta \psi _{g_{\nu } } (\tilde {t}^{q_{\nu } } )\right )\Bigg ] \Delta x_{g_{\nu } } (\tilde {t}^{q_{\nu } } ) \\ &\qquad {}+\sum _{j=1}^{2l_{2} }\sum _{i=1}^{n}\left [\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \hat {x}_{i}^{j} } +\Delta \psi _{i} (\hat {t}^{j} )\right ] \Delta x_{i} (\hat {t}^{j} )+R. \end {aligned}$$
(A.18)

By virtue of the arbitrariness of the vector function \(\psi (t) \), we require that the expressions in the first and in the last two brackets in (A.18) vanish. From the first requirement, we obtain the adjoint system of differential equations (3.7), and from the other two requirements we obtain the expressions

$$ \begin {gathered} \sum _{i=1}^{n}b_{i\nu } \left [\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{k_{i} }^{s_{i} } } +\Delta \psi _{k_{i} } (\tilde {t}^{s_{i} } )\right ]+ \left [\vphantom {\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{k_{i} }^{s_{i} } }}\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{g_{\nu } }^{q_{\nu } } } +\Delta \psi _{g_{\nu } } (\tilde {t}^{q_{\nu } } )\right ]=0,\quad \nu =1,2,\ldots ,l_{1} n, \\ \frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \hat {x}_{i}^{j} } +\Delta \psi _{i} (\hat {t}^{j} )=0,\quad i=1,2,\ldots ,n, \quad j=1,2,\ldots ,2l_{2} . \end {gathered} $$
This implies conditions (3.8) and (3.9).

Then the desired components of the gradient of the functional with respect to \(u(t) \) and \(\vartheta \) will be determined from (A.18) as the linear parts of the increment of the functional for \(\Delta u(t) \) and \(\Delta \vartheta \) by formulas (3.5) and (3.6).

This completes the proof of Theorem 3.\(\quad \blacksquare \)

Proof of Theorem 4. Let us proceed to the optimality conditions for the pair \(\left (u,\vartheta \right ) \) with conditions (2.2) replaced by conditions (3.3) and (3.4). Unlike the above manipulations for the case of \(\bar {n}=n\), in the sequel, to take into account conditions (3.4), we use the Lagrange method and introduce an \((n-\bar {n})\) -dimensional additional vector of parameters—the Lagrange multipliers.

Once again using the method of increment in the parameters \(\left (u,\vartheta \right ) \) to be optimized, for the increment of the functional we obtain formula (A.6). Condition (3.4) in terms of increments now acquires the form

$$ \sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} }\beta _{j}^{2} (t )\Delta x(t)dt =\Delta \vartheta ^{(2)} . $$
(A.19)

We transpose all terms in (A.19) to the left, multiply the resulting expression by as yet arbitrary vector \(\lambda \in \mathrm {R}^{n-\bar {n}}\), and add to (A.14). For the increment of the functional, we obtain

$$ \begin {aligned} \Delta J(u,\vartheta )&=\int \limits _{t^{1} }^{t^{f} }\left [\vphantom { \sum _{j=1}^{l_{2} }}-\dot {\psi }^{\mathrm {T}} (t)-\psi ^{\mathrm {T}} (t)A_{1} (t)+\frac {\partial f^{0} (x(t), u(t),\vartheta , t)}{\partial x}\right . \\ &\qquad \qquad \qquad \qquad {}-\left .\lambda ^{\mathrm {T}} \sum _{j=1}^{l_{2}}\chi _{[\hat {t}^{2j-1},\hat {t}^{2j}} (t) \beta _{j}^{2} (t) \right ]\Delta x(t)dt \\ &\qquad {}+ \int \limits _{t^{1} }^{t^{f} } \left [-\psi ^{\mathrm {T}} (t)A_{2} (t) + \frac {\partial f^{0} (x(t), u(t),\vartheta , t)}{\partial u} \right ] \Delta u(t)dt \\ &\qquad {}+\left [\int \limits _{t^{1} }^{t^{f} }\frac {\partial f^{0} (x(t), u(t),\vartheta , t)}{\partial \vartheta ^{(1)} } dt + \frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \vartheta ^{(1)} } \right ] \Delta \vartheta ^{(1)} \\ &\qquad {}+\left [\int \limits _{t^{1} }^{t^{f} }\frac {\partial f^{0} (x(t), u(t),\vartheta , t)}{\partial \vartheta ^{(2)} } dt+\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \vartheta ^{(2)} } -\lambda ^{\mathrm {T}} \right ]\Delta \vartheta ^{(2)} \\ &\qquad {}+\left \{\vphantom {\sum _{j=1}^{2l_{2} }}\sum _{i=1}^{l_{1} }\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}^{i} } \Delta x(\tilde {t}^{i} )+\sum _{j=1}^{2l_{2} }\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \hat {x}^{j} } \Delta x(\hat {t}^{j} )+\psi ^{\mathrm {T}} (t^{f} )\Delta x(t^{f} )\right . \\ &\quad \quad \qquad \qquad \qquad {}-\psi ^{\mathrm {T}} (t^{1} )\Delta x(t^{1} )+\sum _{i=2}^{l_{1} -1} \left [\psi (\tilde {t}_{-}^{i} )-\psi (\tilde {t}_{+}^{i} )\right ] ^{\mathrm {T}} \Delta x(\tilde {t}^{i} ) \\ &\qquad \qquad \qquad \qquad \qquad \qquad \qquad \qquad \qquad {}+\left .\sum _{j=1}^{2l_{2} } \left [\psi (\hat {t}_{-}^{j} )-\psi (\hat {t}_{+}^{j} )\right ] ^{\mathrm {T}} \Delta x(\hat {t}^{j} ) \right \}+R. \end {aligned} $$
(A.20)

