This paper proposes an approach for piecewise trajectory planning and fractional-order sliding-mode guidance of a two-stage launched miniature munition with the laser beam riding steered guidance technology. In the proposed approach, firstly, the polynomial description is used as an expected trajec- tory. Then consider the random external disturbances/parametric uncertainties and other factors deteriorate the tracking control performances of the miniature munition. To reduce the chatter- ing caused by modeling errors and external disturbances and achieve fast speed and high accuracy tracking performances for miniature munition, based on adaptive multi-power reaching strategy, an integrated guidance and control (IGC) method with the fractional-order sliding-mode guidance law (FOSMGL) is introduced. The designed fractional-order sliding surfaces ultimately and successively allow both the trajectory and the attitude angle converge to expected value in a finite time. In addition, considering the case of a moving target, a feedfor- ward compensation mechanism, which decreases the lag of the guidance system, is imposed using an additional compensated term obtained from the attitude angle error. The convergence and reachability of the proposed algorithm in finite-time are analyzed using the Lyapunov function method. The validity and practicability of the proposed method are verified by a numerical example.

This paper documents a novel nonsingular continuous guidance which can drive the line-of-sight (LOS) angular rate to converge to zero in finite time in the presence of impact angle constraints. More specifically, based on the second-order sliding mode control (SMC) theory, a second-order observer (2-OB) is presented to estimate the unknown target maneuvers, while a super twisting algorithm- (STA-) based guidance law is presented to restrict the LOS angle and angular rate. Compared with other terminal sliding mode guidance laws, the proposed guidance law absorbs the merits of the conventional linear sliding mode (LSM) and terminal sliding mode (TSM) and uses switching technique to avoid singularity. In order to verify the stability of the proposed guidance law, a finite-time bounded (FTB) function is invited to prove the boundedness of the proposed observer-controller system and a Lyapunov approach is presented to prove the finite-time convergence (FTC) of the proposed sliding system. Rigorous theoretical analysis and numerical simulations demonstrate the mentioned properties.

In this study, two different impact-angle-constrained guidance and control strategies using deep reinforcement learning (DRL) are proposed. The proposed strategies are based on the dual-loop and integrated guidance and control types. To address comprehensive flying object dynamics and the control mechanism, a Markov decision process is used to solve the guidance and control problem, and a real-time impact-angle error in the state vector is used to improve the model applicability. In addition, a reasonable reward mechanism is designed based on the state component which reduces both the miss distance and the impact-angle error and solves the problem of sparse rewards in DRL. Further, to overcome the negative effects of unbounded distributions on bounded action spaces, a Beta distribution is used instead of a Gaussian distribution in the proximal policy optimization algorithm for policy sampling. The state initialization is then realized using a sampling method adjusted to engineering backgrounds, and the control strategy is adapted to a wide range of operational scenarios with different impact angles. Simulation and Monte Carlo experiments in various scenarios show that, compared with other methods mentioned in the experiment in this paper, the proposed DRL strategy has smaller impact-angle errors and miss distance, which demonstrates the method’s effectiveness, applicability, and robustness.