Symplectic discrete-time energy-based control for nonlinear mechanical systems

TL;DR

This paper introduces a novel symplectic discrete-time control method for nonlinear mechanical systems, enhancing stability and performance by accurately discretizing the system dynamics using implicit midpoint rule and energy-based control techniques.

Contribution

It develops a new discrete-time control approach based on symplectic discretization and implicit midpoint rule, improving stability and performance in nonlinear mechanical systems.

Findings

Enhanced stability and performance demonstrated in simulations.

Real-time implementation feasibility confirmed through computation time analysis.

Applicable to impedance, energy shaping, and computed torque control methods.

Abstract

In this article we present a novel discrete-time design approach which reduces the deteriorating effects of sampling on stability and performance in digitally controlled nonlinear mechanical systems. 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. In the nonlinear case, both the second order accurate representation of the sampling process and the definition of the target dynamics stem from the application of 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 St\"ormer-Verlet integration scheme on a staggered grid. We present discrete-time versions of impedance or energy shaping plus damping injection control as well as computed torque tracking control. Both the Hamiltonian and the Lagrangian perspective are adopted. Besides a linear example to introduce the concept, the simulations with a planar two link robot model illustrate the performance and stability gain compared to the discrete implementations of continuous-time control laws. A short analysis of computation times shows the real-time capability of our method.