Volume 15, Number 2, 2018
Special Issue on Automation and Computing Advancements for Future Industries (pp.125-193)
The study and application of methods for incorporating nonuniform and delayed information in state estimation techniques are important topics to advance in soft sensor development. Therefore, this paper presents a review of these methods and proposes a taxonomy that allows a faster selection of state estimator in this type of applications. The classification is performed according to the type of estimator, method, and used tool. Finally, using the proposed taxonomy, some applications reported in the literature are described.
A gain-scheduled feedforward controller, based on pseudo-LIDAR (light detection and ranging) wind speed measurement, is designed to augment the baseline feedback controller for wind turbine′s load reduction in above rated operation. The pseudo-LIDAR measurement data are generated from a commercial software – Bladed using a designed sampling strategy. The nonlinear wind turbine model has been simplified and linearised at a set of equilibrium operating points. The feedforward controller is firstly developed based on a linearised model at an above rated wind speed, and then expanded to the full above rated operational envelope by employing gain scheduling strategy. The combined feedforward and baseline feedback control is simulated on a 5 MW industrial wind turbine model. Simulation studies demonstrate that the proposed control strategy can improve the rotor and tower load reduction performance for large wind turbines.
With the increasing number of human-robot interaction applications, robot control characteristics and their effects on safety as well as performance should be taken account into the robot control system. In this paper, a position and torque switching control method was proposed to improve the robot safety and performance, when robots and humans work in the same space. The switching control method includes two modes, the position control mode using a proportion-integral (PI) algorithm, and the torque control mode using sliding mode control (SMC) algorithm for eliminating swing. Under the normal condition, the robot works in position control mode for trajectory tracking with quick response. Once the robot and human collide, the robot will switch to torque control mode immediately, and the impact force will be restricted within a safe range. When the robot and human detach, the robot will resume to position control mode automatically. Moreover, for a better performance, the joint torque is detected from direct-current (DC) motor′s current rather than the torque sensor. The experiment results show that the proposed approach is effective and feasible.
This paper proposes a new set of 3D rotation scaling and translation invariants of 3D radially shifted Legendre moments. We aim to develop two kinds of transformed shifted Legendre moments: a 3D substituted radial shifted Legendre moments (3DSRSLMs) and a 3D weighted radial one (3DWRSLMs). Both are centered on two types of polynomials. In the first case, a new 3D radial complex moment is proposed. In the second case, new 3D substituted/weighted radial shifted Legendre moments (3DSRSLMs/3DWRSLMs) are introduced using a spherical representation of volumetric image. 3D invariants as derived from the suggested 3D radial shifted Legendre moments will appear in the third case. To confirm the proposed approach, we have resolved three issues. To confirm the proposed approach, we have resolved three issues: rotation, scaling and translation invariants. The result of experiments shows that the 3DSRSLMs and 3DWRSLMs have done better than the 3D radial complex moments with and without noise. Simultaneously, the reconstruction converges rapidly to the original image using 3D radial 3DSRSLMs and 3DWRSLMs, and the test of 3D images are clearly recognized from a set of images that are available in Princeton shape benchmark (PSB) database for 3D image.
The parallel computation capabilities of modern graphics processing units (GPUs) have attracted increasing attention from researchers and engineers who have been conducting high computational throughput studies. However, current single GPU based engineering solutions are often struggling to fulfill their real-time requirements. Thus, the multi-GPU-based approach has become a popular and cost-effective choice for tackling the demands. In those cases, the computational load balancing over multiple GPU "nodes" is often the key and bottleneck that affect the quality and performance of the real-time system. The existing load balancing approaches are mainly based on the assumption that all GPU nodes in the same computer framework are of equal computational performance, which is often not the case due to cluster design and other legacy issues. This paper presents a novel dynamic load balancing (DLB) model for rapid data division and allocation on heterogeneous GPU nodes based on an innovative fuzzy neural network (FNN). In this research, a 5-state parameter feedback mechanism defining the overall cluster and node performance is proposed. The corresponding FNN-based DLB model will be capable of monitoring and predicting individual node performance under different workload scenarios. A real-time adaptive scheduler has been devised to reorganize the data inputs to each node when necessary to maintain their runtime computational performance. The devised model has been implemented on two dimensional (2D) discrete wavelet transform (DWT) applications for evaluation. Experiment results show that this DLB model enables a high computational throughput while ensuring real-time and precision requirements from complex computational tasks.
