Combined kinematic and dynamic control of vehicle-manipulator systems

A vehicle-manipulator system (VMS) is a class of mobile robots characterised by their ability to carry or be a robotic arm and therefore also manipulate objects. The VMS class includes vehicles with a robotic manipulator, free-floating space robots, aerial manipulators and underwater vehicle-manipulator systems (UVMSs). All of these systems need a kinematic controller to solve the kinematic redundancy of the VMS and a dynamic controller to follow the reference given by the kinematic controller. In this paper, we propose a combined kinematic and dynamic control approach for VMSs. The approach uses the singularity-robust multiple task-priority (SRMTP) framework to generate a velocity reference combined with a dynamic velocity controller based on a robust sliding mode controller (SMC). Any SMC can be used as long as it can make the velocity vector converge to the velocity reference vector in finite time. This novel approach allows us to analyse the stability properties of the kinematic and dynamic subsystems together in the presence of model uncertainty. We show that the multiple set-point regulation tasks will converge asymptotically to zero without the strict requirement that the velocities are perfectly controlled. This novel approach thus avoids the assumption of perfect dynamic control that is common in kinematic stability analyses for robot manipulators. We present two examples of SMCs that can make the velocity vector converge to the velocity reference vector in finite time. We also demonstrate the applicability of the proposed approach through a simulation study of an articulated intervention-AUV (AIAUV), which is a type of UVMS, by conducting three simultaneous tasks. The results show that both SMC algorithms can make all the regulation tasks converge to their respective set-points. In the simulation study, we also include the results from two standard control methods, a proportional-integral-derivative (PID) controller and a feedback linearisation controller, and we use two different AIAUVs to illustrate the advantages and robustness achieved from using SMC.     

Implementation of sliding mode control using the concept of perturbation

This work presents further improvements on a class of non-linear robust motion controllers, namely Sliding Mode Control (SMC) and Sliding Mode Control with Perturbation Estimation (SMCPE). Typically, the SMC methodology requires a priori knowledge of the uncertainty upper bounds for the system dynamics. It may be a difficult task to accomplish, and sometimes even impossible, due to the complexity in identifying the individual dynamic uncertainties. This work utilizes a perturbation analysis to remedy this hardship. SMCPE, on the other hand, requires the upper bounds of perturbation estimation errors, instead of the uncertainties themselves. We present an enhancement on this process also, by evaluating these bounds based on the given hardware limitations at hand. The contributions of this work appear at two points: (a) new guidelines to obtain uncertainty knowledge, (b) new feedback gain selection strategy to assure robustness without being overly conservative. All critical steps, such as robustness and stability, are rigorously proven. Practicality is the major objective in this new formulation. The claims are verified through experiments on a three-axes industrial manipulator. It is shown that the controller does not have to know more than the hardware specs of the sensory and actuation devices in order to assure robustness of the two control routines. This makes control design process much easier to complete.     

Sufficient stability bounds for slowly varying direct-formrecursive linear filters and their applications in adaptive IIR filters

This article derives a sufficient time-varying bound on the maximum variation of the coefficients of an exponentially stable time-varying direct-form homogeneous linear recursive filter. The stability bound is less conservative than all previously derived bounds for time-varying IIR systems. The bound is then applied to control the step size of output-error adaptive IIR filters to achieve bounded-input bounded-output (BIBO) stability of the adaptive filter. Experimental results that demonstrate the good stability characteristics of the resulting algorithms are included. This article also contains comparisons with other competing output-error adaptive IIR filters. The results indicate that the stabilized method possesses better convergence behavior than other competing techniques     

Interval methods for fault-tree analysis in robotics

This paper describes a novel technique, based on interval methods, for estimating reliability using fault trees. The approach encodes inherent uncertainty in the input data by modeling these data in terms of intervals. Appropriate interval arithmetic is then used to propagate the data through standard fault trees to generate output distributions which reflect the uncertainty in the input data. Through a canonical example of reliability estimation for a robot manipulator system, we show how the use of this novel interval method appreciably improves the accuracy of reliability estimates over existing approaches to the problem of uncertain input data. This method avoids the key problem of loss of uncertainty inherent in some approaches when applied to noncoherent systems. It is further shown that the method has advantages over approaches based on partial simulation of the input-data space because it can provide guaranteed bounds for the estimates in reasonable times     

An entropy-based model for supporting and evaluating route stability in mobile ad hoc wireless networks

We propose an entropy-based modeling framework for supporting route stability in mobile ad hoc wireless networks. The basic motivations of the proposed modeling approach stem from the commonality observed in the location uncertainty in mobile ad hoc wireless networks and the concept of entropy. The corresponding results demonstrate that the proposed approach and parameters provide an accurate and efficient method of estimating and evaluating the route stability in dynamic mobile networks.     

