Moving mass control system in conjunction with brain emotional learning-based intelligent control for rate regulation of suborbital reentry payloads

Mohammadi, Aidin; Tayefi, Morteza

doi:10.1177/0959651812453391

Cited by 3 publications

(1 citation statement)

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“…Their proposed model inspired Lucas et al (2004) to develop BELBIC controller and effectively implement it in various types of single-input and single-output (SISO) and multiple-input and multiple-output (MIMO) linear/nonlinear systems. Since then, BELBIC is implemented in many other systems such as moving-mass control system for a highly nonlinear and time-varying re-entry system (Mohammadi and Tayefi, 2012), space launch vehicle (Mohammadi et al , 2012), stepper motor for motion control of omni-directional three-wheel robots (Sharbafi et al , 2010), electrohydraulic servo system (Sadeghieh et al , 2012) and the quadrotor attitude and altitude control (Jafari et al , 2013). In all these researches, BELBIC demonstrated better tracking performance for nonlinear and real systems, more robustness and almost same energy consumption compared to conventional controllers.…”

Section: Introductionmentioning

confidence: 99%

A real-test and simulation combined platform for developing intelligent tracking control of multirotors

Bajelani

Tayefi²,

Zhu³

2022

IJIUS

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PurposeThis study aims to minimize the risk of costly failures of flight tests during the path tracking control design, and a noble approach has been proposed in this study to put the whole vehicle-in-the simulation loop. Working with the real system is essential for developing intelligent and data-driven controllers for multirotor drones which needs learning the drones' nonlinear complicated dynamics. The vehicle-in-the-loop (VIL) platform developed in this paper is a safe and effective solution to deal with this problem.Design/methodology/approachTo avoid risky flight test during controller design, the multirotor is hinged to a shaft that allows the multirotor's angular motion but restricts translational motion. The test-bed includes the real system attitude dynamics and the simulation of the position dynamics to model the complete flight based on real-time reactions of the vehicle. For the authors' case study, a hexacopter angular motion provides the real-time attitude data in translational motion simulation loop. To test the set-up, a proportional-integral-derivative (PID) and a brain emotional learning-based intelligent controller (BELBIC) is implemented for tracking of circle and 8-shape flight trajectories.FindingsThe results show that the platform helps the intelligent controller to learn the system dynamics without worrying about the failure in the early stages of the design and in the real-world flight test. Although the hexacopter translational dynamics is modeled in simulation, the authors still have highly accurate attitude dynamics matching the requirement of the control loop design. The comparison of the two controllers also shows that the performance of BELBIC is better than PID in this test.Originality/valueThe research background is reviewed in the introduction section. The other sections are originally developed in this paper.

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