Optimal control of a reflection boundary coefficient in an acoustic wave equation

We seek to optimally control a reflection boundary coefficient for an acoustic wave equation. The goal-quantified by an objective functional- is to drive the solution close to a target by adjusting this coefficient, which acts as a control. The problem is solved by finding the optimal control, which minimizes the objective functional. Then the optimal control is used as a an approximation for an inverse “ identification” problem.     

An efficient computational method for the optimal control problem for the Burgers equation

Pointwise control of the viscous Burgers equation in one spatial dimension is studied with the objective of minimizing the distance between the final state function and target profile along with the energy of the control. An efficient computational method is proposed for solving such problems, which is based on special orthonormal functions that satisfy the associated boundary conditions. Employing these orthonormal functions as a basis of a modal expansion method, the solution space is limited to the smallest lower subspace that is sufficient to describe the original problem. Consequently, the Burgers equation is reduced to a set of a minimal number of ordinary nonlinear differential equations. Thus, by the modal expansion method, the optimal control of a distributed parameter system described by the Burgers equation is converted to the optimal control of lumped parameter dynamical systems in finite dimension. The time-variant control is approximated by a finite term of the Fourier series whose unknown coefficients and frequencies giving an optimal solution are sought, thereby converting the optimal control problem into a mathematical programming problem. The solution space obtained is based on control parameterization by using the Runge–Kutta method. The efficiency of the proposed method is examined using a numerical example for various target functions.     

Method for Constructing Piecewise Quadratic Value Functions in a Control Problem for a Switched System

The problem of constructing internal approximations to solvability sets and the control synthesis problem for a piecewise linear system with control parameters and disturbances (uncertainties) are solved. The solution is based on the comparison principle and piecewise quadratic value functions of a special form. Relations defining such functions and, in particular, “continuous binding conditions” for the functions and their first derivatives are obtained. The results are used to construct numerical methods for solving the control synthesis problem for the class of switched systems under study. An example of approximate solution of the control synthesis problem in a target control problem for a nonlinear mathematical model of a pendulum with a flywheel is considered.     

Numerical Scaling Invariance Applied to the van der Pol Model

In this study we use the van der Pol model to explain a novel numerical application of scaling invariance. The model in point is not invariant to a scaling group of transformations, but by introducing an embedding parameter we are able to recover it from an extended model which is invariant to an extended scaling group. As well known, within a similarity analysis we can define a family of solutions from a computed one, so that the solution of a target problem can be obtained by rescaling the solution of a reference problem. The main idea is to use scaling invariance and numerical analysis to find a reference problem easier to solve, from a numerical viewpoint, than the target problem. This allows us to save human efforts and computational resources every time we have to solve a challenging problem. We test our approach using three stiff solvers available within the most recent releases of MATLAB. Independently from the solver used, by employing the described scaling invariance we are able to significantly reduce the computational cost of the numerical solution of the van der Pol model.      

Optimal institutional advertising: Minimum-time problem

This paper considers the problem of optimizing the institutional advertising expenditure for a firm which produces two products. The problem is formulated as a minimum-time control problem for the dynamics of an extended Vidale-Wolfe advertising model, the optimal control being the rate of institutional advertising that minimizes the time to attain the specified target market shares for the two products. The attainable set and the optimal control are obtained by applying the recent theory developed by Hermes and Haynes extending the Green's theorem approach to higher dimensions. It is shown that the optimal control is a strict bang-bang control. An interesting side result is that the singular arc obtained by the Green's theorem application turns out to be a maximum-time solution over the set of all feasible controls. The result clarifies the connection between the Green's theorem approach and the maximum principle approach.     

An Iterative Sparse-Group Lasso

In high-dimensional supervised learning problems, sparsity constraints in the solution often lead to better performance and interpretability of the results. For problems in which covariates are grouped and sparse structure are desired, both on group and within group levels, the sparse-group lasso (SGL) regularization method has proved to be very efficient. Under its simplest formulation, the solution provided by this method depends on two weight parameters that control the penalization on the coefficients. Selecting these weight parameters represents a major challenge. In most of the applications of the SGL, this problem is left aside, and the parameters are either fixed based on a prior information about the data, or chosen to minimize some error function in a grid of possible values. However, an appropriate choice of the parameters deserves more attention, considering that it plays a key role in the structure and interpretation of the solution. In this sense, we present a gradient-free coordinate descent algorithm that automatically selects the regularization parameters of the SGL. We focus on a more general formulation of this problem, which also includes individual penalizations for each group. The advantages of our approach are illustrated using both real and synthetic datasets. Supplementary materials for this article are available online.     

