Application of Optimal Control Theory to the Synthesis of High-Speed Cam-Follower Systems. Part 2: System Optimization

1983 ◽  
Vol 105 (3) ◽  
pp. 585-591 ◽  
Author(s):  
M. Chew ◽  
F. Freudenstein ◽  
R. W. Longman
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Contact Stress ◽  
Optimal Control Theory ◽  
High Speed ◽  
System Optimization ◽  
Design Parameters ◽  
Numerical Examples ◽  
Cam Follower

This part is concerned with the determination of optimum values of the design parameters of cam-follower systems according to the criterion developed in Part 1. The nonlinearities associated with the optimization of contact stress, pressure-angle, and friction-dependent forces, which create difficulties in the simpler approaches, can be tolerated in the optimal-control-theory formulation, which is developed in this investigation. The procedure for the optimization of tuned D-R-D and D-R-R-D cams has been described and the results illustrated by means of numerical examples.

Application of Optimal Control Theory to the Synthesis of High-Speed Cam-Follower Systems. Part 1: Optimality Criterion

1983 ◽  
Vol 105 (3) ◽  
pp. 576-584 ◽  
Author(s):  
M. Chew ◽  
F. Freudenstein ◽  
R. W. Longman
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Dynamic Response ◽  
Optimal Control Theory ◽  
High Speed ◽  
Integrated Approach ◽  
Optimization Criterion ◽  
Design Stage ◽  
Trade Offs ◽  
Cam Follower

The synthesis of the parameters governing the dynamic response of high-speed cam-follower systems ideally involves an integrated approach capable of carrying out the tradeoffs necessary to achieve optimum dynamic response in the design stage. These trade-offs involve a balance between the system characteristics at the output and at the cam-follower interface. In this investigation optimal-control theory has been demonstrated to be a useful tool in developing such a tradeoff. Part 1 describes the development of an optimization criterion while Part 2 describes the application of optimal-control theory to the evaluation of system parameters satisfying the optimization criterion.

A Variable-Speed Method for Reducing Residual Vibrations in Elastic Cam-Follower Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 480-482 ◽  
Author(s):  
Yan-An Yao ◽  
Hong-Sen Yan ◽  
Ce Zhang
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Vibration Control ◽  
Optimal Control Theory ◽  
Variable Speed ◽  
Design Point ◽  
Parameter Variations ◽  
Cam Follower ◽  
Speed Method

This paper applies the concept of variable speeds to vibration control of elastic cam-follower systems. A multi-design-point approach, based on optimal control theory, is developed for selecting suitable input speed functions of the cam that can reduce both primary and residual vibrations of the output in elastic cam-follower systems despite parameter variations. A design example is given to verify the feasibility of the approach.

Optimization of Induction Heating Regarding Typical Quality Criteria: Problem Solution Based on 2D FEM Analysis

2015 ◽  
Vol 792 ◽  
pp. 462-467 ◽  
Author(s):  
Yuliya Pleshivtseva ◽  
Bernard Nacke ◽  
Anton Popov
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Induction Heating ◽  
Optimal Control Theory ◽  
Quality Criteria ◽  
Fem Analysis ◽  
Hot Forming ◽  
Electrical Heating ◽  
Design Parameters ◽  
Distributed Parameters

One of the most widespread methods of heating is induction mass heating because it offers certain advantages over similar technologies, including convectional and electrical heating. A significant economical effect can be achieved through optimization of heating modes and design parameters of induction heaters on the basis of modern optimal control theory for distributed parameters systems. The paper is devoted to the numerical simulation and optimal with respect to typical quality criteria control of thermal modes for metals induction heating before hot forming operations. Two-dimensional non-linear time-optimal control problem, problem of maximum heating accuracy and problem of minimum energy consumption are formulated and reduced to the mathematical programming problems. Optimization procedures are based on the developed at SamSTU alternance method of optimal control theory for distributed parameters systems. 2D FLUX code provides FEM analysis of interrelated electromagnetic and temperature fields during induction heating of a cylindrical billet before its hot forming. The model integrated into optimization procedures provides options for variation of the heating system parameters or billet geometry, and for evaluating the process optimization abilities. Computational results for optimal heating of aluminum cylindrical billets are shown and analyzed.

