Research

19 pages, 8626 KiB

Open AccessArticle

The Research of Air Combat Intention Identification Method Based on BiLSTM + Attention

by Bin Tan, Qiuni Li, Tingliang Zhang and Hui Zhao

Electronics 2023, 12(12), 2633; https://doi.org/10.3390/electronics12122633 - 12 Jun 2023

Cited by 3 | Viewed by 909

In the process of air combat intention identification, expert experience and traditional algorithm are relied on to analyze enemy aircraft combat intention in a single moment, but the identification time and accuracy are not excellent. In this paper, from the dynamic attributes of [...] Read more.

In the process of air combat intention identification, expert experience and traditional algorithm are relied on to analyze enemy aircraft combat intention in a single moment, but the identification time and accuracy are not excellent. In this paper, from the dynamic attributes of an airspace fighter air combat target and the dynamic and time series changing characteristics of the battlefield environment, we introduce the bidirectional long short-term memory neural network (BiLSTM + Attention) intention identification method based on the attention mechanism for air combat intention identification. In this method, five kinds of state parameters, including target maneuver type, distance, flight velocity, altitude and heading angle, were taken as datasets. The BiLSTM + Attention was used to extract enemy aircraft intention features. By introducing attention mechanism, the weight coefficients of characteristic states corresponding to air combat victories were corrected. Finally, it was input into the SoftMax function to obtain the category of the enemy’s intention. Experimental results showed that the proposed method can effectively identify enemy aircraft in the case of high complexity, multidimensional and large amount of data. Compared with bidirectional long short-term memory (BiLSTM), long short-term memory (LSTM), long short-term memory based on attention mechanisms (LSTM + Attention) and support vector machine (SVM) classification, the proposed method had higher accuracy and lower loss value. Full article

(This article belongs to the Special Issue Latest Theoretical and Technological Advancements in Nonlinear Adaptive Control and Decision-Making)

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25 pages, 13806 KiB

Open AccessArticle

Autonomous Air Combat Maneuvering Decision Method of UCAV Based on LSHADE-TSO-MPC under Enemy Trajectory Prediction

by Mulai Tan, Andi Tang, Dali Ding, Lei Xie and Changqiang Huang

Electronics 2022, 11(20), 3383; https://doi.org/10.3390/electronics11203383 - 19 Oct 2022

Cited by 4 | Viewed by 1341

Abstract

In this paper, an autonomous UCAV air combat maneuvering decision method based on LSHADE-TSO optimization in a model predictive control framework is proposed, along with enemy trajectory prediction. First, a sliding window recursive prediction method for multi-step enemy trajectory prediction using a Bi-LSTM [...] Read more.

In this paper, an autonomous UCAV air combat maneuvering decision method based on LSHADE-TSO optimization in a model predictive control framework is proposed, along with enemy trajectory prediction. First, a sliding window recursive prediction method for multi-step enemy trajectory prediction using a Bi-LSTM network is proposed. Second, Model Predictive Control (MPC) theory is introduced, and when combined with enemy trajectory prediction, a UCAV maneuver decision model based on the MPC framework is proposed. The LSHADE-TSO algorithm is proposed by combining the LSHADE and TSO algorithms, which overcomes the problem of traditional sequential quadratic programming falling into local optimum when solving complex nonlinear models. The LSHADE-TSO-MPC air combat maneuver decision method is then proposed, which combines the LSHADE-TSO algorithm with the MPC framework and employs the LSHADE-TSO algorithm as the optimal control sequence solver. To validate the effectiveness of the maneuvering decision method proposed in this paper, it is tested against the test maneuver and the LSHADE-TSO decision algorithm, respectively, and the experimental results show that the maneuvering decision method proposed in this paper can beat the opponent and win the air combat using the same weapons and flight platform. Finally, to demonstrate that LSHADE-TSO can better exploit the decision-making ability of the MPC model, LSHADE-TSO is compared to various optimization algorithms based on the MPC model, and the results show that LSHADE-TSO-MPC can not only help obtain air combat victory faster but also demonstrates better decision-making ability. Full article

