On the properties of solutions to the Goursat-Darboux problem with boundary and distributed controls

We consider a control system described by the Goursat-Darboux equation. The system is controlled by distributed and boundary controls. The controls are subject to the constraints given as multivalued mappings with closed, possibly nonconvex, values depending on the phase variable. Alongside the initial constraints, we consider the convexified constraints and the constraints whose values are the extreme points of the convexified constraints. We study the questions of existence of solutions and establish connections between the solutions under various constraints.     

Solution of an optimal control problem with phase constraints by the double variation method

An optimal control problem with an integral quality index specified in a finite time interval is formulated for a model of economic growth that leads to emission of greenhouse gases. The controlled system is linear with respect to control. The problem contains phase constraints that abandon emission of greenhouse gases above some predefined time-dependent limit. As is known, optimal control problems with phase constraints fall beyond the sphere of efficient application of the Pontryagin maximum principle because, for such problems, this principle is formulated in a complicated form difficult for analytic treatment in particular situations. In this study, the analytic structure of the optimal control and phase trajectories is constructed using the double variation method.     

On intersections of abelian and nilpotent subgroups in finite groups. I

We consider Pontryagin’s generalized nonstationary example with identical dynamic and inertial capabilities of the players under phase constraints on the evader’s states. The boundary of the phase constraints is not a “death line” for the evader. The set of admissible controls is a ball centered at the origin, and the terminal sets are the origin. We obtain sufficient conditions for a multiple capture of one evader by a group of pursuers in the case when some functions corresponding to the initial data and to the parameters of the game are recurrent.     

Optimal control of sterilization of prepackaged food in the case of problem with free final time and phase constraints

This paper is concerned with the optimal control of the sterilization of prepackaged food. The investigated system is constructed as an optimal control problem with free final horizon and phase constraints. Pontryagin’s maximum principle, the necessary optimality condition for the system, is studied by the Dubovitskii and Milyutin functional analytical approach. The derived necessary condition is presented for the problem with both the control constraints and the state constraints.     

Constructing the reachability set for a control problem with phase constraints

A method is proposed for describing the reachability set in a phase-constrained control problem. Sufficient conditions are obtained when the reachability set in a phase-constrained control problem is the intersection of two sets: the reachability set for the corresponding problem without phase constraints and the set of phase constraints. __________ Translated from Nelineinaya Dinamika i Upravlenie, No. 4, pp. 397–399, 2004.     

Effects of volume fraction condition on thermodynamic restrictions in mixture theory

Volume fraction condition is a true constraint that must be taken into consideration in deducing the thermodynamic restrictions of mixture theory applying the axiom of dissipation. For a process to be admissible, the constraints imposed by the volume fraction condition include not only the equation obtained by taking its material derivative with respect to the motion of a given phase, but also those by taking its spatial gradient. The thermodynamic restrictions are deduced under the complete constraints, the results obtained are consistent for the mixtures with or without a compressible phase, and in which the free energy of each phase depends on the densities of all phases.     

Lyapunov function method for control law synthesis under one integral and several phase constraints

We consider the synthesis of linear control laws under one integral and several phase constraints on the basis of the Lyapunov function method and the technique of linear matrix inequalities. In particular, our method permits one to obtain suboptimal state or output feedbacks providing the minimum upper bound for a quadratic functional under phase and control constrains or the minimum bound for the maximum deviation of the controlled variable under one integral and several phase constraints. The synthesis is generalized to the case of nonstationary parametric perturbations in the plant.     

Sequential gradient-restoration algorithm for optimal control problems with nondifferential constraints

This paper considers the numerical solution of optimal control problems involving a functionalI subject to differential constraints, nondifferential constraints, and terminal constraints. The problem is to find the statex(t), the controlu(t), and the parameter π so that the functional is minimized, while the constraints are satisfied to a predetermined accuracy. The approach taken is a sequence of two-phase processes or cycles, composed of a gradient phase and a restoration phase. The gradient phase involves a single iteration and is designed to decrease the functional, while the constraints are satisfied to first order. The restoration phase involves one or several iterations and is designed to restore the constraints to a predetermined accuracy, while the norm of the variations of the control and the parameter is minimized. The principal property of the algorithm is that it produces a sequence of feasible suboptimal solutions: the functionsx(t),u(t), π obtained at the end of each cycle satisfy the constraints to a predetermined accuracy. Therefore, the functionals of any two elements of the sequence are comparable. The stepsize of the gradient phase is determined by a one-dimensional search on the augmented functionalJ, and the stepsize of the restoration phase by a one-dimensional search on the constraint errorP. If α _g is the gradient stepsize and α _r is the restoration stepsize, the gradient corrections are ofO(α _g ) and the restoration corrections are ofO(α _r α _g ²). Therefore, for α _g sufficiently small, the restoration phase preserves the descent property of the gradient phase: the functionalÎ at the end of any complete gradient-restoration cycle is smaller than the functionalI at the beginning of the cycle. To facilitate the numerical solution on digital computers, the actual time ? is replaced by the normalized timet, defined in such a way that the extremal arc has a normalized time length Δt=1. In this way, variable-time terminal conditions are transformed into fixed-time terminal conditions. The actual time τ at which the terminal boundary is reached is regarded to be a component of the parameter π being optimized. The present general formulation differs from that of Ref. 4 because of the inclusion of the nondifferential constraints to be satisfied everywhere over the interval 0 ≤t ≤ 1. Its importance lies in that (i) many optimization problems arise directly in the form considered here, (ii) problems involving state equality constraints can be reduced to the present scheme through suitable transformations, and (iii) problems involving inequality constraints can be reduced to the present scheme through suitable transformations. The latter statement applies, for instance, to the following situations: (a) problems with bounded control, (b) problems with bounded state, (c) problems with bounded time rate of change of the state, and (d) problems where some bound is imposed on an arbitrarily prescribed function of the parameter, the control, the state, and the time rate of change of the state. Numerical examples are presented for both the fixed-final-time case and the free-final-time case. These examples demonstrate the feasibility as well as the rapidity of convergence of the technique developed in this paper.     