About an Optimal Visiting Problem

In this paper we are concerned with the optimal control problem consisting in minimizing the time for reaching (visiting) a fixed number of target sets, in particular more than one target. Such a problem is of course reminiscent of the famous “Traveling Salesman Problem” and brings all its computational difficulties. Our aim is to apply the dynamic programming technique in order to characterize the value function of the problem as the unique viscosity solution of a suitable Hamilton–Jacobi equation. We introduce some “external” variables, one per target, which keep in memory whether the corresponding target is already visited or not, and we transform the visiting problem in a suitable Mayer problem. This fact allows us to overcome the lacking of the Dynamic Programming Principle for the originary problem. The external variables evolve with a hysteresis law and the Hamilton–Jacobi equation turns out to be discontinuous     

Optimal Guidance of the Isotropic Rocket in the Presence of Wind

We address the minimum-time guidance problem for the so-called isotropic rocket in the presence of wind under an explicit constraint on the acceleration norm. We consider the guidance problem to a prescribed terminal position and a circular target set with a free terminal velocity in both cases. We employ standard techniques from optimal control theory to characterize the structure of the optimal guidance law as well as the corresponding minimum time-to-go function. It turns out that the complete characterization of the solution to the optimal control problem reduces to the solution of a system of nonlinear equations in triangular form. Numerical simulations, that illustrate the theoretical developments, are presented.     

Controlled Markov Chains with Weak and Strong Interactions: Asymptotic Optimality and Applications to Manufacturing

This paper deals with the asymptotic optimality of a stochastic dynamic system driven by a singularly perturbed Markov chain with finite state space. The states of the Markov chain belong to several groups such that transitions among the states within each group occur much more frequently than transitions among the states in different groups. Aggregating the states of the Markov chain leads to a limit control problem, which is obtained by replacing the states in each group by the corresponding average distribution. The limit control problem is simpler to solve as compared with the original one. A nearly-optimal solution for the original problem is constructed by using the optimal solution to the limit problem. To demonstrate, the suggested approach of asymptotic optimal control is applied to examples of manufacturing systems of production planning.     

An Open-loop criterion for the solvability of a closed-loop guidance problem with incomplete information: Linear control systems

The method of open-loop control packages is a tool for stating the solvability of guaranteed closed-loop control problems under incomplete information on the observed states. In this paper, a solution method is specified for the problem of guaranteed closed-loop guidance of a linear control system to a convex target set at a prescribed point in time. It is assumed that the observed signal on the system’s states is linear and the set of its admissible initial states is finite. It is proved that the problem under consideration is equivalent to the problem of open-loop guidance of an extended linear control system to an extended convex target set. Using a separation theorem for convex sets, we derive a solvability criterion, which reduces to solving a finite-dimensional optimization problem. An illustrative example is considered.     

A PTAS for Min-<Emphasis Type="Italic">k</Emphasis>-SCCP in Euclidean space of arbitrary fixed dimension

The paper deals with the problem of coordinated control for a flock of control systems that are to realize a joint motion towards a target set under collision avoidance. We consider one of its subproblems, which is formulated as follows. During the motion to the target, the members of the group are obliged to lie within a virtual ellipsoidal container, which realizes a reference motion (a “tube”). The container avoids obstacles, which are known in advance, by means of reconfigurations. In response, the flock must rearrange itself within the container, avoiding collisions between its members. The present paper concerns the behavior of the flock within the container, when the flock coordinates its motions with the evolution of the container.     

Curve following in illiquid markets

In this article the problem of curve following in an illiquid market is addressed. The optimal control is characterised in terms of the solution to a coupled FBSDE involving jumps via the technique of the stochastic maximum principle. Analysing this FBSDE, we further show that there are buy and sell regions. In the case of quadratic penalty functions the FBSDE admits an explicit solution which is determined via the four step scheme. The dependence of the optimal control on the target curve is studied in detail.     

Control synthesis for discrete time systems with control and state bounds in the presence of disturbances

The problem of control in the presence of unknown but limited disturbance for a discrete-time linear system with polyhedral input and state bounds is investigated. Two problems are considered: that of reaching an assigned target set in the state space; and that of keeping the state in a given region using the available controls. In both cases, a solution is given via linear programming. A computational procedure for the control synthesis is proposed which can be implemented to obtain a feedback control.The author thanks Professor G. Leitmann for his helpful suggestions.     

