Dynamic Model of Demand-Supply of Labor and its Optimal Management with Immigration Policy

We begin with an introduction to the historical origin of optimal control theory, the calculus of variations. But it is not our intention to give a comprehensive treatment of this topic. Rather, we introduce the fundamental necessary and sufficient conditions for optimality by fully analyzing two of the cornerstone problems of the theory, the brachistochrone problem and the problem of determining surfaces of revolution with minimum surface area, so-called minimal surfaces. Our emphasis is on illustrating the methods and techniques required for getting complete solutions for these problems. More generally, we use the so-called fixed-endpoint problem, the problem of minimizing a functional over all differentiable curves that satisfy given boundary conditions, as a vehicle to introduce the classical results of the theory: (a) the Euler–Lagrange equation as the fundamental first-order necessary condition for optimality, (b) the Legendre and Jacobi conditions, both in the form of necessary and sufficient second-order conditions for local optimality, (c) the Weierstrass condition as additional necessary condition for optimality for so-called strong minima, and (d) its connection with field theory, the fundamental idea in any sufficiency theory. Throughout our presentation, we emphasize geometric constructions and a geometric interpretation of the conditions. For example, we present the connections between envelopes and conjugate points of a fold type and use these arguments to give a full solution for the minimum surfaces of revolution.

In any optimization problem three basic questions have to be answered. Does an optimal solution exist? How can one restrict the candidates for optimality by way of necessary conditions? Is a candidate found in this way indeed optimal (in a local and/or global sense)? For problems on function spaces by now fairly general existence results are known see, for instance, Cesari [6]) which cover a wide range of realistic problem situations. The theories of necessary and sufficient conditions for optimality, on the other hand, lack a similar completeness of results. For optimal control problems, the Pontryagin Maximum Principle [11] gives first order necessary conditions for optimality. Several higher order conditions for optimality are known as well (cf. Krener [8], Knobloch [7] and the many references therein), but they mainly deal with special situations, like the generalized Legendre-Clebsch condition for singular arcs. Typically the necessary conditions will not suffice to single out the optimal control. In fact, in many cases there exists a significant gap between the structure of extremals (i.e. trajectories which satisfy the necessary conditions for optimality) and the structure of optimal trajectories in a regular synthesis. Roughly speaking, a regular synthesis consists of a family of extremals with the property that a unique extremal trajectory starts from every point of the state-space and which satisfies certain technical conditions which allow to prove that the corresponding feedback control is indeed optimal.

3D pendulum is a rigid body supported at a fixed pivot with three rotational degrees of freedom. The objective is to maneuver the 3D pendulum from an initial attitude and angular rate to a desired attitude and angular rate in minimum time in presence of uniform gravity, subjected to constraints on the control input. We derive the necessary conditions for time optimality for a 3D pendulum by formulating a discrete-time optimal control problem using a Lie group variational integrator. The approach does not use local parameterizations (like Euler angles or quaternion) for attitude representation but necessary conditions for optimality are derived directly on the special orthogonal group. Further, discrete-time, time optimal attitude control satisfying the necessary conditions is computed using geometrically exact technique on special orthogonal group, such that it preserves the geometric properties of a rigid body.

Singular systems of the form E[xdot] = ƒ(x,u,t) are considered, where E is a square matrix and may be singular. It is assumed that for any ‘admissible’ initial state x(t 0), any control u(t) ∊ U yields one and only one continuous state x(t), and there is one and only one continuous adjoint state λ(t). The formulae for functional variation are derived; the necessary condition for optimality—the maximum principle—is obtained; the boundary conditions for the adjoint equations of the singular systems are given; and the necessary and sufficient condition for optimality of linear singular systems is derived.

Рассматривается задача оптимального управления, описываемая системой интегро-дифференциальных уравнений типа Вольтерра с запаздывающим аргументом и многоточечным критерием качества. При предположении открытости области управления вычислены первая и вторая вариации критерия качества. Из равенства нулю первой вариации функционала качества вдоль оптимального процесса выведено необходимое условие оптимальности первого порядка в форме аналога уравнения Эйлера. Далее получено неявное необходимое условие оптимальности второго порядка, с помощью которого установлено довольно общее, но конструктивно проверяемое необходимое условие оптимальности второго порядка. Полученные результаты могут быть использованы для построения легко проверяемых необходимых условий оптимальности особых в классическом смысле управлений.