A phase field approach to shape optimization in Navier–Stokes flow with integral state constraints

We consider the shape optimization of an object in Navier–Stokes flow by employing a combined phase field and porous medium approach, along with additional perimeter regularization. By considering integral control and state constraints, we extend the results of earlier works concerning the existence of optimal shapes and the derivation of first order optimality conditions. The control variable is a phase field function that prescribes the shape and topology of the object, while the state variables are the velocity and the pressure of the fluid. In our analysis, we cover a multitude of constraints which include constraints on the center of mass, the volume of the fluid region, and the total potential power of the object. Finally, we present numerical results of the optimization problem that is solved using the variable metric projection type (VMPT) method proposed by Blank and Rupprecht, where we consider one example of topology optimization without constraints and one example of maximizing the lift of the object with a state constraint, as well as a comparison with earlier results for the drag minimization.     

Primal-dual interior-point method for thermodynamic gas-particle partitioning

A mathematical model for the computation of the phase equilibrium and gas-particle partitioning in atmospheric organic aerosols is presented. The thermodynamic equilibrium is determined by the global minimum of the Gibbs free energy under equality and inequality constraints for a system that involves one gas phase and many liquid phases. A primal-dual interior-point algorithm is presented for the efficient solution of the phase equilibrium problem and the determination of the active constraints. The first order optimality conditions are solved with a Newton iteration. Sequential quadratic programming techniques are incorporated to decouple the different scales of the problem. Decomposition methods that control the inertia of the matrices arising in the resolution of the Newton system are proposed. A least-squares initialization of the algorithm is proposed to favor the convergence to a global minimum of the Gibbs free energy. Numerical results show the efficiency of the approach for the prediction of gas-liquid-liquid equilibrium for atmospheric organic aerosol particles.     

Two-Phase Model Algorithm with Global Convergence for Nonlinear Programming

The family of feasible methods for minimization with nonlinear constraints includes the nonlinear projected gradient method, the generalized reduced gradient method (GRG), and many variants of the sequential gradient restoration algorithm (SGRA). Generally speaking, a particular iteration of any of these methods proceeds in two phases. In the restoration phase, feasibility is restored by means of the resolution of an auxiliary nonlinear problem, generally a nonlinear system of equations. In the minimization phase, optimality is improved by means of the consideration of the objective function, or its Lagrangian, on the tangent subspace to the constraints. In this paper, minimal assumptions are stated on the restoration phase and the minimization phase that ensure that the resulting algorithm is globally convergent. The key point is the possibility of comparing two successive nonfeasible iterates by means of a suitable merit function that combines feasibility and optimality. The merit function allows one to work with a high degree of infeasibility at the first iterations of the algorithm. Global convergence is proved and a particular implementation of the model algorithm is described.     

An assessment of a days off decomposition approach to personnel shift scheduling

This paper studies a two-phase decomposition approach to solving the personnel scheduling problem. The first phase creates a days-off-schedule, indicating working days and days off for each employee. The second phase assigns shifts to the working days in the days-off-schedule. This decomposition is motivated by the fact that personnel scheduling constraints are often divided into two categories: one specifies constraints on working days and days off, while the other specifies constraints on shift assignments. To assess the consequences of the decomposition approach, we apply it to public benchmark instances, and compare this to solving the personnel scheduling problem directly. In all steps we use mathematical programming. We also study the extension that includes night shifts in the first phase of the decomposition. We present a detailed results analysis, and analyze the effect of various instance parameters on the decompositions’ results. In general, we observe that the decompositions significantly reduce the computation time, but the quality, though often good, depends strongly on the instance at hand. Our analysis identifies which aspects in the instance can jeopardize the quality.     

