Authors

Paul Kotyczka, Tobias Thoma

Abstract

We present a novel approach for discrete-time state feedback control implementation which reduces the deteriorating effects of sampling on stability and performance in digitally controlled nonlinear mechanical systems. We translate the argument of energy shaping to discrete time by using the symplectic implicit midpoint rule. The method is motivated by recent results for linear systems, where feedback imposes closed-loop behavior that exactly represents the symplectic discretization of a desired target system. For the nonlinear case, the sampled system and the target dynamics are approximated with second order accuracy using the implicit midpoint rule. The implicit nature of the resulting state feedback requires the numerical solution of an in general nonlinear system of algebraic equations in every sampling interval. For an implementation with pure position feedback, the velocities/momenta have to be approximated in the sampling instants, which gives a clear interpretation of our approach in terms of the Störmer–Verlet integration scheme on a staggered grid. Both the Hamiltonian and the Lagrangian perspective are adopted. We present discrete-time versions of impedance or energy shaping plus damping injection control as well as computed torque tracking control in the simulation examples to illustrate the performance and stability gain compared to the quasi-continuous implementation. We discuss computational aspects and show the structural advantages of the implicit midpoint rule compared to other integration schemes in the appendix.

Keywords

Sampled-data systems; Discrete-time control; Structure-preserving methods; Symplectic integration; Störmer–Verlet; Nonlinear mechanical systems; Energy shaping; Passivity-based control; Computed torque

Citation

BibTeX

@article{Kotyczka_2021,
  title={{Symplectic discrete-time energy-based control for nonlinear mechanical systems}},
  volume={133},
  ISSN={0005-1098},
  DOI={10.1016/j.automatica.2021.109842},
  journal={Automatica},
  publisher={Elsevier BV},
  author={Kotyczka, Paul and Thoma, Tobias},
  year={2021},
  pages={109842}
}

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References