A sliding-mode guidance (SMG) law is designed to intercept maneuvering targets with impact angle constrained flight trajectories under the assumption of ideal missile autopilot. Furthermore, accounting for the autopilot as second-order dynamics, a new guidance law with terminal impact angle constraint is designed using the dynamic surface control method. Some first-order low-pass filters are introduced into the designing process to avoid the occurrence of high-order derivatives of the line of sight (LOS) angle in the expression of the guidance law such that the guidance law can be implemented in practical applications. The proposed guidance law is effective in compensating for the second-order autopilot lag. Simulation results show that it is able to guide a missile to impact a maneuvering target with a desired angle and a small miss distance.

This paper investigates the trajectory tracking problem for quadrotors with finite-time convergence via homogeneous feedback approach. Based on the inherent underactuated cascaded properties of a quadrotor, the tracking control is decomposed into the outer position tracking control loop and the inner attitude tracking control loop. And the orientation of the four thrust inputs for the position loop is determined by the attitude loop. Therefore, an inner geometric tracking controller based on exponential coordinates for the attitude loop is first proposed, which avoids kinematic singularity and redundancy. Then, a homogeneous feedback plus dynamics compensation scheme is utilized to construct the thrust magnitude control input for the position dynamics, which also provides a reference rotation matrix for the attitude tracking loop. The almost global finite time stability of the whole tracking control system is clearly proved. Numerical results are also presented to demonstrate the effectiveness of this finite-time control strategy.

This paper investigates the fixed-time tracking problem for uncertain nonlinear systems with input saturation constraint under the event-triggered scheme. An adaptive fixed-time controller is proposed to drive the tracking error to a residual set in fixed time, which can eliminate the problem of “explosion of complexity”. Furthermore, in order to decrease the communication burden between the controller and the actuator while maintaining the system control performance, we co-design the adaptive controller and the event-triggered strategy, which guarantees that the control input is injected into the actuator only when the predefined event occurs. In addition, the measurement error arisen from event-triggering mechanism is also compensated. Finally, a rigorous mathematical stability proof is given and simulation results are presented to illustrate the effectiveness of the proposed control scheme.

A closed-form analytical method for estimating the time-to-go of a proportional guidance law guided missile is proposed. This method is effective for both stationary and maneuvering targets. It divides the missile flight process into a constant drag-lift ratio flight phase and a constant drag coefficient flight phase, so that the influence of drag can be fully considered when calculating the time-to-go. In this article, the time-to-go estimation for a stationary target is given first. Then, the maneuvering target is transformed into a stationary target by reasonably setting a fictitious target related to the target motion state and a preliminary estimation of the time-to-go for the maneuvering target is obtained. To further improve the accuracy of the time-to-go estimation for the maneuvering target, a compensation item is designed on the basis of the preliminary estimation. The simulation results show that the method proposed in this article can accurately estimate the time-to-go for various targets, and cooperative attacks can be realized by applying the time-to-go estimation to a specific cooperative guidance law.

Accurate estimation and prediction of the lithium-ion (Li-ion) batteries’ performance has important theoretical and practical significance to make better use of lithium-ion battery and to avoid unnecessary losses. State of health (SOH) estimation is used as a qualitative measure of the capability of a lithium-ion battery to store and deliver energy in a system. To evaluate and predict the SOH of batteries, the Gaussian process regression with neural network (GPRNN) as its variance function is proposed. Experimental results confirm that the proposed method can be effectively applied to Li-ion battery monitoring and prognostics by quantitative comparison with basic GPR, combination LGPFR, combination QGPFR, and the multiscale GPR (SMK-GPR, P-MGPR, and SE-MGPR). The criteria of RMSE and MAPE of the proposed three models are reduced significantly compared to those of other existing methods.