Finite Time Robust Control of the Sit-to-Stand Movement for Powered Lower Limb Orthoses

This study presents a technique to safely control the Sit-to-Stand movement of powered lower limb orthoses in the presence of parameter uncertainty. The weight matrices used to calculate the finite time horizon linear-quadratic regulator (LQR) gain in the feedback loop are chosen from a pool of candidates as to minimize a robust performance metric involving induced gains that measure the deviation of variables of interest in a linear time-varying (LTV) system, at specific times within a finite horizon, caused by a perturbation signal modeling the variation of the parameters. Two relevant Sit-to-Stand movements are simulated for drawing comparisons with the results documented in a previous work.Comment: 8 pages, 14 figures, ACC 2018 Submissio

Worst-Case Disturbances for Time-Varying Systems with Application to Flexible Aircraft

The aim of this Paper is to propose a method for constructing worst-case disturbances to analyze the performance of linear time-varying systems on a finite time horizon. This is primarily motivated by the goal of analyzing flexible aircraft, which are more realistically described as time-varying systems, but the same framework can be applied to other fields in which this feature is relevant. The performance is defined by means of a generic quadratic cost function, and the main result consists of a numerical algorithm to compute the worst-case signal verifying that a given performance objective is not achieved. The developed algorithm employs the solution to a Riccati differential equation associated with the cost function. Theoretically, the signal can also be obtained by simulating the related Hamiltonian dynamics, but this does not represent a numerically reliable strategy, as commented in the Paper. The applicability of the approach is demonstrated with a case study consisting of a flexible aircraft subject to gust during a flight-test maneuver.This work has received funding from the Horizon 2020 research and innovation programme under grant agreement number 636307, project FLEXOP. P. Seiler also acknowledges funding from the Hungarian Academy of Sciences, Institute for Computer Science and Control. The authors are thankful for Sérgio Waitman for providing the controller used in the analyses

The balanced mode decomposition algorithm for data-driven LPV low-order models of aeroservoelastic systems

A novel approach to reduced-order modeling of high-dimensional systems with time-varying properties is proposed. It combines the problem formulation of the Dynamic Mode Decomposition method with the concept of balanced realization. It is assumed that the only information available on the system comes from input, state, and output trajectories, thus the approach is fully data-driven. The goal is to obtain an input-output low dimensional linear model which approximates the system across its operating range. Time-varying features of the system are retained by means of a Linear Parameter-Varying representation made of a collection of state-consistent linear time-invariant reduced-order models. The algorithm formulation hinges on the idea of replacing the orthogonal projection onto the Proper Orthogonal Decomposition modes, used in Dynamic Mode Decomposition-based approaches, with a balancing oblique projection constructed from data. As a consequence, the input-output information captured in the lower-dimensional representation is increased compared to other projections onto subspaces of same or lower size. Moreover, a parameter-varying projection is possible while also achieving state-consistency. The validity of the proposed approach is demonstrated on a morphing wing for airborne wind energy applications by comparing the performance against two recent algorithms. Analyses account for both prediction accuracy and closed-loop performance in model predictive control applications