Automatic Differentiation In The Automatic Generation Of The Linearized Equations Of Motion

2021 ◽  
Author(s):  
Bruce Minaker
2021 ◽  
Author(s):  
Bruce Minaker ◽  
Francisco González

Abstract In the ongoing search for mathematically efficient methods of predicting the motion of vehicle and other multibody systems, and presenting the associated results, one of the avenues of continued interest is the linearization of the equations of motion. While linearization can potentially result in reduced fidelity in the model, the benefits in computational speed often make it the pragmatic choice. Linearization techniques are also useful in modal and stability analysis, model order reduction, and state and input estimation. This paper explores the application of automatic differentiation to the generation of the linearized equations of motion. Automatic differentiation allows one to numerically evaluate the derivative of any function, with no prior knowledge of the differential relationship to other functions. It exploits the fact that every computer program must evaluate every function using only elementary arithmetic operations. Using automatic differentiation, derivatives of arbitrary order can be computed, accurately to working precision, with minimal additional computational cost over the evaluation of the base function. There are several freely available software libraries that implement automatic differentiation in modern computing languages. In the paper, several example multibody systems are analyzed, and the computation times of the stiffness matrix are compared using direct evaluation and automatic differentiation. The results show that automatic differentiation can be surprisingly competitive in terms of computational efficiency.


2014 ◽  
Vol 611 ◽  
pp. 40-45
Author(s):  
Darina Hroncová ◽  
Jozef Filas

The paper describes an algorithm for automatic compilation of equations of motion. Lagrange equations of the second kind and the transformation matrices of basic movements are used by this algorithm. This approach is useful for computer simulation of open kinematic chains with any number of degrees of freedom as well as any combination of bonds.


Author(s):  
Julie J. Parish ◽  
John E. Hurtado ◽  
Andrew J. Sinclair

Nonlinear equations of motion are often linearized, especially for stability analysis and control design applications. Traditionally, the full nonlinear equations are formed and then linearized about the desired equilibrium configuration using methods such as Taylor series expansions. However, it has been shown that the quadratic form of the Lagrangian function can be used to directly linearize the equations of motion for discrete dynamical systems. This procedure is extended to directly generate linearized equations of motion for both continuous and hybrid dynamical systems. The results presented require only velocity-level kinematics to form the Lagrangian and find equilibrium configuration(s) for the system. A set of selected partial derivatives of the Lagrangian are then computed and used to directly construct the linearized equations of motion about the equilibrium configuration of interest, without first generating the entire nonlinear equations of motion. Given an equilibrium configuration of interest, the directly constructed linearized equations of motion allow one to bypass first forming the full nonlinear governing equations for the system. Examples are presented to illustrate the method for both continuous and hybrid systems.


Author(s):  
A Sinaie ◽  
A Ziaie

The equations of particle motion in an elastic isotropic stressed medium are first derived in Cartesian coordinates and then transformed into cylindrical coordinates. The three components of the equations of motion are non-linear partial differential equations and cannot be of use in practical applications. However, noting that the particle displacement is composed of a small dynamic part superimposed on a large static part, these equations are linearized via a simple perturbation method. The linearized equations are presented in closed form. They contain variables, which may be measured and experimented upon in practice, in the field of acoustoelasticity.


1977 ◽  
Vol 8 (3) ◽  
pp. 129-140 ◽  
Author(s):  
Lars Gottschalk

Correlation structure of river runoff is a complicated set of different persistence phenomena in the watershed itself and in the meteorological input to the watershed. Correlation functions and time scale of isolated processes in a watershed (groundwater level and river runoff) are derived analytically from the linearized equations of motion for these processes. Nonlinear effects on the correlation functions are shown for river runoff and for the watershed as a whole.


1979 ◽  
Vol 23 (03) ◽  
pp. 175-187
Author(s):  
Carl A. Scragg

This paper discusses the error introduced into maneuvering predictions by the use of a set of linearized equations of motion which ignore memory effects. After incorporating certain improvements into the impulse-response technique for the determination of hydrodynamic coefficients, experiments were conducted to measure a complete set of the coefficients. Maneuvering predictions were then made using two different sets of linearized equations of motion, one of which included memory effects and one which excluded memory effects. It was determined that significant errors occurred only during the initial phase of the maneuver, and that memory effects could be safely ignored for most deepwater maneuvering problems.


Author(s):  
Márton Kuslits ◽  
Dieter Bestle

Multibody systems and associated equations of motion may be distinguished in many ways: holonomic and nonholonomic, linear and nonlinear, tree-structured and closed-loop kinematics, symbolic and numeric equations of motion. The present paper deals with a symbolic derivation of nonlinear equations of motion for nonholonomic multibody systems with closed-loop kinematics, where any generalized coordinates and velocities may be used for describing their kinematics. Loop constraints are taken into account by algebraic equations and Lagrange multipliers. The paper then focuses on the derivation of the corresponding linear equations of motion by eliminating the Lagrange multipliers and applying a computationally efficient symbolic linearization procedure. As demonstration example, a vehicle model with differential steering is used where validity of the approach is shown by comparing the behavior of the linearized equations with their nonlinear counterpart via simulations.


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