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IROS 2016

Numerical search for local (partial) differential flatness

Conference Paper Accepted Paper Artificial Intelligence ยท Robotics

Abstract

Differential flatness is a property of certain systems that greatly simplifies the generation of optimal and dynamically feasible trajectories. Using a differentially flat model, there is no need to integrate the system dynamics to retrieve the states and the constraints of the optimization problem are simpler. Recently, the concept of partial differential flatness has been introduced covering a broader class of systems. In particular, it allows to reduce the need for integration by limiting it to a subset of the states. However, finding an analytical expression for the (partial) differential flatness requires the manipulation of the equations of motion in a very specific manner such that a series of properties are fulfilled. In general, finding such analytical model is not straightforward nor compatible with algorithmic models. In order to tackle this problem, in this paper we present a numerical method to find a (partially) differentially flat model of a system around a collection of states and inputs trajectories. We present results on three underactuated nonlinear systems (cart-pole, planar ballbot and a 3D quadrotor). As use case examples, we show online trajectory re-planning tasks. The validity of the trajectories obtained with the locally flat models is verified by forward integrating the original equations of motion together with an optimal stabilizer.

Authors

Keywords

  • Trajectory
  • Mathematical model
  • Numerical models
  • Robots
  • Optimization
  • Analytical models
  • System dynamics
  • Locally Flat
  • Differential Flatness
  • Optimization Problem
  • Equations Of Motion
  • Class Of Systems
  • Collection Conditions
  • Optimal Stability
  • Flat Model
  • Underactuated Systems
  • Model Parameters
  • Numerical Simulations
  • Horizontal Plane
  • Weight Matrix
  • Set Of Functions
  • Matrix Form
  • Multi-core
  • Numerical Approach
  • Nonlinear Programming
  • Output Function
  • Feasibility Problem
  • State Trajectories
  • Sample Trajectories
  • Input Trajectory
  • Combination Of Basis Functions
  • Trajectory Optimization
  • Basis Function Vector
  • Roll Angle
  • Combination Of Functions
  • Standard Laptop

Context

Venue
IEEE/RSJ International Conference on Intelligent Robots and Systems
Archive span
1988-2025
Indexed papers
26578
Paper id
1126330394530833149