Model-free observer for state estimation applied to a quadrotor

Younes, Younes Al; Noura, Hassan; Muflehi, Mohamad; Rabhi, Abdelhamid

doi:10.1109/icuas.2015.7152433

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…According to the criteria of the squared errors and energies (see Table 6), it can be observed that, the SMO has proven its effectiveness in terms of accuracy and consumed energy compared to the results obtained by the conventional PID. Unlike the obtained results in works [33,34,35,36,37,38,39,40,41,42,43,44], where the initial conditions are zero, the SMO has presented good performance.…”

Section: Results Interpretationcontrasting

confidence: 84%

Sliding Mode Controller Based on the Sliding Mode Observer for a QBall 2+ Quadcopter with Experimental Validation

Daadi¹,

Boulebtinai²,

Derrouaoui³

et al. 2022

IJRCS

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This paper studies a particular Unmanned Aerial Vehicle (UAV), called QBall 2+ quadcopter. This vehicle is a complex system, non-linear, strongly coupled, and under-actuated. First, a non-linear model was developed to represent the dynamics of the studied drone. Once the latter is established, the linear model was used to obtain the best gains of the Proportional Integral Derivative (PID) controller. This controller was applied after on the non-linear model of the UAV. Moreover, a Sliding Mode Controller (SMC) based on Sliding Mode Observer (SMO) was designed for retrieving the system unknown variables. Through these latter, the QBall 2+ was controlled, taking into account the observer errors. The first contribution in this work is to implement the PID regulator on the QBall 2+ flight controller to validate the results obtained by simulation. Secondly, due to the limitations of the Flex 3 cameras, especially when the drone is outside their working environment, the sliding mode observer was implemented to replace the cameras in order to measure the states of the system considered in this work. Simulation results of the different applied controllers were displayed to evaluate their effectiveness.

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Section: Results Interpretationcontrasting

confidence: 84%

Sliding Mode Controller Based on the Sliding Mode Observer for a QBall 2+ Quadcopter with Experimental Validation

Daadi¹,

Boulebtinai²,

Derrouaoui³

et al. 2022

IJRCS

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show abstract

“…The demand for methodology applications in productive plants and the equipment industry can be observed in the energy, aeronautical, and chemical sectors. Specifically, applications can be found in quadcopters, rocket tracking, aircrafts, space stations and the agro-industry [20]- [22].…”

Section: Introductionmentioning

confidence: 99%

Data Driven State Reconstruction of Dynamical System Based on Approximate Dynamic Programming and Reinforcement Learning

Silva

Neto

2021

IEEE Access

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A data-driven approximation formulation for the state reconstruction problem of dynamical systems is presented in this paper. Without the assumption of an explicit mathematical model, the Hamilton-Jacobi-Bellman (HJB) based approach of a data-driven state reconstruction design method for monitoring and output-feedback control of dynamical systems is presented. The proposed state reconstruction design is based on a dynamic programming approach. To evaluate the proposed state reconstruction, computational experiments are conducted using only dynamical system model output data. The sensitivity of the algorithm parameters is also analyzed and discussed. The performance evaluation is analyzed in terms of the error metrics of the discrete linear quadratic regulator with output feedback under the value iteration algorithm through a reinforcement learning strategy.

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“…During the development of the sliding mode observer, Utkin [15] first used discontinuous switching items. Subsequently, Walcott and Zak [16] used a Lyapunov-based method to construct observers with discontinuous terms, and the Thau observer [17] was designed for nonlinear systems. The sliding mode observer feeds the output estimation error back via a nonlinear switching term, which effectively solves the problem that the state estimation cannot approach the system state due to unknown interference [18], and increases robustness.…”

Section: Introductionmentioning

confidence: 99%

Multi-Fault Diagnosis of an Aero-Engine Control System Using Joint Sliding Mode Observers

et al. 2020

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An aero-engine is a complex aerodynamic thermal system, which can operate in extreme environments for long periods. It is crucial to diagnose any faults of the aero-engine control system accurately. At present, most aero-engine control system fault diagnosis schemes suffer from large interference, significant chattering, and low estimation accuracy. To diagnose multi-faults of the control system effectively, we introduce and investigate a new fault diagnosis scheme in this paper, which uses joint sliding mode observers. First, we develop a mathematical model for multi-faults in the control system, which can describe actuator and sensor faults in detail. Then, we design the joint sliding mode observers for fault detection and isolation (FDI), using the sliding mode variable structure term to reduce the coupling effect. Finally, during the fault estimation process, we use a pseudo-sliding form to reduce the chattering problem and suppress the impact of interference, which leads to an accurate estimation of the multi-fault characteristics. The simulation results show that, the proposed scheme can effectively detect and isolate faults, which enables superior timeliness and accuracy compared to a conventional sliding mode observer scheme. During the process of fault estimation, the effect of chattering is reduced, which shows the advantages of strong sensitivity and high estimation accuracy. INDEX TERMS Aero-engine, control system, fault diagnosis, sliding mode observer.

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Model-free observer for state estimation applied to a quadrotor

Cited by 7 publications

References 12 publications

Sliding Mode Controller Based on the Sliding Mode Observer for a QBall 2+ Quadcopter with Experimental Validation

Sliding Mode Controller Based on the Sliding Mode Observer for a QBall 2+ Quadcopter with Experimental Validation

Data Driven State Reconstruction of Dynamical System Based on Approximate Dynamic Programming and Reinforcement Learning

Multi-Fault Diagnosis of an Aero-Engine Control System Using Joint Sliding Mode Observers

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