Now let us deal with the terms in curly braces having in mind that the rank of the augmented matrix \(\alpha =\left [\alpha _{1} ,\alpha _{2} ,\ldots ,\alpha _{l_{1} } \right ]\) in conditions (3.3) is \(\bar {n}\). From the expression in increments

$$ \sum _{i=1}^{l_{1} }\alpha _{i} \Delta x(\tilde {t}^{i} )+\sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} }\beta _{j}^{1} (t )\Delta x(t)dt =\Delta \vartheta ^{(1)} , $$
obtained from (3.3), we express some \(\bar {n} \) components of the \(nl_{1} \)-dimensional vector \(\Delta \tilde {x}(\tilde {t}) \) via the remaining \(nl_{1}-\bar {n} \) components. As was done above, from the matrix \(\alpha \) we isolate a \(\bar {n}\times \bar {n} \) submatrix (minor) \({\overset{{}_\frown}{\alpha}{}} \). Let the remaining \(nl_{1} -\bar {n} \) columns of the matrix \(\alpha \) form a matrix \(\breve {\alpha } \). We denote the components of the vector \(\Delta \tilde {x}(\tilde {t})\) corresponding to the matrix \({\overset{{}_\frown}{\alpha}{}} \) by \(\Delta {\overset{{}_\frown}{x}{}} (\tilde {t}) \); the remaining components form the vector \(\Delta \breve {x}(\tilde {t})\). Then
$$ \Delta \, {\overset{{}_\frown}{x}{}} (\tilde {t})= {\overset{{}_\frown}{\alpha}{}} ^{-1} \Delta \vartheta ^{(1)} - {\overset{{}_\frown}{\alpha}{}} ^{-1} \breve {\alpha }\Delta \breve {x}(\tilde {t})- {\overset{{}_\frown}{\alpha}{}} ^{-1} \sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} }\beta _{j}^{1} (t )\Delta x(t)dt , $$
$$ \Delta \, {\overset{{}_\frown}{x}{}} =C \Delta \vartheta +B\Delta \breve {x}-\sum _{j=1}^{l_{2} }\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} } {\overset{{}_\frown}{\alpha}{}} ^{-1} \beta _{j}^{(1)} (t)\Delta x(t)dt,$$
or, in componentwise form,
$$ \begin {aligned} \Delta \,{\overset{{}_\frown}{x}{}} _{i}=\Delta x_{k_{i} } (\tilde {t}^{s_{i} } )&=\sum _{k=1}^{n}c_{ik} \Delta \vartheta _{k} +\sum _{\nu =1}^{l_{1} n-\bar {n}}b_{i\nu } \Delta x_{g_{\nu } } (\tilde {t}^{q_{\nu } } ) \\ &\qquad {}-\sum _{j=1}^{l_{2} }\sum _{k=1}^{n}\;\int \limits _{\hat {t}^{2j-1} }^{\hat {t}^{2j} } {\overset{{}_\frown}{\alpha}{}} ^{-1} \beta _{jik}^{(1)} (t) \Delta x_{k} (t)dt , \quad i=1,2,\ldots ,\bar {n} , \quad 1\le g_{\nu } \le n. \end {aligned} $$
(A.21)
Taking (A.21) into account in (A.20), we obtain
$$ \begin {aligned} \Delta J(u,\vartheta )&=\int \limits _{t^{1} }^{t^{f}} \left [\vphantom {\sum _{j=1}^{l_{2} }} -\dot {\psi }^{\mathrm {T}}(t)-\psi ^{\mathrm {T}}(t)A_{1}(t)+ \frac {\partial f^{0} (x(t),u(t),\vartheta , t)}{\partial x}\right . \\ &\qquad \qquad \quad {}-\lambda ^{\mathrm {T}} \sum _{j=1}^{l_{2} }\chi _{[\hat {t}^{2j-1} , \hat {t}^{2j}]} (t) \beta _{j}^{2} (t) \\ &\qquad \qquad \qquad {}+ \left ( \sum _{i=1}^{n} \left ( \frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{k_{i} }^{s_{i} } } +(\psi _{k_{i} } (\tilde {t}_{-}^{s_{i} } )-\psi _{k_{i} } (\tilde {t}_{+}^{s_{i} } ))^{\mathrm {T}} \right ) \right ) \\ &\qquad \qquad \qquad \qquad \qquad {}\times \left . \sum _{j=1}^{l_{2} }\chi _{[\hat {t}^{2j-1} , \hat {t}^{2j}]} (t) {\overset{{}_\frown}{\alpha}{}} ^{-1} \beta _{j}^{1} (t) \right ]\Delta x(t)dt \\ &\qquad {}+\int \limits _{t^{1} }^{t^{f} }\left [-\psi ^{\mathrm {T}} (t)A_{2} (t)+\frac {\partial f^{0} (x(t), u(t),\vartheta , t)}{\partial u} \right ]\Delta u(t)dt \\ &\qquad {}+\sum _{k=1}^{\bar {n}}\left [\vphantom {\int \limits _{t^{1} }^{t^{f} }}\sum _{i=1}^{\bar {n}}\left [\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{k_{i} }^{s_{i} } } +\Delta \psi _{k_{i} } (\tilde {t}^{s_{i} } )\right ] c_{ik}\right . \\ &\qquad \qquad \qquad {}+\left .\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \vartheta _{k}^{(1)} } +\int \limits _{t^{1} }^{t^{f} }\frac {\partial f^{0} (x(t),u(t),\vartheta ,t)}{\partial \vartheta _{k}^{(1)} } dt\right ] \Delta \vartheta _{k}^{(1)} \\ &\qquad {}+\sum _{k=1}^{n-\bar {n}} \left [-\lambda _{k} +\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \vartheta _{k}^{(2)} } +\int \limits _{t^{1} }^{t^{f} }\frac {\partial f^{0} (x(t),u(t),\vartheta ,t)}{\partial \vartheta _{k}^{(2)} } dt\right ]\Delta \vartheta _{k}^{(2)} \\ &\qquad {}+\sum _{\nu =1}^{l_{1} n-\bar {n}} \left [\sum _{i=1}^{n}b_{i\nu } \left (\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{k_{i} }^{s_{i} } } +\Delta \psi _{k_{i} } (\tilde {t}^{s_{i} } )\right )\right . \\ &\qquad \qquad \qquad {}+\left .\left (\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{g_{\nu } }^{q_{\nu } } } +\Delta \psi _{g_{\nu } } (\tilde {t}^{q_{\nu } } )\right )\vphantom {\sum _{i=1}^{n}}\right ] \Delta x_{g_{\nu } } (\tilde {t}^{q_{\nu } } ) \\ &\qquad {}+\sum _{j=1}^{2l_{2} }\sum _{i=1}^{n}\left [\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \hat {x}_{i}^{j} } +\Delta \psi _{i} (\hat {t}^{j} )\right ] \Delta x_{i} (\hat {t}^{j} )+R. \end {aligned}$$
(A.22)