(This article belongs to the Special Issue Latest Theoretical and Technological Advancements in Nonlinear Adaptive Control and Decision-Making)

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11 pages, 705 KiB

Open AccessFeature PaperArticle

Arrhythmia Classification and Diagnosis Based on ECG Signal: A Multi-Domain Collaborative Analysis and Decision Approach

by Hongpeng Ruan, Xueying Dai, Shengqi Chen and Xiang Qiu

Electronics 2022, 11(19), 3251; https://doi.org/10.3390/electronics11193251 - 09 Oct 2022

Cited by 2 | Viewed by 1683

Abstract

Electrocardiogram (ECG) signal plays a key role in the diagnosis of arrhythmia, which will pose a great threat to human health. As an effective feature extraction method, deep learning has shown excellent results in processing ECG signals. However, most of these methods neglect [...] Read more.

Electrocardiogram (ECG) signal plays a key role in the diagnosis of arrhythmia, which will pose a great threat to human health. As an effective feature extraction method, deep learning has shown excellent results in processing ECG signals. However, most of these methods neglect the cooperation between the multi-lead ECG series correlation and intra-series temporal patterns. In this work, a multi-domain collaborative analysis and decision approach is proposed, which makes the classification and diagnosis of arrhythmia more accurate. With this decision, we can realize the transition from the spatial domain to the spectral domain, and from the time domain to the frequency domain, and make it possible that ECG signals can be more clearly detected by convolution and sequential learning modules. Moreover, instead of the prior method, the self-attention mechanism is used to learn the relation matrix between the sequences automatically in this paper. We conduct extensive experiments on eight advanced models in the same field to demonstrate the effectiveness of our method. Full article

(This article belongs to the Special Issue Latest Theoretical and Technological Advancements in Nonlinear Adaptive Control and Decision-Making)

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18 pages, 4003 KiB

Open AccessArticle

Finite-Time Adaptive Neural Control Scheme for Uncertain High-Order Systems with Input Nonlinearities and Unmodeled Dynamics

by Hantong Mei, Hanqiao Huang, Yunhe Guo, Guan Huang and Feihong Xu

Electronics 2022, 11(18), 2835; https://doi.org/10.3390/electronics11182835 - 08 Sep 2022

Viewed by 992

Abstract

This paper proposes a novel finite-time adaptive neural control method for a class of high-order nonlinear systems with high powers in the presence of dead zone input nonlinearities and unmodeled dynamics. By utilizing prescribed performance functions and radial basis function neural networks, the [...] Read more.

This paper proposes a novel finite-time adaptive neural control method for a class of high-order nonlinear systems with high powers in the presence of dead zone input nonlinearities and unmodeled dynamics. By utilizing prescribed performance functions and radial basis function neural networks, the tracking error and state errors are limited within the preassigned range in a finite time, which can be specified by the designer in advance according to the chosen the parameters of the novel prescribed performance functions. Nonlinear transformed error surfaces are designed to counteract the effects of dead zone input nonlinearities in nonlinear high-order systems with unknown system nonlinearities and unmodeled dynamics. Based on the Lyapunov theorem, the tracking errors are proven to converge into a preassigned set in a finite time previously specified by the novel prescribed performance function. Finally, simulation results demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Latest Theoretical and Technological Advancements in Nonlinear Adaptive Control and Decision-Making)

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14 pages, 2070 KiB

Open AccessArticle

Adaptive Fuzzy Control for Flexible Robotic Manipulator with a Fixed Sampled Period

by Jiaming Zhang and Xisheng Dai

Electronics 2022, 11(14), 2270; https://doi.org/10.3390/electronics11142270 - 20 Jul 2022

Cited by 3 | Viewed by 1071

Abstract

In this paper, a backstepping sampled data control method is developed for a flexible robotic manipulator whose internal dynamic is completely unknown. To address the internal uncertainties, the fuzzy logical system (FLS) is considered. Moreover, considering the limited network bandwidth, the designed controller [...] Read more.

