Laplace ℓ1 Huber based cubature Kalman filter for attitude estimation of small satellite

Cao, Lu; Dong, Qiao; Chen, Xiaoqian

doi:10.1016/j.actaastro.2018.04.020

Cited by 66 publications

(35 citation statements)

References 28 publications

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“…In this paper, we developed a state-dependent approach. To seek methods requiring only sensor output information, one can design observers as Laplace 1 Huber based Kalman filter [35] and sliding mode observers [36], [37]. Currently, we are working on developing observer based algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…This can be viewed as an estimation of 1/ζ ≥ 1, which is designed to compensate the energy fading ofū c caused by actuator faults and failures. Note that the adaptation law (35) guarantees that ζ ≥ 1 forζ(0) ≥ 1, which corresponds to 1/ζ ≥ 1. Note that the term −γ ζζ in ζ is used to prevent the increase of adaptive gainζ.…”

Section: Adaptive Nn-based Finite-time Control Under Actuator Failmentioning

confidence: 99%

“…Suppose that Assumptions 1-3 and 5 hold. Then, one can design a TPVS and controller dynamics (30)- (35) such that the following holds: 1) for any positive constant u max , the designed control input satisfies |u c,i (•)| ≤ u max , i = 1, . .…”

Section: Adaptive Nn-based Finite-time Control Under Actuator Failmentioning

confidence: 99%

“…Then we choose the design parameters K φ = 0.01, K S = 40, φ θ = 0.1, ξ = 1.1, K ρ = 0.01, and ε c = 0.007, which are used to computeū c in (32). Next, we give the initial value of the adaptive parametersθ(0) = 0.1,η(0) = 0.001, ζ(0) = 1.1, and select the design parameters γ S = 0.1, γ θ = 0.003, α = 10, γ η = 0.06, β = 0.08, γ ζ = 0.08, which are used to calculateθ,η, andζ according to (33)- (35). Fig.…”

Section: Thrusters With Limited Thrusts and Actuator Faults/failuresmentioning

confidence: 99%

See 3 more Smart Citations

Neural Network-Based Adaptive Control for Spacecraft Under Actuator Failures and Input Saturations

Zhou

Kawano

Cao

2020

IEEE Trans. Neural Netw. Learning Syst.

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In this paper, we develop attitude tracking control methods for spacecraft as rigid bodies against model uncertainties, external disturbances, subsystem faults/failures, and limited resources. A new intelligent control algorithm is proposed using approximations based on radial basis function neural networks (RBFNN) and adopting the tunable parameter-based variable structure (TPVS) control techniques. By choosing different adaptation parameters elaborately, a series of control strategies are constructed to handle the challenging effects due to actuator faults/failures and input saturations. With the help of Lyapunov theory, we show that our proposed methods guarantee both finitetime convergence and fault-tolerance capability of the closed-loop systems. Finally, benefits of the proposed control methods are illustrated through five numerical examples. Index Terms-Attitude tracking, fault-tolerant control, input saturations, neural network control, finite-time control. Defineδ 1 := min{δ 1,1 ,δ 1,2 }. Note that thisδ 1 can be made arbitrary small by making K S , K ρ sufficiently large. According to Lemma 3, for any positive constantsδ 1 , ε 1 and ε 2 , and any S * (0) , there existsT 2 :=T 2 (S * (0),θ * (0),η * (0),δ 1) > 0 such that S * (t) ≤δ 1 for all t ≥T 2 .

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Section: Discussionmentioning

confidence: 99%

Section: Adaptive Nn-based Finite-time Control Under Actuator Failmentioning

confidence: 99%

Section: Adaptive Nn-based Finite-time Control Under Actuator Failmentioning

confidence: 99%

Section: Thrusters With Limited Thrusts and Actuator Faults/failuresmentioning

confidence: 99%

See 2 more Smart Citations

Neural Network-Based Adaptive Control for Spacecraft Under Actuator Failures and Input Saturations

Zhou

Kawano

Cao

2020

IEEE Trans. Neural Netw. Learning Syst.

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“…The tracking control has become one of the most important schemes, and a lot of research achievements have been achieved in previous works. [10][11][12][13][14][15][16] Safa et al 10 present a position-tracking control strategy using output feedback and adaptive sliding-mode approach. Sun et al 11 propose a novel hybrid coordinated control method, in which backstepping scheme and Hamilton control were utilized to improve the performance of the asymptotic position tracking.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy finite-time fault-tolerant funnel control of nonlinear systems with actuators failures

Chen

Wang

et al. 2019

Advances in Mechanical Engineering

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This article investigates adaptive fuzzy finite-time fault-tolerant funnel control of nonlinear systems with actuators failures, which contain outage, loss of effectiveness and stuck modes. The principal control objectives are ensure that the trajectory of the tracking error can be limited to the predefined area in finite time and whether the actuators are at faults or not. First, the original error equation is changed into a new one by introducing the revised funnel variable, which will be employed to design the controller later. Second, the required conditions for the existence of the adaptive fuzzy finite-time fault-tolerant controller are provided using backstepping technology, fuzzy logic system, and finite-time Lyapunov stability theory. Theoretical analysis verifies that all the signals of the closed-loop system are semi-globally practically finite-time stable. Finally, a numerical example illustrates that the proposed method can effectively improve the steady-state and transient-state performance of the system in finite time.

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Fault severity estimation in a vehicle cooling system

Nemati

Seyedtabaii

2019

Asian Journal of Control

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On-time water pump malfunction, thermostat sticking, fan defect, and radiator clogging severity estimation in a vehicle cooling system prevents system breakdown and saves repair costs and service time. In this respect, to minimize the required number of engine variable measurements, the engine body temperature, T b is introduced as a single driving force of the thermal system and a modified adaptive second order sliding mode (SOSM) fault observer is utilized to estimate the (single case) faults despite the system nonlinear uncertainty. The faults are applied and the estimates are obtained within the defined temperature ranges. The results of extensive simulations indicate that the algorithm works well in assessing the occurred damages to the uncertain system. KEYWORDS adaptive second order sliding mode, automobile cooling system, fault detection, observer 2 | AUTOMOBILE COOLING SYSTEM MODEL The engine heat, not released through the exhaust, is dissipated by the cooling system. Since the engine body is massive with high thermal conductivity, it is approximately modeled by a variable temperature source, T b. This possibility simplifies the model and lets a simpler estimation algorithm to be built.

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Laplace ℓ1 Huber based cubature Kalman filter for attitude estimation of small satellite

Cited by 66 publications

References 28 publications

Neural Network-Based Adaptive Control for Spacecraft Under Actuator Failures and Input Saturations

Neural Network-Based Adaptive Control for Spacecraft Under Actuator Failures and Input Saturations

Adaptive fuzzy finite-time fault-tolerant funnel control of nonlinear systems with actuators failures

Fault severity estimation in a vehicle cooling system

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