Design of a perturbation estimator using the theory ofvariable-structure systems and its application to magnetic levitationsystems

A perturbation estimator using the theory of variable structure systems is proposed to enhance the robustness of a pole-placement controller design. In its ideal form, the pole-placement design using feedback-linearization technique achieves a desired performance in nonlinear time-varying systems. However, its performance deteriorates rapidly with the presence of disturbance and parametric uncertainties, referred to as perturbation. The estimate generated by the proposed perturbation estimator is incorporated as an additional input to rectify the uncertainties in the nominal control model of the pole-placement design. The proposed scheme requires neither the measurement of the time derivative of the state vector nor the precise knowledge of system parameters, hut rather the bounds on system perturbation. Chatter and the adverse effects of conservative bounds on system perturbation, often encountered in conventional sliding-mode control (SMC), are alleviated for the controlled plant by the proposed scheme. The benefits of this scheme are demonstrated in this study practically on a magnetic levitation system and its performance is compared with that of the conventional SMC scheme     

Energy-efficiency bounds for deep submicron VLSI systems in the presence of noise

In this paper, we present an algorithm for computing the bounds on energy-efficiency of digital very large scale integration (VLSI) systems in the presence of deep submicron noise. The proposed algorithm is based on a soft-decision channel model of noisy VLSI systems and employs information-theoretic arguments. Bounds on energy-efficiency are computed for multimodule systems, static gates, dynamic circuits and noise-tolerant dynamic circuits in 0.25-/spl mu/m CMOS technology. As the complexity of the proposed algorithm grows linearly with the size of the system, it is suitable for computing the bounds on energy-efficiency for complex VLSI systems. A key result presented is that noise-tolerant dynamic circuits offer the best trade off between energy-efficiency and noise-immunity when compared to static and domino circuits. Furthermore, employing a 16-bit noise-tolerant Manchester adder in a CDMA receiver, we demonstrate a 31.2%-51.4% energy reduction over conventional systems when operating in the presence of noise. In addition, we compute the lower bounds on energy dissipation for this CDMA receiver and show that these lower bounds are 2.8/spl times/ below the actual energy consumed, and that noise-tolerance reduces the gap between the lower bounds and actual energy dissipation by a factor of 1.9/spl times/.     

The Smooth Variable Structure Filter

In this paper, a new method for state estimation, referred to as the smooth variable structure filter (SVSF), is presented. The SVSF method is model based and applies to smooth nonlinear dynamic systems. It allows for the explicit definition of the source of uncertainty and can guarantee stability given an upper bound for uncertainties and noise levels. The performance of the SVSF improves with more refined definition of upper bounds on parameter variations or uncertainties. Furthermore, most filtering methods provide as their measure of performance the filter innovation vector or (output) estimation error. However in addition to the innovation vector, the SVSF has a secondary set of performance indicators that correlate to the modeling errors specific to each state or parameter that is being estimated. The combined robustness and multiple indicators of performance allow for dynamic refinement of internal models in the SVSF. Dynamic refinement and robustness are features that are particularly advantageous in fault diagnosis and prediction. In this paper, the applications of the SVSF to linear and nonlinear systems, including one pertaining to fault detection, are provided. The characteristics of this filter in terms of its accuracy and rate of convergence are discussed.     

Sequential Monte Carlo inference of internal delays innonstationary data networks

On-line, spatially localized information about internal network performance can greatly assist dynamic routing algorithms and traffic transmission protocols. However, it is impractical to measure network traffic at all points in the network. A promising alternative is to measure only at the edge of the network and infer internal behavior from these measurements. We concentrate on the estimation and localization of internal delays based on end-to-end delay measurements from a source to receivers. We propose a sequential Monte Carlo (SMC) procedure capable of tracking nonstationary network behavior and estimating time-varying, internal delay characteristics. Simulation experiments demonstrate the performance of the SMC approach     

Order-P: an algorithm to order network partitionings

A network partitioning occurs when failures fragment the network into at least 2 sub-networks. This causes the network performance to degrade; many techniques have been proposed to combat this degradation. The number of possible partitionings in a fully connected network of n nodes is greater than 2', for large n. Thus, analysis of partitioning-resilient algorithms is extremely difficult due to the difficulty of computing the probabilities of occurrence of the partitionings. The authors propose an algorithm that orders network partitionings in decreasing order of probability. This algorithm is similar to the Most Probable State Enumeration (MPSE) algorithm of Li & Silvester (1984). By looking at only the most probable partitionings, the performance of the network can be estimated well. This approach also gives bounds on the network performance. Two distinct equally-important goals have been attained: the algorithm Order-P is proposed, and the algorithm is applied in the real world and demonstrates its value in performance modeling of distributed systems     

