Iterative Learning based feedforward control for Transition of a Biplane-Quadrotor Tailsitter UAS

Raj, Nidhish; Simha, Ashutosh; Kothari, Mangal; Abhishek, Amit; Banavar, Ravi N.

doi:10.1109/icra40945.2020.9196671

Cited by 16 publications

(6 citation statements)

References 19 publications

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“…where c 1 >0. Next, by replacing equation (32) into equation (31), _ v 1 can be computed as follows:…”

Section: Faults Tolerant Control System Designmentioning

confidence: 99%

“…A cooperative adaptive iterative learning fault-tolerant control algorithm with the radial basis function neural network is proposed for multiple subway trains subject to the time-iteration-dependent actuator faults [31]. In [32], to compensate the systematic modeling errors, the iterative learning algorithm by contribution of feed forward controller was used until the error of states was zero. To reject the external disturbances and improve the accuracy of performance, ILC was used for control of multiquadrotor where each vehicle learns from the experience of its own and its neighbours' previous task repetitions [33].…”

Section: Introductionmentioning

confidence: 99%

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Fault-Tolerant Control of Quadrotor UAVs Based on Back-Stepping Integral Sliding Mode Approach and Iterative Learning Algorithm

Allahverdy

Fakharian

Menhaj

2021

Mathematical Problems in Engineering

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In this paper, a fault-tolerant control system based on back-stepping integral sliding mode controller (BISMC) is designed and analyzed for both nonlinear translational and rotational subsystems of the quadrotor unmanned aerial vehicles (UAVs). The novelty of this paper is about combination of a classic controller with a repetitive algorithm to reduce the response time to actuator faults and have better tracking performance. The actuator fault is defined based on the loss of effectiveness and bias fault. Next, the iterative learning control algorithm (ILCA) is used to compensate for the unknown fault input according to previous recorded experiences. In the normal condition (without actuators fault), BISMC can force the actual trajectories toward the desired commands and reduce chattering about control signals, and in the presence of the actuators fault or external disturbances, the mentioned learning algorithm can incline the accuracy of the tracking performance and compensate for the occurred error. The Lyapunov theory illustrates that the proposed control strategy can stabilize the system despite the actuators’ fault and external disturbances. The simulation results show the effectiveness of the proposed scheme in comparison with another method.

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“…where c 1 >0. Next, by replacing equation (32) into equation (31), _ v 1 can be computed as follows:…”

Section: Faults Tolerant Control System Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Control of Quadrotor UAVs Based on Back-Stepping Integral Sliding Mode Approach and Iterative Learning Algorithm

Allahverdy

Fakharian

Menhaj

2021

Mathematical Problems in Engineering

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“…Mcintosh et al [7] present a strategy for an optimization-based trajectory planner for an autonomous transition of Bi-Quadrotor. Finally, Raj et al [8] offer a real-time onboard process for a Bi-Quadrotor to iteratively learn forward changeovers through recurring flight trials to demonstrate robustness.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, Raj et al. [8] offer a real‐time onboard process for a Bi‐Quadrotor to iteratively learn forward changeovers through recurring flight trials to demonstrate robustness.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of RW‐Quadrotor and Bi‐Quadrotor for payload delivery

Dalwadi,

Deb,

Ozana

2023

IET Intelligent Trans Sys

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Payload delivery is one of the many Unmanned Aerial Vehicle (UAV) applications that save time, energy, and human resources. The present simulation study compares rotary‐wing quadrotors (RW‐Quadrotors) and tail‐sitter biplane quadrotors (Bi‐Quadrotors) under payload delivery conditions that help to choose the best UAV for the particular mission. A comparative study is performed based on (i) time taken to accomplish the given mission, (ii) trajectory tracking performance, (iii) motor speed throughout the entire flight envelope, and (iv) remains SoC (State of Charge) after the mission. Furthermore, a Backstepping Controller (BSC) and an Adaptive Backstepping Controller (ABSC) are developed for both UAVs to handle mass changes during flight while Genetic Algorithm (GA) is used for the gain optimization. MATLAB Simulink‐based numerical simulation reveals that the Bi‐Quadrotor has a lower average rotor speed than the RW‐Quadrotor. However, the Bi‐Quadrotor's SoC after the mission is higher than the RW‐Quadrotor, and it took less time to accomplish the mission. The ABSC designed for RW‐Quadrotor and Bi‐Quadrotor effectively handles mass change during the mission. However, the BSC controller increases energy consumption for the Bi‐Quadrotor in the presence of wind gusts.

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“…Not surprisingly, ILC proved to be an excellent tool for repetitive tasks. Indeed, its field of applications are multiple, e.g., robotic manipulation [3], the wafer stage [4], manufacturing process [5], quadrotors [6], and soft robotics [7]- [10].…”

Section: Introductionmentioning

confidence: 99%

A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances

Pierallini¹,

Angelini

Mengacci

et al. 2021

IEEE Access

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Iterative Learning based feedforward control for Transition of a Biplane-Quadrotor Tailsitter UAS

Cited by 16 publications

References 19 publications

Fault-Tolerant Control of Quadrotor UAVs Based on Back-Stepping Integral Sliding Mode Approach and Iterative Learning Algorithm

Fault-Tolerant Control of Quadrotor UAVs Based on Back-Stepping Integral Sliding Mode Approach and Iterative Learning Algorithm

Performance evaluation of RW‐Quadrotor and Bi‐Quadrotor for payload delivery

A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances

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