Adaptive attitude control of spacecraft without velocity measurements using Chebyshev neural network

Zou, An‐Min; Kumar, Krishna Dev

doi:10.1016/j.actaastro.2009.08.020

Cited by 80 publications

(44 citation statements)

References 31 publications

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“…Theorem 1: Considering a rigid spacecraft system described by (3) and (5) with actuator fault, for any given attitude reference trajectory σ d , if Assumptions 1 and 2 are satisfied, the control scheme (46) and (47) can guarantee that all the signals in the closed-loop system remain bounded, and the tracking and observer errors converge to a small neighborhood of the origin by choosing the design parameters appropriately.…”

Section: B Tracking Controller Designmentioning

confidence: 99%

“…As can be seen from (46) and (47), the proposed control scheme only involves simple functions and variables. So, the control scheme needs less computing power compared to the others existing methods [40], [42], [44].…”

Section: B Tracking Controller Designmentioning

confidence: 99%

“…2) Specify a positive definite matrix Q and by solving the Lyapunov equation (14), a positive definite matrix P is obtained. 3) Choose the design parameters c 1 , c 2 , and γ i , (i = 1, 2, 3) in (37), (46), and (47) to satisfy that c 1 > 0, c 2 > 0, and γ i > 0, respectively. 4) Choose the design parameters r i > 2 in (47) are positive constants, which are used for the σ -modification.…”

Section: B Tracking Controller Designmentioning

confidence: 99%

“…In this section, to verify the effectiveness of the proposed control scheme (37), (46), and (47), numerical simulations are analyzed for a rigid spacecraft, and the control parameters are shown in Table I. We choose the fuzzy membership functions as 2, 3, l = 1, .…”

Section: Simulation Studiesmentioning

confidence: 99%

See 3 more Smart Citations

Fuzzy Adaptive Fault-Tolerant Output Feedback Attitude-Tracking Control of Rigid Spacecraft

Huo

Xia

Yin

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

102

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This paper proposes a stable adaptive fuzzy faulttolerant attitude-tracking controller for a rigid spacecraft in the presence of unavailable velocities, external disturbance, actuator faults, and actuator saturation. Fuzzy logic systems are applied to approximate the unknown nonlinear function vector, and a fuzzy adaptive observer is designed to estimate the unmeasured velocity of the rigid body. By using the backstepping technique, a novel adaptive fuzzy attitude-tracking fault-tolerant control scheme is developed. It has been testified that this control approach guarantees that all signals of the rigid spacecraft are bounded and the tracking error between the system output and the reference signal converges to a small neighborhood of zero. Simulation examples with constant faults and time-variant faults are provided to show the fault-tolerant effectiveness of the control method.Index Terms-Actuator faults, actuator saturation, adaptive fuzzy control, attitude tracking, fault-tolerant control (FTC).

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Section: B Tracking Controller Designmentioning

confidence: 99%

Section: B Tracking Controller Designmentioning

confidence: 99%

Section: B Tracking Controller Designmentioning

confidence: 99%

Section: Simulation Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Adaptive Fault-Tolerant Output Feedback Attitude-Tracking Control of Rigid Spacecraft

Huo

Xia

Yin

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

102

View full text Add to dashboard Cite

show abstract

“…The fuzzy decision makers have to choose the best configuration of performance indices coefficients. Also, this configuration balanced performance indices intelligently based on decision rules by mission designer in user-friendly manner [15][16][17][18][19][20]. Moreover, other intelligent tool is considered as GA-PSO optimization loop to demonstrate this novel method.…”

Section: Introductionmentioning

confidence: 99%

Design of the LQR controller and observer with fuzzy logic GA and GA-PSO algorithm for triple an inverted pendulum and cart system

Molazadeh

Banazadeh

Shafieenejad

2014

Proceedings of the 2014 International Conference on Advanced Mechatronic Systems

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In this paper, designing of the LQR controller and observer with intelligent tools for the triple inverted pendulum is investigated. Intelligent tools are considered as GA and GA-PSO optimization algorithms and fuzzy logic to qualify achieving LQR gains. The pendulum is swung up from the vertical position to the unstable position. The rules for the controlled swing up are heuristically achieved such that each swing results are controlled. The inverted pendulum and cart system are modeled and constructed their equation based on energy method. Also, the performances of the proposed fuzzy logic controller, GA and GA-PSO tuning LQR gains are investigated and compared. Simulation results show that the fuzzy Logic Controllers are far more superior in terms of overshoot, settling time and response to parameter changes.

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Actuator failure‐tolerant control of an all‐thruster satellite in coupled translational and rotational motion using neural networks

Tavakoli

Assadian

2018

Adaptive Control & Signal

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SummaryThe nonlinear model predictive control (MPC) approach is used to control the coupled translational‐rotational motion of an all‐thruster spacecraft when one of the actuators fails. In order to model the dynamical response of the spacecraft in MPC, instead of direct integration, a neural network (NN) model is utilized. This model is built of a static NN, followed by a dynamic NN. The static NN is used to find the changes of the mapping of “the demanded forces to the thrusters” and “the real torques/forces produced by the remaining thrusters” after the failure occurrence through online training. In this manner, the effect of failed thruster on the dynamics can be found and the need for conventional and separate “fault detection and isolation” in previous works is eliminated. The dynamic NN is used to replicate the dynamical equations of the spacecraft excluding the effects of the mapping of thrusters demand to the real generated force and torque. Using updated model of the spacecraft through the learning capability of the NNs, the nonlinear MPC is able to compensate the failure of the thruster with the help of the remaining active thrusters. Through numerical simulations, it is shown that in the presence of thruster failure(s) this approach can effectively control the spacecraft with the remaining actuators.

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Adaptive attitude control of spacecraft without velocity measurements using Chebyshev neural network

Cited by 80 publications

References 31 publications

Fuzzy Adaptive Fault-Tolerant Output Feedback Attitude-Tracking Control of Rigid Spacecraft

Fuzzy Adaptive Fault-Tolerant Output Feedback Attitude-Tracking Control of Rigid Spacecraft

Design of the LQR controller and observer with fuzzy logic GA and GA-PSO algorithm for triple an inverted pendulum and cart system

Actuator failure‐tolerant control of an all‐thruster satellite in coupled translational and rotational motion using neural networks

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