Lagrangean Based Methods for Solving Large-Scale Cellular Network Design Problems

The cellular network design (CND) problem is formulated as a comprehensive linear mixed integer programming model integrating the base station location (BSL) problem, the frequency channel assignment (FCA) problem and the topological network design (TND) problem. A solution algorithm based on Lagrangean relaxation is proposed for solving this complex cellular network design problem. Pursuing the optimum solution through exact algorithms to this problem appears to be unrealistic considering the large scale nature and NP-hardness of the problem. Therefore, the solution algorithm strategy consists in computing effective lower and upper bounds for the problem. Lower bounds are evaluated through a Lagrangean relaxation technique and subgradient method. A Lagrangean heuristic is developed to compute upper bounds based on the Lagrangean solution. The bounds are improved through a customized branch and bound algorithm which takes in account specific knowledge of the problem to improve its efficiency. Thirty two random test instances are solved using the proposed algorithm and the CPLEX optimization package. The results show that the duality gap is excessive, so it cannot guarantee the quality of the solution. However, the proposed algorithm provides optimal or near optimal solutions for the problem instances for which CPLEX also provides the optimal solution. It further suggests that the proposed algorithm provides optimal or near optimal solutions for the other instances too. Finally, the results demonstrate that the proposed algorithm is superior to CPLEX as a solution approach for the CND problem.     

Computing lower bounds on functional units before scheduling

The authors present a new polynomial-time algorithm for computing lower bounds on the number of functional units (FUs) of each type required to schedule a data flow graph in a specified number of control steps. A formal approach is presented that is guaranteed to find the tightest possible bounds that can be found by relaxing either the precedence constraints or integrality constraints on the scheduling problem. This tight, yet fairly efficient, bounding method can be used to estimate FU area, to generate resource constraints for reducing the search space, or in conjunction with exact techniques for efficient optimal design space exploration     

New Sliding-Mode Learning Law for Dynamic Neural Network Observer

This brief deals with a state observation problem when the dynamic model of a plant contains an uncertainty or it is completely unknown (only smoothness properties are assumed to be in force). The dynamic neural network approach is applied in this informative situation. A new learning law, containing relay (signum) terms, is suggested to be in use. The nominal parameters of this procedure are adjusted during the preliminary "training process" where the sliding-mode technique as well as the least-squares method are applied to obtain the "best" nominal parameter values using training experimental data. The upper bounds for the weights as well as for the averaged estimation error are derived. Two numeric examples illustrate this approach: first, the nonlinear third-order electrical system (Chua's circuit) with noises in the dynamics as well as in the output, and, second, the water ozone-purification process supplied by a bilinear model with unknown parameters     

Application of functional link neural network to HVAC thermaldynamic system identification

Recent efforts to incorporate aspects of artificial intelligence into the design and operation of automatic control systems have focused attention on techniques such as fuzzy logic, artificial neural networks and expert systems. The use of computers for direct digital control highlights the recent trend toward more effective and efficient heating, ventilating and air-conditioning (HVAC) control methodologies. Researchers in the HVAC field have stressed the importance of self-learning in building control systems and have encouraged further studies in the integration of optimal control and other advanced techniques into the formulation of such systems. Artificial neural networks can also be used to emulate the plant dynamics, in order to estimate future plant outputs and obtain plant input/output sensitivity information for online neural control adaptation. This paper describes a functional link neural network approach to performing the HVAC thermal dynamic system identification. Methodologies to reduce inputs of the functional link network to reduce the complexity and speed up the training speed are presented. Analysis and comparison between the functional link network approach and the conventional network approach for the HVAC thermal modeling are also presented     

