一种新的角加速度估计方法及其应用

首先提出了一种新的基于卡尔曼滤波及牛顿预测的角加速度估计方法,在已知电机驱动系统位置信息的情况下,利用卡尔曼滤波实时估计系统的角加速度;同时采用牛顿预测方法解决估计算法的滞后问题,进一步提高了估计加速度的响应频带．以此为基础,本文进一步分析了利用估计加速度进行反馈控制以增强系统对外扰动的鲁棒性问题,提出了加速度反馈控制策略的设计准则并分析了稳定性．在一个直接驱动机器人关节上针对上述加速度估计及控制方法进行了实验研究:将估计加速度的实验结果与实测加速度(利用加速度计)的实验结果进行了比较分析,从而定量地揭示出估计加速度及其反馈控制在实际系统中的可行性及有效性．     

State and parameter estimation for phosphorus removal in an alternating activated sludge process

Biological phosphorus removal involved in the advanced activated sludge process requires more efficient on-line monitoring and process control. However, the substantial complexity of the process behaviour and the limited availability of reliable on-line measurement challenge the application of advanced process control techniques. This paper presents an application of state and parameter estimation techniques in an altering activated sludge process with regard to biological phosphorus removal. A simplified model describing the phosphorus dynamics in an alternating activated sludge process is proposed based on insight into the process with a mechanistic activated sludge model. State and parameter estimation problems relating to the non-measurable dynamics of a most important limiting substrate poly-hydroxy-alkanoate (PHA) are formulated and discussed. Several schemes are presented which involve a state estimator designed with the extended Kalman filter algorithm, two specific parameter estimation procedures and an adaptive scheme for simultaneous state and parameter estimation. All these schemes are oriented to practical application and therefore some techniques dealing with the implementation are also provided. Simulations and experimental demonstrations are presented which illustrate the performance of these procedures and schemes.     

基于RANSAC和Kalman滤波的足球机器人球速估计算法

针对中型组足球机器人如何有效地估计足球速度的问题,提出了一种基于Kalman滤波和RANSAC算法的新方法。首先对存储的若干帧足球位置信息作Kalman滤波,接着利用这些足球位置信息,建立若干个可能的足球速度模型并运用随机采样一致(RANSAC)算法选出最优的速度模型作为速度值。实验结果验证了该算法的有效性,同时由于RANSAC算法可以有效地去除外点的干扰,因此当足球位置信息具有较大噪声时,该方法可以较准确地估计足球的速度,较以往球速估计的算法具有更高的鲁棒性。     

Channel estimation and tracking with nested sampling for fast-moving users in millimeter-wave communication

Channel estimation is very challenging in millimeter-wave (mmWave) communications since the receiver is usually equipped with a limited number of radio-frequency (RF) chains and a large number of antennas. The receiver can only observe a low-dimensional projection of the received signals due to the huge gap between the numbers of RF chains and antennas. Instead of using the phase-shifter which is not a simple circuit at mmWave, we employ a switch network for analog design, which is equivalent to an antenna selection process. To increase the resolution and accuracy, a nested sampling strategy is used to formulate a virtual array with a larger aperture, aiming to reduce the complexity and power consumption of the system. We also consider the Doppler effect caused by the fast-moving user which may seriously deteriorate the channel estimation accuracy. Based on the covariance fitting criterion, a joint Doppler and channel estimation method is proposed without need of discretizing the angle space, and thus the model mismatch effect can be totally eliminated. Finally, we excavate the temporal variation law of the user to estimate the line-of-sight (LoS) channel in the current time slot by using the estimates from the previous two time slots. Numerical simulation results are provided to demonstrate the effectiveness and superiority of our proposed method.     

A hybrid framework for process monitoring: Enhancing data-driven methodologies with state and parameter estimation

In this study we bridge traditional standalone data-driven and knowledge-driven process monitoring approaches by proposing a novel hybrid framework that exploits the advantages of both simultaneously. Namely, we design a process monitoring system based on a data-driven model that includes two different data types: i) “actual” data coming from sensor measurements, and ii) “virtual” data coming from a state estimator, based on a first-principles model of the system under investigation. We test the proposed approach on two simulated case studies: a continuous polycondensation process for the synthesis of poly-ethylene terephthalate, and a fed-batch fermentation process for the manufacturing of penicillin. The hybrid monitoring model shows superior fault detection and diagnosis performances with respect to conventional monitoring techniques, even when the first-principles model is relatively simple and process/model mismatch exists.     

