Coverage-based cooperative guidance law for intercepting hypersonic vehicles with overload constraint

Traditional guidance algorithms for hypersonic glide vehicles face the challenge of real-time requirements and robustness to multiple deviations or tasks. In this paper, an intelligent online multiconstrained reentry guidance is proposed to strikingly reduce computational burden and enhance the effectiveness with multiple constraints. First, the simulation environment of reentry including dynamics, multiconstraints, and control variables is built. Different from traditional decoupling methods, the bank angle command including its magnitude and sign is designed as the sole guidance variable. Secondly, a policy neural network is designed to output end-to-end guidance commands. By transforming the reentry process into a Markov Decision Process (MDP), the policy network can be trained by deep reinforcement learning (DRL). To address the sparse reward issue caused by multiconstraints, the improved Hindsight Experience Replay (HER) method is adaptively combined with Deep Deterministic Policy Gradient (DDPG) algorithm by transforming multiconstraints into multigoals. As a result, the novel training algorithm can realize higher utilization of failed data and improve the rate of convergence. Finally, simulations for typical scenes show that the policy network in the proposed guidance can output effective commands in much less time than the traditional method. The guidance is robust to initial bias, different targets, and online aerodynamic deviation.

Section: Introductionmentioning

confidence: 99%

Intelligent Online Multiconstrained Reentry Guidance Based on Hindsight Experience Replay

Jiang

Wang

Bai

et al. 2023

“…To improve the hitting accuracy of interceptors, numerous guidance approaches have been proposed over the decades [1][2][3][4]. However, with the increasing complexity of the operational environment, current guidance approaches have gradually shown limitations in the following cases:…”

Section: Introductionmentioning

confidence: 99%

“…The contribution of the manuscript is described in the following: (1) by decomposing the optimal guidance problem with obstacle avoidance constraints into two subproblems associated with the triggering condition, we present the overall scheme of guidance. (2) To make sure the interceptor can accurately hit the target, a MPC controller with improved objective function (to enhance control effect) and an auxiliary controller (to guarantee the system stability) is designed, and an 2…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Guidance Approach with Obstacle Avoidance against Maneuvering Targets

Jiang¹,

Yang²,

Zhang³

et al. 2023

A guidance approach with obstacle avoidance is proposed for maneuvering target interception. Firstly, by decomposing the constrained optimal guidance problem into two associated subproblems, the overall scheme of guidance is presented. Secondly, to improve the guidance accuracy against the maneuvering target, an MPC controller with disturbance estimation is designed, which transforms the global optimization problem into a finite-horizon optimal control problem. An auxiliary controller is also developed to ensure the system’s stability. Then, to achieve obstacle avoidance without affecting the guidance accuracy, a unified function is built to assess the threats from obstacles of different shapes, based on which the optimal guidance-obstacle avoidance model is established. Finally, to solve the above optimization problems, a hybrid solver is developed based on the adaptive moment estimation (Adam) algorithm, whose output will serve as the real-time guidance command. The numerical simulation results show that the guidance approach presented in this paper can achieve precise guidance and obstacle avoidance in various scenarios, and the Monte Carlo experiment further demonstrates the robustness and computation efficiency of the approach.

“…In the field of civil aviation, one of the key drivers for the development of aeroengines is to comprehend and be proficient in the operational protocols of different systems. Enhancing the maintenance efficiency of aeroengines, ensuring their safety and airworthiness, and optimizing the key system functions are paramount for the improvement of aeroengines' design ability and for guaranteeing flight safety [1][2][3][4][5]. As the basic control system of the engine, the compressor geometric variable system has the responsibility of stabilizing the flow state of the gas path.…”

Section: Introductionmentioning

confidence: 99%

Research on Forecasting of the Compressor Geometric Variable System Based on the MAE Model

Xia

Zhan

Tan

et al. 2023

The compressor geometric variable system is vital for aeroengines, as it affects their performance and design. To monitor the compressor geometric variable system states and detect anomalies in real time, a t -step forecasting method based on the MAE (masked autoencoders) model was proposed in this article. Unlike previous studies that used simulated or lab-generated data, we use actual flight data recorded by the aircraft data acquisition system to make our results more realistic. Through our experimental efforts, the feasibility of forecasting the compressor geometric variable system based on the MAE model is verified. That is not only the first application of transformer models with a masked pretraining mechanism in time series forecasts but also taking the lead in exploring the possibility of this key system forecast. We also test the generalizability of our method across different types of aeroengines. Finally, to make our theories more reasonable and convincing, experiments on different aeroengine states, including the transition state and the steady state, are carried out.