Model reference adaptive predictive control for a variable-frequency oil-cooling machine

This paper develops methodologies and techniques for the design, analysis, and implementation of a model reference adaptive predictive temperature controller for a variable-frequency oil-cooling machine, suited for cooling high-speed machine tools. The oil-cooling process is modeled experimentally as a first-order system model with a time delay and its system parameters are identified using the recursive least-square method. Based on this model, a model reference adaptive predictive controller is proposed for achieving set-point tracking and robustness. A real-time model reference adaptive predictive control algorithm is then presented and implemented utilizing a stand-alone digital signal processor TMS320F243 from Texas Instruments Incorporated. The experimental results show that the proposed control method is proven capable of giving satisfactory performance under set-point changes, fixed loads, and load changes.     

时钟改进模型辅助RAIM算法研究

提出采用"滑动窗"方式建立参数实时更新的接收机钟差改进模型,把基于该模型的钟差预测值引入到卫星导航中辅助进行自主完好性监测(RAIM).文中给出了相关流程图,并提出新的预测误差计算方法.基于GPS实测数据的验证分析表明,这种方法不仅可以提高RAIM算法的可用性,而且可以提高故障检测和识别效率,可检测的最小故障偏差从50米降到40米, 可完全检测和识别的最小故障偏差从70米降到60米,并且系统的其它性能指标也有明显改善.     

Sparse Least Logarithmic Absolute Difference Algorithm with Correntropy-Induced Metric Penalty

Sparse adaptive filtering algorithms are utilized to exploit system sparsity as well as to mitigate interferences in many applications such as channel estimation and system identification. In order to improve the robustness of the sparse adaptive filtering, a novel adaptive filter is developed in this work by incorporating a correntropy-induced metric (CIM) constraint into the least logarithmic absolute difference (LLAD) algorithm. The CIM as an \(l_{0}\)-norm approximation exerts a zero attraction, and hence, the LLAD algorithm performs well with robustness against impulsive noises. Numerical simulation results show that the proposed algorithm may achieve much better performance than other robust and sparse adaptive filtering algorithms such as the least mean p-power algorithm with \(l_{1}\)-norm or reweighted \(l_{1}\)-norm constraints.     

Efficient Scheduling Algorithms for Real-Time Service on WDM Optical Networks

One of the important issues in the design of future generation high-speed networks is the provision of real-time services to different types of traffic with various time constraints. In this paper we study the problem of providing real-time service to hard and soft real-time messages in Wavelength-Division-Multiplexing (WDM) optical networks. We propose a set of scheduling algorithms which prioritize and manage message transmissions in single-hop WDM passive star networks based on specific message time constraints. In particular, we develop time-based priority schemes for scheduling message transmissions in order to increase the real-time performance of a WDM network topology. We formulated an analytical model and conducted extensive discrete-event simulations to evaluate the performance of the proposed algorithms. We compared their performances with that of the state-of-the-art WDM scheduling algorithms which typically do not consider the time constraint of the transmitted messages. This study suggests that when scheduling real-time messages in WDM networks, one has to consider not only the problem of resources allocation in the network but also the problem of sequencing messages based on their time constraints.     

Sequential Monte Carlo Methods for Tracking Multiple Targets With Deterministic and Stochastic Constraints

In multitarget scenarios, kinematic constraints from the interaction of targets with their environment or other targets can restrict target motion. Such motion constraint information could improve tracking performance if effectively used by the tracker. In this paper, we propose three particle filtering methods that incorporate constraint information in their proposal and weighting process; the number of targets is fixed and known in all methods. The reproposed constrained motion proposal (RCOMP) utilizes an accept/reject method to propose particles that meet the constraints. The truncated constraint motion proposal (TCOMP) uses proposal densities truncated to satisfy the constraints. The constraint likelihood independent partitions (CLIP) method simply rejects proposed partitions that do not meet the constraints. We use simulation to evaluate the performance of these three methods for two constrained motion scenarios: a vehicle convoy and soldiers executing a leapfrog motion. Moreover, we demonstrate the utility of constraint information by comparing the proposed algorithms with the independent partition (IP) proposal method that does not use constraint information. The simulation results demonstrate that the root mean square error (RMSE) tracking performance of the RCOMP and the TCOMP methods is much better than the CLIP and IP methods; this is due to their more efficient proposal process.     

