Assoc. Prof. Dr. Abdul Rashid Husain
A quadrotor is known to be sensitive to external disturbances due to its small size and lightweight. This is particularly more critical when multiple quadrotors are flying simultaneously (smarm) in formation close to each other as the disturbances could cause a collision between the quadrotors. In this work, the design and analysis of distributed formation controls with disturbance rejection capability of swarm quadrotors perturbed by external disturbances are discussed. This covers the control scheme that consists of two control loops: inner-loop control and outer-loop control. In the inner-loop control, the feedback linearization technique is employed to linearize the nonlinear quadrotor dynamics. However, the presence of external disturbances may cause inexact linearization resulting in an unknown disturbance.
Thus, an improved disturbance rejection, namely intelligent disturbance observer-based control (iDOBC), is implemented in the outer-loop control to estimate and reject the unknown
disturbance parts. The iDOBC consists of a disturbance observer (DO) that estimates the unknown disturbance part and augmented with a radial basis function neural network (RBFNN) compensator. In this work, the RBFNN is utilized to improve the disturbance rejection by compensating the estimation error produced by DO when estimating time-varying disturbances. The iDOBC is augmented with distributed formation regulation (FR-iDOBC) and formation tracking (FTiDOBC) control to improve the robustness of the formation control algorithms by using the consensus-based algorithm that only requires local neighbour-to-neighbour
communication. The main merits of the proposed methodologies are twofold: the disturbance estimation and rejection using iDOBC requires no prior knowledge of the disturbance, and the introduction of decoupling gain in the formation controls for design flexibility. Several simulation case studies are carried out to show the efficacy of the proposed algorithms.
Abdul Rashid Husain (M’06) received the B.Sc. degree in Electrical and Computer Engineering from The Ohio State University, Columbus, Ohio, U.S.A, in 1997, M.Sc. degree in Mechatronics from University of Newcastle Upon Tyne, U.K., in 2003, and Ph.D. in Electrical Engineering (Control) from Universiti Teknologi Malaysia (UTM) in 2009. He is a member of IEEE and Graduate member of IEM. Prior joining UTM, he worked as an engineer in semiconductor industry for several years specializing in precision molding and IC trimming process. His research interests include application of control in dynamic systems, robot navigation, mechatronic system design and electrical drives and motion control.
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