Guaranteed trajectory tracking control based on interval observer for quadrotors

Abadi, Amine; Mekki, Hassen; Ramdani, Nacim

doi:10.1080/00207179.2019.1610903

Cited by 9 publications

(10 citation statements)

References 39 publications

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“…). From conditions (30)(31)(32) and Property 1, the estimation error e(k + 1) ∈ B n x ([e(k + 1), e(k + 1)]), which is computed as conditions (33)(34)(35). Furthermore, the relationship between the box B n x ([e(k + 1), e(k + 1)]) and the zonotope ⟨H e (k + 1)⟩ is given in (36).…”

Section: Box Computations To Obtain Interval Estimation Error Boundsmentioning

confidence: 99%

“…20,21 Most of the researches on interval observer systems based on cooperativity systems. 22,23 The researches on the interval observer systems have been extensively investigated, such as state estimation, [24][25][26][27][28][29] fault detection, 30,31 robust control, [32][33][34] fault-tolerant control, 35,36 and so forth. Compared with the Luenberger observer system, the interval observer systems should ensure that estimation error systems are both cooperative and stable.…”

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confidence: 99%

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Interval state estimation‐based robust model predictive control for linear parameter varying systems

Ping

Yang

Xiao³

et al. 2021

Intl J Robust & Nonlinear

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For linear parameter varying (LPV) systems with unknown system states, this article investigates an interval state estimation-based robust model predictive control algorithm. Two interval state estimation approaches, including interval observer systems and zonotope-based box computations, are considered to estimate the upper and lower bounds of system states. The on-line interval estimation error boxes are contained within the scaled and time-varying ellipsoidal robust positively invariant sets. Then, the centers of the state constraint boxes are steered to a region near the origin. When the interval estimation error boxes and the centers of state constraint boxes simultaneously converge to the neighborhood of the origin, the controlled LPV systems are robust stable. K E Y W O R D Sinterval observer, LPV systems, model predictive control, output feedback, set-membership estimation INTRODUCTIONRobust model predictive control (RMPC) has the abilities to predict system future behaviors based on process models, and incorporate both system uncertainties and physical constraints in receding horizon optimal control problems. [1][2][3][4][5] In some practical processes, full system states or partial system states are often difficult to measure and exogenous disturbances and/or noises exist. In these situations, output feedback RMPC is often more practical for real processes with unknown system states. For the researches on output feedback RMPC approaches, it is often required to design the state observer system to estimate system true states. Nevertheless, to satisfy robust stability and physical constraints, it is important to obtain time-varying estimation error bounds. Linear parameter varying (LPV) systems, whose characteristics are dependent on scheduling parameters and bounded in prespecified convex sets, are applicable for representing system nonlinearity and uncertainty. 6 Output feedback RMPC approaches for LPV systems with bounded disturbances and/or noises have been extensively studied. The output feedback RMPC in works 7-10 consider that ellipsoidal estimation error sets are time-varying ellipsoidal robust positively invariant 7026

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Section: Box Computations To Obtain Interval Estimation Error Boundsmentioning

confidence: 99%

mentioning

confidence: 99%

Interval state estimation‐based robust model predictive control for linear parameter varying systems

Ping

Yang

Xiao³

et al. 2021

Intl J Robust & Nonlinear

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“…The unknown input interval observer can be designer considering (18) in a way that ensures the simultaneous estimation of Equations (8) and (9). The following theorem is introduced to secure the stability analysis and robustness in the presence of unknown entries.…”

Section: Observer Designmentioning

confidence: 99%

“…The authors in [8] design an interval sliding mode observer for sensor fault detection and applied it to an electrical traction device. Another interesting application of interval observers is given in [9], where a trajectory control based on an interval observer is designed for a quadrotor. The interval observer is synthesized by using an uncertain model where all the uncertainties (parameters, disturbance, noise) are unknown but bounded with known bounds.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Actuator and System Faults Via an Unknown Input Interval Observer for Takagi–Sugeno Systems

et al. 2020

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This paper presents a simultaneous state variables and system and actuator fault estimation, based on an unknown input interval observer design for a discrete-time parametric uncertain Takagi–Sugeno system under actuator fault, with disturbances in the process and measurement noise. The observer design is synthesized by considering unknown but bounded process disturbances, output noise, as well as bounded parametric uncertainties. By taking into account these considerations, the upper and lower bounds of the considered faults are estimated. The gain of the unknown input interval observer is computed through a linear matrix inequalities (LMIs) approach using the robust H ∞ criteria in order to ensure attenuation of process disturbances and output noise. The interval observer scheme is experimentally evaluated by estimating the upper and lower bounds of a torque load perturbation, a friction parameter and a fault in the input voltage of a permanent magnet DC motor.

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Interval Observer-based Robust Trajectory Tracking Control for Quadrotor Unmanned Aerial Vehicle

Yan,

Zhang,

Ren

2024

Int. J. Control Autom. Syst.

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Guaranteed trajectory tracking control based on interval observer for quadrotors

Cited by 9 publications

References 39 publications

Interval state estimation‐based robust model predictive control for linear parameter varying systems

Interval state estimation‐based robust model predictive control for linear parameter varying systems

Estimation of Actuator and System Faults Via an Unknown Input Interval Observer for Takagi–Sugeno Systems

Interval Observer-based Robust Trajectory Tracking Control for Quadrotor Unmanned Aerial Vehicle

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