Multiscale Regularization of Flooding Optimization for Smart Field Management

SPE Journal ◽  
2008 ◽  
Vol 13 (02) ◽  
pp. 195-204 ◽  
Author(s):  
Martha E. Lien ◽  
D. Roald Brouwer ◽  
Trond Mannseth ◽  
Jan-Dirk Jansen
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Objective Function ◽  
Optimal Control Theory ◽  
Degrees Of Freedom ◽  
Management Strategies ◽  
Multiscale Methods ◽  
Regularization Methods ◽  
Production Rates ◽  
Numerical Examples

Summary Smart fields can provide enhanced oil recovery through the combined use of optimization and data assimilation. In this paper, we focus on the dynamic optimization of injection and production rates during waterflooding. In particular, we use optimal control theory in order to find an optimal well management strategy over the life of the reservoir that maximizes an objective function (e.g., recovery or net present value). Optimal control requires the determination of a potentially large number of (groups of) well rates for a potentially large number of time periods. However, the optimal number of well groups and time steps is not known a priori. Moreover, taking these numbers too large can slow down the optimization process and increase the chance of achieving a suboptimal solution. We investigate the use of multiscale regularization methods to achieve grouping of the control settings of the wells in both space and time. Starting out with a very coarse grouping, the resolution is subsequently refined during the optimization. The regularization is adaptive in that the multiscale parameterization is chosen based on the gradients of the objective function. Results for the numerical examples studied indicate that the regularization may lead to significantly simpler optimum strategies, while resulting in a better or similar cumulative oil production. Introduction We consider the secondary recovery phase of a heterogeneous oil reservoir, where water is injected into the reservoir for pressure maintenance and sweep improvement. In a smart field scenario, we consider injectors and producers with both single and multiple completions. The flow rates of the different well completions can be adjusted individually. In the following, an individual well completion will be referred to as "well segment." This implies that in case of conventional single-completion wells the term "well segment" is therefore equivalent to "well." Ideally, the injected water will displace the remaining oil in the reservoir on its way from the injection wells to the production wells. Rock heterogeneities will, however, influence the path of the injected water. The water will mainly flow in the high-permeability channels, which causes only part of the oil to be produced. Recently, smart field concepts have been proposed as a means to improve control over the waterfront through detailed adjustments of the injection and production rates in time using a combination of model-based flooding optimization and model updating (Brouwer et al. 2004; Sarma et al. 2005b). For the optimization part, these "closed-loop" reservoir management strategies rely on optimal control theory, which has been proposed before as a flooding optimization method by various authors (Asheim 1988; Virnovski 1991; Sudaryanto 1998; Brouwer et al. 2004; Sarma et al. 2005a). However, optimization by means of optimal control theory is computationally expensive, and detailed management of every individual well segment of a smart field at every moment in time is economically and technically demanding. Moreover, there may not be enough information in the system to determine the optimal production strategy uniquely. Hence, we seek to develop management strategies with a restricted number of degrees of freedom, which at the same time maintain the advantages of the smart field technology. In this paper, multiscale estimation techniques are utilized to attempt to find the optimal well management level. These are hierarchical regularization methods where the number of degrees of freedom in the estimation is gradually increased as the optimization proceeds. Multiscale methods were first applied for solving partial differential equations to speed up convergence (Brandt 1977; Briggs 1987). Later, through the development of wavelets, multiscale approaches have also been widely used within inverse problems (Emsellem and de Marsily 1971; Chavent and Liu 1989; Liu 1993; Yoon et al. 2001). The outline of the paper is as follows: First, the theory behind the solution of the problem in terms of optimal control and gradient-based optimization is presented. Thereafter we present methods to regularize the optimization problem in terms of multiscale reparameterization of the control variable. Finally, the performance of the proposed regularization strategies is illustrated through a line of numerical examples before we summarize and conclude.

scholarly journals Kontrol Optimal Upaya Pengobatan Penyakit Campak Menggunakan Model Endemi SIR

2019 ◽  
Vol 9 (2) ◽  
pp. 94
Author(s):  
Ida Ayu Putu Ari Utari
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Optimal Control Theory ◽  
Minimum Principle ◽  
System Of Differential Equations ◽  
Pontryagin Minimum Principle ◽  
Main Target ◽  
Model Determination ◽  
Efficiency Rate

Measles is an acute highly contagious disease caused by Paramyxovirus. Measles is considered as a dangerous disease because it cause complications, brain and other organs damage, lifelong disability, paralysis and even death. In the previous studies, it was known that the spread of measles highly dependent on number of infected individuals so it is necessary to control measles through treatment. In this paper, we study about the application of the optimal control theory on the system of differential equations of the SIR endemic model. Determination of the optimal control is obtained through the application of the Pontryagin minimum principle. The main target in this paper is to find a unique optimal control where the optimal control can be described as an efficiency rate of treatment in individuals infected with measles to decrease the number of infected individuals.

Sign in / Sign up

Export Citation Format

Share Document