2,302 research outputs found
Analysis, Simulation and Control of a New Measles Epidemic Model
In this paper the problem of modeling and controlling the measles epidemic spread is faced. A new model
is proposed and analysed; besides the categories usually considered in measles modeling, the susceptible,
the exposed, the infected, the removed and, less frequently, the quarantine individuals, two new categories
are herein introduced: the immunosuppressed subjects, that can not be vaccinated, and the patients with an
additional complication, not risky by itself but dangerous if caught togeter with the measles. These two
novelties are taken into account in designing and scheduling suitably control actions such as vaccination,
whenever possible, prevention, quarantine and treatment, when limited resources are available. An analysis of
the model is developed and the optimal control strategies are compared with other not optimized actions. By
using the Pontryagin principle, it is shown the prevailing role of the vaccination in guaranteeing the protection
to immunosuppressed individuals, as well as the importance of a prompt response of the society when an
epidemic spread occurs, such as the quarantine intervention
Optimal Control to Limit the Propagation Effect of a Virus Outbreak on a Network
The aim of this paper is to propose an optimal control strategy to face the propagation effects of a virus
outbreak on a network; a recently proposed model is integrated and analysed. Depending on the specific
model caracteristics, the epidemic spread could be more or less dangerous leading to a virus free or to a virus
equilibrium. Two possible controls are introduced: a test on the computers connected in a network and the
antivirus. In a condition of limited resources the best allocation strategy should allow to reduce the spread of
the virus as soon as possible
State Feedback Optimal Control with Singular Solution for a Class of Nonlinear Dynamics
The paper studies the problem of determining the optimal control when singular arcs are present in the solution.
In the general classical approach the expressions obtained depend on the state and the costate variables at the
same time, so requiring a forward-backward integration for the computation of the control. In this paper,
sufficient conditions on the dynamics structure are provided and discussed in order to have both the control
and the switching function depending on the state only, so simplifying the computation avoiding the necessity
of the backward integration. The approach has been validated on a classical SIR epidemic model
An Improvement in a Local Observer Design for Optimal State Feedback Control: The Case Study of HIV/AIDS Diffusion
The paper addresses the problem of an observer design for a nonlinear system for which a preliminary linear
state feedback is designed but the full state is not measurable. Since a linear control assures the fulfilment of
local approximated conditions, usually a linear observer is designed in these cases to estimate the state with
estimation error locally convergent to zero. The case in which the control contains an external reference, like
in regulations problems, is studied, showing that the solution obtained working with the linear approximation
to get local solutions produces non consistent results in terms of local regions of convergence for the system
and for the observer. A solution to this problem is provided, proposing a different choice for the observer
design which allows to obtain all conditions locally satisfied on the same local region in the neighbourhood of
a new equilibrium point. The case study of an epidemic spread control is used to show the effectiveness of the
procedure. The linear control with regulation term is present in this case because the problem is reconducted to
a Linear Quadratic Regulation problem. Simulation results show the differences between the two approaches
and the effectiveness of the proposed on
Inventory routing problem with non-stationary stochastic demands
In this paper we solve Stochastic Periodic Inventory Routing Problem (SPIRP) when the accuracy of expected demand is changing among the periods. The variability of demands increases from period to period. This variability follows a certain rate of uncertainty. The uncertainty rate shows the change in accuracy level of demands during the planning horizon. To deal with the growing uncertainty, we apply a safety stock based SPIRP model with different levels of safety stock. To satisfy the service level in the whole planning horizon, the level of safety stock needs to be adjusted according to the demand's variability. In addition, the behavior of the solution model in long term planning horizons for retailers with different demand accuracy is taken into account. We develop the SPIRP model for one retailer with an average level of demand, and standard deviation for each period. The objective is to find an optimum level of safety stock to be allocated to the retailer, in order to achieve the expected level of service, and minimize the costs. We propose a model to deal with the uncertainty in demands, and evaluate the performance of the model based on the defined indicators and experimentally designed cases
Auto-tuning of PID Controllers for Robotic Manipulators Using PSO and MOPSO
This work proposes two approaches to automatic tuning of PID position controllers based on different global optimization strategies. The chosen optimization algorithms are PSO and MOPSO, i. e. the problem is handled as a single objective problem in the first implementation and as a multiobjective problem in the second one. The auto-tuning is performed without assuming any previous knowledge of the robot dynamics. The objective functions are evaluated depending on real movements of the robot. Therefore, constraints guaranteeing safe and stable robot motion are necessary, namely: a maximum joint torque constraint, a maximum position error constraint and an oscillation constraint. Because of the practical nature of the problem in hand, constraints must be observed online. This requires adaptation of the optimization algorithm for reliable observance of the constraints without affecting the convergence rate of the objective function. Finally, Experimental results of a 3-DOF robot for different trajectories and with different settings show the validity of the two approaches and demonstrate the advantages and disadvantages of every method
Hidden attractors in fundamental problems and engineering models
Recently a concept of self-excited and hidden attractors was suggested: an
attractor is called a self-excited attractor if its basin of attraction
overlaps with neighborhood of an equilibrium, otherwise it is called a hidden
attractor. For example, hidden attractors are attractors in systems with no
equilibria or with only one stable equilibrium (a special case of
multistability and coexistence of attractors). While coexisting self-excited
attractors can be found using the standard computational procedure, there is no
standard way of predicting the existence or coexistence of hidden attractors in
a system. In this plenary survey lecture the concept of self-excited and hidden
attractors is discussed, and various corresponding examples of self-excited and
hidden attractors are considered
A Practical Approach for the Auto-tuning of PD Controllers for Robotic Manipulators using Particle Swarm Optimization
An auto-tuning method of PD controllers for robotic manipulators is proposed. This method suggests a practical implementation of the particle swarm optimization technique in order to find optimal gain values achieving the best tracking of a predefined position trajectory. For this purpose, The integral of the absolute error IAE is used as a cost function for the optimization algorithm. The optimization is achieved by performing the desired movement of the robot iteratively and evaluating the cost function for every iteration. Therefor, the necessary constraints that guarantee a safe and stable movement of the robot are defined, which are: a maximum joint torque constraint, a maximum position error constraint and an oscillation constraint. A constraint handling approach is suggested for the optimization algorithm in order to adapt it to the problem in hand. Finally, the efficiency of the proposed method is verified through a practical experiment on a real robot
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