Optimal design of efficient acoustic antenna arrays

Minimax optimal design of sonar transducer arrays can be formulated as a nonlinear program with many convex quadratic constraints and a nonconvex quadratic efficiency constraint. The variables of this problem are a scaling and phase shift applied to the output of each sensor. This problem is solved by applying Lagrangian relaxation to the convex quadratic constraints. Extensive computational experience shows that this approach can efficiently find near-optimal solutions of problems with up to 391 variables and 579 constraints. This work was supported by ONR Contracts N00014-83-C-0437 and N00014-82-C-824.     

Optimal Control Problems with Integral Functional and Phase Constraints: Reduction to Optimal Consistency Parameter Problems

An iteration method to solve a class of optimal control problems with integral functional and phase constraints is developed in the paper.     

Construction of the viability kernel for a generalized dynamical system

The problem of the approximate construction of the viability kernel for a generalized dynamical system, the evolution of which is specified directly by an attainability set, is investigated under phase constraints. A backward grid method, based on the substitution of the phase space by pixels and a consideration of “inverse” attainability sets, is proposed. The convergence of the method is proved.     

Modeling and passivity analysis of nonholonomic Hamiltonian systems with rheonomous affine constraints

This paper is devoted to modeling and theoretical analysis of Hamiltonian systems subject to nonholonomic rheonomous affine constraints. We first define rheonomous affine constraints and explain geometric representation of them. Next, a complete nonholonomicity condition for the rheonomous affine constraints is developed in terms of the rheonomous bracket. Then, the nonholonomic Hamiltonian system with rheonomous affine constraints (NHSRAC) is derived via a transformation and model reduction for the expanded Hamiltonian system defined on the expanded phase space. After that, we investigate passivity of the NHSRAC with the control input term and the output equation. Finally, in order to confirm the application potentiality of our new results, we show an example, a radius-variable ball on rotating table with a time-varying angular velocity.     

Dynamics,Phase Constraints,and Linear Programming

Computational Mathematics and Mathematical Physics - A new approach to solving terminal control problems with phase constraints, using sufficient optimality conditions, is considered. The approach...     

Stabilization by a linear feedback under phase constraints

We suggest a new method for solving the stabilization problem under phase constraints with the use of linear matrix inequalities. We consider the cases of complete and incomplete state measurements and the presence of nonstationary parametric perturbations. In the synthesis of linear control laws, the suggested method permits one to cover all possible quadratic Lyapunov functions and indicate a set of initial values for trajectories satisfying the phase constraints.     

Sequential gradient-restoration algorithm for optimal control problems with general boundary conditions

This paper considers the numerical solution of two classes of optimal control problems, called Problem P1 and Problem P2 for easy identification.Problem P1 involves a functionalI subject to differential constraints and general boundary conditions. It consists of finding the statex(t), the controlu(t), and the parameter so that the functionalI is minimized, while the constraints and the boundary conditions are satisfied to a predetermined accuracy. Problem P2 extends Problem P1 to include nondifferential constraints to be satisfied everywhere along the interval of integration. Algorithms are developed for both Problem P1 and Problem P2.The approach taken is a sequence of two-phase cycles, composed of a gradient phase and a restoration phase. The gradient phase involves one iteration and is designed to decrease the value of the functional, while the constraints are satisfied to first order. The restoration phase involves one or more iterations and is designed to force constraint satisfaction to a predetermined accuracy, while the norm squared of the variations of the control, the parameter, and the missing components of the initial state is minimized.The principal property of both algorithms is that they produce a sequence of feasible suboptimal solutions: the functions obtained at the end of each cycle satisfy the constraints to a predetermined accuracy. Therefore, the values of the functionalI corresponding to any two elements of the sequence are comparable.The stepsize of the gradient phase is determined by a one-dimensional search on the augmented functionalJ, while the stepsize of the restoration phase is obtained by a one-dimensional search on the constraint errorP. The gradient stepsize and the restoration stepsize are chosen so that the restoration phase preserves the descent property of the gradient phase. Therefore, the value of the functionalI at the end of any complete gradient-restoration cycle is smaller than the value of the same functional at the beginning of that cycle.The algorithms presented here differ from those of Refs. 1 and 2, in that it is not required that the state vector be given at the initial point. Instead, the initial conditions can be absolutely general. In analogy with Refs. 1 and 2, the present algorithms are capable of handling general final conditions; therefore, they are suited for the solution of optimal control problems with general boundary conditions. Their importance lies in the fact that many optimal control problems involve initial conditions of the type considered here.Six numerical examples are presented in order to illustrate the performance of the algorithms associated with Problem P1 and Problem P2. The numerical results show the feasibility as well as the convergence characteristics of these algorithms.This research was supported by the Office of Scientific Research, Office of Aerospace Research, United States Air Force, Grant No. AF-AFOSR-76-3075. Partial support for S. Gonzalez was provided by CONACYT, Consejo Nacional de Ciencia y Tecnologia, Mexico City, Mexico.