Optimal Control of Uniformly Heated Granular Fluids in Linear Response

We present a detailed analytical investigation of the optimal control of uniformly heated granular gases in the linear regime. The intensity of the stochastic driving is therefore assumed to be bounded between two values that are close, which limits the possible values of the granular temperature to a correspondingly small interval. Specifically, we are interested in minimising the connection time between the non-equilibrium steady states (NESSs) for two different values of the granular temperature by controlling the time dependence of the driving intensity. The closeness of the initial and target NESSs make it possible to linearise the evolution equations and rigorously—from a mathematical point of view—prove that the optimal controls are of bang-bang type, with only one switching in the first Sonine approximation. We also look into the dependence of the optimal connection time on the bounds of the driving intensity. Moreover, the limits of validity of the linear regime are investigated.

Liouville Integrability in a Four-Dimensional Model of the Visual Cortex

We consider a natural extension of the Petitot–Citti–Sarti model of the primary visual cortex. In the extended model, the curvature of contours is taken into account. The occluded contours are completed via sub-Riemannian geodesics in the four-dimensional space M of positions, orientations, and curvatures. Here, M=R2×SO(2)×R models the configuration space of neurons of the visual cortex. We study the problem of sub-Riemannian geodesics on M via methods of geometric control theory. We prove complete controllability of the system and the existence of optimal controls. By application of the Pontryagin maximum principle, we derive a Hamiltonian system that describes the geodesics. We obtain the explicit parametrization of abnormal extremals. In the normal case, we provide three functionally independent first integrals. Numerical simulations indicate the existence of one more first integral that results in Liouville integrability of the system.

Development of a Method for Evaluating the Technical Condition of a Car’s Hybrid Powertrain

The article presents the results of a study performed and substantiated based on the principles of a new method of diagnostics of technical conditions of a hybrid powertrain regardless of the structural diagram and design features of a hybrid vehicle. The presented new technology of the diagnostics of hybrid powertrains allows an objective complex assessment of their technical condition by diagnostic parameters in contrast to existing diagnostic methods. In the proposed method, a mechanism for the general standardization of diagnostic parameters has been developed as well as for determining the numerical values of the parameters of the powertrain. The control subset was used to control the learning error. As a result of debugging the system, the scatter of experimental and calculated points has decreased, which confirms the quality of debugging the tested fuzzy model. As a result of training the artificial neural network, the standard deviation of the error in the control sample was 0.012·Pk. A symmetry method of diagnostics of the technical state of a hybrid propulsion system was developed based on the concept of a neural network together with a neuro-fuzzy control with an adaptive criteria based on the method of training a neural network with reinforcement. The components of the vector functional include the criteria for control accuracy, the use of traction battery energy, and the degree of toxicity of exhaust gases. It is proposed to use the principle of symmetry of the guaranteed result and the linear inversion of the vector criterion into a supercriterion to determine the technical state of a hybrid powertrain on a set of Pareto-optimal controls under unequal conditions of optimality.

Optimal Control in a Mathematical Model of Smoking

This paper presents a dynamic model of smoking with optimal control. The mathematical model is divided into 5 sub-classes, namely, non-smokers, occasional smokers, active smokers, individuals who have temporarily stopped smoking, and individuals who have stopped smoking permanently. Four optimal controls, i.e., anti-smoking education campaign, anti-smoking gum, anti-nicotine drug, and government prohibition of smoking in public spaces are considered in the model. The existence of the controls is also presented. The Pontryagin maximum principle (PMP) was used to solve the optimal control problem. The fourth-order Runge-Kutta was employed to gain the numerical solutions.

Mathematical Analysis and Optimal Control of Giving up the Smoking Model

In this study, we are going to explore mathematically the dynamics of giving up smoking behavior. For this purpose, we will perform a mathematical analysis of a smoking model and suggest some conditions to control this serious burden on public health. The model under consideration describes the interaction between the potential smokers P , the occasional smokers L , the chain smokers S , the temporarily quit smokers Q T , and the permanently quit smokers Q P . Existence, positivity, and boundedness of the proposed problem solutions are proved. Local stability of the equilibria is established by using Routh–Hurwitz conditions. Moreover, the global stability of the same equilibria is fulfilled through using suitable Lyapunov functionals. In order to study the optimal control of our problem, we will take into account a two controls’ strategy. The first control will represent the government prohibition of smoking in public areas which reduces the contact between nonsmokers and smokers, while the second will symbolize the educational campaigns and the increase of cigarette cost which prevents occasional smokers from becoming chain smokers. The existence of the optimal control pair is discussed, and by using Pontryagin minimum principle, these two optimal controls are characterized. The optimality system is derived and solved numerically using the forward and backward difference approximation. Finally, numerical simulations are performed in order to check the equilibria stability, confirm the theoretical findings, and show the role of optimal strategy in controlling the smoking severity.

Analysis and Optimal Control of φ-Hilfer Fractional Semilinear Equations Involving Nonlocal Impulsive Conditions

The goal of this paper is to consider a new class of φ-Hilfer fractional differential equations with impulses and nonlocal conditions. By using fractional calculus, semigroup theory, and with the help of the fixed point theorem, the existence and uniqueness of mild solutions are obtained for the proposed fractional system. Symmetrically, we discuss the existence of optimal controls for the φ-Hilfer fractional control system. Our main results are well supported by an illustrative example.

On a discrete game problem with non-convex control vectograms

In a normed space of finite dimension, a discrete game problem with fixed duration is considered. The terminal set is determined by the condition that the norm of the phase vector belongs to a segment with positive ends. In this paper, a set defined by this condition is called a ring. At each moment, the vectogram of the first player's controls is a certain ring. The controls of the second player at each moment are taken from balls with given radii. The goal of the first player is to lead a phase vector to the terminal set at a fixed time. The goal of the second player is the opposite. In this paper, necessary and sufficient termination conditions are found, and optimal controls of the players are constructed.

Parameter calibration with stochastic gradient descent for interacting particle systems driven by neural networks

We propose a neural network approach to model general interaction dynamics and an adjoint-based stochastic gradient descent algorithm to calibrate its parameters. The parameter calibration problem is considered as optimal control problem that is investigated from a theoretical and numerical point of view. We prove the existence of optimal controls, derive the corresponding first-order optimality system and formulate a stochastic gradient descent algorithm to identify parameters for given data sets. To validate the approach, we use real data sets from traffic and crowd dynamics to fit the parameters. The results are compared to forces corresponding to well-known interaction models such as the Lighthill–Whitham–Richards model for traffic and the social force model for crowd motion.

ADAPTIVE GRADIENT METHOD FOR THE OPTIMAL CONTROL OF AN ANTICANCER THERAPY MODEL

Cancer is a set of diseases whose mechanisms of emergence and growth are not completely known. Mathematical and computational models aim to contribute to a better understanding of these mechanisms in tumor dynamics. They can also be used to analyze the impact of anticancer therapeutic protocols. In this sense, we investigate a mathematical model of tumor growth dynamics from the optimal control point of view. We apply the Switching-Time-Variation method to solve the control problem and modify its implementation in order to reduce computational time. Our results show that the execution time of the adaptive method is significantly shorter, and optimal controls for the observed scenario are of the bang-bang type with administration by maximum tolerated dose. The analysis also suggests that the application of another drug capable of acting on resistant tumor cells is required.