In this paper, an innovative error-based virtual composite-axis disturbance rejection backstepping control strategy is proposed for electro-optical tracking systems. Tracking accuracy cannot be improved by conventional composite axis structures where target position, velocity and acceleration are unknown and immeasurable. Our proposed method, however, operates without the need for target trajectory input signals or additional sensors. It solely relies on error information to adeptly simulate the compound axis system's functionality. Notably, its error suppression characteristics amalgamate dual-axis suppression features, substantially augmenting tracking performance. Moreover, to further optimize trajectory tracking and counteract the disturbances and uncertainties within the virtual composite axis, a backstepping control strategy is integrated with disturbance rejection. Remarkably, this approach achieves a 31.89% leap in tracking accuracy and a 73.87% boost in disturbance rejection performance. The effectiveness and superiority of the method have been thoroughly corroborated via simulations and experiments.
{"title":"Error-Based Virtual Compound Axis With Backstepping Control for Electro-Optical Tracking System","authors":"Zhijun Li, Jiachen Li, Jiuqiang Deng, Yao Mao","doi":"10.1049/cth2.70012","DOIUrl":"https://doi.org/10.1049/cth2.70012","url":null,"abstract":"<p>In this paper, an innovative error-based virtual composite-axis disturbance rejection backstepping control strategy is proposed for electro-optical tracking systems. Tracking accuracy cannot be improved by conventional composite axis structures where target position, velocity and acceleration are unknown and immeasurable. Our proposed method, however, operates without the need for target trajectory input signals or additional sensors. It solely relies on error information to adeptly simulate the compound axis system's functionality. Notably, its error suppression characteristics amalgamate dual-axis suppression features, substantially augmenting tracking performance. Moreover, to further optimize trajectory tracking and counteract the disturbances and uncertainties within the virtual composite axis, a backstepping control strategy is integrated with disturbance rejection. Remarkably, this approach achieves a 31.89% leap in tracking accuracy and a 73.87% boost in disturbance rejection performance. The effectiveness and superiority of the method have been thoroughly corroborated via simulations and experiments.</p>","PeriodicalId":50382,"journal":{"name":"IET Control Theory and Applications","volume":"19 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cth2.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143456138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the advancement of unmanned aerial vehicle (UAV) technology, research on adversarial interactions within UAV swarms has gained significant attention domestically and internationally. However, the existing decision-making algorithms are primarily tailored to homogeneous UAV swarm adversarial scenarios, facing challenges such as complex reward function design and limited decision-making timeliness when applied to more intricate scenarios. This article investigates the real-time control decision-making issues in UAV swarm adversarial interactions. First, an adversarial simulation environment for UAV swarms is constructed, which effectively unifies the environment and state representation, enhancing the response speed of our UAVs. Second, a distributed UAV swarm collaborative control algorithm based on multi-agent reinforcement learning is proposed, and an effective sparse reward function is designed to guide UAVs in adversarial gaming, making the UAV strategies more aggressive, enhancing the adversarial intensity, and further optimizing the control strategy to meet real-world demands better. Finally, the real-time performance and scalability of the proposed method are validated through simulations.
{"title":"Collaborative decision-making for UAV swarm confrontation based on reinforcement learning","authors":"Yongkang Jiao, Wenxing Fu, Xinying Cao, Qiangqing Su, Yusheng Wang, Zixiang Shen, Lanlin Yu","doi":"10.1049/cth2.12781","DOIUrl":"https://doi.org/10.1049/cth2.12781","url":null,"abstract":"<p>With the advancement of unmanned aerial vehicle (UAV) technology, research on adversarial interactions within UAV swarms has gained significant attention domestically and internationally. However, the existing decision-making algorithms are primarily tailored to homogeneous UAV swarm adversarial scenarios, facing challenges such as complex reward function design and limited decision-making timeliness when applied to more intricate scenarios. This article investigates the real-time control decision-making issues in UAV swarm adversarial interactions. First, an adversarial simulation environment for UAV swarms is constructed, which effectively unifies the environment and state representation, enhancing the response speed of our UAVs. Second, a distributed UAV swarm collaborative control algorithm based on multi-agent reinforcement learning is proposed, and an effective sparse reward function is designed to guide UAVs in adversarial gaming, making the UAV strategies more aggressive, enhancing the adversarial intensity, and further optimizing the control strategy to meet real-world demands better. Finally, the real-time performance and scalability of the proposed method are validated through simulations.</p>","PeriodicalId":50382,"journal":{"name":"IET Control Theory and Applications","volume":"19 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cth2.12781","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143456140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates the problem of asymptotic stabilization for a class of uncertain nonlinear systems involving logarithmic quantization at the system input. Different from the existing results and approaches, a Lyapunov function candidate and an adaptive control law are developed to adaptively estimate the uncertain parameters and to asymptotically stabilize the uncertain nonlinear system, in which the control input also involves uncertain parameters, possibly in the nonlinear form. It is shown that asymptotic stabilization can be achieved under some mild conditions, even though the adaptively estimated parameters do not converge to the true system parameters. A sufficient condition is obtained for the asymptotic stabilizability, in terms of the quantization density and the multiplicative parameter error bound at the control input. More importantly, the proposed adaptive control law is suboptimal for the corresponding LQR control and achieves the