scholarly journals Inverse Estimation of 3-D Traction Stress Field of Adhered Cell based on Optimal Control Technique using Image Intensities

2019 ◽  
Vol 16 (S2) ◽  
pp. 49-49
Author(s):  
Satoshi Ii ◽  
Keisuke Ito ◽  
Naoya Takakusaki ◽  
Naoya Sakamoto
Keyword(s):  
Optimal Control ◽  
Stress Field ◽  
Control Technique ◽  
Inverse Estimation ◽  
Traction Stress

scholarly journals Production and process management: An optimal control approach

2016 ◽  
Vol 26 (3) ◽  
pp. 331-342 ◽  
Author(s):  
Haider Biswas ◽  
Ahad Ali
Keyword(s):  
Optimal Control ◽  
Process Management ◽  
Nonlinear Dynamical Systems ◽  
State Constraint ◽  
Control Technique ◽  
Optimal Control Strategy ◽  
Nonlinear Dynamical ◽  
Control Approach ◽  
Time Operation ◽  
Operation Process

Optimal control and efficient management of industrial products are the key for sustainable development in industrial and process engineering. It is well-known that proper maintenance of process performance, ensuring the quality products after a long time operation of the system, is desirable in any industry. Nonlinear dynamical systems may play crucial role to appropriately design the model and obtain optimal control strategy in production and process management. This paper deals with a mathematical model in terms of ordinary differential equations (ODEs) that describe control of production and process arising in industrial engineering. The optimal control technique in the form of maximum principle, used to control the quality products in the operation processes, is applied to analyze the model. It is shown that the introduction of state constraint can be advantageous for obtaining good products during the longer operation process. We investigate the model numerically, using some known nonlinear optimal control solvers, and we present the simulation results to illustrate the significance of introducing state constraint onto the dynamics of the model.

Advanced modeling and optimal control of a cart-double pendulum system

2019 ◽  
Author(s):  
◽  
Cecil Jr. Shy
Keyword(s):  
Optimal Control ◽  
Lagrange Equation ◽  
Control Technique ◽  
Tuning Parameter ◽  
Microgravity Environment ◽  
Double Pendulum ◽  
Overhead Crane ◽  
Material Transport ◽  
Pendulum System ◽  
Industry Standards

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The Overhead Crane has evolved in scope since its inception in the late 1800's. Its early use as a hoist for material transport is now proceeded by new found applications, such as in the Active Response Gravity Offload System (ARGOS) at the NASA Johnson Space Center. ARGOS is an astronaut training facility designed to simulate reduced gravity environments such as Lunar, Martian, or microgravity. By industry standards, it is essentially a repurposed Overhead Crane; in academia it can be conceptualized as a cart-double pendulum system. Anti-sway control of cart-pendulum systems has been heavily researched; however, these methods are not typically designed for space simulation. The goal of this research is to design a controller that provides both energy and error minimization for the cart-pendulum, so that its payload moves as if it were floating freely in a microgravity environment (according to Newton's 1st law). The Euler-Lagrange equation is used to model the system and an optimal control technique called the [alpha]-shift is used to control the system. Most treatments on optimal linear control do not include the [alpha]-shift, but its addition allows one to stabilize the system faster and provides an extra tuning parameter while maintaining the simplicity of the solution. Numerical experiments show that the [alpha]-shift method significantly improves the cart-pendulum's ability to control its payload; especially for payloads in the cart-double-pendulum case.

scholarly journals Multi-Timescale-Based Partial Optimal Control of a Proton-Exchange Membrane Fuel Cell

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 166
Author(s):  
Milos Milanovic ◽  
Verica Radisavljevic-Gajic
Keyword(s):  
Optimal Control ◽  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Proton Exchange ◽  
Operating Conditions ◽  
Pole Placement ◽  
Control Technique ◽  
Transient Model ◽  
Linearized Model ◽  
Exchange Membrane

