Hamiltonian Formalism for Optimal Control of Nonlinear Loaded Integro-PDE Systems

A new approach to locally design a control pulse is proposed. This locally optimized control pulse is explicitly derived, starting with optimal control formalism, and satisfies the necessary condition for a solution to the optimal control problem. Our method requires a known function, g(t), a priori, which gives one of the possible paths within the functional space of the objective functional. A special choice of g(t)≡0 reduces the expression of the control pulse to that derived by Kosloff et al. For numerical application, we restrict ourselves to this special case; however, by combining an appropriate choice of the target operator together with the backward time-propagation technique, we apply the local control method to population inversion and to wave packet shaping. As an illustrative example, we adopt a two-electronic-surface model with displaced harmonic potentials and that with displaced Morse potentials. It is shown that our scheme successfully controls the wave packet dynamics and that it can be a convenient alternative to the optimal control method for wave packet shaping.

A systematic approach is presented to describe nonresonant multiphoton transitions, i.e., transitions between two electronic states without the presence of additional intermediate states resonant with the single-photon energy. The method is well suited to describe femtosecond spectroscopic experiments and, in particular, attempts to achieve laser pulse control of molecular dynamics. The obtained effective time-dependent Schrodinger equation includes effective couplings to the radiation field which combine powers of the field strength and effective transition dipole operators between the initial and final states. To arrive at time-local equations our derivation combines the well-known rotating wave approximation with the approximation of slowly varying amplitudes. Under these terms, the optimal control formalism can be readily extended to also account for nonresonant multiphoton events. Exemplary, nonresonant two- and three-photon processes, similar to those occurring in the recent femtosecond pulse-shaping experiments on CpMn(CO)(3), are treated using related ab initio potential energy surfaces.

Lagrangian particle formulations of the large deformation diffeomorphic metric mapping algorithm only allow for the study of a single shape. In this paper, we introduce and discuss both a theoretical and practical setting for the simultaneous study of multiple shapes that are either stitched to one another or slide along a submanifold. The method is described within the optimal control formalism, and optimality conditions are given, together with the equations that are needed to implement augmented Lagrangian methods. Experimental results are provided for stitched and sliding surfaces.

The results of previous research [ J. Chem. Phys.88, 6870 ( 1988)] on optimal control of harmonic molecular motion are extended. A closed-form solution for the optimal optical field is derived for a quadratic cost criterion, and an asymptotic form for this field is obtained for large target times. The dynamics of a molecule are shown to be controllable if no normal mode has zero optical absorption intensity. The theoretical formulation yields optical field designs that are to be executed ultimately in the laboratory. In this regard, a critical issue is the robustness of the field designs. Therefore the optimal control formalism is extended further to yield optimal fields that exhibit minimal sensitivity of the desired molecular objectives with respect to force constants and dipole derivatives. Examples of sensitivity minimization are shown, using a linear chain molecule. Finally, optimal control of the molecule vinylidene fluoride is demonstrated.

In the optimal control theory, the Hamiltonian formulation is a famous one convenient to find an optimal designed control force. However, when the performance index is a complicated function of control force, the Hamiltonian method is not easy to find the optimal solution, because one may encounter a two-point boundary value problem of nonlinear differential algebraic equations (DAEs). In this paper we address this issue via a quite novel and effective approach, of which the optimally controlled vibration problem of Duffing oscillator is recast into a two-point nonlinear DAEs by identifying the unknown control force. We develop the corresponding SL(n, R) and GL(n, R) shooting methods, as well as a Lie-group differential algebraic equations (LGDAE) method to numerically solve the optimal control forces. Eight examples of a single Duffing oscillator and one coupled Duffing oscillators are used to test the performance of the present method.