In this paper, a backstepping sampled data control method is developed for a flexible robotic manipulator whose internal dynamic is completely unknown. To address the internal uncertainties, the fuzzy logical system (FLS) is considered. Moreover, considering the limited network bandwidth, the designed controller and adaptive laws only contain the sampled data with a fixed sampled period. By invoking the Lyapunov stability theory, all signals of the flexible robotic manipulator are semi-global uniformly ultimately bounded (SGUUB). Ultimately, an application to a flexible robotic manipulator is given to verify the validity of the sampled data controller. Full article

(This article belongs to the Special Issue Latest Theoretical and Technological Advancements in Nonlinear Adaptive Control and Decision-Making)

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18 pages, 3679 KiB

Open AccessArticle

Deep Reinforcement Learning for Intelligent Dual-UAV Reconnaissance Mission Planning

by Xiaoru Zhao, Rennong Yang, Ying Zhang, Mengda Yan and Longfei Yue

Electronics 2022, 11(13), 2031; https://doi.org/10.3390/electronics11132031 - 28 Jun 2022

Cited by 16 | Viewed by 2695

Abstract

The reconnaissance of high-value targets is prerequisite for effective operations. The recent appreciation of deep reinforcement learning (DRL) arises from its success in navigation problems, but due to the competitiveness and complexity of the military field, the applications of DRL in the military [...] Read more.

The reconnaissance of high-value targets is prerequisite for effective operations. The recent appreciation of deep reinforcement learning (DRL) arises from its success in navigation problems, but due to the competitiveness and complexity of the military field, the applications of DRL in the military field are still unsatisfactory. In this paper, an end-to-end DRL-based intelligent reconnaissance mission planning is proposed for dual unmanned aerial vehicle (dual UAV) cooperative reconnaissance missions under high-threat and dense situations. Comprehensive consideration is given to specific mission properties and parameter requirements through the whole modelling. Firstly, the reconnaissance mission is described as a Markov decision process (MDP), and the mission planning model based on DRL is established. Secondly, the environment and UAV motion parameters are standardized to input the neural network, aiming to deduce the difficulty of algorithm convergence. According to the concrete requirements of non-reconnaissance by radars, dual-UAV cooperation and wandering reconnaissance in the mission, four reward functions with weights are designed to enhance agent understanding to the mission. To avoid sparse reward, the clip function is used to control the reward value range. Finally, considering the continuous action space of reconnaissance mission planning, the widely applicable proximal policy optimization (PPO) algorithm is used in this paper. The simulation is carried out by combining offline training and online planning. By changing the location and number of ground detection areas, from 1 to 4, the model with PPO can maintain 20% of reconnaissance proportion and a 90% mission complete rate and help the reconnaissance UAV to complete efficient path planning. It can adapt to unknown continuous high-dimensional environmental changes, is generalizable, and reflects strong intelligent planning performance. Full article

(This article belongs to the Special Issue Latest Theoretical and Technological Advancements in Nonlinear Adaptive Control and Decision-Making)

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23 pages, 7685 KiB

Open AccessArticle

The Joint Phantom Track Deception and TDOA/FDOA Localization Using UAV Swarm without Prior Knowledge of Radars’ Precise Locations

by Yubing Wang, Weijia Wang, Xudong Zhang, Lirong Wu and Hang Yin

Electronics 2022, 11(10), 1577; https://doi.org/10.3390/electronics11101577 - 14 May 2022

Cited by 3 | Viewed by 1758

Abstract

This paper develops the model of the joint phantom track deception and the joint techniques of time-difference of arrival (TDOA) and frequency-difference of arrival (FDOA) localization to deceive air defense radar networks under the condition that an unmanned aerial vehicle (UAV) swarm has [...] Read more.