The object of research is the linear optimal control problem described by discrete two-parameter systems under the assumption that the controlled process is stepwise.The work is aimed at deriving the necessary first-order optimality conditions in the case of a non-smooth quality function. And also to establish the necessary conditions of second-order optimality in stepwise control problems for discrete two-parameter systems. The paper investigates one linear two-parameter discrete optimal control problem with a non-smooth quality criterion. A special increment of the quality functional is calculated. Cases under the condition of a convex set are considered. The concept of special control in the problem under study is given. A number of necessary optimality conditions for the first and second orders are established. And also the necessary second-order optimality conditions are obtained in terms of directional derivatives. In the case of a linear quality criterion, the necessary and sufficient optimality condition is proved using the increment formula by analogous arguments. Under the assumption that the set is convex, a special increment of the quality criterion for admissible control is defined.The methods of calculus of variations and optimal control, the theory of difference equations are used. The result is obtained for the optimality of a special, first-order control, in the case of convexity of the set. The case when the minimized functional is linear is considered. In this case, a necessary and sufficient condition is obtained for the optimality of the admissible control.Thanks to the research results, it is possible to obtain the necessary first-order optimality conditions in terms of directional derivatives in the stepwise problem of optimal control of discrete two-parameter systems. As well as the necessary conditions of optimality of the second order in the case of convexity of the control domain and the necessary optimality conditions of special controls.The theoretical results obtained in the work are of interest in the theory of optimal control of step systems and can be used in the further development of the theory of necessary optimality conditions for step control problems.

This paper investigates a relationship between the maximum principle with an infinite horizon and dynamic programming and sheds new light upon the role of the transversality condition at infinity as necessary and sufficient conditions for optimality with or without convexity assumptions. We first derive the nonsmooth maximum principle and the adjoint inclusion for the value function as necessary conditions for optimality. We then present sufficiency theorems that are consistent with the strengthened maximum principle, employing the adjoint inequalities for the Hamiltonian and the value function. Synthesizing these results, necessary and sufficient conditions for optimality are provided for the convex case. In particular, the role of the transversality conditions at infinity is clarified.

Optimization arises naturally when process performance needs improvement. This is often the case in industry because of competition – the product has to be proposed at the lowest possible cost. From the point of view of control, optimization consists in designing a control policy that best satisfies the chosen objectives. Most optimization schemes rely on a process model, which, however, is always an approximation of the real plant. Hence, the resulting optimal control policy is suboptimal for the real process. The fact that accurate models can be prohibitively expensive to build has triggered the development of a field of research known as Optimization under Uncertainty. One promising approach in this field proposes to draw a strong parallel between optimization under uncertainty and control. This approach, labeled NCO tracking, considers the Necessary Conditions of Optimality (NCO) of the optimization problem as the controlled outputs. The approach is still under development, and the present work is today's most recent contribution to this development. The problem of NCO tracking can be divided into several subproblems that have been studied separately in earlier works. Two main categories can be distinguished : (i) tracking the NCO associated with active constraints, and (ii) tracking the NCO associated with sensitivities. Research on the former category is mature. The latter problem is more difficult to solve since the sensitivity part of the NCO cannot be directly measured on the real process. The present work proposes a method to tackle these sensitivity problems based on the theory of Neighboring Extremals (NE). More precisely, NE control provides a way of calculating a first-order approximation to the sensitivity part of the NCO. This idea is developed for static and both nonsingular and singular dynamic optimization problems. The approach is illustrated via simulated examples: steady-state optimization of a continuous chemical reactor, optimal control of a semi-batch reactor, and optimal control of a steered car. Model Predictive Control (MPC) is a control scheme that can accommodate both process constraints and nonlinear process models. The repeated solution of a dynamic optimization problem provides an update of the control variables based on the current state, and therefore provides feedback. One of the major drawbacks of MPC lies in the expensive computations required to update the control policy, which often results in a low sampling frequency for the control loop. This limitation of the sampling frequency can be dramatic for fast systems and for systems exhibiting a strong dispersion between the predicted and the real state such as unstable systems. In the MPC framework, two main methods have been proposed to tackle these difficulties: (i) The use of a pre-stabilizing feedback operating in combination with the MPC scheme, and (ii) the use of robust MPC. The drawback of the former approach is that there exists no systematic way of designing such a feedback, nor is there any systematic way of analyzing the interaction between the MPC controller and this additional feedback. This work proposes to use the NE theory to design this additional feedback, and it provides a systematic way of analyzing the resulting control scheme. The approach is illustrated via the control of a simulated unstable continuous stirred-tank reactor and is applied successfully to two laboratory-scale set-ups, an inverted pendulum and a helicopter model called Toycopter. The stabilizing potential of NE control to handle fast and unstable systems is well illustrated. In the case of a strong dispersion between the state trajectories predicted by the model and the real process, robust MPC becomes infeasible. This problem can be addressed using robust MPC based on multiple input profiles, where the inherent feedback provided by MPC is explicitly taken into account, thereby increasing the size of the set of feasible inputs. The drawback of this scheme is its very high computational complexity. This work proposes to use the NE theory in the robust MPC framework as an efficient way of dealing with the feasibility issue, while limiting the computational complexity of the approach. The approach is illustrated via the control of a simulated unstable continuous stirred-tank reactor, and of an inverted pendulum.

