Direct Linearization of Continuous and Hybrid Dynamical Systems

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.

2010 ◽  
Vol 6 (1) ◽  
Author(s):  
Julie J. Parish ◽  
John E. Hurtado

Linearized governing equations are often used in analysis, design, and control applications for dynamical systems. Linearized equations of motion can be formed in either an indirect or direct manner, that is, by first forming or bypassing the full nonlinear equations. Direct linearization is useful for easing the computation of linearized equations, particularly when the full nonlinear equations are not immediately desired. Currently, direct linearization methods derived from a Lagrangian perspective are available. In this paper, these methods are extended to reflect a Gibbs/Appell viewpoint. The resulting directly linearized equations take advantage of features of a Gibbs/Appellian formulation such as the ability to handle nonholonomic constraints and use of quasi-velocities. The Gibbs function and resulting equations are reviewed, the direct linearization method is explained, and a new method for directly linearizing equations via an augmented Gibbs function is presented with examples.


Author(s):  
Kazuyuki Aihara ◽  
Hideyuki Suzuki

In this introductory article, we survey the contents of this Theme Issue. This Theme Issue deals with a fertile region of hybrid dynamical systems that are characterized by the coexistence of continuous and discrete dynamics. It is now well known that there exist many hybrid dynamical systems with discontinuities such as impact, switching, friction and sliding. The first aim of this Issue is to discuss recent developments in understanding nonlinear dynamics of hybrid dynamical systems in the two main theoretical fields of dynamical systems theory and control systems theory. A combined study of the hybrid systems dynamics in the two theoretical fields might contribute to a more comprehensive understanding of hybrid dynamical systems. In addition, mathematical modelling by hybrid dynamical systems is particularly important for understanding the nonlinear dynamics of biological and medical systems as they have many discontinuities such as threshold-triggered firing in neurons, on–off switching of gene expression by a transcription factor, division in cells and certain types of chronotherapy for prostate cancer. Hence, the second aim is to discuss recent applications of hybrid dynamical systems in biology and medicine. Thus, this Issue is not only general to serve as a survey of recent progress in hybrid systems theory but also specific to introduce interesting and stimulating applications of hybrid systems in biology and medicine. As the introduction to the topics in this Theme Issue, we provide a brief history of nonlinear dynamics and mathematical modelling, different mathematical models of hybrid dynamical systems, the relationship between dynamical systems theory and control systems theory, examples of complex behaviour in a simple neuron model and its variants, applications of hybrid dynamical systems in biology and medicine as a road map of articles in this Theme Issue and future directions of hybrid systems modelling.


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