scholarly journals Design and Control of Nonlinear Mechanical Systems for Minimum Time

2008 ◽  
Vol 15 (3-4) ◽  
pp. 315-323 ◽  
Author(s):  
J.B. Cardoso ◽  
P.P. Moita ◽  
A.J. Valido
Keyword(s):  
Dynamic Response ◽  
Mechanical Systems ◽  
Nonlinear Behavior ◽  
Adjoint System ◽  
Minimum Time ◽  
Design Parameters ◽  
Time Interval ◽  
Minimum Time Problems ◽  
And Control ◽  
Time Invariant

This paper presents an integrated methodology for optimal design and control of nonlinear flexible mechanical systems, including minimum time problems. This formulation is implemented in an optimum design code and it is applied to the nonlinear behavior dynamic response. Damping and stiffness characteristics plus control driven forces are considered as decision variables. A conceptual separation between time variant and time invariant design parameters is presented, this way including the design space into the control space and considering the design variables as control variables not depending on time. By using time integrals through all the derivations, design and control problems are unified. In the optimization process we can use both types of variables simultaneously or by interdependent levels. For treating minimum time problems, a unit time interval is mapped onto the original time interval, then treating equally time variant and time invariant problems. The dynamic response and its sensitivity are discretized via space-time finite elements, and may be integrated either by at-once integration or step-by-step. Adjoint system approach is used to calculate the sensitivities.

scholarly journals A Space-Time Finite Element Model for Design and Control Optimization of Nonlinear Dynamic Response

2008 ◽  
Vol 15 (3-4) ◽  
pp. 307-314 ◽  
Author(s):  
P.P. Moita ◽  
J.B. Cardoso ◽  
A.J. Valido
Keyword(s):  
Dynamic Response ◽  
Optimum Design ◽  
Nonlinear Dynamic ◽  
Adjoint System ◽  
Element Model ◽  
Optimality Criteria ◽  
Space Time ◽  
Control Variables ◽  
Nonlinear Dynamic Response ◽  
And Control

A design and control sensitivity analysis and multicriteria optimization formulation is derived for flexible mechanical systems. This formulation is implemented in an optimum design code and it is applied to the nonlinear dynamic response. By extending the spatial domain to the space-time domain and treating the design variables as control variables that do not change with time, the design space is included in the control space. Thus, one can unify in one single formulation the problems of optimum design and optimal control. Structural dimensions as well as lumped damping and stiffness parameters plus control driven forces, are considered as decision variables. The dynamic response and its sensitivity with respect to the design and control variables are discretized via space-time finite elements, and are integrated at-once, as it is traditionally used for static response. The adjoint system approach is used to determine the design sensitivities. Design optimization numerical examples are performed. Nonlinear programming and optimality criteria may be used for the optimization process. A normalized weighted bound formulation is used to handle multicriteria problems.

Development of a Jacobian Module for Advanced Pulp and Paper Simulation and Control

TAPPI Journal ◽  
2009 ◽  
Vol 8 (1) ◽  
pp. 4-11
Author(s):  
MOHAMED CHBEL ◽  
LUC LAPERRIÈRE
Keyword(s):  
Control System ◽  
Nonlinear Behavior ◽  
Simulation Software ◽  
Pulp And Paper ◽  
Pid Controllers ◽  
Tuning Parameters ◽  
Pid Tuning ◽  
Fixed Set ◽  
And Control ◽  
Operating Points

Pulp and paper processes frequently present nonlinear behavior, which means that process dynam-ics change with the operating points. These nonlinearities can challenge process control. PID controllers are the most popular controllers because they are simple and robust. However, a fixed set of PID tuning parameters is gen-erally not sufficient to optimize control of the process. Problems related to nonlinearities such as sluggish or oscilla-tory response can arise in different operating regions. Gain scheduling is a potential solution. In processes with mul-tiple control objectives, the control strategy must further evaluate loop interactions to decide on the pairing of manipulated and controlled variables that minimize the effect of such interactions and hence, optimize controller’s performance and stability. Using the CADSIM Plus™ commercial simulation software, we developed a Jacobian sim-ulation module that enables automatic bumps on the manipulated variables to calculate process gains at different operating points. These gains can be used in controller tuning. The module also enables the control system designer to evaluate loop interactions in a multivariable control system by calculating the Relative Gain Array (RGA) matrix, of which the Jacobian is an essential part.

