A Comparison of Closed-Loop Performance of Multirotor Configurations Using Non-Linear Dynamic Inversion Control

Ireland, Murray; Vargas, Aldo; Anderson, David

doi:10.3390/aerospace2020325

Cited by 17 publications

(14 citation statements)

References 14 publications

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“…A State reconstruction module is active at all times which reconstructs the dynamic states of the quadrotor from the available sensor measurements. The vehicle control laws determine the rotor inputs u using a feedback-linearised controller, described in [IVA15,Voo09].…”

Section: Autonomous Guidance Systemmentioning

confidence: 99%

Collaborative models for autonomous systems controller synthesis

et al. 2020

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We show how detailed simulation models and abstract Markov models can be developed collaboratively to generate and implement effective controllers for autonomous agent search and retrieve missions. We introduce a concrete simulation model of an Unmanned Aerial Vehicle (UAV). We then show how the probabilistic model checker PRISM is used for optimal strategy synthesis for a sequence of scenarios relevant to UAVs and potentially other autonomous agent systems. For each scenario we demonstrate how it can be modelled using PRISM, give model checking statistics and present the synthesised optimal strategies. We then show how our strategies can be returned to the controller for the simulation model and provide experimental results to demonstrate the effectiveness of one such strategy. Finally we explain how our models can be adapted, using symmetry, for use on larger search areas, and demonstrate the feasibility of this approach.

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Section: Autonomous Guidance Systemmentioning

confidence: 99%

Collaborative models for autonomous systems controller synthesis

et al. 2020

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“…Additionally, they presented a backstepping control, which is a nonlinear controller design algorithm to design a course-keeping controller for ships, such as a nonlinear control for the steering wheel, which is also carried out in the Control System of Autonomous Vehicles. Similarly, references [15][16][17] researched nonlinear control, such as a robust adaptive fuzzy neural network control algorithm to design a PID controller for heading control of unmanned marine vehicles and a Non-Linear Dynamic Inversion Control to compare the primary differences between three multi-rotor platforms, and a closed-loop control system were implemented where applicable. Some other nonlinear control models were also established and proposed from different perspectives [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Inverse Tangent Functional Nonlinear Feedback Control and Its Application to Water Tank Level Control

Zhao

Zhang

2020

Processes

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This paper explores the significance and feasibility of addressing a notion that the system error of a nonlinear feedback control can be decorated by an inverse tangent function in order to attain a sound energy-efficient performance. The related mathematical model and relevant evaluation of this concept are further illustrated by demonstrating a case study about the control performance of water tank level. The rationale of robust control and theoretical algorithm of Lyapunov stability theorem are outlined to evaluate the effectiveness of nonlinear feedback with inverse tangent function in terms of improving robustness of PID (Proportional–Integral–Derivative) controller and energy-saving capability. By demonstrating five simulations of different scenarios, it ultimately proves that the modified robust PID controller by inverse tangent function meets the requirement of energy-saving capacity. Comparing with the routine PID control, the mean control input of controlling water tank level can be reduced up to 39.2% by using modified nonlinear feedback controller. This nonlinear feedback PID controller is energy efficient and concise for its convenient use, which is feasible to expand its utility to other applications.

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“…The identification and control of the variables is challenging since the quantities involved are not always available, known and accurate. Proportional-integral-derivative (PID) control is sufficient for most performance requirements of these systems; however, it is challenging as well to design lowlevel flight controllers for quadrotors while ensuring stability and performance (Ireland, Vargas, & Anderson, 2015). Effective fields of investigation include the multirotor robustness against disturbances, transient response analysis, measurement errors of magnitudes and unmodulated dynamics (Ireland et al, 2015).…”

Section: Introductionmentioning

confidence: 99%