基于连续时间广义预测校正的水下非线性追踪博弈控制
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摘要
针对复杂环境下的追踪控制问题,提出了一种基于连续时间广义预测校正的水下非线性追踪博弈控制算法.利用连续时间广义预测对目标机动偏离趋势进行在线预测补偿校正,将机动目标紧缩于最大捕获概率扇面之内,同时引入零效控制参数和连续时间广义预测校正算法,解决了微分对策动态博弈剩余时间难于估计的问题,提高了系统的响应速度.将算法应用于水下非线性追踪博弈的验证结果表明,该算法兼顾了控制约束与干扰抑制性能,能够实时有效地对抗初始偏差和随机扰动,不仅具有良好的导引效果,而且有效提高了系统对环境干扰的鲁棒性.
In order to overcome the difficulty in real-time effectively acquiring the target parameters of differential game guidance in a complex underwater environment, the differential game guidance of underwater nonlinear tracking control based on continuous time generalised predictive correction is proposed. Since the target parameter and the detection precision are seriously affected by the acoustic homing device detection period, noise, and interference, it is easy to lose or misjudge the target signal. Hence a combination of the dynamic tracking game model for differential games and the acoustic homing detection method of underwater tracking is used for making the on-line prediction and compensation correction to the deviation tendency of target manoeuvres deviating from the self-guided sound zero axis. This is carried out by using a continuous time generalized predictive control algorithm, according to the discrepancy between the predicted advance angle and the expected value. The manoeuvring target can then be located in the maximum capture probability sector of the tracker device in real time. In order to solve the estimation difficulty problem of the remaining time of the dynamic differential game antagonism, and improve the response speed and the control precision of the system, the zero-efficiency control parameter and the predictive control algorithm are introduced to optimize the differential game. In this way, the infinite time domain differential game can be transformed into a multiple-time domain differential game with feedback correction. Through the complementing advantages of dynamic programming and predictive optimization, the real-time compensation and correction to the interceptor differential game guidance is realised, and the disadvantages of the differential game in the process constraints and stochastic disturbance are overcome.In order to adjust the favourable advance angle of the self-guided detection rapidly, the learning prediction function of rolling optimization feedback correction is adopted. The initial moment of the differential response is pushed forward along with the entire forecast period by rolling optimization. To verify the validity of the algorithm, this is applied to the underwater nonlinear tracking game, and the guidance performance is compared with the differential game guidance and the integrated control algorithm of differential game and discrete predictive control. The results show that this can achieve the optimum control of the high precision underwater manoeuvring target on-line tracking and prediction correction with the detection mode limited in uncertain disturbances, because this is flexible in the choosing of sampling time and does not need control weighting for non-minimum phase system. This can also solve the problem of the initial bias and random disturbance taking into account the control constraints and interference suppression performance, and can improve the robustness to environmental interference.
In order to overcome the difficulty in real-time effectively acquiring the target parameters of differential game guidance in a complex underwater environment, the differential game guidance of underwater nonlinear tracking control based on continuous time generalised predictive correction is proposed. Since the target parameter and the detection precision are seriously affected by the acoustic homing device detection period, noise, and interference, it is easy to lose or misjudge the target signal. Hence a combination of the dynamic tracking game model for differential games and the acoustic homing detection method of underwater tracking is used for making the on-line prediction and compensation correction to the deviation tendency of target manoeuvres deviating from the self-guided sound zero axis. This is carried out by using a continuous time generalized predictive control algorithm, according to the discrepancy between the predicted advance angle and the expected value. The manoeuvring target can then be located in the maximum capture probability sector of the tracker device in real time. In order to solve the estimation difficulty problem of the remaining time of the dynamic differential game antagonism, and improve the response speed and the control precision of the system, the zero-efficiency control parameter and the predictive control algorithm are introduced to optimize the differential game. In this way, the infinite time domain differential game can be transformed into a multiple-time domain differential game with feedback correction. Through the complementing advantages of dynamic programming and predictive optimization, the real-time compensation and correction to the interceptor differential game guidance is realised, and the disadvantages of the differential game in the process constraints and stochastic disturbance are overcome.In order to adjust the favourable advance angle of the self-guided detection rapidly, the learning prediction function of rolling optimization feedback correction is adopted. The initial moment of the differential response is pushed forward along with the entire forecast period by rolling optimization. To verify the validity of the algorithm, this is applied to the underwater nonlinear tracking game, and the guidance performance is compared with the differential game guidance and the integrated control algorithm of differential game and discrete predictive control. The results show that this can achieve the optimum control of the high precision underwater manoeuvring target on-line tracking and prediction correction with the detection mode limited in uncertain disturbances, because this is flexible in the choosing of sampling time and does not need control weighting for non-minimum phase system. This can also solve the problem of the initial bias and random disturbance taking into account the control constraints and interference suppression performance, and can improve the robustness to environmental interference.