Adaptive control is enhanced by background estimation

The automated control of physiological variables must often contend with an unknown and time-varying background (i.e., the output level corresponding to no input). To allow for simultaneous real-time identification of background as well as the parameters of an autoregressive moving average model with exogenous inputs (ARMAX model) during adaptive control, a "floating identifier" (FI) approach was developed which may be used with most recursive identification algorithms. This method separates input and output data into low- and high-frequency components. The high-frequency components are used to identify the ARMAX model parameters and the low-frequency components to identify background. This approach was evaluated in computer simulations and animal experiments comparing an adaptive controller coupled to the FI with the same controller coupled to two other standard least squares identifiers. In the animal experiments, sodium nitroprusside was used to control mean arterial pressure of anesthetized dogs in the presence of background changes. Results showed that with the FI, the controller performed satisfactorily, while with the other identifiers, it sometimes failed. It is concluded that the FI approach is useful when applying ARMAX-based adaptive controllers to systems in which a change in background is likely.     

Identification of electrically stimulated quadriceps muscles inparaplegic subjects

This work establishes a method for the noninvasive in vivo identification of parametric models of electrically stimulated muscle in paralyzed individuals, when significant inertial loads and/or load transitions are present. The method used differs from earlier work, in that both the pulse width and stimulus period (interpulse interval) modulation are considered. A Hill-type time series model, in which the output is the product of two factors (activation and torque-angle) is used. In this coupled model, the activation dynamics depend upon velocity. Sequential nonlinear least squares methods are used in the parameter identification. The ability of the model, using identified time-varying parameters, to accurately predict muscle torque outputs is evaluated, along with the variability of the identified parameters. This technique can be used to determine muscle parameter models for biomechanical computer simulations, and for real-time adaptive control and monitoring of muscle response variations such as fatigue     

坚实性宽带自适应阵列处理器设计

在这篇文章中，我们提出了一种新的自适应阵列处理器的设计方法．这种方法以输出最小功率为准则，并服从幅度的或功率的等式和不等式约束．约束可以在空域进行，也可以在频域进行．通过调整约束条件，可以方便地得到各种不同的性能指标，如坚实性的宽带波束，高抗干扰性能，简化硬件结构等．     

A discrete model of induction motors for real-time controlapplications

The real-time digital control of induction motors and AC servomotor drives often involves estimation, identification, and adaptive control algorithms. An efficient and numerically stable discrete model of the induction motor is required to implement these algorithms in real time. A predictor-corrector discrete model of induction motors is developed here for real-time model is analyzed by a discrete root locus technique. The digital simulation of the model is presented and compared with a rigorous solution, and satisfactory results are obtained     

Probabilistic delay budget assignment for synthesis of soft real-time applications

Unlike their hard real-time counterparts, soft real-time applications are only expected to guarantee their "expected delay" over input data space. This paradigm shift calls for customized statistical design techniques to replace the conventional pessimistic worst case analysis methodologies. We present a novel statistical time-budgeting algorithm to translate the application expected delay constraint into its components' local delay constraints. We utilize the mathematical properties of the problem to quickly calculate the system expected delay and incrementally estimate the component utility variation with its timing relaxation. Our algorithm determines the optimal maximum weighted timing relaxation of an application under expected delay constraint. Experimental results on core-based synthesis of several multimedia applications targeting field-programmable gate arrays show that our technique always improves the design area. Furthermore, it consistently outperforms optimal time budgeting under hard real-time constraint, which is the best existing competitor. Design area improvements were up to 26% and averaged about 17% on several MediaBench applications.     

