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The dynamics analysis of the rear suspension system of the Fiat Panda III with electric motors mounted in wheels is presented in the paper. The simplified model of this system modeled using the multibody system dynamics method and the MSC. Adams package is proposed. In order to validate the proposed numerical model, the road tests were carried out consisting on passing the vehicle without motors in wheels at constant speed through the obstacle. The vertical displacement of the center of the vehicle wheel was measured during the tests. During the validation, parameters of the wheel-to-road contact, stiffness coefficients of springs and shock absorber damping coefficients of the suspension of the simulation model were modified so that the numerical results were consistent with the experiment. Further, such a tuned model was used to simulate the motion of suspension with the motors mounted into the wheels. The obtained results were validated, obtaining the accepted compatibility. In the following, a series of calculations was carried out in order to analyze the influence of stiffness coefficients of springs and shock absorber damping coefficients on the dynamic response of the suspension.

SummaryThis paper details a new automated generation method of dynamic models of structures allowing presence of friction and damping in their models as well as subjected to position or kinematic constraints. The constraints may be material, which come from the system interaction with environment, or may be programmed. The novelty of the presented dynamics generation method is to include friction and damping into modeling and to require predefined constraint satisfaction as well. The latter requirement is critical, since when friction and damping are neglected, controllers may be inadequately designed, constraints may be violated, and overall system performance may be poor. The developed method is applied to a manipulator model, whose end‐effector performance is predefined by programmed constraints. Simulation results present manipulator constrained motion. The advantage of this method is that it serves both reference and control oriented dynamics derivation, and the final dynamics models are obtained in the reduced state form, ie, constraint reaction forces are eliminated. This is the fundamental difference between the presented approach and the Lagrange based approaches to constrained system modeling.

Background The paper presents vibration analysis of dynamic models of systems with flexible mechanical components, friction modeled and subjected to position and kinematic programmed constraints, which can be imposed as control goals, work or service task demands. Methods The constrained dynamics is derived using an automated computational procedure dedicated to constrained systems. The procedure was successfully implemented to rigid system models. A class of systems composed of flexible parts and subjected to programmed motions is considered in the paper. Their motion analysis has to be accompanied by vibration inspection. The novelty of the presented approach is in the possibility of analyzing system motions and vibrations that can be induced by the presence of programmed constraints. Conclusions The constrained motion is examined by the example of a crane model equipped with a flexible link, e.g. a jib, friction modeled in its joints and subjected to programmed constraints. The example delivers a realistic work situation, in which the crane carries loads and moves according to the programmed constraint put on motion.

The paper presents a development of a computational based procedure for generation of constrained system dynamical models. The constraints may be both holonomic and first order nonholonomic, either material or nonmaterial. The latter ones are referred to as programmed and they are imposed by a designer, a control engineer as a control goal, or may come from controlled system performance requirements. The procedure for generation of constrained dynamics provides then reference dynamical models, i.e. models whose solutions satisfy all the constraints put upon them. These models may serve as motion planners for control. The distinctions between the presented approach and the ones reported in the literature are that the constraints may be material or nonmaterial and the final equations of motion are derived in the reduced state form, i.e. constraint reaction forces are eliminated at the equations derivation level but not afterwards as in the case of the Lagrange approach. This is the essential advantage of our approach and this one computational procedure may serve both reference and control oriented dynamical models derivation. The procedure is applied to a manipulator model whose end effector is subjected to a programmed constrained.