Performance of 3-D PPN Against Arbitrarily Maneuvering Target for Homing Phase

Li, Kebo; Shin, Hyo‐Sang; Tsourdos, Antonios; Tahk, Min-Jea

doi:10.1109/taes.2020.2987404

Cited by 17 publications

(6 citation statements)

References 34 publications

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“…Considering homing missiles are generally axisymmetric and controlled by a skid-to-turn method, the longitudinal and lateral models are usually similar in structure. 19 Thus, this paper is mainly centered on the longitudinal plane.…”

Section: Model Derivationmentioning

confidence: 99%

Adaptive control law based integrated guidance and control design for missile with the radome error compensation

Guo,

Zhou,

Zhou

2024

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper presents an integrated guidance and control design for the homing missiles with the radome error compensation to address the guidance problems caused by radome. Firstly, a mathematics model of the integrated guidance and control system with the compensation for the radome error is built by integrating the missile dynamics and the engagement kinematics between the missile and ground target on the longitudinal motion. Secondly, the model is transformed into a normal form by nonlinear coordinate transformation because the system is a time-varying system with mismatched uncertainties. Thirdly, an adaptive control law of the system is designed by adopting the sliding mode control theory, and the stability of the closed-loop system is also conducted. Finally, the simulation proves that the proposed integrated guidance and control scheme can effectively compensate for the radome error and ensure the hit-to-kill attack to the target.

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Section: Model Derivationmentioning

confidence: 99%

Adaptive control law based integrated guidance and control design for missile with the radome error compensation

Guo,

Zhou,

Zhou

2024

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…The intersection plane is formed by r and v which are shown in Figure 1, and the details are illustrated in Figure 2. The plane between the target and interceptor will rotate as a function of their relative motion [34]. Figure 2 illustrates the centroid of the interceptor at the origin o m , with unit vectors perpendicular and International Journal of Aerospace Engineering parallel to r denoted by e θ and e r , respectively.…”

Section: Motion Model Of Interceptionmentioning

confidence: 99%

Homing Guidance Law Design against Maneuvering Targets Based on DDPG

Liang

Tang

Bai³

et al. 2023

International Journal of Aerospace Engineering

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A novel homing guidance law against maneuvering targets based on the deep deterministic policy gradient (DDPG) is proposed. The proposed guidance law directly maps the engagement state information to the acceleration of the interceptor, which is an end-to-end guidance policy. Firstly, the kinematic model of the interception process is described as a Markov decision process (MDP) that is applied to the deep reinforcement learning (DRL) algorithm. Then, an environment of training, state, action, and network structure is reasonably designed. Only the measurements of line-of-sight (LOS) angles and LOS rotational rates are used as state inputs, which can greatly simplify the problem of state estimation. Then, considering the LOS rotational rate and zero-effort-miss (ZEM), the Gaussian reward and terminal reward are designed to build a complete training and testing simulation environment. DDPG is used to deal with the RL problem to obtain a guidance law. Finally, the proposed RL guidance law’s performance has been validated using numerical simulation examples. The proposed RL guidance law demonstrated improved performance compared to the classical true proportional navigation (TPN) method and the RL guidance policy using deep-Q-network (DQN).

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“…It changes the way of random sampling from the sample pool and adds weight to the sample data so that the samples with large absolute value of temporal difference (TD) error have a greater probability of being selected. Therefore, the prioritized experience replay DQN can solve the problems of larger fluctuation and slow convergence in the training process, and the obtained policy is more stable [36].…”

Section: Prioritized Experience Replay Dqnmentioning

confidence: 99%

“…According to the relevant definitions of 3D interception process, the plane composed of the relative position vector r and the relative velocity vector v of the interceptor and target is called the intersection plane. The intersection plane may rotate with the relative motion of the target and interceptor [36][37][38]. The schematic diagram of the intersection plane is shown in Figure 2.…”

Section: Model Of Relative Motionmentioning

confidence: 99%

An Exoatmospheric Homing Guidance Law Based on Deep Q Network

Tang

Bai²,

Liang

et al. 2022

International Journal of Aerospace Engineering

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A homing guidance law for exoatmospheric interceptor based on the Deep Q Network (DQN) algorithm is proposed in this paper. Aiming at the exoatmospheric interception problem, the guidance agent is built with the help of the deep reinforcement learning theory, and the action command is given according to the measurement information of the exoatmospheric interceptor for the accurate interception of the target. The homing guidance problem is first transformed into a Markov decision process, and a three-dimensional (3D) interception scenario is established. Then, the reward function considering the line-of-sight (LOS) rate and the final zero-effort-miss (ZEM) is designed, and the homing guidance problem is transferred to the reinforcement learning framework. After that, DQN is utilized to solve the exoatmospheric interception problem, and the guidance agent is obtained through a large amount of training. Finally, the guidance performance of DQN homing guidance law is verified by numerical simulation examples and compared with the classical true proportional navigation (TPN) guidance law. The results show that the guidance performance of the homing guidance law is better than that of TPN.

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Performance of 3-D PPN Against Arbitrarily Maneuvering Target for Homing Phase

Cited by 17 publications

References 34 publications

Adaptive control law based integrated guidance and control design for missile with the radome error compensation

Adaptive control law based integrated guidance and control design for missile with the radome error compensation

Homing Guidance Law Design against Maneuvering Targets Based on DDPG

An Exoatmospheric Homing Guidance Law Based on Deep Q Network

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