Adaptive control of nonlinear time-delay systems in the presence of output constraints and actuators faults

In this paper, the problem of fixed-time attitude consensus control is addressed for a group of rigid spacecraft in the presence of inertia uncertainties and external disturbances. By applying the adaptive technique and neural network approximation technique to handle the disturbances and uncertainties, an adaptive neural network-based distributed protocol is proposed to achieve attitude consensus control for multiple rigid spacecraft. The proposed distributed attitude consensus protocol is composed of a group of distributed fixed-time observers for followers to estimate the leader's information and an adaptive neural network-based fixed-time sliding mode control law to realize attitude tracking control. Rigorous proofs are provided to demonstrate that the estimation errors of the proposed observers are convergent in a fixed time. Further, it is also proven that attitude tracking errors reach some adjustable regions in a fixed time under the proposed attitude consensus protocol. Numerical simulations are conducted to illustrate the performance of the proposed distributed attitude consensus protocol.

Section: Fixed‐time Attitude Tracking Control Law Designmentioning

confidence: 99%

Distributed adaptive neural network fixed‐time leader‐follower attitude consensus control for multiple rigid spacecraft

Dong

2022

“…F I G U R E 1 Block diagram of the above proposed distributed control law (13) and parameter estimation law (22) F I G U R E 2 Block diagram for bias estimated consensus control scheme…”

Section: Bias Estimationmentioning

confidence: 99%

“…with bias adaptation law (22), guarantees that lim t→∞ (q i − q j ) = 0 (for all i, j ∈ {1, 2, … , n}), lim t→∞ q = 0 and lim t→∞ (b i − bk i ) = 0 (for all i, k ∈ {1, 2, … , n}) exponentially (beyond the collective initial excitation window, i.e., t ⩾ T) for sufficiently large 𝜇 IF > 0, while ensuring that the trajectories of the closed-loop system given by ( 9), (12), and ( 23) are uniformly bounded. Remark 4.…”

Section: F I G U R E 3 Flowchart Of the Proposed Control Algorithm Fo...mentioning

confidence: 99%

“…Considering a constant communication graph and in the presence of unknown nonlinearities, unknown time‐varying actuator defects, it is demonstrated that the tracking errors converge to a small neighborhood of the origin. The References 21 and 22 effectively address designing neural and barrier Lyapunov function controllers, respectively, for nonlinear time‐delay systems, in the presence of actuator faults and various other constraints. However, References 20‐22 use Lyapunov–Krasvoskii functionals to compensate the unknown faults and time‐delays and not explicitly take into account bias estimation.…”

Section: Introductionmentioning

confidence: 99%

“…The References 21 and 22 effectively address designing neural and barrier Lyapunov function controllers, respectively, for nonlinear time‐delay systems, in the presence of actuator faults and various other constraints. However, References 20‐22 use Lyapunov–Krasvoskii functionals to compensate the unknown faults and time‐delays and not explicitly take into account bias estimation. An approach to estimating measurement inconsistencies using an output regulation‐based technique is presented in Reference 23.…”

Section: Introductionmentioning

confidence: 99%

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