Learning to Control a Quadcopter Qualitatively

Šoberl, Domen; Bratko, Ivan; Žabkar, Jure

doi:10.1007/s10846-020-01228-7

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…A quadcopter autonomously learned a qualitative model for navigating through space and qualitatively planned and executed an escape out of an enclosed space. [6] Controlling the cart-pole system. The well known inverted pendulum problem was implemented as a cart-andpole system, which learned to stabilize within seconds.…”

Section: Resultsmentioning

confidence: 99%

Automated Planning with Induced Qualitative Models in Dynamic Robotic Domains

Šoberl

2021

IJCAI

Self Cite

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Section: Resultsmentioning

confidence: 99%

Automated Planning with Induced Qualitative Models in Dynamic Robotic Domains

Šoberl

2021

IJCAI

Self Cite

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“…While there is a defined structure for flight control (auto-pilot architecture), the way controllers are designed also varies depending on the specific UAV platform technology (e.g., fixed-wing, rotors, VTOL, tailsitters [20]). In addition, and in particular with the advent of AI/ML based control for UAVs, "explainable" control (design/solution) offers advantages [21]. The methodology of controller design also depends on the UAV technology/application, and not much work has been previously published regarding IMC control strategies for NMP UAV systems.…”

Section: Methodology and Structure Of The Papermentioning

confidence: 99%

“…f (s) = 0.932(1 + 0.033s + 0.0024s 2 ) s(1 + 0.073s), (21) clearly in the form of an ideal PID + low-pass filter. The controller could be further simplified by a P + I equivalent if necessary, i.e., K…”

Section: Inner Loop (Pitch Rate)mentioning

confidence: 99%

Simple Internal Model-Based Robust Control Design for a Non-Minimum Phase Unmanned Aerial Vehicle

Zolotas

2023

Machines

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Robust control has been successful in enabling flight stability and performance for UAVs. This paper presents a simple explainable robust control design for UAV platforms with non-minimum phase (NMP) zero characteristics in their model. The paper contributes to economic (simple) robust control design by addressing the NMP model’s characteristics via Internal Model Control (IMC) and its impact on the UAV pitch response performance. The proposed design is compared with a Parallel Feedback Control Design (PFCD) scheme for the same vehicle platform, for fair comparison. Simulation results illustrate the achievement of the proposed control designs for the UAV platform; only the pitch control is addressed. A by-product of this work is the interpretation of different ways of manipulating the non-minimum phase plant model, so-called ‘modelling for control’, to enable the simple controller design. The work in this paper underpins the simplicity and robustness of the IMC technique for the NMP UAV platform, which further supports the explainability of the control structure relative to performance.

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Transferring a Learned Qualitative Cart-Pole Control Model to Uneven Terrains

Šoberl,

Bratko

2023

Lecture Notes in Computer Science

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Learning to Control a Quadcopter Qualitatively

Cited by 4 publications

References 40 publications

Automated Planning with Induced Qualitative Models in Dynamic Robotic Domains

Automated Planning with Induced Qualitative Models in Dynamic Robotic Domains

Simple Internal Model-Based Robust Control Design for a Non-Minimum Phase Unmanned Aerial Vehicle

Transferring a Learned Qualitative Cart-Pole Control Model to Uneven Terrains

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