New estimation and compensation method for quadrature modulator and demodulator errors in digital predistortion system

This article proposes a new method to estimate and compensate for the in‐phase and quadrature (IQ) imbalance errors of the quadrature modulator (QM) and demodulator (QDM) without interrupting the transmission. It uses two groups of captured signals, which transmit through two feedback loops with different propagation delays, to separate and estimate the IQ imbalance errors. In comparison with the previous methods, the greatest advantage of the proposed method is that both compensators of the QM and QDM can be obtained without additional feedback loop, and then the digital predistorter can be designed without the disturbance of IQ imbalance errors. This method can improve the compensation performance of digital predistortion with lower costs and less complexity. Experimental results show the correctness and effectiveness of the proposed method. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

A diffusion subband adaptive filtering algorithm for distributed estimation using variable step size and new combination method based on the MSD

This paper proposes a novel diffusion subband adaptive filtering algorithm for distributed networks. To achieve a fast convergence rate and small steady-state errors, a variable step size and a new combination method is developed. For the adaptation step, the upper bound of the mean-square deviation (MSD) of the algorithm is derived and the step size is adaptive by minimizing it in order to attain the fastest convergence rate on every iteration. Furthermore, for a combination step realized by a convex combination of the neighbor-node estimates, the proposed algorithm uses the MSD, which contains information on the reliability of the estimates, to determine combination coefficients. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of the convergence rate and the steady-state errors.     

Precise and efficient pose estimation of stacked objects for mobile manipulation in industrial robotics challenges

Object manipulation tasks such as picking up, carrying and placing should be executed based on the information of objects which are provided by the perception system. A precise and efficient pose estimation system has been developed to address the requirements and to achieve the objectives for autonomous packaging, specifically picking up of stacked non-rigid objects. For fine pose estimation, a drawing pin shaped kernel and pinhole filtering methods are used on the roughly estimated pose of objects. The system has been applied in a realistic industrial environment as a challenging scenario for the Challenge 2 – Shop Floor Logistics and Manipulation on a mobile manipulator in the context of the European Robotics Challenges (EuRoC) project.     

Non-rigid isometric ICP: A practical registration method for the analysis and compensation of form errors in production engineering

The unprecedented success of the iterative closest point (ICP) method for registration in geometry processing and related fields can be attributed to its efficiency, robustness, and wide spectrum of applications. Its use is however quite limited as soon as the objects to be registered arise from each other by a transformation significantly different from a Euclidean motion. We present a novel variant of ICP, tailored for the specific needs of production engineering, which registers a triangle mesh with a second surface model of arbitrary digital representation. Our method inherits most of ICP’s practical advantages but is capable of detecting medium-strength bendings i.e. isometric deformations. Initially, the algorithm assigns to all vertices in the source their closest point on the target mesh and then iteratively establishes isometry, a process which, very similar to ICP, requires intermediate re-projections. A NURBS-based technique for applying the resulting deformation to arbitrary instances of the source geometry, other than the very mesh used for correspondence estimation, is described before we present numerical results on synthetic and real data to underline the viability of our approach in comparison with others.     

Sequential boundary eigenvalue destabilisation (SeBEDes): An expert method for parameter screening and estimation in complex ecosystem models

We present a new method to find parameter sets that allow all populations to co-exist in multi-trophic level food web models in which the outcome of competition between populations at each trophic level is determined by R* theory. The method involves sequentially destabilising an eigenvalue at the boundary equilibrium point of the winning population at each trophic level. We illustrate the procedure on a six population, three trophic level ecosystem model of a pelagic Antarctic ecosystem.We used the method to find an initial parameter set for which all populations coexisted. Only three model evaluations were required to find a parameter set that allowed coexistence. In contrast, a random search of parameter space required an average of 250 model evaluations to find each coexistence parameter set. The method is useful for identifying regions of parameter space that have high densities of coexistence solutions.