Application of the PC-based symbolic dynamics program Autolev to spacecraft attitude dynamics simulation

Schaechter, D. B.; Levinson, David A.

doi:10.2514/6.1988-4224

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…Equations of motion are derived using symbolic dynamics software AUTOLEV (Schaechter et al, 1996). Jumping simulations begin from a balanced posture which generally matches measured average initial postures, with slight differences due to a model's rigid-segment assumption.…”

Section: Methodsmentioning

confidence: 99%

Optimal compliant-surface jumping: a multi-segment model of springboard standing jumps

Cheng

Hubbard

2005

Journal of Biomechanics

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Section: Methodsmentioning

confidence: 99%

Optimal compliant-surface jumping: a multi-segment model of springboard standing jumps

Cheng

Hubbard

2005

Journal of Biomechanics

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“…12 Jumping from three different starting postures (high squat, squat, and low squat; denoted as HS, SQ, and LS) are simulated with the initial joint angles specified in the Appendix. The initial center of mass (c.m.)…”

Section: Model Descriptionmentioning

confidence: 99%

Optimal Standing Long Jump Simulation From Different Starting Postures

Cheng

Chen

2005

J. Mech. Med. Biol.

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A planar 4-segment human body model is used to simulate and study the effects of starting posture on standing long jumping performance. The model consists of frictionless hinge joints and is driven by joint torque actuators. The four segments represent feet, shanks, thighs, and trunk with head and arms. Movement simulations start from three different postures: high squat, squat, and low squat. The control variables are the joint torque activation levels and takeoff time. The objective function is the maximum horizontal distance from the toe point at takeoff to the center of mass (c.m.) position at landing. Optimal simulation results agree reasonably well with measurements. Different from previous high jump simulation study, slight dependence of initial posture on jump distance is found. Longer jump distance from a higher initial posture is probably due to greater range of countermovement that results in larger extension joint torque generation.

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“…It can provide powerful graphical support and be close to the mental model of the human designer, for example as realized within FLUIDSIM: based on CAD drawings of even large and complex electro-fluidic systems, FLUIDSIM generates the related algorithmic models without human support [44]. AUTOLEV [38] and the IMECH toolbox [3] are tools for textually describing mechanical multibody systems. Models in AUTOLEV are formulated within a proprietary mathematical language, models in the IMECH toolbox are formulated by instantiating C++ objects.…”

Section: Top-down Model Construction Support: a Classification Schemementioning

confidence: 99%

Model Construction for Knowledge-Intensive Engineering Tasks

Stein

2008

Advances of Computational Intelligence in Industrial Systems

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Summary. The construction of adequate models to solve engineerings tasks is a field of paramount interest. The starting point for an engineering task is a given system, S, or a set of systems, S, along with a shortcoming of information, often formulated as a question:• Which component is broken in S? (diagnosis ∼ analysis) • How does S react on the input u? (simulation ∼ analysis) • Does a system with the desired functionality exist in S? (design ∼ synthesis)If such an analysis or synthesis question shall be answered automatically, both adequate algorithmic models along with the problem solving expertise of a human problem solver must be operationalized on a computer. Often, the construction of an adequate model turns out to be the key challenge when tackling the engineering task. Model construction-also known as model creation, model formation, model finding, or model building-is an artistic discipline that highly depends on the reasoning job in question.Model construction can be supported by means of a computer, and in this chapter we present a comprehensive view on model construction, characterize both existing and new paradigms, and give examples for the state of the art of the realization technology. Our contributions are as follows:• In Section 2 we classify existing model construction approaches with respect to their position in the model hierarchy. Nearly all of the existing methods support a top-down procedure of the human modeler; they can be characterized as being either structuredefining (top), structure-filling (middle), or structure propagating (down).• Domain experts and knowledge engineers rarely start from scratch when constructing a new model; instead, they develop an appropriate model by modifying an existing one. Following this observation we analyzed various projects and classified the found model construction principles as model simplification, model compilation, and model reformulation. In Section 3 we introduce these principles as horizontal modeling construction and provide a generic characterization of each.• Section 4 presents real-world case studies to show horizontal model construction principles at work. The underlying technology includes, among others, hybrid knowledge representations, case-based as well as rule-based reasoning, and machine learning.

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Application of the PC-based symbolic dynamics program Autolev to spacecraft attitude dynamics simulation

Cited by 5 publications

References 3 publications

Optimal compliant-surface jumping: a multi-segment model of springboard standing jumps

Optimal compliant-surface jumping: a multi-segment model of springboard standing jumps

Optimal Standing Long Jump Simulation From Different Starting Postures

Model Construction for Knowledge-Intensive Engineering Tasks

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