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In a multi-source localization system, direction of arrival (DOA) estimation of angles always suffers from errors due to noise interference, sensor position inaccuracies, and other factors. When the distance between target sources is much smaller than the distance between sensors and target sources, the accuracy of traditional localization algorithms based on direction finding and cross-fixing deteriorates. In this paper, we propose a localization algorithm based on K-means clustering. To tackle the problem of unknown initial positions of target sources, we employ a grid density peak clustering(DPC) method for initial localization. In the K-means algorithm, we integrate a quartile range anomaly detection algorithm to address interference signal issues. Finally, we propose an invalid compensation algorithm to filter out invalid signals, thereby compensating for the estimation errors in angles. Through the collection of real-world data, we compare the performance of the traditional direction finding and cross-fixing algorithms with the proposed algorithm in the localization of nearby target points. Experimental results demonstrate that the proposed algorithm significantly improves localization accuracy.

Owing to the tolerance of circuit components in analogue circuits, feature overlap occurs between different component faults, especially in the case of early-stage faults. To address this issue, we present an improved model that utilizes the channel attention mechanism in conjunction with a fully convolutional network. The proposed model conducts end-to-end diagnosis by utilizing the response signals generated from the tested circuit. To diagnose different fault categories, we add a global average pooling layer to the last layer of the fully convolutional network to obtain the probabilities of various fault categories. Furthermore, we incorporating a channel attention, which allocates distinct weight coefficients to each channel to amplify crucial features while dampening less significant ones, within the network, which allocates distinct weight coefficients to each channel to amplify crucial features while dampening less significant ones. The proposed method is validated with four-opamp biquad high-pass filter circuit and leapfrog filter circuit. The results demonstrate that the proposed model achieves a fault diagnosis accuracy of 95.24% in Case 1,87.09% in Case 2. This method exhibits higher diagnostic accuracy and reliability than existing methods.

In Unmanned Aerial Vehicle (UAV) systems, packet loss during sensor data transmission causes data missing, which reduces fault features in sensor signals and causes the accuracy of state monitoring to decrease. This study proposes a state monitoring algorithm combining a convolutional neural network (CNN) with a random forest (RF) for data missing scenarios. CNN algorithm is designed to extract the distributed fault information from the available signals and acquire the state features of the system. Random forest algorithm processes the state features and judges the system state. The integrating strategy utilizes the automatic feature extraction capability of CNN and the superior discrimination capability of an RF classifier to improve the state monitoring accuracy. The experimental results show that the accuracy of state monitoring in data missing condition reaches 92.74%. The comparative experiments verify the validity of the proposed algorithm.

In this paper, a hybrid nonlinear control scheme combining nonlinear dynamic inversion (NDI) and adaptive sliding mode (ASM) control is proposed for high angle of attack fighter system with center of gravity perturbation and aerodynamic parameter uncertainty. By introducing the affine nonlinear model of inner loop angular velocity with center of gravity perturbation, an adaptive sliding mode control scheme based on the online estimation of radial basis function (RBF) neural network is designed, which can reduce the dynamic inversion error of NDI control and rapidly compensate the multi-coupled channel oscillation. The aerodynamic force and moment coefficients are estimated via the iterative weighted least squares (IRLS) method. Then an outer loop integral sliding mode (ISM) NDI controller is designed, which can be robust to disturbances and mismatched uncertainties through adjusting adaptive gain, and the rudder yaw chattering caused by the uncertainty of aerodynamic parameters is reduced simultaneously. The Lyapunov stability theory and Barbarat’s lemma prove the stability of the designed control scheme, and simulation based on F-16 model verifies the effectiveness of the control scheme.

Due to oversaturated traffic condition, few models are capable of describing the complexity and control strategies are difficult to design. This article proposes an arterial coordination control for unsaturated and oversaturated traffic condition via discretization analysis model. The Robertson discrete formula is introduced to describe the discrete relationship between the upstream and downstream flow. The downstream vehicle arrival rate satisfies the cyclical relationship when the turning traffic flow is low. On this basis, the traffic parameters of the upstream and downstream can be calculated by the `look-up table' method instead of the `recursion' method in using the periodic array to save the intermediate variables in the model. As a result, the computation time significantly decreases. For unsaturated situation, delays are accumulated in each time period and reduced through adjusting offsets. For oversaturated traffic condition caused by lane occupancy accidents, green time is optimized by genetic algorithm after the road capacity decline is estimated. Simulation experimental results verify the effectiveness of the control strategies and the practicability of the proposed model.

This study investigated minimum-entropy hybrid fault-tolerant control (FTC) theory for non-Gaussian stochastic systems with compound faults. After fuzzy linearization for the singular systems, the output probability density function (PDF) is generated by rational square root B-splines. To deal with the compound faults consisting of single sensor fault and intermittent multiple actuator faults, an active-passive hybrid adaptive FTC scheme is proposed: A passive compensation function can directly reconstruct the algorithm to mask the sensor fault; then, actuator fault estimation accurately tracks the multiple actuator faults. Hence, the hybrid FTC combines estimated information and passive compensation simultaneously implements active actuator fault repair and passive sensor fault shielding. A novel variable parameter algorithm that mimics animal predation behavior is designed and incorporated into learning rates, making the controller more sensitive to the incipient deviations in actuator faults. Finally, with the optimal indicators containing entropy and mean of non-Gaussian PDF, the minimum-entropy FTC is achieved. Lyapunov and indicator functions prove the stability, simulation verifies the effectiveness of the methods.

This paper presents a nonlinear fault-tolerant control (FTC) and sensor fault diagnosis for longitudinal dynamics of hypersonic flight vehicle. A nominal sliding mode control is developed to track the reference command of velocity and altitude on the basis of an affine nonlinear dynamic model. A nonlinear adaptive observer is proposed to estimate the fault in velocity system. The fault in altitude system is estimated using the coupling item as an estimation item through a backstepping sliding-mode observer to achieve an estimation performance better than that of the original adaptive observer. The estimation results are used to design a nonlinear FTC to compensate the multi-sensor faults. The stability of the controller is analyzed through Lyapunov theory. Numerical simulation results demonstrate the validity and robustness of the proposed controller and observer.

This paper provides a mathematical model for a hypersonic flight vehicle system with actuator faults and then, proposes a fault-tolerant algorithm to increase the reliability of the attitude system. A terminal sliding mode controller for attitude angular loop is introduced to obtain the virtual control input. Thus, the required deflection angle of aerodynamic surfaces can be calculated via dynamic surface technique in inner angular rate loop. When the aero-surfaces are saturated and unable to supply the moment vector commanded by the fault tolerant controller, a reaction control system will be driven by quadratic programming allocator to provide the residual control torque. The system stability is analyzed utilizing the Lyapunov function. Several simulation results validate that the system output can track the desired command even under fault situations.