最小方差自适应的视觉导盲诱导控制方法研究
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摘要
准确诱导盲人跟踪给定路径行走是穿戴式导盲系统安全导盲的重要基础和前提.为了实现盲人的安全出行,对盲人的行走动力学模型以及自适应控制算法进行了研究.针对不同使用者在实际行走过程中,心理生理的差异以及与环境的交互作用,导致自身模型参数发生了变化,要求控制器具有一定的自适应性,提出了一种基于最小方差自校正控制算法的诱导方法,首先运用增广最小二乘法对被控对象的模型参数进行估计,然后在线求解Diophantine方程得到控制器的参数,最后根据最小方差控制律求得控制器的输出.仿真结果表明最小方差自校正控制算法在参数辨识完毕后能够很好地跟踪圆轨迹,实验结果表明多名实验者的平均行走轨迹误差范围为(-0.5957m,0.4811m),验证了该方法的准确性和适应性.
Accurately inducing the blind to track a given path is an important basis and premise of wearable blind guide systems. In order to achieve safe travel for the blind, the walking dynamics model of the blind and an adaptive control algorithm were studied. For different users in the actual walking process, the model parameters change due to psychological and physiological differences and interactions with the environment, and thus an adaptive controller is required. An induction method based on minimum variance self-tuning control algorithm was proposed. Firstly, the parameters of the controlled object were estimated by the extended least squares. Then the Diophantine equation was solved to obtain the controller parameters. Finally, the output of the controller was obtained by the minimum variance control algorithm.The simulation results show that the minimum variance self-tuning control algorithm can track the circular trajectory well after identifying the model parameters, and the average travel-trajectory error range of experimental tests is(-0.5957 m, 0.4811 m), which verifies the accuracy and adaptability of the proposed method.
Accurately inducing the blind to track a given path is an important basis and premise of wearable blind guide systems. In order to achieve safe travel for the blind, the walking dynamics model of the blind and an adaptive control algorithm were studied. For different users in the actual walking process, the model parameters change due to psychological and physiological differences and interactions with the environment, and thus an adaptive controller is required. An induction method based on minimum variance self-tuning control algorithm was proposed. Firstly, the parameters of the controlled object were estimated by the extended least squares. Then the Diophantine equation was solved to obtain the controller parameters. Finally, the output of the controller was obtained by the minimum variance control algorithm.The simulation results show that the minimum variance self-tuning control algorithm can track the circular trajectory well after identifying the model parameters, and the average travel-trajectory error range of experimental tests is(-0.5957 m, 0.4811 m), which verifies the accuracy and adaptability of the proposed method.
引文
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11 靖固,冯晓雪,张学松.导盲系统方向与速度综合控制及FPGA实现.哈尔滨理工大学学报,2016,21(6):55～61 (Jing G,Feng X X,Zhang X S.The direction and speed control of the guide system and the implementation of FPGA.Journal of Harbin Institute of Technology,2016,21(6):55～61 (in Chinese))
12 王漩,申晓红,闰永胜等.基于多传感器的导盲避碰系统设计.声学技术,2011,30(4):126～129 (Wang X,Shen X H,Yan Y S,et al.Design of guided blind collision avoidance system based on multi-sensor.Acoustic Technology,2011,30(4):126～129 (in Chinese))
13 Alegria F C.Precision of harmonic amplitude estimation on jitter corrupted data using sine fitting.Signal Processing,2012,92(3):807～818
14 Miroslav J,Lucie O,Jan B.Parameter identification for pilot behavior model using the MATLAB system identification toolbox.International Conference on Military Technologies,2017,Brno,Czech Republic
15 倪博溢,萧德云.MATLAB环境下的系统辨识仿真工具箱.系统仿真学报,2006,18(6):1493～1496 (Ni B Y,Xiao D Y.System identification simulation toolbox under MATLAB.Journal of System Simulation,2006,18(6):1493～1496 (in Chinese))
16 Sendjaja A Y,Kariwala V.Minimum variance benchmark for performance assessment of decentralized controllers.Industrial & Engineering Chemistry Research,2012,51(11):4288～4298
2 Amjed S.Al,Heba B.Al,Liang H.A smart infrared microcontroller-based blind guidance system.Active and Passive Electronic Components,2013,10(1155):98～105
3 Pradeep V,Medioni G,Weiland J.Robot vision for the visually impaired//Computer vision and pattern recognition workshops (CVPRW),2010 IEEE Computer Society Conference on IEEE,2010:15～22
4 Vivek P,Gerard M,James W.A wearable system for the visually impaired.International conference of the IEEMBS,2010,8(10):91～95
5 Pradeep V,Lim J.Egomotion estimation using assorted features.International Journal of Computer Vision,2012,98(2):202～216
6 王冠生.基于Haptic和空间认知的盲人路径诱导服务研究[硕士学位论文].乌鲁木齐:新疆大学,2013 (Wang G S.Research on blind path guidance service based on haptic and spatial cognition[Master Thesis].Urumqi:Xinjiang University,2013(in Chinese))
7 Liu J H,Sun X Y.A survey of vision aids for the blind.In:Proceedings of the 6th World Congress on Intelligent Control and Automation,2006:4312～4316
8 Dunai L,Lengua I,Tortajada I,et al.Obstacle detectors for visually impaired people.14th International Conference on ptimization of Electrical and Electronic Equipment OPTIM 2014,2014,Brasov,Romania
9 张志美,程立英,赵以恒等.基于模糊PID控制算法的导盲机器人研究.沈阳师范大学学报,2015,33(1):81～85 (Zhang Z M,Cheng L Y,Zhao Y H,et al.Research on guide blind robot based on fuzzy PID control algorithm.Journal of Shenyang Normal University,2015,33(1):81～85 (in Chinese))
10 胡侠,王佳.基于模糊控制导盲机器人控制方法.机械设计与制造,2007(11):148～150 (Hu X,Wang J.Control of blind robot based on fuzzy control.Mechanical Design and Manufacturing,2007(11):148～150 (in Chinese))
11 靖固,冯晓雪,张学松.导盲系统方向与速度综合控制及FPGA实现.哈尔滨理工大学学报,2016,21(6):55～61 (Jing G,Feng X X,Zhang X S.The direction and speed control of the guide system and the implementation of FPGA.Journal of Harbin Institute of Technology,2016,21(6):55～61 (in Chinese))
12 王漩,申晓红,闰永胜等.基于多传感器的导盲避碰系统设计.声学技术,2011,30(4):126～129 (Wang X,Shen X H,Yan Y S,et al.Design of guided blind collision avoidance system based on multi-sensor.Acoustic Technology,2011,30(4):126～129 (in Chinese))
13 Alegria F C.Precision of harmonic amplitude estimation on jitter corrupted data using sine fitting.Signal Processing,2012,92(3):807～818
14 Miroslav J,Lucie O,Jan B.Parameter identification for pilot behavior model using the MATLAB system identification toolbox.International Conference on Military Technologies,2017,Brno,Czech Republic
15 倪博溢,萧德云.MATLAB环境下的系统辨识仿真工具箱.系统仿真学报,2006,18(6):1493～1496 (Ni B Y,Xiao D Y.System identification simulation toolbox under MATLAB.Journal of System Simulation,2006,18(6):1493～1496 (in Chinese))
16 Sendjaja A Y,Kariwala V.Minimum variance benchmark for performance assessment of decentralized controllers.Industrial & Engineering Chemistry Research,2012,51(11):4288～4298
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