Penalty Functions in a Control Problem

Karelin, Vladimir V.

doi:10.1023/b:aurc.0000019381.32610.e9

Cited by 9 publications

(16 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main results on exact penalty functions for various variational problems from other works [13][14][15][16][17][18][19] are based on the assumptions that the objective function is Lipschitz continuous on a rather complicated and possibly unbounded set, and a penalty function attains a global minimum in the space of piecewise continuous functions for any sufficiently large value of the penalty parameter, and it is, once again, unclear how to verify these assumptions in any particular case. The same remark is true for the main results of the papers [9][10][11][12]20,21 devoted to exact penalty functions for optimal control problems. To the best of authors' knowledge, the only verifiable sufficient conditions for the global exactness of an exact penalty function in the infinite dimensional setting were obtained in the work of Gugat and Zuazua, 26 where the exact penalisation of the terminal constraint for optimal control problems involving linear evolution equations was considered.…”

mentioning

confidence: 65%

Exact penalty functions for optimal control problems I: Main theorem and free‐endpoint problems

Долгополик

Fominyh

2019

Optim Control Appl Methods

View full text Add to dashboard Cite

Summary In this two‐part study, we develop a general approach to the design and analysis of exact penalty functions for various optimal control problems, including problems with terminal and state constraints, problems involving differential inclusions, and optimal control problems for linear evolution equations. This approach allows one to simplify an optimal control problem by removing some (or all) constraints of this problem with the use of an exact penalty function, thus allowing one to reduce optimal control problems to equivalent variational problems and apply numerical methods for solving, eg, problems without state constraints, to problems including such constraints, etc. In the first part of our study, we strengthen some existing results on exact penalty functions for optimisation problems in infinite dimensional spaces and utilise them to study exact penalty functions for free‐endpoint optimal control problems, which reduce these problems to equivalent variational ones. We also prove several auxiliary results on integral functionals and Nemytskii operators that are helpful for verifying the assumptions under which the proposed penalty functions are exact.

show abstract

mentioning

confidence: 65%

Exact penalty functions for optimal control problems I: Main theorem and free‐endpoint problems

Долгополик

Fominyh

2019

Optim Control Appl Methods

View full text Add to dashboard Cite

show abstract

“…) < 0, (see equality (40) and inequality (41)). Thus, 0 ∈ core K(x*, u*), and the proof is complete.…”

Section: Variable-endpoint Problemsmentioning

confidence: 97%

“…41 However, the main results of these papers are based on the assumptions that the penalty function attains a global minimum in the space of piecewise continuous functions for any sufficiently large value of the penalty parameter, and the cost functional is Lipschitz continuous on a possibly unbounded and rather complicated set. It is unclear how to verify these assumptions in particular cases, which makes it very difficult to apply the main results of papers [37][38][39][40][41] to real problems. To the best of author's knowledge, the only verifiable sufficient conditions for the global exactness of penalty functions for optimal control problems were obtained by Gugat 42 for an optimal control of the wave equation, by Gugat and Zuazua 43 for optimal control problems for general linear evolution equations, and by Jayswal and Preeti 44 for an optimal control problem for a system described by a partial differential equation state inequality constraints.…”

mentioning

confidence: 99%

Exact penalty functions for optimal control problems II: Exact penalization of terminal and pointwise state constraints

Долгополик

2020

Optim Control Appl Methods

View full text Add to dashboard Cite

The second part of our study is devoted to an analysis of the exactness of penalty functions for optimal control problems with terminal and pointwise state constraints. We demonstrate that with the use of the exact penalty function method one can reduce fixed-endpoint problems for linear time-varying systems and linear evolution equations with convex constraints on the control inputs to completely equivalent free-endpoint optimal control problems, if the terminal state belongs to the relative interior of the reachable set. In the nonlinear case, we prove that a local reduction of fixed-endpoint and variable-endpoint problems to equivalent free-endpoint ones is possible under the assumption that the linearized system is completely controllable, and point out some general properties of nonlinear systems under which a global reduction to equivalent free-endpoint problems can be achieved. In the case of problems with pointwise state inequality constraints, we prove that such problems for linear time-varying systems and linear evolution equations with convex state constraints can be reduced to equivalent problems without state constraints, provided one uses the L ∞ penalty term, and Slater's condition holds true, while for nonlinear systems a local reduction is possible, if a natural constraint qualification is satisfied. Finally, we show that the exact L p -penalization of state constraints with finite p is possible for convex problems, if Lagrange multipliers corresponding to the state constraints belong to L p ′ , where p ′ is the conjugate exponent of p, and for general nonlinear problems, if the cost functional does not depend on the control inputs explicitly.

show abstract

“…Constraint handling is an important step for a control problem before being optimized. In general, the constraints are disposed by penalty function method, in which a penalty factor is introduced to convert the original problem into an unconstrained optimization problem [29]. But there is no definite theory to determine the penalty factor, which is usually obtained by experience.…”

Section: Introductionmentioning

confidence: 99%

A Numerical Computation Approach for the Optimal Control of ASP Flooding Based on Adaptive Strategies

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

A numerical computation approach based on constraint aggregation and pseudospectral method is proposed to solve the optimal control of alkali/surfactant/polymer (ASP) flooding. At first, all path constraints are aggregated into one terminal condition by applying a Kreisselmeier-Steinhauser (KS) function. After being transformed into a multistage problem by control vector parameter, a normalized time variable is introduced to convert the original problem into a fixed final time optimal control problem. Then the problem is discretized to nonlinear programming by using Legendre-Gauss pseudospectral method, whose numerical solutions can be obtained by sequential quadratic programming (SQP) method through solving the KKT optimality conditions. Additionally, two adaptive strategies are applied to improve the procedure: (1) the adaptive constraint aggregation is used to regulate the parameter in KS function and (2) the adaptive Legendre-Gauss (LG) method is used to adjust the number of subinterval divisions and LG points. Finally, the optimal control of ASP flooding is solved by the proposed method. Simulation results show the feasibility and effectiveness of the proposed method.

show abstract

Penalty Functions in a Control Problem

Cited by 9 publications

References 6 publications

Exact penalty functions for optimal control problems I: Main theorem and free‐endpoint problems

Exact penalty functions for optimal control problems I: Main theorem and free‐endpoint problems

Exact penalty functions for optimal control problems II: Exact penalization of terminal and pointwise state constraints

A Numerical Computation Approach for the Optimal Control of ASP Flooding Based on Adaptive Strategies

Contact Info

Product

Resources

About