引文
[1]Dong Z P,Wan L,Li Y M,Liu T 2015 Int.J.Nav.Arch.Ocean 7 817
[2]Qin J X,Katsnelson B,Li Z L,Zhang R H,Luo W2016 Acta Acustica 41 145(in Chinese)[秦继兴,Boris Katsnelson,李整林,张仁和,骆文2016声学学报41 145]
[3]Hu Z G,Li Z L,Zhang R H,Ren Y,Qin J X,He L 2016Acta Phys.Sin.65 014303(in Chinese)[胡治国,李整林,张仁和,任云,秦继兴,何利2016物理学报65 014303]
[4]Chen Y F,Li G J,Wang Z S,Zhang M W,Jia B 2013Acta Phys.Sin.62 084302(in Chinese)[陈云飞,李桂娟,王振山,张明伟,贾兵2013物理学报62 084302]
[5]Shojaei K,Dolatshahi M 2017 Ocean Eng.133 244
[6]Zhang P,Fang Y W,Zhang F M,Xiao B S 2012 Chinese J.Aeronaut.25 739
[7]Zhang Y,Xu Q,Sun M W,Chen Z Q 2015 Acta Phys.Sin.64 010502(in Chinese)[张园,徐琦,孙明玮,陈增强2015物理学报64 010502]
[8]Yu G F,Li D F,Qiu J M,Ye Y F 2016 Control Decis.31 2013(in Chinese)[余高锋,李登峰,邱锦明,叶银芳2016控制与决策31 2013]
[9]Wei H,Gao F,Wang D K,Wen G H,Pan L D 2007 J.Syst.Sim.19 27(in Chinese)[魏环,高峰,王东凯,闻光辉,潘立登2007系统仿真学报19 27]
[10]Yong K H,Farouq S M,Hark K Y 2012 Chem.Eng.84479
[11]Oksendal B,Sulem A 2014 J.Optimiz.Theory Appl.161 22
[12]Kamel O,Toufik R,Mohand O 2014 ISA Trasac.53 76
[13]Chen Y,Zhang R M,Zhao X Y 2016 Ocean Eng.15 123
[14]Zhang H G,Wei Q L,Liu D R 2011 Acta47207
[15]Fu Y,Chai Y 2015 Control Theory Appl.32 196(in Chinese)[富月,柴佑2015控制理论与应用32 196]
[16]Liu X D,Li K,Sun J,Fu X L,Zhang C H 2015 Control Theory Appl.32 1640(in Chinese)[刘旭东,李柯,孙静,符晓玲,张承慧2015控制理论与应用32 1640]
[17]Zhang P,Fang Y W,Hui X B 2013 Acta Automatica Sin.39 391(in Chinese)[张平,方洋旺,惠晓滨2013自动化学报39 391]
[18]Ping X B,Ding B C,Han C Z 2012 Acta Automatica Sin.38 31(in Chinese)[平续斌,丁宝苍,韩崇昭2012自动化学报38 31]
[19]Krid M,Benamar F,Lenain R 2017 Int.J.Control Autom.15 303
[20]Yan Z P,Liu Y B,Zhou J J,Zhang W,Wang L 2017Chin.Phys.B 4 79
[21]Sarkar M,Nandy S,Vadal S R K 2016 Math.Comput.Simulat.121 34
[22]Li H P,Yan W S,Shi Y 2017 Syst.Control Lett.75 144
[23]Gao J,Liu C X 2015 J.Northwest Polytechnical Univ.33 861(in Chinese)[高剑,刘昌鑫2015西北工业大学报33 861]
[24]He J Z,Fu T P,Wu X H 2011 Comput.Dig.Eng.3921(in Chinese)[何建忠,傅调平,吴晓海2011计算机与数字工程39 21]
[25]Fan H,Zhang Y W,Li W Z 2008 J.Northwest Polytechnical Univ.26 743(in Chinese)[范辉,张宇文,李文哲2008西北工业大学学报26 743]