In the optimal control theory, the Hamiltonian formalism is a famous one to find an optimal solution. However, when the performance index is complicated or for a degenerate case with a non-convexity of the Hamiltonian function with respect to the control force the Hamiltonian method does not work to find the solution. In this paper we will address this important issue via a quite different approach, which uses the optimal control problem of nonlinear Duffing oscillator as a demonstrative example. The optimally controlled vibration problem of nonlinear oscillator is recast into a nonlinear inverse problem by identifying the unknown heat source in a nonlinear parabolic partial differential equation (PDE). Then through a semi-discretization of the resultant PDE, the inverse problem is further reformulated to be a system of n-dimensional ODEs with n unknown pointwise sources, which allows a Lie-group adaptive method (LGAM) to recover the point-wise sources. The present method has three-fold advantages: it can easily minimize a complicated performance index to find an optimal control force of the nonlinear vibration system, it is effective for highly nonlinear optimal control problem, and it does not resort on the classical Hamiltonian formulation, which provides only a necessary condition, but not a sufficient condition, for the optimality of the control law. Numerical examples show that the LGAM may find a better performance than the classical one.

Auxiliary matrix exponential method is used to derive simple and numerically efficient general expressions for the following, historically rather cumbersome, and hard to compute, theoretical methods: (1) average Hamiltonian theory following interaction representation transformations; (2) Bloch-Redfield-Wangsness theory of nuclear and electron relaxation; (3) gradient ascent pulse engineering version of quantum optimal control theory. In the context of spin dynamics, the auxiliary matrix exponential method is more efficient than methods based on matrix factorizations and also exhibits more favourable complexity scaling with the dimension of the Hamiltonian matrix.

Hamilton–Jacobi–Bellman formalism for optimal climate control of greenhouse crop

Given the increasingly dense environment in both low-earth orbit (LEO) and geostationary orbit (GEO), a sudden change in the trajectory of any existing resident space object (RSO) may cause potential collision damage to space assets. With a constellation of EO/IR sensor platforms and ground radar surveillance systems, it is important to design optimal estimation algorithm for updating nonlinear object states and allocating sensing resources to effectively avoid collisions among many RSOs. We consider N space objects being observed by M sensors whose task is to provide the minimum mean square estimation error of the overall system subject to the cost associated with each measurement. To simplify the analysis, we assume that sensors can switch between objects instantaneously subject to additional resource and sensing geometry constraints. We first formulate the sensor scheduling problem using the optimal control formalism and then derive a tractable relaxation of the original optimization problem, which provides a lower bound on the achievable performance. We propose an open-loop periodic switching policy whose performance can approach the theoretical lower bound closely. We also discuss a special case of identical sensors and derive an index policy that coincides with the general solution to restless multi-armed bandit problem by Whittle. Finally, we demonstrate the effectiveness of the resulting sensor management scheme for space situational awareness using a realistic space object tracking simulator with both unintentional and intentional maneuvers by RSOs that may lead to collision. Our sensor scheduling scheme outperforms the conventional information gain and covariance control based schemes in the overall tracking accuracy as well as making earlier declaration of collision events.

Many control problems in the robotics field can be cast as partially observed Markovian decision problems (POMDPs), an optimal control formalism. Finding optimal solutions to such problems in general, however is known to be intractable. It has often been observed that in practice, simple structured controllers suffice for good sub-optimal control, and recent research in the artificial intelligence community has focused on policy search methods as techniques for finding sub-optimal controllers when such structured controllers do exist. Traditional model-based reinforcement learning algorithms make a certainty equivalence assumption on their learned models and calculate optimal policies for a maximum-likelihood Markovian model. We consider algorithms that evaluate and synthesize controllers under distributions of Markovian models. Previous work has demonstrated that algorithms that maximize mean reward with respect to model uncertainty leads to safer and more robust controllers. We consider briefly other performance criterion that emphasize robustness and exploration in the search for controllers, and note the relation with experiment design and active learning. To validate the power of the approach on a robotic application we demonstrate the presented learning control algorithm by flying an autonomous helicopter. We show that the controller learned is robust and delivers good performance in this real-world domain.