Real-Time Solution of Bi-Level Optimal Control Problems

Bi-level optimal control problems are presented as an extension to classical optimal control problems. Hereby, additional constraints for the primary problem are considered, which depend on the optimal solution of a secondary optimal control problem. A demanding problem is the numerical complexity, since at any point in time the solution of the optimal control problem as well as a complete solution of the secondary problem have to be determined. Hence we deal with two dependent variables in time. The numerical solution of the bi-level problem is illustrated by an application of a container crane. Jerk and energy optimal trajectories with free final time are calculated under the terminal condition that the crane system comes to be at rest at a predefined location. In enlargement additional constraints are investigated to ensure that the crane system can be brought to a rest position by a safety stop at a free but admissible location in minimal time from any state of the trajectory. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Covering a polygonal region by rectangles

The problem of covering a compact polygonal region, called target region, with a finite family of rectangles is considered. Tools for mathematical modeling of the problem are provided. Especially, a function, called Γ-function, is introduced which indicates whether the rectangles with respect to their configuration form a cover of the target region or not. The construction of the Γ-function is similar to that of Φ-functions which have been proved to be an efficient tool for packing problems. A mathematical model of the covering problem based on the Γ-function is proposed as well as a solution strategy. The approach is illustrated by an example and some computational results are presented.     

An approach-evasion differential game: Stochastic guide

An approach-evasion positional differential game is considered for a conflict-controlled motion and a target set within a given set. Use is made of a solution of the associated boundary-value problem for a parabolic equation degenerating as the diffusion term vanishes to a Hamilton-Jacobi type equation, which is typical for techniques in the theory of differential games. Based on this, a control scheme with a stochastic guide is developed.     

Bilinear minimax control problems for a class of parabolic systems with applications to control of nuclear reactors

This paper considers the bilinear minimax control problem of an important class of parabolic systems with Robin boundary conditions. Such systems are linear on state variables when the control and disturbance are fixed, and linear on the control or disturbance when the state variables are fixed. The objective is to maintain target state variables by taking account the influence of noises in data, while a desired power level and adjustment costs are taken into consideration. Firstly we introduce some classes of bilinear systems and obtain the existence and the uniqueness of the solution, as well as stability under mild assumptions. Afterwards the minimax control problem is formulated. We show the existence of an optimal solution, and we also find necessary optimality conditions. Finally, to illustrate the abstract results, we present two examples of neutron fission systems.     

A Two-Level Production Control Model with Target Amount Rescheduling

This paper deals with the problem of two-level production control with disturbances. A production system is considered with several machines on the lower level and a section on the upper one. Each machine produces a given target amount by a given due date (common to all machines). Each machine has several possible speeds, which are subject to disturbances. On the machine level, at the routine control point, decision-making centres on introducing the proper speed and determining the next control point. It is assumed that all the target amounts are transferable; i.e. a part of each target amount can be processed by any other machine. In a case when it is realized, for a certain machine, that the target cannot be completed on time, the section reschedules the remaining target amounts among the machines. New target amounts are determined for the machines so that the overall target can be accomplished on time. A stochastic optimization formulation is presented, followed by a heuristic solution and simulation results.     

A group decision making model based on goal programming with fuzzy hierarchy: an application to regional forest planning

In this paper a multi-criteria group decision making model is presented in which there is a heterogeneity among the decision makers due to their different expertise and/or their different level of political control. The relative importance of the decision makers in the group is handled in a soft manner using fuzzy relations. We suppose that each decision maker has his/her preferred solution, obtained by applying any of the techniques of distance-based multi-objective programming [compromise, goal programming (GP), goal programming with fuzzy hierarchy, etc.]. These solutions are used as aspiration levels in a group GP model in which the differences between the unwanted deviations are interpreted in terms of the degree of achievement of the relative importance amongst the group members. In this way, a group GP model with fuzzy hierarchy (Group-GPFH) is constructed. The solution for this model is proposed as a collective decision. To show the applicability of our proposal, a regional forest planning problem is addressed. The objective is to determine tree species composition in order to improve the values achieved by Pan-European indicators for sustainable forest management. This problem involves stakeholders with competing interests and different preference schemes for the aforementioned indicators. The application of our proposal to this problem allows us to be able to comfortably address all these issues. The results obtained are consistent with the preferences of each stakeholder and their hierarchy within the group.     

Effect of bifurcation on the semi-active optimal control problem

For nonlinear dynamic systems near bifurcation, the basins of attraction of fixed points as well as the steady-state responses can change considerably with a small variation of the bifurcating parameter. This paper studies the effect of bifurcation on the semi-active optimal control problem with fixed final state by using the cell mapping method. A system parameter is taken as the control. The admissible control values considered encompass a bifurcation point of the system. Global changes in the optimal control solution for different targets are studied. Saddle node, supercritical pitchfork and subcritical Hopf bifurcations are considered in the examples. It has been found that the global topology of the optimal control solution is strongly dependent on the state of the target.