By virtue of the arbitrariness of the vector function \(\psi (t) \) and the vector \(\lambda \), we require that they be such that the expression in the first bracket in (A.22) vanishes, and, based on this, produce the adjoint differential equation (3.28). At the same time, by virtue of the arbitrariness of the components of the remainder vector \(\Delta x(\tilde {t}) \) and increments \(\Delta x(\hat {t}^{i} ) \), \(j=1,2,\ldots ,2l_{2} \), we require that the expressions in the last two brackets in (A.22) be zero,

$$ \begin {gathered} \sum _{i=1}^{\bar {n}}b_{i\nu } \left [\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{k_{i} }^{s_{i} } } +\Delta \psi _{k_{i} } (\tilde {t}^{s_{i} } )\right ]+\left [\frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \tilde {x}_{g_{\nu } }^{q_{\nu } } } +\Delta \psi _{g_{\nu } } (\tilde {t}^{q_{\nu } } )\right ]=0 , \quad \nu =1, 2, \ldots , l_{1} n-\bar {n}, \\ \frac {\partial \Phi (\tilde {x},\hat {x},\vartheta )}{\partial \hat {x}_{i}^{j} } +\Delta \psi _{i} (\hat {t}^{j} )=0, \quad i=1, 2, \ldots ,n, \quad j=1, 2, \ldots , 2l_{2} . \end {gathered}$$
Based on this, we obtain the boundary conditions (3.29)–(3.30) for the adjoint equation (3.28).

It is clear from (A.22) that the formula for the gradient of the functional with respect to \(u(t) \) will be the same as in (3.5), and the components of the gradient with respect to \(\vartheta \) are given by formulas (3.26) aand (3.27). Thus, the proof of Theorem 4 is complete.\(\quad \blacksquare \)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aida-zade, K.R., Abdullayev, V.M. Optimization of Right-Hand Sides of Nonlocal Boundary Conditions in a Controlled Dynamical System. Autom Remote Control 82, 375–397 (2021). https://doi.org/10.1134/S0005117921030012

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0005117921030012

Keywords

Navigation