Effect of artificial-intelligence planning-procedures on systemreliability

For an embedded real-time process-control system incorporating artificial-intelligence programs, the system reliability is determined by both the software-driven response computation time and the hardware-driven response execution time. A general model, based on the probability that the system can accomplish its mission under a time constraint without incurring failure, is proposed to estimate the software/hardware reliability of such a system. The factors which influence the proposed reliability measure are identified, and the effects of mission time, heuristics and real-time constraints on the system reliability with artificial-intelligence planning procedures are illustrated. An optimal search procedure might not always yield a higher reliability than that of a nonoptimal search procedure. Hence, design parameters and conditions under which one search procedure is preferred over another, in terms of improved software/hardware reliability, are identified     

Blind adaptive algorithms for minimum variance CDMA receivers

Constrained optimization methods have received considerable attention as a means to derive blind multiuser receivers with low complexity. The receiver's output variance is minimized subject to appropriate constraints which depend on the multipath structure of the signal of interest. When multipath is present, the constraint equations can be written in parametric form, and the constraint parameters jointly optimized with the linear receiver's parameters. We develop adaptive solutions for this joint, constrained optimization problem. Both stochastic gradient and recursive least-square-type algorithms are developed. The performance of the proposed methods is compared with other blind and trained methods and turns out to be close to the trained minimum mean-square-error receiver     

变流量节能系统动态监测及实时调节系统设计分析

为提高变流量节能系统自适应性能和实时调节能力,提出基于双启发式动态规划的变流量节能系统动态监测及实时调节系统设计方法,构建变流量节能动态监测及实时调节控制对象模型,以供水流量及一次侧的供水温度、供水流量为初始约束参量设计系统的控制规则。在此基础上,结合双启发式动态规划模式并采用模糊优化分配控制策略,对系统进行调节和控制,通过线性规划学习方法,实现变流量节能动态监测及实时调节。仿真结果表明,采用该方法进行变流量节能系统动态监测及实时调节的自适应性能较好,各时段流量预测误差曲线波动介于-1.5至1.5之间,且热负荷值与预测热负荷之间相对误差最大值为2.5%,误差较低。     

等式约束FIR滤波器设计的投影最小二乘算法

本文考虑具有频域和时域等式约束的FIR滤波器设计问题,提出一个非常有效的新算法——投影最小二乘算法.该算法由两部分组成,前一部分产生一个解析的最小二乘解,后一部分将此解逐次投影到每个等式约束上.该算法有两个显著特点:一是目标函数的Hessian矩阵不要求正定;二是由于采用平方根因子分解来计算增广Hessian矩阵及投影算子矩阵,算法具有很好的数字稳定性.以此算法为核心构成了一个迭代算法,用于实现FIR Nyquist滤波器的minimax设计.设计例子表明了所提算法的有效性和数字稳定性.     

Real-time Reliability Prediction for a Dynamic System Based on the Hidden Degradation Process Identification

This paper introduces a real-time reliability prediction method for a dynamic system which suffers from a hidden degradation process. The hidden degradation process is firstly identified by use of particle filtering based on measurable outputs of the considered dynamic system. Then the system's reliability is predicted according to the model of the degradation path. We analyze the identification algorithm mathematically, and validate the effectiveness of this method through computer simulations of a three-vessel water tank. This real-time reliability prediction method is beneficial to the dynamic system's condition monitoring, and may be further helpful to make a proper predictive maintenance policy for the system.     

改进的“当前”统计模型变采样率目标跟踪算法

为满足实际雷达系统对高精度和高实时性的要求,提出了一种改进的"当前"统计模型变采样率机动目标跟踪算法。该算法针对"当前"统计模型必须预设加速度极值和机动频率的问题,提出一种加速度方差和机动频率在线同步自适应方法,建立改进的"当前"统计模型机动目标跟踪算法;针对在线自适应方法计算量大的问题,结合采样周期的大小与目标机动特性的关系,引入变采样率方法。仿真结果表明,与传统"当前"统计模型相比,改进的"当前"统计模型机动目标跟踪算法能显著提高对不同机动强度目标的跟踪精度;变采样率方法通过减少采样点数,节省了系统资源,提高了跟踪实时性;所提算法将两者结合,用传统的"当前"统计模型1.5~2倍的平均采样周期得到了更小的位置均方根误差,实现了用单模型方法同时改善跟踪精度和实时性的目的。     

