Resource scheduling and load balancing in distributed robotic control systems

This paper describes the latest advances made to a software architecture designed to control multiple miniature robots. As the robots themselves have very limited computational capabilities, a distributed control system is needed to coordinate tasks among a large number of robots. Two of the major challenges facing such a system are the scheduling of access to system resources and the distribution of work across multiple workstations. This paper discusses solutions to these problems in the context of a distributed surveillance task.     

Modular dynamic virtual-reality modeling of robotic systems

This article presents the results of research into the field of dynamic simulation of mechanical and robotic systems. The main goal of the whole project is to achieve an efficient modular approach to modeling, in order to make the modeling process easier, ensure its traceability and inspectability, and support model reuse. This is achieved through the declarative definition of models, the standardization of model interfaces, and the object-oriented approach to model development and model data management. The article presents the specific features ofa general-purpose simulation environment, including an overview of the 3D solid modeling software interfaces. The modeling and simulation of a 3-DOF gripper designed for Space robotics applications is also discussed to show the performance of the whole simulation environment in terms of complexity management, efficiency, and accuracy     

Functional reproducibility in nonlinear systems using dynamic compensation

A new sufficient condition for the existence of a control to generate a given real analytic function as the output of multivariable nonlinear systems with dynamic compensation is derived. Furthermore, a prefilter (right-inverse system) is constructed to generate the required control.     

Nonlinear adaptive control for dynamic and dead-zone uncertainties in robotic systems

Accurate position tracking performance of robotic systems with both dynamic and dead-zone uncertainties is difficult to achieve by traditional adaptive control. In this paper, for nonlinear robotic systems in the presence of uncertain dynamics and dead-zone, an adaptive control scheme is employed to guarantee accurate and stable position tracking. The dynamics of the robot and dead-zone parameters are concurrently estimated in the adaptation laws and the estimated values are subsequently applied in the control law. In order to demonstrate the practicability of this paper, an experimental setup composed of Phantom Omni robots are built. The experimental results show that even when the dynamic and dead-zone uncertainties simultaneously appear, the robot can well track any desired position trajectory.

     

一种用于冲击性负荷的动态无功功率补偿方法

针对冲击性负荷造成的供电系统功率因数低、电压跌落和闪变等电能质量问题,提出了一种快速晶闸管投切电容器动态无功补偿方法。该方法采用基于改进瞬时无功理论的无功电流检测算法和新型不完全微分PID控制算法对冲击性负荷进行快速无功补偿。实验结果表明,该方法不仅可以提高系统功率因数,而且可以稳定变压器输出端电压,增强系统的控制精度和响应速度。     

Modeling and compensation of dynamic effects in camera-based position measurement

Cameras are used as position sensors for camera-based control or metrology, the goal being to estimate the position of some markers or features. When one of these markers has significant displacement over the exposure time, the measurement delivered by image processing cannot be considered as a sample of the actual trajectory as dynamic effects become effective. In this paper, models are proposed that allow to reproduce accurately the measurements provided by a camera. Various cases are considered: with full or partial exposure time; with global shutter mode or rolling shutter mode. An experimental evaluation in the global shutter case shows the accuracies of the models.When the continuous-time trajectory of a device needs to be reconstructed, these accurate models of the camera-based position measurement can be used in order to improve the trajectory provided by the camera. A methodology based on a hybrid Kalman filter is proposed to solve this general issue. An experimental evaluation is provided for the global shutter case, based on real images inspired from the problem of heart motion reconstruction.     

基于分散动态补偿的矩形广义系统的正则化与镇定

采用代数方法研究基于分散动态补偿的矩形广义系统的正则化、无脉冲, 以及镇定问题. 首先给出了补偿后闭环系统正则与无脉冲的充要条件, 进而给出矩形广义系统能通过分散动态补偿镇定的充要条件. 这些条件涉及一系列简单不等式与等式是否存在正整数解问题. 所得结果揭示矩形系统及其动态补偿器的许多新的性质, 而且进一步说明对应方形系统与方形或矩形补偿器的结果仍是矩形系统结果的特例. 因而, 本文结果可以认为是方形系统相应结果的自然推广. 另外, 给出几个数字例子说明所得结果.     

