Robust trajectory control of an unmanned aerial vehicle using acceleration feedback

Zaki, Hammad; Alcan, Gökhan; Ünel, Mustafa

doi:10.1504/ijmms.2019.103495

Cited by 3 publications

(2 citation statements)

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“…In this section, we statistically evaluate our Safe-CDDP algorithm on three different robot dynamics in simulation: (i) 2D point robot (4 states, 2 inputs) [13], (ii) 2D car-like robot (4 states, 2 inputs) [13], and (iii) 3D quadrotor robot (12 states, 4 inputs) [29], [30]. Prediction horizons (N ) for the robots are selected as 100, 120 and 50, respectively.…”

Section: A Dynamical Systems In Simulationmentioning

confidence: 99%

Differential Dynamic Programming With Nonlinear Safety Constraints Under System Uncertainties

Alcan

Kyrki

2022

IEEE Robot. Autom. Lett.

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Safe operation of systems such as robots requires them to plan and execute trajectories subject to safety constraints. When those systems are subject to uncertainties in their dynamics, it is challenging to ensure that the constraints are not violated. In this letter, we propose Safe-CDDP, a safe trajectory optimization and control approach for systems under additive uncertainties and nonlinear safety constraints based on constrained differential dynamic programming (DDP). The safety of the robot during its motion is formulated as chance constraints with user-chosen probabilities of constraint satisfaction. The chance constraints are transformed into deterministic ones in DDP formulation by constraint tightening. To avoid overconservatism during constraint tightening, linear control gains of the feedback policy derived from the constrained DDP are used in the approximation of closed-loop uncertainty propagation in prediction. The proposed algorithm is empirically evaluated on three different robot dynamics with up to 12 degrees of freedom in simulation. The computational feasibility and applicability of the approach are demonstrated with a physical hardware implementation.

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Section: A Dynamical Systems In Simulationmentioning

confidence: 99%

Differential Dynamic Programming With Nonlinear Safety Constraints Under System Uncertainties

Alcan

Kyrki

2022

IEEE Robot. Autom. Lett.

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“…Nevertheless, [Oh, 2010] compensates sensor dynamics (such as drifting) by adding some modifications to the algorithm. In [Zanoni and de Barros, 2014], the EKF is applied for the fusion of the sample data from different sensors, and in [Zaki et al, 2019], angular acceleration is estimated through a cascaded filter structure.…”

Section: Filtering and Estimationmentioning

confidence: 99%

Control arquitecture for the navigation system of robotic airship using incremental controllers

Silva Marton

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This thesis is in the context of autonomous airships. Airships are advantageous platforms for long duration missions because the lifting gas sustain part of the vehicle weight, reducing energy consumption. Also, airships are platforms with low degree of intrusion in the environment, which is an important characteristic for environmental monitoring applications. This thesis is placed in the context of the project DRONI. This project aims to develop an autonomous airship for the enviromental monitoring in the Mamirauá reserve in Amazon rainforest. Airships are known to have a nonlinear dynamics with many uncertain parameters. The identification of such parameters require complex testing, such as wind tunnel tests. This thesis proposes Incremental Controllers (ICs) as a solution for the control loop in order to overcome the modeling lack. ICs reduce the sensitivity to model uncertainties by adding the dependency of derivative measuring, which increases the sensitivity to measurement noise and delay. Furthermore, actuator redundancy and saturation are issues when designing ICs for airships. Therefore, in this thesis, we discuss how to design ICs presenting solutions for the design issues mentioned before. Nevertheless, the control and guidance loops require access to linear and angular velocities. Meanwhile, the guidance must have access to positions, attitude, and incident wind in the airship body. These information can be retrieved by inertial sensors (such as gyroscope, accelerometer and magnetometer), GPS, and barometer. Thus, a comparison between different filtering strategies is presented in order to define which technique provides better feedback for the control and guidance loops. Also, the wind estimation problem is addressed. Traditional model-based estimators are presented, then a data-driven strategy is proposed. The results obtained led us to propose a novel estimation strategy that performs a fusion between both model-based and data-driven approaches resulting in a hybrid version of a wind estimator. Finally, the guidance problem is addressed. A modified version of the so-called Line-of-sight strategy is presented. This strategy receives adaptations for considering the kinematic restrictions of the airship and the varying behavior when transitioning between hovering and cruise flights. As a final result, we obtained a closed loop system addressing Guidance, Control and Estimation problems. As demonstrated in simulation results, this final control architecture is capable of covering all flight phases of a complete mission, namely: cruise, hovering, vertical take-off and landing.

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Lyapunov-based fractional-order PID controller design for coupled nonlinear system

Zaki,

Rashid,

Masud

2024

Transactions of the Institute of Measurement and Control

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Coupled nonlinear systems are difficult to control due to the adverse effects of uncertainties and coupling effects with increased sensor noise. This paper proposes an improved Lyapunov-based composite controller consisting of fractional-order proportional–integral–derivative (FOPID) and velocity-based disturbance observer to deal with the motion control of uncertain, nonlinear, and coupled system. FOPID utilizes the stable filtered error to facilitate the control development and stability analysis for the multi-input multi-output (MIMO) coupled system. Moreover, a disturbance observer is developed by utilizing the velocity signals to provide robustness against the disturbances and parametric uncertainties. Enhanced infinite order disturbance observer (EIFDOB) structure is used to improve the robustness of the introduced technique despite the high-frequency sensor noise. Stability analysis is provided to verify the introduced controller through the Lyapunov stability theorem, LaSalle’s invariance principle, and Barbalat’s lemma. Signal chasing is also presented to show that all signals are ultimately bounded. Comprehensive numerical simulations are performed on high-fidelity and coupled nonlinear model of the twin rotor MIMO system where the efficiency of the presented technique is examined against the external disturbances, matched uncertainties, and sensor noise. The results presented with different scenarios show that the proposed technique performed better with more robustness than FOPID and integer order proportional–integral–derivative.

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Robust trajectory control of an unmanned aerial vehicle using acceleration feedback

Cited by 3 publications

References 0 publications

Differential Dynamic Programming With Nonlinear Safety Constraints Under System Uncertainties

Differential Dynamic Programming With Nonlinear Safety Constraints Under System Uncertainties

Control arquitecture for the navigation system of robotic airship using incremental controllers

Lyapunov-based fractional-order PID controller design for coupled nonlinear system

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