This paper analyzes a mathematical model for the growth of bone marrow cells under cell-cycle-specific cancer chemotherapy originally proposed by Fister and Panetta [8]. The model is formulated as an optimal control problem with control representing the drug dosage ( respectively its effect ) and objective of Bolza type depending on the control linearly, a so-called L(1)- objective. We apply the Maximum Principle, followed by high-order necessary conditions for optimality of singular arcs and give sufficient conditions for optimality based on the method of characteristics. Singular controls are eliminated as candidates for optimality, and easily verifiable conditions for strong local optimality of bang-bang controls are formulated in the form of transversality conditions at switching surfaces. Numerical simulations are given.

A necessary condition of optimality—the variational maximum principle—for continuous dynamic optimization problems under linear unbounded control and trajectory terminal constraints is studied. It holds for optimal control problems, which are characterized by the commutativity of vector fields corresponding to the components of a linear control in the dynamic system (Frobenius-type condition). For these problems, the variational maximum principle, being a first-order necessary condition of optimality, is a stronger version of the Pontryagin maximum principle. Examples are given.

Abstract. One boundary value problem of optimal control of Goursat-Darboux systems is considered, under the assumption that the control domain is open. An analogue of the Euler equation is proved and necessary conditions for second-order optimality are derived. The case of degeneracy of an analogue of the Legendre-Klebsch condition is studied separately.

The main purpose of necessary conditions of optimality (NCO) is to identify a ‘small’ set of candidates to minimizers among the overall set of admissible solutions. However, for certain optimal control problems with state constraints, it might happen that the set of all admissible solutions coincides with the set of candidates satisfying the NCO. This phenomenon is known as the degeneracy phenomenon and there has been some literature proposing stronger forms of NCO that can be informative in such cases: the so-called nondegenerate NCO. The nondegenerate NCO proposed here are valid under a different set of hypothesis and under a constraint qualification of an integral-type that, in relation to some previous literature, is easier to verify.

Necessary Conditions of Optimality (NCO) play an important role in the characterization of and search for solutions of Optimization Problems. They enable us to identify a small set of candidates to local minimizers among the overall set of admissible solutions. However, in constrained optimization problems it may happen that necessary conditions of optimality are merely state a relation between the constraints and do not use the objective function to select candidates to minimizers. To avoid this phenomenon, it is necessary to strengthened the NCO. Here, we overview and describe strengthened forms NCO for calculus of variations with inequality constraints.

Optimality conditions for the control of a double-membrane complex system by a modal decomposition technique

The article considers the problem of resource allocation in a two-sector economic model with a nonlinear production function of a special type. The main mathematical apparatus is Pontryagin’s maximum principle, i.e., the theorem on necessary conditions of optimality. It is shown that in the given problem the maximum principle provides a necessary and sufficient condition of optimality. A possible singular solution of the problem is found. An extremum solution is constructed in explicit form under various assumptions about the initial values. A “sufficiently long” planning horizon is assumed. An alternative approach is described, which does not use the maximum principle and instead investigates the integral representation of the optimand functional. The detailed theoretical investigation of the problem is accompanied by numerous illustrations.