This paper introduces an electrical drives control architecture combining a fractional-order controller and a setpoint pre-filter. The former is based on a fractional-order proportional-integral(PI) unit, with a non-i...This paper introduces an electrical drives control architecture combining a fractional-order controller and a setpoint pre-filter. The former is based on a fractional-order proportional-integral(PI) unit, with a non-integer order integral action, while the latter can be of integer or non-integer type. To satisfy robustness and dynamic performance specifications, the feedback controller is designed by a loop-shaping technique in the frequency domain. In particular, optimality of the feedback system is pursued to achieve input-output tracking. The setpoint pre-filter is designed by a dynamic inversion technique minimizing the difference between the ideal synthesized command signal(i.e., a smooth monotonic response) and the prefilter step response. Experimental tests validate the methodology and compare the performance of the proposed architecture with well-established control schemes that employ the classical PIbased symmetrical optimum method with a smoothing pre-filter.展开更多
Sensitivity loop shaping using add-on peak filters is a simple and effective method to reject narrow-band disturbances in hard disk drive （HDD） servo systems. The parallel peak filter is introduced to provide high-g...Sensitivity loop shaping using add-on peak filters is a simple and effective method to reject narrow-band disturbances in hard disk drive （HDD） servo systems. The parallel peak filter is introduced to provide high-gain magnitude in the concerned frequency range of open-loop transfer function. Different from almost all the known peak filters that possess second-order structures, we explore in this paper bow high-order peak filters can be designed to improve the loop shaping performance. The main idea is to replace some of the constant coefficients of common second-order peak filter by frequency-related transfer functions, and then differential evolution （DE） algorithm is adopted to perform optimal design. We creatively introduce chromosome coding and fitness function design, which are original and the key steps that lead to the success of DE applications in control system design. In other words, DE is modified to achieve a novel design for hard disk drive control. Owing to the remarkable searching ability of DE, the expected shape of sensitivity function can be achieved by incorporating the resultant high-order peak filter in parallel with baseline feedback controller. As a result, a seventh-order peak filter is designed to compensate for contact-induced vibration in a high-density HDD servo system, where the benefits of high-order filter are clearly demonstrated.展开更多
在井下斜坡道无人驾驶卡车往往存在因信号传输困难、道路倾斜且缺乏有效特征信息等问题而导致难以稳定高精定位,严重影响井下无人矿卡安全高效作业。为解决上述问题,提出一种基于激光SLAM的井下斜坡道无人矿卡定位与建图算法GFRMINE-LIO...在井下斜坡道无人驾驶卡车往往存在因信号传输困难、道路倾斜且缺乏有效特征信息等问题而导致难以稳定高精定位,严重影响井下无人矿卡安全高效作业。为解决上述问题,提出一种基于激光SLAM的井下斜坡道无人矿卡定位与建图算法GFRMINE-LIO,首先,针对井下斜坡道口两侧均为光滑水泥墙壁,特征点稀少问题,设计了一种基于人工路标的辅助增强定位方法,有效增加特征点云数量,从而优化位姿估计结果,避免建图过程中出现漂移现象;其次,提出融合坡度与曲率信息的SCSA(Slope and Curvature based Segmentation Algorithm)算法,通过分析激光雷达采集的点云数据中的几何特征,精确计算每个点的坡度角和曲率值,有效识别井下倾斜坑洼路面,确保在复杂环境中实现更精确的点云过滤,显著提升算法在复杂地形中的鲁棒性和精度;最后,在已构建地图的基础上利用GICP算法对实时采集的点云数据进行配准,融合GFRMINE-LIO算法修正点云畸变,从而实现高效重定位,相较于原算法定位精度大幅提升。实验结果表明:此算法能够在恶劣环境下更稳定、更快速地实现高精度定位。实际应用表明:在中钢集团山东某井下斜坡道的现场,与原算法相比,该算法精度提升2.90%,Z轴误差降低20.8%,地图质量明显提高,定位精度和鲁棒性均有显著提升,能有效解决井下无人驾驶建图及定位的难题。展开更多
基金partially supported by the Australian Research Council(DP160104994)
文摘This paper introduces an electrical drives control architecture combining a fractional-order controller and a setpoint pre-filter. The former is based on a fractional-order proportional-integral(PI) unit, with a non-integer order integral action, while the latter can be of integer or non-integer type. To satisfy robustness and dynamic performance specifications, the feedback controller is designed by a loop-shaping technique in the frequency domain. In particular, optimality of the feedback system is pursued to achieve input-output tracking. The setpoint pre-filter is designed by a dynamic inversion technique minimizing the difference between the ideal synthesized command signal(i.e., a smooth monotonic response) and the prefilter step response. Experimental tests validate the methodology and compare the performance of the proposed architecture with well-established control schemes that employ the classical PIbased symmetrical optimum method with a smoothing pre-filter.
基金supported by National Natural Science Foundation of China(Nos.61640310 and 61433011)
文摘Sensitivity loop shaping using add-on peak filters is a simple and effective method to reject narrow-band disturbances in hard disk drive （HDD） servo systems. The parallel peak filter is introduced to provide high-gain magnitude in the concerned frequency range of open-loop transfer function. Different from almost all the known peak filters that possess second-order structures, we explore in this paper bow high-order peak filters can be designed to improve the loop shaping performance. The main idea is to replace some of the constant coefficients of common second-order peak filter by frequency-related transfer functions, and then differential evolution （DE） algorithm is adopted to perform optimal design. We creatively introduce chromosome coding and fitness function design, which are original and the key steps that lead to the success of DE applications in control system design. In other words, DE is modified to achieve a novel design for hard disk drive control. Owing to the remarkable searching ability of DE, the expected shape of sensitivity function can be achieved by incorporating the resultant high-order peak filter in parallel with baseline feedback controller. As a result, a seventh-order peak filter is designed to compensate for contact-induced vibration in a high-density HDD servo system, where the benefits of high-order filter are clearly demonstrated.
文摘在井下斜坡道无人驾驶卡车往往存在因信号传输困难、道路倾斜且缺乏有效特征信息等问题而导致难以稳定高精定位,严重影响井下无人矿卡安全高效作业。为解决上述问题,提出一种基于激光SLAM的井下斜坡道无人矿卡定位与建图算法GFRMINE-LIO,首先,针对井下斜坡道口两侧均为光滑水泥墙壁,特征点稀少问题,设计了一种基于人工路标的辅助增强定位方法,有效增加特征点云数量,从而优化位姿估计结果,避免建图过程中出现漂移现象;其次,提出融合坡度与曲率信息的SCSA(Slope and Curvature based Segmentation Algorithm)算法,通过分析激光雷达采集的点云数据中的几何特征,精确计算每个点的坡度角和曲率值,有效识别井下倾斜坑洼路面,确保在复杂环境中实现更精确的点云过滤,显著提升算法在复杂地形中的鲁棒性和精度;最后,在已构建地图的基础上利用GICP算法对实时采集的点云数据进行配准,融合GFRMINE-LIO算法修正点云畸变,从而实现高效重定位,相较于原算法定位精度大幅提升。实验结果表明:此算法能够在恶劣环境下更稳定、更快速地实现高精度定位。实际应用表明:在中钢集团山东某井下斜坡道的现场,与原算法相比,该算法精度提升2.90%,Z轴误差降低20.8%,地图质量明显提高,定位精度和鲁棒性均有显著提升,能有效解决井下无人驾驶建图及定位的难题。