A method to incorporate low-thrust propulsion into the invariant manifolds technique is presented in this paper. The low-thrust propulsion is introduced by means of special attainable sets that are used in conjunction with invariant manifolds to define a first-guess solution. This is later optimized in a more refined model where an optimal control formalism is used. Planar low-energy low-thrust transfers to the moon, as well as spatial low-thrust stable manifold transfers to halo orbits in the Earth–moon system, are presented. These solutions are not achievable via patched-conics methods or standard invariant manifolds techniques. The aim of the work is to demonstrate the usefulness of the proposed method in delivering efficient solutions, which are compared with known examples.

The combination of nonadiabatic Ehrenfest-path molecular dynamics (EMD) based on time-dependent density functional theory (TDDFT) and quantum optimal control formalism (QOCT) was used to optimize the shape of ultra-short laser pulses to achieve photodissociation of a hydrogen molecule and the trihydrogen cation H3 (+) . This work completes a previous one [A. Castro, ChemPhysChem, 2013, 14, 1488-1495], in which the same objective was achieved by demonstrating the combination of QOCT and TDDFT for many-electron systems on static nuclear potentials. The optimization model, therefore, did not include the nuclear movement and the obtained dissociation mechanism could only be sequential: fast laser-assisted electronic excitation to nonbonding states (during which the nuclei are considered to be static), followed by field-free dissociation. Here, in contrast, the optimization was performed with the QOCT constructed on top of the full dynamic model comprised of both electrons and nuclei, as described within EMD based on TDDFT. This is the first numerical demonstration of an optimal control formalism for a hybrid quantum-classical model, that is, a molecular dynamics method.

In this study, we pay attention to novel explicit closed-form solutions of optimal control problems in economic growth models described by Hamiltonian formalism by utilizing mathematical approaches based on the theory of Lie groups. For this analysis, the Hamiltonian functions, which are used to define an optimal control problem, are considered in two different types, namely, the current and present value Hamiltonians. Furthermore, the first-order conditions (FOCs) that deal with Pontrygain maximum principle satisfying both Hamiltonian functions are considered. FOCs for optimal control in the problem are studied here to deal with the first-order coupled systems. This study mainly focuses on the analysis of these systems concerning for to the theory of symmetry groups and related analytical approaches. First, Lie point symmetries of the first-order coupled systems are derived, and then by using the relationships between symmetries and Jacobi last multiplier method, the first integrals and corresponding invariant solutions for two different economic models are investigated. Additionally, the solutions of initial-value problems based on the transversality conditions in the optimal control theory of economic growth models are analyzed.

An optimal switching control formalism combined with the stochastic dynamic programming is, for the first time, applied to modelling life cycle of migrating population dynamics with non-overlapping generations. The migration behaviour between habitats is efficiently described as impulsive switching based on stochastic differential equations, which is a new standpoint for modelling the biological phenomenon. The population dynamics is assumed to occur so that the reproductive success is maximized under an expectation. Finding the optimal migration strategy ultimately reduces to solving an optimality equation of the quasi-variational type. We show an effective linkage between our optimality equation and the basic reproduction number. Our model is applied to numerical computation of optimal migration strategy and basic reproduction number of an amphidromous fish Plecoglossus altivelis altivelis in Japan as a target species.

We consider the optimal-control problem with time-homogeneous linear (and in general non-Markov) dynamics and risk-sensitive criterion. Appeal to the extremal principle prescribed by the risk-sensitive certainty equivalence principle (RSCEP) yields a symmetric equation system, indicating that the extended hamiltonian formulation generalizes naturally to the risk-sensitive case. The conjugate variable of a hamiltonian formulation now has an interpretation in terms of forecasted process noise, and the RSCEP in fact provides a stochastic maximum principle for which all variables have a clear interpretation and the desired measurability properties. In the infinite-horizon case (meaningful under generalized controllability conditions) optimal control is determined explicitly in terms of a canonical factorization. For the case of imperfect process observation, the RSCEP leads to coupled-equation systems that can again be solved in terms of canonical factorizations in the time-invariant (stationary) case.