[2]Qin J X,Katsnelson B,Li Z L,Zhang R H,Luo W2016 Acta Acustica 41 145(in Chinese)[秦继兴,Boris Katsnelson,李整林,张仁和,骆文2016声学学报41 145]
[3]Hu Z G,Li Z L,Zhang R H,Ren Y,Qin J X,He L 2016Acta Phys.Sin.65 014303(in Chinese)[胡治国,李整林,张仁和,任云,秦继兴,何利2016物理学报65 014303]
[4]Chen Y F,Li G J,Wang Z S,Zhang M W,Jia B 2013Acta Phys.Sin.62 084302(in Chinese)[陈云飞,李桂娟,王振山,张明伟,贾兵2013物理学报62 084302]
[5]Shojaei K,Dolatshahi M 2017 Ocean Eng.133 244
[6]Zhang P,Fang Y W,Zhang F M,Xiao B S 2012 Chinese J.Aeronaut.25 739
[7]Zhang Y,Xu Q,Sun M W,Chen Z Q 2015 Acta Phys.Sin.64 010502(in Chinese)[张园,徐琦,孙明玮,陈增强2015物理学报64 010502]
[8]Yu G F,Li D F,Qiu J M,Ye Y F 2016 Control Decis.31 2013(in Chinese)[余高锋,李登峰,邱锦明,叶银芳2016控制与决策31 2013]
[9]Wei H,Gao F,Wang D K,Wen G H,Pan L D 2007 J.Syst.Sim.19 27(in Chinese)[魏环,高峰,王东凯,闻光辉,潘立登2007系统仿真学报19 27]
[10]Yong K H,Farouq S M,Hark K Y 2012 Chem.Eng.84479
[11]Oksendal B,Sulem A 2014 J.Optimiz.Theory Appl.161 22
[12]Kamel O,Toufik R,Mohand O 2014 ISA Trasac.53 76
[13]Chen Y,Zhang R M,Zhao X Y 2016 Ocean Eng.15 123
[14]Zhang H G,Wei Q L,Liu D R 2011 Acta47207
[15]Fu Y,Chai Y 2015 Control Theory Appl.32 196(in Chinese)[富月,柴佑2015控制理论与应用32 196]
[16]Liu X D,Li K,Sun J,Fu X L,Zhang C H 2015 Control Theory Appl.32 1640(in Chinese)[刘旭东,李柯,孙静,符晓玲,张承慧2015控制理论与应用32 1640]
[17]Zhang P,Fang Y W,Hui X B 2013 Acta Automatica Sin.39 391(in Chinese)[张平,方洋旺,惠晓滨2013自动化学报39 391]
[18]Ping X B,Ding B C,Han C Z 2012 Acta Automatica Sin.38 31(in Chinese)[平续斌,丁宝苍,韩崇昭2012自动化学报38 31]
[19]Krid M,Benamar F,Lenain R 2017 Int.J.Control Autom.15 303
[20]Yan Z P,Liu Y B,Zhou J J,Zhang W,Wang L 2017Chin.Phys.B 4 79
[21]Sarkar M,Nandy S,Vadal S R K 2016 Math.Comput.Simulat.121 34
[22]Li H P,Yan W S,Shi Y 2017 Syst.Control Lett.75 144
[23]Gao J,Liu C X 2015 J.Northwest Polytechnical Univ.33 861(in Chinese)[高剑,刘昌鑫2015西北工业大学报33 861]
[24]He J Z,Fu T P,Wu X H 2011 Comput.Dig.Eng.3921(in Chinese)[何建忠,傅调平,吴晓海2011计算机与数字工程39 21]
[25]Fan H,Zhang Y W,Li W Z 2008 J.Northwest Polytechnical Univ.26 743(in Chinese)[范辉,张宇文,李文哲2008西北工业大学学报26 743]