Optimal and Approximate Mobility-Assisted Opportunistic Scheduling in Cellular Networks

This paper considers the problem of scheduling multiple users in the downlink of a time-slotted cellular data network. For such a network, opportunistic scheduling algorithms improve system performance by exploiting time variations of the radio channel. We present novel optimal and approximate opportunistic scheduling algorithms that combine channel fluctuation and user mobility information in their decision rules. The algorithms modify the opportunistic scheduling framework of Liu et al., (1993) with dynamic constraints for fairness. These fairness constraints adapt according to the user mobility. The adaptation of constraints in the proposed algorithms implicitly results in giving priority to the users that are in the most favorable locations. The optimal algorithm is an offline algorithm that precomputes constraint values according to a known mobility model. The approximate algorithm is an online algorithm that relies on the future prediction of the user mobility locations in time. We show that the use of mobility information in opportunistic scheduling increases channel capacity. We also provide analytical bounds on the performance of the approximate algorithm using the fundamental inequality of Dyer et al., (1986) for linear programs. Simulation results on high data rate (HDR) illustrate the usefulness of the proposed schemes for elastic traffic and macrocell structures     

Randomized gossip algorithms

Motivated by applications to sensor, peer-to-peer, and ad hoc networks, we study distributed algorithms, also known as gossip algorithms, for exchanging information and for computing in an arbitrarily connected network of nodes. The topology of such networks changes continuously as new nodes join and old nodes leave the network. Algorithms for such networks need to be robust against changes in topology. Additionally, nodes in sensor networks operate under limited computational, communication, and energy resources. These constraints have motivated the design of "gossip" algorithms: schemes which distribute the computational burden and in which a node communicates with a randomly chosen neighbor. We analyze the averaging problem under the gossip constraint for an arbitrary network graph, and find that the averaging time of a gossip algorithm depends on the second largest eigenvalue of a doubly stochastic matrix characterizing the algorithm. Designing the fastest gossip algorithm corresponds to minimizing this eigenvalue, which is a semidefinite program (SDP). In general, SDPs cannot be solved in a distributed fashion; however, exploiting problem structure, we propose a distributed subgradient method that solves the optimization problem over the network. The relation of averaging time to the second largest eigenvalue naturally relates it to the mixing time of a random walk with transition probabilities derived from the gossip algorithm. We use this connection to study the performance and scaling of gossip algorithms on two popular networks: Wireless Sensor Networks, which are modeled as Geometric Random Graphs, and the Internet graph under the so-called Preferential Connectivity (PC) model.     

Constrained least squares in adaptive, imperfect arrays

Two new constrained deterministic least-squares algorithms are presented which are capable of enabling a narrow-band zero-order generalized sidelobe canceller (SLC), in the presence of array imperfections, to null out jammers while preserving the friendly look-direction signal with minimal a priori knowledge of the signal environment. The algorithms are capable of solving deterministic least-squares optimization problems subject to an equality constraint in an iterative, adaptive manner by imposing a `soft' constraint via the quadratic penalty function optimization method. The first algorithm is based on the matrix inversion lemma while the second is obtained by means of QR-decomposition using new three-dimensional Givens (1958) rotations and implemented with a systolic array architecture. These new constrained algorithms improve system performance when an artificial injection of a receiver noise vector is introduced     

等式约束最小二乘在“北斗”姿态测量中的应用

针对传统无约束的姿态测量中整周模糊度求解成功率不高的问题,提出利用等式约束快速求解整周模糊度的算法,并将其应用于“北斗”姿态测量。该算法充分利用基线的先验信息,在整周模糊度的求解过程中加入等式约束,同时利用拉格朗日乘子法求解约束整数最小二乘问题,提高了姿态测量中整周模糊度和姿态角的求解成功率。采用静态测试和动态测试验证该算法,结果表明在“北斗”单历元条件下,整周模糊度及姿态角的求解成功率提升30%左右。