In recent years, there have been a lot of interests in incorporating semantics into simultaneous localization and mapping (SLAM) systems. This paper presents an approach to generate an outdoor large-scale 3D dense semantic map based on binocular stereo vision. The inputs to system are stereo color images from a moving vehicle. First, dense 3D space around the vehicle is constructed, and the motion of camera is estimated by visual odometry. Meanwhile, semantic segmentation is performed through the deep learning technology online, and the semantic labels are also used to verify the feature matching in visual odometry. These three processes calculate the motion, depth and semantic label of every pixel in the input views. Then, a voxel conditional random field (CRF) inference is introduced to fuse semantic labels to voxel. After that, we present a method to remove the moving objects by incorporating the semantic labels, which improves the motion segmentation accuracy. The last is to generate the dense 3D semantic map of an urban environment from arbitrary long image sequence. We evaluate our approach on KITTI vision benchmark, and the results show that the proposed method is effective.
This paper investigates the problem of estimation of the wheelchair position in indoor environments with noisy measurements. The measuring system is based on two odometers placed on the axis of the wheels combined with a magnetic compass to determine the position and orientation. Determination of displacements is implemented by an accelerometer. Data coming from sensors are combined and used as inputs to unscented Kalman filter (UKF). Two data fusion architectures:measurement fusion (MF) and state vector fusion (SVF) are proposed to merge the available measurements. Comparative studies of these two architectures show that the MF architecture provides states estimation with relatively less uncertainty compared to SVF. However, odometers measurements determine the position with relatively high uncertainty followed by the accelerometer measurements. Therefore, fusion in the navigation system is needed. The obtained simulation results show the effectiveness of proposed architectures.
In order to improve the precision of guidance for the missile intercepting maneuvering targets, this paper proposes a sliding mode guidance law with impact angle constraints based on the equation of the relative motion of the missile and the target in a 2D plane. Two finite-time convergent guidance laws are proposed based on the nonsingular terminal sliding mode, while, two exponential convergent guidance laws involving dynamic delay are developed through applying the higher-order nonsingular terminal sliding mode. The simulations denote that, in all the four scenarios of the target s maneuvering, the guidance laws are able to inhibit the chattering phenomenon of the sliding modes effectively; and from an expected aspect angle, the missiles could attack the targets with high precision and fast speed.
Register allocation is a major step for all compilers. Various register allocation algorithms have been developed over the decades. This work describes a new class of rapid register allocation algorithms and presents experimental data on their behavior. Our research encourages the avoidance of graphing and graph-coloring based on the fact that precise graph-coloring is nondeterministic polynomial time-complete (NP-complete), which is not suitable for real-time tasks. In addition, practical graph-coloring algorithms tend to use polynomial-time heuristics. In dynamic compilation environments, their super linear complexity makes them unsuitable for register allocation and code generation. Existing tools for code generation and register allocation do not completely fulfill the requirements of fast compilation. Existing approaches either do not allow for the optimization of register allocation to be achieved comprehensively with a sufficient degree of performance or they require an unjustifiable amount of time and/or resources. Therefore, we propose a new class of register allocation and code generation algorithms that can be performed in linear time. These algorithms are based on the mathematical foundations of abstract interpretation and the computation of the level of abstraction. They have been implemented in a specialized library for just-in-time compilation. The specialization of this library involves the execution of common intermediate language (CIL) and low level virtual machine (LLVM) with a focus on embedded systems.
This paper mainly focuses on designing a sliding mode boundary controller for a single flexible-link manipulator based on adaptive radial basis function (RBF) neural network. The flexible manipulator in this paper is considered to be an Euler-Bernoulli beam. We first obtain a partial differential equation (PDE) model of single-link flexible manipulator by using Hamiltons approach. To improve the control robustness, the system uncertainties including modeling uncertainties and external disturbances are compensated by an adaptive neural approximator. Then, a sliding mode control method is designed to drive the joint to a desired position and rapidly suppress vibration on the beam. The stability of the closed-loop system is validated by using Lyapunov s method based on infinite dimensional model, avoiding problems such as control spillovers caused by traditional finite dimensional truncated models. This novel controller only requires measuring the boundary information, which facilitates implementation in engineering practice. Favorable performance of the closed-loop system is demonstrated by numerical simulations.