Fully coupled simulation of mechatronic and flexible multibody systems: An extended finite element approach

In this talk, I will discuss some extensions and refinements of classical finite element tools for the numerical simulation of complex mechatronic systems. The presentation will be divided into three main parts. In the first part, I will show how this approach allows the modeling of dynamic systems with large amplitude motions composed of rigid bodies, flexible bodies, kinematic joints, actuators, sensors and control units. A fully coupled model of a semi-active suspension will be used to illustrate the methodology. The second part will focus on some numerical aspects concerning the time integration of the equations of motion which have the structure of strongly coupled differential-algebraic equations on a Lie group. The treatment of large rotation variables and of the coupling between control state variables and mechanical generalized coordinates will be discussed in more detail. In the third part, the simulation tool will be exploited for the topology optimization of structural components embedded in multibody systems. Generally, topology optimization techniques use simplified quasi-static load cases to mimic the complex dynamic loadings in service. In contrast, I will present an optimization procedure which properly accounts for the actual dynamic interactions which occur during the motion of the flexible multibody system. In order to illustrate the benefits of this integrated design approach, the optimization of a two degrees-of-freedom robot arm with flexible truss linkages will be analyzed

Modelling of frictional unilateral contact in automotive differentials

audience: researche

Analysis of temporal gait features extracted from accelerometer-based signals during ambulatory walking in Parkinson’s disease

Objective: To perform a proof-of-concept study showing the utility of versatile algorithms aimed at objectively quantifying the duration of refined gait features during ambulatory walking in a patient with Parkinson’s disease (PD) in ON and OFF medication states as compared with an age-matched control subject

Integrated structure and control design for mechatronic systems with configuration-dependent dynamics

This paper considers the optimal design of mechatronic systems with configuration-dependent dynamics. An optimal mechatronic design requires that, among the structural and control parameters, an optimal choice has to be made with respect to design specifications in the different domains. Two main challenges are treated in this paper: the non-convex nature of the optimization problem and the difficulty in modeling serial machines with flexible components and their embedded controllers. The optimization problem is treated using the direct design strategy which considers simultaneously structural and control parameters as variables and adopts non-convex optimization algorithms. Linear time-invariant and gain-scheduling PID controllers are addressed. This methodology is exploited for the multi-objective optimization of a pick-and-place assembly robot with a gripper carried by a variable-length flexible beam. The resulting design tradeoffs between system accuracy and control efforts demonstrate the advantage of an integrated design approach for mechatronic systems with configuration-dependent dynamics. 2009 Elsevier Ltd. All rights reserved

Simulation-based Bayesian inference for multi-fingered robotic grasping

Multi-fingered robotic grasping is an undeniable stepping stone to universal picking and dexterous manipulation. Yet, multi-fingered grippers remain challenging to control because of their rich nonsmooth contact dynamics or because of sensor noise. In this work, we aim to plan hand configurations by performing Bayesian posterior inference through the full stochastic forward simulation of the robot in its environment, hence robustly accounting for many of the uncertainties in the system. While previous methods either relied on simplified surrogates of the likelihood function or attempted to learn to directly predict maximum likelihood estimates, we bring a novel simulation-based approach for full Bayesian inference based on a deep neural network surrogate of the likelihood-to-evidence ratio. Hand configurations are found by directly optimizing through the resulting amortized and differentiable expression for the posterior. The geometry of the configuration space is accounted for by proposing a Riemannian manifold optimization procedure through the neural posterior. Simulation and physical benchmarks demonstrate the high success rate of the procedure