This paper presents a Proton-Exchange Membrane Fuel Cell (PEMFC) transient model in stack current cycling conditions and its partial optimal control. The derived model is used for a specific application of the recently published multistage control technique developed by the authors. The presented control-oriented transient PEMFC model is an extension of the steady-state control-oriented model previously established by the authors. The new model is experimentally validated for transient operating conditions on the Greenlight Innovation G60 testing station where the comparison of the experimental and simulation results is presented. The derived five-state nonlinear control-oriented model is linearized, and three clusters of eigenvalues can be clearly identified. This specific feature of the linearized model is known as the three timescale system. A novel multistage optimal control technique is particularly suitable for this class of systems. It is shown that this control technique enables the designer to construct a local LQR, pole-placement or any other linear controller type at the subsystem level completely independently, which further optimizes the performance of the whole non-decoupled system.

scholarly journals Stability Analysis and Optimal Control of a Vector-Borne Disease with Nonlinear Incidence

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Muhammad Ozair ◽  
Abid Ali Lashari ◽  
Il Hyo Jung ◽  
Kazeem Oare Okosun
Keyword(s):  
Optimal Control ◽  
Sensitivity Analysis ◽  
Control Measures ◽  
Basic Reproductive Number ◽  
Control Technique ◽  
Global Dynamics ◽  
Nonlinear Incidence ◽  
Vector Borne Disease ◽  
Vector Borne ◽  
The Impact

The paper considers a model for the transmission dynamics of a vector-borne disease with nonlinear incidence rate. It is proved that the global dynamics of the disease are completely determined by the basic reproduction number. In order to assess the effectiveness of disease control measures, the sensitivity analysis of the basic reproductive numberR0and the endemic proportions with respect to epidemiological and demographic parameters are provided. From the results of the sensitivity analysis, the model is modified to assess the impact of three control measures; the preventive control to minimize vector human contacts, the treatment control to the infected human, and the insecticide control to the vector. Analytically the existence of the optimal control is established by the use of an optimal control technique and numerically it is solved by an iterative method. Numerical simulations and optimal analysis of the model show that restricted and proper use of control measures might considerably decrease the number of infected humans in a viable way.

A New Hierarchical Optimal Control Technique of Stochastic Large-Scale Systems *

1996 ◽  
Vol 29 (1) ◽  
pp. 5470-5475
Author(s):  
San-Ming Xie ◽  
Cang-Pu Wu ◽  
Julian L. Xu
Keyword(s):  
Optimal Control ◽  
Large Scale ◽  
Control Technique ◽  
Large Scale Systems

scholarly journals Comparison of a Triple Inverted Pendulum Stabilization Using Optimal Control Technique

2020 ◽  
Author(s):  
Mustefa Jibril ◽  
Messay Tadese ◽  
Eliyas Alemayehu Tadese
Keyword(s):  
Optimal Control ◽  
Impulse Response ◽  
Inverted Pendulum ◽  
Control Method ◽  
Pole Placement ◽  
Control Technique ◽  
Comparison Results ◽  
Highly Nonlinear ◽  
Pole Placement Controller ◽  
Triple Inverted Pendulum

In this paper, modelling design and analysis of a triple inverted pendulum have been done using Matlab/Script toolbox. Since a triple inverted pendulum is highly nonlinear, strongly unstable without using feedback control system. In this paper an optimal control method means a linear quadratic regulator and pole placement controllers are used to stabilize the triple inverted pendulum upside. The impulse response simulation of the open loop system shows us that the pendulum is unstable. The comparison of the closed loop impulse response simulation of the pendulum with LQR and pole placement controllers results that both controllers have stabilized the system but the pendulum with LQR controllers have a high overshoot with long settling time than the pendulum with pole placement controller. Finally the comparison results prove that the pendulum with pole placement controller improve the stability of the system.

Sign in / Sign up

Export Citation Format

Share Document