Adaptive chaos synchronization of an attitude control of satellite: A backstepping based sliding mode approach

Pal, Pikaso; Jin, Gang Gyoo; Bhakta, S.; Mukherjee, V.

doi:10.1016/j.heliyon.2022.e11730

Cited by 11 publications

(3 citation statements)

References 52 publications

(52 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, satellite kinematics is responsible for ascertaining the satellite attitude by handling the angular velocity data, which is the output of dynamics equations [ [28] , [29] , [30] , [31] ]. The underlying equation defines the basic rotational motion of a satellite [ 32 ].

represents the moment of inertia (kgm 2 ) with its diagonal elements being 0.0067, 0.0419 & 0.0419 in x, y, and z directions respectively,

signifies the angular velocity vector of satellite concerning the body frame of reference,

denotes skew-symmetric matrix,

is the angular momentum and

shows input control torque of satellite defined by

where

is control input torque provided by three reaction wheels called

and

is the total low earth orbital disturbances that cover gravity gradient torque

, atmospheric drag torque

, solar radiation torque

, and residual magnetic dipole torque

.…”

Section: Cubesat Attitude Model With External Perturbationsmentioning

confidence: 99%

Neuro-fuzzy system based proportional derivative gain optimized attitude control of CubeSat under LEO perturbations

Shehzad,

Asghar,

Jaffery

et al. 2023

Heliyon

View full text Add to dashboard Cite

represents the moment of inertia (kgm 2 ) with its diagonal elements being 0.0067, 0.0419 & 0.0419 in x, y, and z directions respectively,

signifies the angular velocity vector of satellite concerning the body frame of reference,

denotes skew-symmetric matrix,

is the angular momentum and

shows input control torque of satellite defined by

where

is control input torque provided by three reaction wheels called

and

is the total low earth orbital disturbances that cover gravity gradient torque

, atmospheric drag torque

, solar radiation torque

, and residual magnetic dipole torque

.…”

Section: Cubesat Attitude Model With External Perturbationsmentioning

confidence: 99%

Neuro-fuzzy system based proportional derivative gain optimized attitude control of CubeSat under LEO perturbations

Shehzad,

Asghar,

Jaffery

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…Furthermore, a distributed passivity-based controller for constrained robotics networks is shown in [57], and in [58], new results in passivity-based control for robots are presented. Finally, other control strategies such as backstepping control, sliding mode control, output feedback robust control, among others, can be found in [23,[59][60][61][62][63].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Modeling and Passivity-Based Control of an RV-3SB Robot

Cardona,

Serrano,

García Cena

2023

Actuators

View full text Add to dashboard Cite

This paper shows the dynamic modeling and design of a passivity-based controller for the RV-3SB robot. Firstly, the dynamic modeling of a Mitsubishi RV-3SB robot is conducted using Euler–Lagrange formulation in order to obtain a decoupled dynamic model, considering the actuator orientation besides the position of the analyzed robot. It is important to remark that the dynamic model of the RV-3SB robot is conducted based on kinematic model obtention, which is developed by the implementation of screw theory. Then, the passivity-based controller is obtained by separating the end effector variables and the actuator variables by making an appropriate coordinate transformation. The passivity-based controller is obtained by selecting an appropriate storage function, and by using Lyapunov theory, the passivity-based control law is obtained in order to drive the error variable, which is the difference between the measured end effector position variable and the desired end effector position variable. The passivity-based controller makes the error variable reach the origin in finite time, taking into consideration the dissipation properties of the proposed controller in order to stabilize the desired end effector position. A numerical simulation experiment is performed in order to validate the theoretical results obtained in this research. Using numerical experimentation, it is verified that the proposed control strategy is efficient and effective in driving the error variable to the origin in comparison with other modified techniques found in the literature. Finally, an appropriate discussion and conclusion of this research study are provided.

show abstract

“…However, the adaptive controller is combined with other controllers to achieve ideal performance against uncertainties and parameter changes. Pal et al [38] combined adaptive control with the back‐stepping controller to improve synchronization of the chaotic satellite's attitude with external disturbances. However, the back‐stepping method is not applicable for high‐order nonlinear systems due to the basic problem of “terms explosion” [39].…”

Section: Introductionmentioning

confidence: 99%

Adaptive barrier function‐based fractional‐order chattering‐free finite‐time control for uncertain chaotic systems

Sepestanaki

Soofi

Barhaghtalab

et al. 2023

Math Methods in App Sciences

View full text Add to dashboard Cite

This study proposes the adaptive continuous barrier function as the fractional‐order control system using the terminal sliding mode control technique with chattering‐free property to stabilize chaotic systems that have unknown uncertainties. The important reason for using the fractional‐order controller is its greater flexibility than the integer‐order controller. Using adaptive approach and Lyapunov's stability theory, an adaptive continuous barrier fractional‐order chattering‐free finite‐time controller for a category of chaotic systems with the unknown uncertainties and external disturbances is presented. The suggested controller can stabilize the chaotic system excellently with a continuous and smooth control law even without knowing the boundaries and in the presence of unknown disturbances due to the model uncertainties. According to MATLAB simulation results, the high efficiency of the suggested control technique to control the chaotic systems in the attendance of unknown perturbations is obviously confirmed.

show abstract

Adaptive chaos synchronization of an attitude control of satellite: A backstepping based sliding mode approach

Cited by 11 publications

References 52 publications

Neuro-fuzzy system based proportional derivative gain optimized attitude control of CubeSat under LEO perturbations

Neuro-fuzzy system based proportional derivative gain optimized attitude control of CubeSat under LEO perturbations

Dynamic Modeling and Passivity-Based Control of an RV-3SB Robot

Adaptive barrier function‐based fractional‐order chattering‐free finite‐time control for uncertain chaotic systems

Contact Info

Product

Resources

About