Anti-saturation adaptive finite-time neural network based fault-tolerant tracking control for a quadrotor UAV with external disturbances

In this paper, a robust fault tolerant control, which provides a global fixed-time stability, is proposed for robot manipulators. This approach is constructed based on an integration between a fixed-time second-order sliding mode observer (FxTSOSMO) and a fixed-time sliding mode control (FxTSMC) design strategy. First, the FxTSOSMO is developed to estimate the lumped disturbance with a fixed-time convergence. Then, based on the obtained disturbance estimation, the FxTSMC is developed based on a fixed-time sliding surface and a fixed-time reaching strategy to form a global fixed-time convergence of the system. The proposed approach is then applied for fault tolerant control of a PUMA560 robot and compared with other state-of-the-art controllers. The simulation results verify the outstanding fault estimation and fault accommodation capability of the proposed fault diagnosis observer and fault tolerant strategy, respectively.

Robust Fault Tolerant Control of Robot Manipulators with Global Fixed-Time Convergence

In this work, we propose a novel strategy of adaptive sparse array beamformer design, referred to as regularized complementary antenna switching (RCAS), to swiftly adapt both array configuration and excitation weights in accordance to the dynamic environment for enhancing interference suppression. In order to achieve an implementable design of array reconfiguration, the RCAS is conducted in the framework of regularized antenna switching, whereby the full array aperture is collectively divided into separate groups and only one antenna in each group is switched on to connect with the processing channel. A set of deterministic complementary sparse arrays with good quiescent beampatterns is first designed by RCAS and full array data is collected by switching among them while maintaining resilient interference suppression. Subsequently, adaptive sparse array tailored for the specific environment is calculated and reconfigured based on the information extracted from the full array data. The RCAS is devised as an exclusive cardinality-constrained optimization, which is reformulated by introducing an auxiliary variable combined with a piece-wise linear function to approximate the $l_0$ -norm function. A regularization formulation is proposed to solve the problem iteratively and eliminate the requirement of feasible initial search point. A rigorous theoretical analysis is conducted, which proves that the proposed algorithm is essentially an equivalent transformation of the original cardinality-constrained optimization. Simulation results validate the effectiveness of the proposed RCAS strategy.

Adaptive Sparse Array Beamformer Design by Regularized Complementary Antenna Switching

Synchronization and anti-synchronization for complex-valued inertial neural networks with time-varying delays

Active fault tolerant control based on adaptive interval observer for uncertain systems with sensor faults

In this paper, a fault-tolerant control scheme is proposed for the rigid spacecraft attitude control system subject to external disturbances, multiple system uncertainties, and actuator faults. The angular velocity measurement is unavailable, which increases the complexity of the problem. An observer is first designed based on the super-twisting sliding mode method, which can provide accurate estimates of the angular velocity in finite time. Then, an adaptive fault-tolerant controller is proposed based on neural networks using the information from the observer. It is shown that the attitude orientations converge to the desired values exponentially. Finally, a simulation example is utilized to verify the effectiveness of the proposed scheme.

Jiandong Wang

Papers

Fault-Tolerant Attitude Control for Rigid Spacecraft Without Angular Velocity Measurements