Adaptive Fuzzy Control of Spacecraft Proximity Operations Using Hierarchical Fuzzy Systems

Abstract

The six degrees-of-freedom (6DOF) relative motion control of a chaser spacecraft approaching to a free tumbling target in deep space is investigated in this paper. In view of unknown model uncertainties and complex dynamic couplings in the dynamical model, a direct adaptive fuzzy nonlinear controller is constructed by using fuzzy logic systems to approximate the uncertainties and couplings, where the parameter vectors of fuzzy systems are estimated online by using a projection-based adaptive control method. Due to the great dimension of the system variables, hierarchical fuzzy logic systems are employed in the fuzzy control to reduce the amount of fuzzy rules and alleviate the on-line computation burden in the proposed control algorithm. It is proved that tracking errors of the chaser spacecraft and adaptive parameters of the fuzzy systems are uniformly ultimately bounded. Numerical simulations are performed to demonstrate the performance of the proposed control strategy.

Velocity-Free Fault-Tolerant Rendezvous Law Based on Dual-Layer Adaptive Algorithm

“…Motived by socalled model reference adaptive control (MRAC) theory, Ulrich et al [15] proposed a simple adaptive control for spacecraft proximity operations. Using a hierarchical fuzzy system and a simple adaptive algorithm, Sun and Hou [16] designed a nonlinear controller to attenuated the bad performance resulted from unknown model uncertainty and complex dynamic couplings. Considering the case that the sensors cannot measure the relative velocity accurately, Wang and Ji [17] designed an integrated relative position and attitude control for spacecraft rendezvous with input-tostate (ISS) and finite-time convergence.…”

“…For above system dynamics, following backstepping controller is presented to counteract the oscillation phenomenon of the second-order dynamics[48]. virtual rendezvous law and its formulation is same as(16). Similarly, the * u in ISSG is same as (62).…”

Minimum-Learning-Parameter-Based Fault-Tolerant Control for Spacecraft Rendezvous With Unknown Inertial Parameters

“…However, it is still a challenging work to construct such kind of controllers owing to the complexity of the external disturbance, unknown system dynamics and unexpected actuator faults. Despite of these difficulties, researchers have developed numerous strategies for spacecraft tracking control, including adaptive control [1][2][3][4][5], backstepping control [11][12][13], output feedback control [25,26] and sliding mode control [4,18,19,[31][32][33].…”

“…It must be noted that the unknown inertia parameters were handled via adaptive laws in [7,8]. As an alternative for this issue, neural network and fuzzy logic possess desirable abilities in approximating uncertain nonlinear dynamics caused by unknown parameters, leading to numerical applications in aerospace engineering [9][10][11][12][13][14][15]. In [9], the convolutional neural network has been applied to obtain the pose estimation for spacecraft during rendezvous process.…”

Adaptive fuzzy finite-time control for spacecraft formation with communication delays and changing topologies

“…Moreover, among various fuzzy control methods, in particular, the approach based on Takagi-Sugeno (T-S) model has drawn rapidly growing attention in recent years for its capability of approximating any smooth nonlinear functions over a compact set to arbitrary accuracy [27,28]. Recently, a number of significant results of engineering applications have A C C E P T E D M A N U S C R I P T been reported by using T-S fuzzy-model-based technique such as, the adaptive fuzzy controllers proposed in [29][30][31], which can be applied to industrial processes, real-time observer-based fault detection problems were solved in [32,33] with the help of fuzzy control, reliable and robust control for nonlinear stochastic systems with actuator faults in [34,35], controller design for network-based systems with communication constraints including time delays, packet dropouts, and signal quantization in [36,37], controller design for nonlinear systems with time-delays in [38,39], and also spacecraft control in [40][41][42][43]. More specifically, the fuzzy control schemes have been applied successfully to approximate the disturbance of spacecraft in [40] and [41], and the adaptive fuzzy controllers combined with NFTSMC to reject system uncertainties in [42] and [43] were effective.…”