This paper develops the model of the joint phantom track deception and the joint techniques of time-difference of arrival (TDOA) and frequency-difference of arrival (FDOA) localization to deceive air defense radar networks under the condition that an unmanned aerial vehicle (UAV) swarm has no prior knowledge of the radars’ precise locations, and related performance experiment and analysis are presented to demonstrate the effectiveness of the proposed method and to clarify the influence factors of phantom track deception. The main contributions of this paper are as follows. Firstly, the model of phantom track deception against a radar network by UAV swarm without prior knowledge of the radars’ positions are established. Secondly, TDOA/FDOA are adapted to locate networked enemy radars using UAV swarm, where the Fisher information matrix (FIM) is derived to evaluate the estimation accuracy. Thirdly, the uncertainty analysis consisting of radar location error and UAV position error is deduced. With these efforts, the integrated capability of sensing and jamming is realized. Moreover, the same source testing using space resolution cell (SRC) from the perspective of a radar network is executed to provide guidance for phantom track design. Finally, performance experiment and analysis are given to verify the theoretical analysis with simulation results. Full article

(This article belongs to the Special Issue Latest Theoretical and Technological Advancements in Nonlinear Adaptive Control and Decision-Making)

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15 pages, 6413 KiB

Open AccessArticle

Adaptive NN Control of Electro-Hydraulic System with Full State Constraints

by Chenyang Jiang, Shuai Sui and Shaocheng Tong

Electronics 2022, 11(9), 1483; https://doi.org/10.3390/electronics11091483 - 05 May 2022

Cited by 3 | Viewed by 1512

Abstract

This paper presents an adaptive neural network (NN) control approach for an electro-hydraulic system. The friction and internal leakage are nonlinear uncertainties, and the states in the considered electro-hydraulic system are fully constrained. In the control design, the NNs are utilized to approximate [...] Read more.

This paper presents an adaptive neural network (NN) control approach for an electro-hydraulic system. The friction and internal leakage are nonlinear uncertainties, and the states in the considered electro-hydraulic system are fully constrained. In the control design, the NNs are utilized to approximate the nonlinear uncertainties. Then, by constructing barrier Lyapunov functions and based on the adaptive backstepping control design technique, a novel adaptive NN control scheme is formulated. It has been proven that the developed adaptive NN control scheme can sustain the controlled electro-hydraulic system to be stable and make the system output track the desired reference signal. Furthermore, the system states do not surpass the given bounds. The computer simulation results verify the effectiveness of the proposed controller. Full article

(This article belongs to the Special Issue Latest Theoretical and Technological Advancements in Nonlinear Adaptive Control and Decision-Making)

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28 pages, 3583 KiB

Open AccessArticle

The Application of Improved Harmony Search Algorithm to Multi-UAV Task Assignment

by Yujuan Cui, Wenhan Dong, Duoxiu Hu and Haibo Liu

Electronics 2022, 11(8), 1171; https://doi.org/10.3390/electronics11081171 - 07 Apr 2022

Cited by 11 | Viewed by 1665

Abstract

In this work, aiming at the problem of cooperative task assignment for multiple unmanned aerial vehicles (UAVs) in actual combat, battlefield tasks are divided into reconnaissance tasks, strike tasks and evaluation tasks, and a cooperative task-assignment model for multiple UAVs is built. Meanwhile, [...] Read more.

In this work, aiming at the problem of cooperative task assignment for multiple unmanned aerial vehicles (UAVs) in actual combat, battlefield tasks are divided into reconnaissance tasks, strike tasks and evaluation tasks, and a cooperative task-assignment model for multiple UAVs is built. Meanwhile, heterogeneous UAV-load constraints and mission-cost constraints are introduced, the UAVs and their constraints are analyzed and the mathematical model is established. The exploration performance and convergence performance of the harmony search algorithm are analyzed theoretically, and the more general formulas of exploration performance and convergence performance are proved. Based on theoretical analysis, an algorithm called opposition-based learning parameter-adjusting harmony search is proposed. Using the algorithm to test the functions of different properties, the value range of key control parameters of the algorithm is given. Finally, four algorithms are used to simulate and solve the assignment problem, which verifies the effectiveness of the task-assignment model and the excellence of the designed algorithm. Simulation results show that while ensuring proper assignment, the proposed algorithm is very effective for the multi-objective optimization of heterogeneous UAV-cooperation mission planning with multiple constraints. Full article

(This article belongs to the Special Issue Latest Theoretical and Technological Advancements in Nonlinear Adaptive Control and Decision-Making)

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