Motion control of elastic joint based on kalman optimization with evolutionary algorithm

After obtaining pre-processed marker positions approximately fitted to the robot structure, the main opti- mization process generates also marker motions that respect the

The reason for separating these two problem parameters is the increased control over the optimization process. By configuring the probabilities for the use of the two different

The methods of linear optimization were applied and tested to solve the problem of cam- era positioning for maximum coverage. In some cases, due to a well-adapted formulation or

This list of specifications is assumed to translate into a finite set of (possibly nonlinear) inequalities to be satisfied by c and p. This makes it possible

Summarizing, the main contribution of this work is that we systematically provide i) a set of exact linearizing (i.e., flat) outputs for the all the four cases, ii) the

Algorithms for nonconvex nonsmooth optimization have been developed and inter- faced for linear robust multiobjective control in the public domain Matlab package HIFOO released in

Driven by these facts, the goal of this paper is to rigorously study the tracking capabilities of aerial robots equipped with either a rigid- or an elastic-joint. Our main

The IEnKS (often significantly) outperforms EnKF and the standard en- semble Kalman smoother (EnKS) in all these regimes, not only regarding the smoothing performance