A Dynamic Load Balancing Mechanism for Distributed Systems

It is desirable in a distributed system to have the system load balanced evenly among the nodes so that the mean job response time is minimized.In this paper,we present a dynamic load balancing mechanism(DLB).It adopts a cntralized approach and is network topology independent.The DLB mechanism employs a set of threscholds which are automatically adjusted as the system load changes.It also provides a simple mechanism for the system to switch between periodic and instantaneous load balancing policies with ease.The performance of the proposed algorithm is evaluated by intensive simulations for various parameters.Te simulation results show that the mean job response time in a system implementing DLB algorithm is significantly lower than the same system without load balancings.Furthermore,compared with a previously proposed algorithm,DLB algorithm demonstrates improved performance,especially when the system is heavily loaded and the load is unevenly distributed.     

非线性系统近似最优PD动态补偿控制

本文研究了一类基于动态补偿的非线性系统的近似最优PD控制的问题.用微分方程的逐次逼近理论将非线性系统的最优控制问题转化为求解线性非齐次两点边值序列问题,并提供了从时域最优状态反馈到频域最优PD控制器参数的优化方法,从而获取系统最优的动态补偿网络,设计出最优PD整定参数,给出其实现算法.最后仿真示例将所提出的方法与传统的线性二次型调节器（LQR）逐次逼近方法相比较,表明该方法具有良好的动态性能和鲁棒性.     

基于动态补偿的线性系统最优干扰抑制

本文针对受外部干扰的线性时不变系统研究了基于动态补偿的最优干扰抑制问题,其中干扰信号为已知动态特性的扰动信号.首先,将原系统与扰动系统联立构成增广系统,进而转化为无扰动的标准线性二次最优问题.其次,给出了经具有适当动态阶的补偿器补偿后的闭环系统渐近稳定并且相关的Lyapunov方程正定对称解存在的条件,进一步给定的二次性能指标可写成一个与该解和闭环系统初值相关的表达式.为了得到系统的最优解,将该Lyapunov方程转化为一个双线性矩阵不等式形式,并给出了相应的路径跟踪算法以求得性能指标最小值以及补偿器参数.最后,通过数值算例说明应用本文方法可以不仅能够最小化线性二次指标,而且能够使得系统的干扰得到抑制.     

A novel dynamic load balancing scheme for parallel systems

Adaptive mesh refinement (AMR) is a type of multiscale algorithm that achieves high resolution in localized regions of dynamic, multidimensional numerical simulations. One of the key issues related to AMR is dynamic load balancing (DLB), which allows large-scale adaptive applications to run efficiently on parallel systems. In this paper, we present an efficient DLB scheme for structured AMR (SAMR) applications. This scheme interleaves a grid-splitting technique with direct grid movements (e.g., direct movement from an overloaded processor to an underloaded processor), for which the objective is to efficiently redistribute workload among all the processors so as to reduce the parallel execution time. The potential benefits of our DLB scheme are examined by incorporating our techniques into a SAMR cosmology application, the ENZO code. Experiments show that by using our scheme, the parallel execution time can be reduced by up to 57% and the quality of load balancing can be improved by a factor of six, as compared to the original DLB scheme used in ENZO.     

Decoupling with dynamic compensation for strong invertible affine non-linear systems

In this paper we tackle, for affine non-linear systems, the ‘Morgan Problem’, i.e. the scalar-input-scalar-output decoupling problem for square systems, with dynamic compensation. The result provided here (Theorem 3.1) generalizes that one previously given by Wang (1970) for linear systems.     

Synchronization and load imbalance effects in distributed memory multi-processor systems

Synchronization is a major cause of wasted computing cycles and of diminished performance in parallel computing. This paper investigates the effects of synchronization upon the performance of iterative methods on distributed memory MIMD machines. A quantitative analysis of the effects of the communication latency and of the load imbalance due to the non-deterministic execution times for iterative methods is presented. This analysis explains the rather poor performance observed often in actual implementations of such methods and suggests better ways to achieve convergence without frequent synchronization.     

Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.     

Learning control in robotic systems

The concept of learning and training machines, and some early methodologies were introduced about two decades ago. Robotic systems, undoubtedly, can be developed to a more advanced and intelligent stage. The realization of the learning capability, analogous to the human learning and thinking process, is a desired primary function. A betterment process has been investigated in the literature for applications of learning control to robotic systems. The existing schemes are a type of one-step learning process. A neighboring (2m+1)-step learning control scheme for robotic systems is presented in this paper. For each process, a betterment algorithm which chooses a generalized momentum as an output function, is executed. Also, it is associated with a conceptual learning process by adding a self-teaching knowledge base for speeding up the convergence, so that the learning capability of the resulting robotic systems can be enhanced.     

Input-output decoupling of non-linear systems via a general class of dynamic compensation

An algorithm is presented for input-output decoupling of non-linear control systems with the use of a dynamic precompensator. The precompensator used is of non-linear form. Moreover, this algorithm solves the above problem in a local fashion, i.e. we find a neighbourhood of the initial state of the overall system (the original system together with the dynamic compensator) where we can achieve input-output decoupling using the appropriate feedback law.     

Distributed dynamic load balancing for pipelined computations on heterogeneous systems

One of the most significant causes for performance degradation of scientific and engineering applications on high performance computing systems is the uneven distribution of the computational work to the resources of the system. This effect, which is known as load imbalance, is even more noticeable in the case of irregular applications and heterogeneous distributed systems. This motivated the parallel and distributed computing research community to focus on methods that provide good load balancing for scientific and engineering applications running on (heterogeneous) distributed systems. Efficient load balancing and scheduling methods are employed for scientific applications from various fields, such as mechanics, materials, physics, chemistry, biology, applied mathematics, etc. Such applications typically employ a large number of computational methods in order to simulate complex phenomena, on very large scales of time and magnitude. These simulations consist of routines that perform repetitive computations (in the form of DO/FOR loops) over very large data sets, which, if not properly implemented and executed, may suffer from poor performance. The number of repetitive computations in the simulation codes is not always constant. Moreover, the computational nature of these simulations may be in fact irregular, leading to the case when one computation takes (unpredictably) more time than others. For successful and timely results, large scale simulations require the use of large scale computing systems, which often are widely distributed and highly heterogeneous. Moreover, large scale computing systems are usually shared among multiple users, which causes the quality and quantity of the available resources to be highly unpredictable. There are numerous load balancing methods in the literature for different parallel architectures. The most recent of these methods typically follow the master-worker paradigm, where a single coordinator (master) is responsible for making all the scheduling decisions based on information provided by the workers. Depending on the application requirements, the scheduling policy and the computational environment, the benefits of this paradigm may be limited as follows: (1) its efficiency may not scale as the number of processors increases, and (2) it is quite probable that the scheduling decisions are made based on outdated information, especially on systems where the workload changes rapidly. In an effort to address these limitations, we propose a distributed (master-less) load balancing scheme, in which the scheduling decisions are made by the workers in a distributed fashion. We implemented this method along with other two master-worker schemes (a previously existing one and a recently modified one) for three different scientific computational kernels. In order to validate the usefulness and efficiency of the proposed scheme, we conducted a series of comparative performance tests with the two master-worker schemes for each computational kernel. The target system is an SMP cluster, on which we simulated three different patterns of system load fluctuation. The experiments strongly support the belief that the distributed approach offers greater performance and better scalability on such systems, showing an overall improvement ranging from 13% to 24% over the master-worker approaches.     

Strategy and Simulation of Adaptive RID for Distributed Dynamic Load Balancing in Parallel Systems

Dynamic load balancing schemes are significant for efficiently executing nonuniform problems in highly parallel multicomputer systems.The objective is to minimize the total exectuion time of single applications.This paper has proposed an ARID strategy for distributed dynamic load balancing.Its principle and control protocol are described,and te communication overhead,the effect on system stability and the performance efficiency are analyzed.Finally,simulation experiments are carried out to compare the adaptive strategy with other dynamic load balancing schemes.