Pose Consensus of Multiple Robots with Time-Delays Using Neural Networks

Abstract

SummaryThis paper proposes a novel control scheme based on Radial Basis Artificial Neural Network to solve the leader–follower and leaderless pose (position and orientation) consensus problems in the Special Euclidean space of dimension three (SE(3)). The controller is designed for robot networks composed of heterogeneous (kinematically and dynamically different) and uncertain robots with variable time-delays in the interconnection. The paper derives a sufficient condition on the controller gains and the robot interconnection, and using Barbalat’s Lemma, both consensus problems are solved. The proposed approach employs the singularity-free, unit-quaternions to represent the orientation of the end-effectors in the SE(3). The significance and advantages of the proposed control scheme are that it solves the two pose consensus problems for heterogeneous robot networks considering variable time-delays in the interconnection without orientation representation singularities, and the controller does not require to know the dynamic model of the robots. The performance of the proposed controller is illustrated via simulations with a heterogeneous robot network composed of robots with 6-DoF and 7-DoF.

A linear regression variable time delay estimation algorithm for the analysis of hydraulic manipulators

“…Again referring to the second segment φ2 of Fig. 2, equation (3) shows that the first pair of lines would be for samples 79−80 and 81−126, the second 79−82 and 83−126, and so on through to 79 − 124 and 125 − 126, i.e. 23 pairs in total.…”

“…Delays are sometimes referred to as an after-effect or a dead-time, and the system itself can be referred to as a hereditary system. Time delays typically manifest as a mixture of communication delays [3], calculation delays, and delays that occur due to mechanical or other dynamics. These include systems with, for example, actuators, sensors, field networks involved in feed-back loops, and networked control systems [4].…”

Pole assignment control design for time–varying time–delay systems using radial basis functions

“…In the latter case, the complex nature of the time delay variability can appear stochastic (rather than, say, dependant on a measured system state). More generally, system time delays typically consist of an accumulation of delays, including communication delays [5], calculation delays and other internal system delays. In fact, time-varying time delays have been observed across a wide array of domains, from the hydraulic actuators alluded to above to e.g.…”

“…The present authors have also found a few examples in the literature of radial basis functions being used in the context of variable time-delay systems. However, these are usually for controllers based on neural networks, in which the basis function is an activation function or weighting term [5][6][7]. By contrast, the present article exploits the existing NMSS modelbased design approach [8][9][10][11], adapted here in a novel way so that the input signal is a weighted sum of the partial control inputs for different time delay models.…”

Adaptive non‐singular fast terminal sliding mode control for multi‐agent systems with unknown non‐linear dynamics

“…In recent years, the consensus problem of multi‐agent systems (MASs) has become a research hotspot, along with the application of MASs in several aspects, such as formation flight of unmanned air vehicles [1], spacecraft [2], autonomous underwater vehicles [3] and multiple robots [4]. The objective of consensus problem is to make a group of agents reach a same state by designing proper distributed control algorithms.…”