Search arXivSearch

arXiv · 1709.06407

Modeling and Control of Inverted Flight of a Variable-Pitch Quadrotor

Abstract

This paper carries out the mathematical modeling, simulation, and control law design for a quadrotor with variable-pitch propellers. The use of variable-pitch propeller for thrust variation instead of RPM regulation facilitates generation of negative thrust, thereby augmenting the rate of change of thrust generation amenable for aggressive maneuvering. Blade element theory along with momentum theory is used to estimate propeller thrust and torque essential for formulating equation of motion of the vehicle. The proposed flight dynamics model is used for non-linear control design using dynamic inversion technique, which is then used to stabilize, track reference trajectory, and simulate flip maneuver. The rotor torque is an irrational function of the control input which makes the control design challenging. To address this problem, the control design employs three loops. The outer loop solves the translational dynamics to generate the thrust, pitch angle, and roll angle commands required to track the prescribed trajectory. Using the command generated in the outer loop, the inner loop simplifies the rotational dynamics to provide the desired rate of angular velocities. A control allocation loop is added to address the problem of nonlinearity associated with rotor torque. This is done by introducing the derivative of thrust coefficient as a virtual control input. These virtual inputs determine the derivatives of thrust and body moments, which in turn is used to generate the required thrust and body moments. The concept is validated by showing attitude stabilization in real flight for a variable pitch quadrotor. The performance of the proposed design is shown through simulated results for attitude stabilization and trajectory following. Reverse thrust capability of variable-pitch quadrotor is also shown by performing flip maneuver in which quadrotor roll angle changes from 0 to 180 degrees.

Explore related subjects

Keep this discovery

BibTeXRIS

Namrata Gupta, Mangal Kothari, Abhishek. 2017-09-19. Modeling and Control of Inverted Flight of a Variable-Pitch Quadrotor. https://arxiv.org/abs/1709.06407

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Structured Stochastic Representations of Integrated Dynamic Strategies

Dynamic allocation decisions couple present resource use to evolving internal conditions, delayed returns, and future costs. We represent this interaction by four probability localizations linked through regime-indexed, graph-constrained column-stochastic operators. Pre-action state or context selects a locally affine model, while action-dependent changes update subsequent regimes, yielding a causal switched representation of nonlinear evolution. We characterize operator identifiability relative to the graph, the stochastic constraints, and the sampled embedding, separating coefficient recovery from predictive equivalence on the decision domain. Decision making is then formulated through implementable return--cost acceptability regions. Finite-horizon error propagation supplies conservative classification margins, and simultaneous intervals distinguish model-relative near-optimality from certified $\epsilon$-optimality over a declared finite policy class. Regime-indexed stochastic feedback is admitted when it satisfies the same certification test. Reproducible synthetic laboratories for personal preparation, supplier participation, and customer retention illustrate exact, operator-supplied, and noisy feedback cases. Multinomial experiments show improving recovery of the feedback function and fewer unresolved decisions with increasing sample size, while unrestricted off-policy recovery remains limited. The contribution is a structure-preserving representation--identification--decision workflow, not a domain-specific physiological or commercial calibration.

eess.SY

Fusion Estimation in Multi-sensor Systems for Data Packets with Disrupted Identities

In this paper, we explore the problem of fusion estimation for a multi-sensor system where the identity of the data packet received by each sensor may be disrupted or incorrect due to confusion in device identity allocation, communication protocol defects, or the lack of a clear sensor identifier. This can result in a random shuffle of the data components during the fusion estimation process, compromising the performance of the fusion estimation. To address this issue, we introduce the concepts of permutations and symmetry groups to describe this phenomenon as data packet permutation. We construct statistics to simplify the information set, developing two algorithms: a Bayesian approach, which performs fusion using posterior arrangement probabilities, and a greedy approach, which effectively improves estimation performance by guessing the likely data arrangement. We compare these two algorithms and demonstrate that both are expectation error-bounded. We improve algorithms for information-scarce scenarios. By employing the expectation-maximization algorithm, we fill in the prior information of data arrangement where the correct convergence is proven. Finally, we present numerical simulations to validate our results.

eess.SY

Quantifying the Reality Gap for RL-Based UAV Placement at mmWave and Sub-THz

Reinforcement learning (RL) policies for unmanned aerial vehicle (UAV) placement in mmWave and sub-terahertz networks are typically trained on simplified analytical channels. We quantify the resulting sim-to-real gap on a real urban map of Doha, Qatar, at carriers {28, 140, 183, 300} GHz and altitudes {50, 75, 100, 125} m, evaluating three channel pipelines: an analytical model (FSPL + atmospheric absorption + cuboid LoS), full Monte-Carlo ray tracing in Sionna RT with ITU-R P.676-13 absorption, and a deterministic-LoS hybrid that reuses Sionna's mesh under a closed-form path-gain expression. We formalize the gap on the spatial SNR distribution via four metrics, namely bias, RMSE, Jensen-Shannon divergence, and optimum-deployment displacement. Three findings emerge: at 28/140 GHz, $\sim$70% of the apparent -5.6/-4.8 dB Sionna bias is Monte-Carlo undersampling and shrinks to -1.7/-1.5 dB after mitigation; at 183 GHz a -9.2 dB residual isolates the atmospheric absorption / ITU-R P.676 line-shape disagreement; at 300 GHz the stochastic ray tracer agrees with the analytical model only coincidentally, with a +3.8 dB structural offset exposed by the deterministic-LoS pipeline. Across all carriers the linear-domain regret of the analytical-trained policy stays $\geq$ 0.93, indicating practical near-optimality but with a carrier-resolved SNR bias that warrants explicit reporting.

eess.SY