A Simplified Design Model for Modular Steel Buildings Subject to Vapor Cloud Explosions (VCEs)

2020 ◽  
Vol 14 ◽  
Author(s):  
Osama Bedair
Keyword(s):  
Dynamic Response ◽  
Minimum Cost ◽  
Design Parameters ◽  
Front Wall ◽  
Steel Buildings ◽  
Element Analysis ◽  
Analysis And Design ◽  
Vapor Cloud ◽  
Building Components ◽  
Analytical Expressions

Background: Modular steel buildings (MSB) are extensively used in petrochemical plants and refineries. Limited guidelines are available in the industry for analysis and design of (MSB) subject to accidental vapor cloud explosions (VCEs). Objectives: The paper presents simplified engineering model for modular steel buildings (MSB) subject to accidental vapor cloud explosions (VCEs) that are extensively used in petrochemical plants and refineries. Method: A Single degree of freedom (SDOF) dynamic model is utilized to simulate the dynamic response of primary building components. Analytical expressions are then provided to compute the dynamic load factors (DLF) for critical building elements. Recommended foundation systems are also proposed to install the modular building with minimum cost. Results: Numerical results are presented to illustrate the dynamic response of (MSB) subject to blast loading. It is shown that (DLF)=1.6 is attained at (td/t)=0.4 for front wall (W1) with (td/T)=1.25. For side walls (DLF)=1.41 and is attained at (td/t)=0.6. Conclusions: The paper presented simplified tools for analysis and design of (MSB) subject accidental vapor cloud blast explosions (VCEs). The analytical expressions can be utilized by practitioners to compute the (MSB) response and identify the design parameters. They are simple to use compared to Finite Element Analysis.

Multivariable modeling and control of an activated sludge process

1998 ◽  
Vol 37 (12) ◽  
pp. 149-156 ◽  
Author(s):  
Carl-Fredrik Lindberg
Keyword(s):  
Activated Sludge ◽  
Nitrate Concentration ◽  
State Space Model ◽  
Activated Sludge Process ◽  
Invariant State ◽  
Linear Quadratic ◽  
Modeling And Control ◽  
Space Model ◽  
And Control ◽  
Time Invariant

This paper contains two contributions. First it is shown, in a simulation study using the IAWQ model, that a linear multivariable time-invariant state-space model can be used to predict the ammonium and nitrate concentration in the last aerated zone in a pre-denitrifying activated sludge process. Secondly, using the estimated linear model, a multivariable linear quadratic (LQ) controller is designed and used to control the ammonium and nitrate concentration.

Using Artificial Intelligence to Estimate Nonlinear Resilient Modulus Parameters from Common Index Properties

2021 ◽  
pp. 036119812110237
Author(s):  
Laura Camarena
Keyword(s):  
Machine Learning ◽  
Material Properties ◽  
Resilient Modulus ◽  
Nonlinear Behavior ◽  
Machine Learning Techniques ◽  
Design Parameters ◽  
Nonlinear Parameters ◽  
State Highway ◽  
Learning Techniques ◽  
State Highway Agencies

The Mechanistic–Empirical Pavement Design Guide (MEPDG) considers a hierarchical approach to determine the input values necessary for most design parameters. Level 1 requires site-specific measurement of the material properties from laboratory testing, whereas other levels make use of equations developed from regression models to estimate the material properties. Resilient modulus is a mechanical property that characterizes the unbound and subgrade materials under loading that is essential for the mechanistic design of pavements. The MEPDG resilient modulus model makes use of a three-parameter constitutive model to characterize the nonlinear behavior of the geomaterials. As the resilient modulus tests are complex, expensive, and require lengthy preparation time, most state highway agencies are unlikely to implement them as routine daily applications. Therefore, it is imperative to make use of models to calculate these nonlinear parameters. Existing models to determine these parameters are frequently based on linear regression. With the development of machine learning techniques, it is feasible to develop simpler equations that can be used to estimate the nonlinear parameters more accurately. This study makes use of the Long-Term Pavement Performance database and machine learning techniques to improve the equations utilized to determine the nonlinear parameters crucial to estimate the resilient modulus of unbound base and subgrade materials.

Momentum exchange impact damper design methodology for object-wall-collision problems

2017 ◽  
Vol 24 (14) ◽  
pp. 3206-3218
Author(s):  
Yohei Kushida ◽  
Hiroaki Umehara ◽  
Susumu Hara ◽  
Keisuke Yamada
Keyword(s):  
Optimal Design ◽  
Design Methodology ◽  
Design Parameters ◽  
Momentum Exchange ◽  
Impact Damper ◽  
One Dimensional ◽  
Practical Design ◽  
Wall Collision ◽  
Damper Design ◽  
And Control

Momentum exchange impact dampers (MEIDs) were proposed to control the shock responses of mechanical structures. They were applied to reduce floor shock vibrations and control lunar/planetary exploration spacecraft landings. MEIDs are required to control an object’s velocity and displacement, especially for applications involving spacecraft landing. Previous studies verified numerous MEID performances through various types of simulations and experiments. However, previous studies discussing the optimal design methodology for MEIDs are limited. This study explicitly derived the optimal design parameters of MEIDs, which control the controlled object’s displacement and velocity to zero in one-dimensional motion. In addition, the study derived sub-optimal design parameters to control the controlled object’s velocity within a reasonable approximation to derive a practical design methodology for MEIDs. The derived sub-optimal design methodology could also be applied to MEIDs in two-dimensional motion. Furthermore, simulations conducted in the study verified the performances of MEIDs with optimal/sub-optimal design parameters.

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