Predefined-Time Formation Control for Multiagent Systems-Based on Distributed Optimization.

This article presents a solution to the leaderless formation control problem for first-order multiagent systems, which minimizes a global function composed of a sum of local strongly convex functions for each agent under weighted undirected graphs within a predefined time. The proposed distributed optimization process consists of two steps: 1) the controller initially leads each agent to the minimizer of its local function and 2) then guides all agents toward achieving leaderless formation and reaching the global function's minimizer. The proposed scheme requires fewer adjustable parameters than most existing methods in the literature without the need for auxiliary variables or time-variable gains. Additionally, one can consider highly nonlinear multivalued strongly convex cost functions, while the agents do not share the gradients and Hessians. Extensive simulations and comparisons with state-of-the-art algorithms demonstrate the effectiveness of our approach.

Robust tracking control for a quadrotor subjected to disturbances using new hyperplane-based fast Terminal Sliding Mode.

This paper presents a finite-time approach for tracking control of a quadrotor system subjected to external disturbances and model uncertainties. The proposed approach offers a preassigned performance guarantee. Firstly, integral terminal sliding manifolds and nonsingular terminal sliding manifolds are considered to produce the new hyperplane sliding variables for both position and attitude of a quadrotor. The designed hyperplane sliding variables guaranteed a finite-time convergence. The objective is to develop a finite-time control scheme for a disturbed quadrotor to follow a predefined trajectory based on a nonlinear sliding mode controller. The main contribution of this paper is to design a hyperplane-based nonlinear sliding mode control strategy for a quadrotor subjected to disturbances. A concept of robust controllers for a quadrotor is presented based on Lyapunov theory, which proves finite-time stability of the proposed control technique. Numerical simulations with two different scenarios verify the accuracy of the proposed hyperplane-based sliding mode control approach. The simulations study also included a comparison with another nonlinear controller. Results demonstrated overperformance of the proposed control strategy.

Self adaptive learning scheme for early diagnosis of simple and multiple switch faults in multicellular power converters.

This paper proposes a scheme based on the use of unsupervised machine learning approach and a drift detection mechanism in order to perform an early fault diagnosis of simple and multiple stuck-opened/stuck-closed switches in multicellular converters. Only the data samples representing the normal operation conditions are used in order to be adapted to the case where no data is available about faulty behaviors. A health indicator measuring the dissimilarity between normal and current operation conditions is built in order to detect a drift (degradations) in early stage. When a degradation (fault) is detected, the isolation is achieved by taking into account the discrete dynamics of switches. The features related to the latter are extracted in order to build a feature space allowing to separate the faulty behavior (zone or class) of the different switches. The proposed scheme is evaluated using real data samples representing different normal/simple/multiple switch fault scenarios issued from a test rig.