Adaptive incremental sliding mode control for a robot manipulator

An adaptive incremental sliding mode control (AISMC) scheme for a robot manipulator is presented in this paper. Firstly, an incremental backstepping (IBS) controller is designed using time-delay estimation (TDE) to reduce dependence on the mathematical model. After substituting IBS controller into the nonlinear system, a linear system w.r.t. tracking errors is obtained while TDE error is the disturbance. Then, the AISMC scheme, including a nominal controller and an SMC, is developed for the resulted linear system to improve control performance. According to the equivalent control method, the SMC in the AISMC scheme is to handle TDE error. To receive optimal control performance at the sliding manifold, an LQR controller is selected as the nominal controller. The SMC is designed based on positive semi-definite barrier function (PSDBF) since it prevents switching gains from being over/under-estimated, and two practical problems are addressed in this paper: A new PSDBF is designed and conservative (large) setting bounds affecting tracking precision and/or system stability are avoided; An improved PSDBF based SMC is developed where the PSDBF and an adaptive parameter are used simultaneously to regulate switching gains, and the system is still stable when sliding variable occasionally exceeds the predefined vicinity. Moreover, finite-time convergence property of the sliding variable is strictly analyzed. Finally, real-time experiments are conducted to verify the effectiveness of the proposed control method.     

Estimation for maximum instantaneous current through supply linesfor CMOS circuits

We present new techniques for estimating the maximum instantaneous current through the power supply lines for CMOS circuits. We investigate four different approaches: (1) timed-ATPG-based approach; (2) probability-based approach; (3) genetic algorithm-based approach; and (4) integer linear programming (ILP) approach. The first three approaches produce a tight lower bound on the maximum current. The ILP-based approach produces the exact solutions for small circuits, and tight upper bounds of the solutions for large circuits. Our experimental results show that the upper bounds produced by the ILP approach combined with the lower bounds produced by the other three approaches confine the exact solution for the maximum instantaneous current to a small range     

A prognostic methodology for power MOSFETs under thermal stress using echo state network and particle filter

Reinforcing the reliability of power semiconductor devices is crucial for extending the lifetime of the power-converter based electrical systems. This paper aims at developing a novel prognostics methodology for estimating the Remaining Useful Life (RUL) of the power Metal-Oxide Field-Effect Transistors (MOSFETs). The variation of on-state resistance as an important fault indicator under thermal overstress is utilized as the main database. A recently proposed neural network paradigm, namely Echo State Network (ESN) is utilized here to derive a degradation model, taking into account its high efficiency in modeling nonlinear dynamical systems. Meanwhile, a particle filter approach is developed to update the initially trained ESN model and to quantify the uncertainty of the RUL prediction online. The accuracy and efficiency of the proposed prognostic methodology has been verified based on an accelerated aging experimental dataset.     

Okumura-Hata传播预测模型的可视化仿真研究

对大尺度传播预测模型的可视化计算问题进行了研究。应用先进仿真技术,通过对图形建模型波涛及的多种资源如算法、参数有结果数据等进行可视化处理,构造出Okumura-Hata传播预测模型的可视化计算环境。与基于传高级程序设计语言（如C、Fortran)的数值预测方法相比,该方法不仅实现了Okumura-Hata模型的图形建模和可视化预测,而且无需算法编程及调试,为蜂窝和陆地移动无线通信径损耗预测提供一个全面的图形化解决方案。     

云计算中的按需服务

云计算为计算系统软硬件基础设施的设计和部署及用户对信息系统的规划和使用提供了一种新的模式。方便、灵活的方式,高效、价格低廉和保障质量的服务是云计算模式的典型特征。文章从服务的角度提出一个云计算中按需服务系统的架构,并基于架构探讨云计算中的按需服务问题,包括分布式服务资源的组织管理与监控、情境感知的按需服务建模、大规模网络环境中的按需服务组合,以及基于复杂系统理论的服务系统。文章还以地球空间信息系统中的连续运行参考站网为例,用云计算的视点分析了其系统的体系结构,并对其服务中出现的问题进行了探讨。     

Soft error modeling and remediation techniques in ASIC designs

Soft errors due to cosmic radiations are the main reliability threat during lifetime operation of digital systems. Fast and accurate estimation of soft error rate (SER) is essential in obtaining the reliability parameters of a digital system in order to balance reliability, performance, and cost of the system. Previous techniques for SER estimation are mainly based on fault injection and random simulations. In this paper, we present an analytical SER modeling technique for ASIC designs that can significantly reduce SER estimation time while achieving very high accuracy. This technique can be used for both combinational and sequential circuits. We also present an approach to obtain uncertainty bounds on estimated error propagation probability (EPP) values used in our SER modeling framework. Comparison of this method with the Monte-Carlo fault injection and simulation approach confirms the accuracy and speed-up of the presented technique for both the computed EPP values and uncertainty bounds.Based on our SER estimation framework, we also present efficient soft error hardening techniques based on selective gate resizing to maximize soft error suppression for the entire logic-level design while minimizing area and delay penalties. Experimental results confirm that these techniques are able to significantly reduce soft error rate with modest area and delay overhead.