scholarly journals Prescribed Performance-Based Event-Driven Fault-Tolerant Robust Attitude Control of Spacecraft under Restricted Communication

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1709
Author(s):  
Syed Muhammad Amrr ◽  
Abdulrahman Alturki ◽  
Ankit Kumar ◽  
M. Nabi
Keyword(s):  
Attitude Control ◽  
Data Transfer ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Control Input ◽  
Prescribed Performance ◽  
Event Trigger ◽  
Steady State Responses ◽  
Complex Nonlinear Systems ◽  
State Responses

This paper explores the problem of attitude stabilization of spacecraft under multiple uncertainties and constrained bandwidth resources. The proposed control law is designed by combining the sliding mode control (SMC) technique with a prescribed performance control (PPC) method. Further, the control input signal is executed in an aperiodic time framework using the event-trigger (ET) mechanism to minimize the control data transfer through a constrained wireless network. The SMC provides robustness against inertial uncertainties, disturbances, and actuator faults, whereas the PPC strategy aims to achieve a predefined system performance. The PPC technique is developed by transforming the system attitude into a new variable using the prescribed performance function, which acts as a predefined constraint for transient and steady-state responses. In addition, the ET mechanism updates the input value to the actuator only when there is a violation of the triggering rule; otherwise, the actuator output remains at a fixed value. Moreover, the proposed triggering rule is constituted through the Lyapunov stability analysis. Thus, the proposed approach can be extended to a broader class of complex nonlinear systems. The theoretical analyses prove the uniformly ultimately bounded stability of the closed-loop system and the non-existence of the Zeno behavior. The effectiveness of the proposed methodology is also presented along with the comparative studies through simulation results.

Fault-tolerant attitude control for flexible spacecraft subject to input and state constraint

2020 ◽  
Vol 42 (14) ◽  
pp. 2660-2674
Author(s):  
Mehdi Golestani ◽  
Seyed Majid Esmaeilzadeh ◽  
Bing Xiao
Keyword(s):  
Attitude Control ◽  
Actuator Saturation ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Fixed Time ◽  
State Constraint ◽  
Flexible Spacecraft ◽  
Convergence Time ◽  
Prescribed Performance ◽  
High Attitude

This paper considers the problem of fault-tolerant attitude control for a flexible spacecraft subject to input and state constraint. Particularly, a new sliding mode-based attitude control with fixed-time convergent for the flexible spacecraft is developed in which the convergence rate of the system state is improved both far from and at close range of the origin. In contrast to the existing complicated prescribed performance controls (PPC), the proposed PPC possesses a much simpler structure due to the use of a novel constraint concept without employing error transformation. It also introduces a modified prescribed performance function (MPPF) to explicitly determine the settling time. It is rigorously proved that the attitude variable is kept within the predefined constraint boundaries even when the actuator saturation is taken into account. Moreover, the proposed controller is inherently continuous and the chattering is effectively reduced. An adaptive mechanism is developed in which no prior knowledge of the lumped uncertainties is required. Finally, numerical simulations are presented to demonstrate that the proposed controller is able to successfully accomplish attitude control with high attitude pointing accuracy and stability. More specifically, it provides faster convergence (improvement percentage of convergence time (IP_CT) is about 18%) and more accurate control (improvement percentages of MRPs (IP_MRPs) and angular velocity (IP_AV) are about 60% and 80%, respectively) under healthy actuators. Values of IP_CT, IP_CT, and IP_AV are 50%, 99.9% and 99.9% under faulty actuators, respectively.

Prescribed performance-based distributed fault-tolerant cooperative control for multi-UAVs

2018 ◽  
Vol 41 (4) ◽  
pp. 975-989 ◽  
Author(s):  
Ziquan Yu ◽  
Youmin Zhang ◽  
Yaohong Qu
Keyword(s):  
Cooperative Control ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Input Saturation ◽  
Dynamic Surface Control ◽  
Tracking Errors ◽  
Dynamic Surface ◽  
Prescribed Performance ◽  
Surface Control ◽  
Control Scheme

In this paper, a prescribed performance-based distributed neural adaptive fault-tolerant cooperative control (FTCC) scheme is proposed for multiple unmanned aerial vehicles (multi-UAVs). A distributed sliding-mode observer (SMO) technique is first utilized to estimate the leader UAV’s reference. Then, by transforming the tracking errors of follower UAVs with respect to the estimated references into a new set, a distributed neural adaptive FTCC protocol is developed based on the combination of dynamic surface control (DSC) and minimal learning parameters of neural network (MLPNN). Moreover, auxiliary dynamic systems are exploited to deal with input saturation. Furthermore, the proposed control scheme can guarantee that all signals of the closed-loop system are bounded, and tracking errors of follower UAVs with respect to the estimated references are confined within the prescribed bounds. Finally, comparative simulation results are presented to illustrate the effectiveness of the proposed distributed neural adaptive FTCC scheme.

scholarly journals Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control

2021 ◽  
Vol 4 (3) ◽  
pp. 51
Author(s):  
Junxia Yang ◽  
Youpeng Zhang ◽  
Yuxiang Jin
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Fault Tolerant Control ◽  
Control Input ◽  
Terminal Sliding Mode ◽  
Train Operation ◽  
Automatic Train Operation

Aiming at the problem of the large tracking error of the desired curve for the automatic train operation (ATO) control strategy, an ATO control algorithm based on RBF neural network adaptive terminal sliding mode fault-tolerant control (ATSM-FTC-RBFNN) is proposed to realize the accurate tracking control of train operation curve. On the one hand, considering the state delay of trains in operation, a nonlinear dynamic model is established based on the mechanism of motion mechanics. Then, the terminal sliding mode control principle is used to design the ATO control algorithm, and the adaptive mechanism is introduced to enhance the adaptability of the system. On the other hand, RBFNN is used to adaptively approximate and compensate the additional resistance disturbance to the model so that ATO control with larger disturbance can be realized with smaller switching gain, and the tracking performance and anti-interference ability of the system can be enhanced. Finally, considering the actuator failure and the control input limitation, the fault-tolerant mechanism is introduced to further enhance the fault-tolerant performance of the system. The simulation results show that the control can compensate and process the nonlinear effects of control input saturation, delay, and actuator faults synchronously under the condition of uncertain parameters, external disturbances of the system model and can achieve a small error tracking the desired curve.

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