Indirect adaptive control for synchronous Generator: comparison of MLP/RBF neural networks approach with Lyapunov stability analysis

This paper compares two indirect adaptive neurocontrollers, namely a multilayer perceptron neurocontroller (MLPNC) and a radial basis function neurocontroller (RBFNC) to control a synchronous generator. The different damping and transient performances of two neurocontrollers are compared with those of conventional linear controllers, and analyzed based on the Lyapunov direct method.     

A modified direct torque control with fault tolerance

The use of inverters in induction motor control has reduced classical motor faults, such as broken rotor bars or windings short-circuit, besides improving control performance. The control becomes faster and more precise, reducing peaks in current and torque, so that the motor can have a softer operation. On the other hand, new elements are included in the system and it will be necessary to take into account their faults. These elements are sensors and power electronic devices that since a control point of view are the system sensors and actuators. Fault tolerance tries to maintain the system under control in case a fault appears in the system. If this is not possible, it takes the system to a safe operational point. In this paper a fault-tolerant control for induction motors is designed. Based on a direct torque control, new control strategies have been added in case current sensor and power switch faults are detected. The challenge is to overcome these faults without any physical redundancy of sensors or power switches as other authors propose. With the proposed control, it will be possible to guarantee the motor operation in the whole speed-torque range with one or none current sensors instead of the two usually used, though the performance will be slightly worsened. In case of inverter faults, the operation range will be restricted but the performance with respect to the fault situation is improved.     

The development of a fault-tolerant control approach and its implementation on a flexible arm robot

This article presents a robust sensor fault-tolerant control (FTC) scheme and its implementation on a flexible arm robot. Sensor faults affect the system's performance in the closed loop when the faulty sensor readings are used to generate the control input. In this article, the non-faulty sensors are used to reconstruct the faults on the potentially faulty sensors. The reconstruction is subtracted from the faulty sensors to generate a 'virtual sensor' which (instead of the normally used faulty sensor output) is then used to generate the control input. A design method is also presented in which the virtual sensor is made insensitive to any system uncertainties (which could corrupt the fault reconstruction) that cannot fit into the framework of the model used. Two fault conditions are tested: total failure and incipient faults. Then the scheme robustness is tested and evaluated through its implementation on two flexible arm systems, one with a flexible joint and the other with a flexible link. Excellent results have been obtained for both cases (joint and link); the FTC scheme produced system performance almost identical to the fault-free scenario, whilst providing an indication that a fault is present, even for simultaneous faults.     

Comparison of heuristic dynamic programming and dual heuristicprogramming adaptive critics for neurocontrol of a turbogenerator

This paper presents the design of an optimal neurocontroller that replaces the conventional automatic voltage regulator (AVR) and the turbine governor for a turbogenerator connected to the power grid. The neurocontroller design uses a novel technique based on the adaptive critic designs (ACDs), specifically on heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Results show that both neurocontrollers are robust, but that DHP outperforms HDP or conventional controllers, especially when the system conditions and configuration change. This paper also shows how to design optimal neurocontrollers for nonlinear systems, such as turbogenerators, without having to do continually online training of the neural networks, thus avoiding risks of instability.     

Selecting useful groups of features in a connectionist framework.

Suppose for a given classification or function approximation (FA) problem data are collected using l sensors. From the output of the ith sensor, ni features are extracted, thereby generating p = sigma li = 1 ni features, so for the task we have X subset Rp as input data along with their corresponding outputs or class labels Y subset Rc. Here, we propose two connectionist schemes that can simultaneously select the useful sensors and learn the relation between X and Y. One scheme is based on the radial basis function (RBF) network and the other uses the multilayered perceptron (MLP) network. Both schemes are shown to possess the universal approximation property. Simulations show that the methods can detect the bad/derogatory groups of features online and can eliminate the effect of these bad features while doing the FA or classification task.     

状态反馈控制系统的容错控制策略

本文提出了在状态反馈控制系统中,当某个传感器失效时,由其余完好的状态反馈回路 平均分担已失效回路的控制作用的容错控制思想.由此所设计的线性状态反馈容错控制系 统在任一传感器失效的情况下,均具有从正常系统的控制到散障系统的控制的无扰动切换功 能和良好的静动态性能.该法简单、实用,具有广泛的工程应用价值.     

Algorithms for Fault-Tolerant Topology in Heterogeneous Wireless Sensor Networks

This paper addresses fault-tolerant topology control in a heterogeneous wireless sensor network consisting of several resource-rich supernodes, used for data relaying, and a large number of energy-constrained wireless sensor nodes. We introduce the k-degree anycast topology control (fc-ATC) problem, with the objective of selecting each sensor's transmission range such that each sensor is k-vertex supernode connected and the total power consumed by sensors is minimized. Such topologies are needed for applications that support sensor data reporting, even in the event of failures of up to k - 1 sensor nodes. We propose three solutions for the k-ATC problem: a k-approximation algorithm, a greedy centralized algorithm that minimizes the maximum transmission range between all sensors, and a distributed and localized algorithm that incrementally adjusts sensors' transmission range such that the k-vertex supernode connectivity requirement is met. Extended simulation results are presented to verify our approaches.     

基于自适应评价UPFC神经网络控制器设计

对含UPFC(统一潮流控制器)的电力系统提出一种新型的非线性最优神经网络控制器。启发式动态规划(HDP)是自适应评价设计(ACDs)体系中的一员,采用HDP来设计UPFC神经网络控制器。和传统的PI控制器相比,这种神经网络控制器能够提供非线性最优控制。仿真结果表明,此种控制器具有很好的控制效果。     

Sensor fault masking of a ship propulsion system

This paper presents the results of a study on fault-tolerant control of a ship propulsion benchmark [Izadi-Zamanabadi, R., & Blanke, M. (1999). A ship propulsion system as a benchmark for fault tolerant control. Control Engineering Practice, 7 (2), 227–239] which uses estimated or virtual measurements as feedback variables. The estimator operates on a self-adjustable design model so that its outputs can be made immune to the effects of a specific set of component and sensor faults. The adequacy of sensor redundancy is measured using the control reconfigurability [Wu, N. E., Zhou, K., & Salomon, G. (2000). Reconfigurability in linear time-invariant systems. Automatica, 36 (11), 1767–1771] and the number of sensor based measurements are increased when this level is found inadequate. As a result, sensor faults that are captured in the estimator's design model can be tolerated without the need for any reconfiguration actions. Simulations for the ship propulsion benchmark show that, with additional sensors added as described and the estimator in the loop, satisfactory fault-tolerance is achieved under two additive sensor faults, an incipient fault, and a parametric fault, without having to alter the original controller in the benchmark.     

Sensor fault-tolerant control of a rail traction drive

The problem of sensor faults on an AC-drive system for an electric train is considered here. Intermittent disconnections of these sensors produce severe transient errors in the estimator for the control loop. This paper uses a bilinear model of the motors and model-based techniques to produce estimates of control variables that are tolerant to intermittent disconnections, without degrading performance. The paper shows how such a system can be verified in hardware, on a small test-rig with a DSP used to run the fault-tolerant algorithm.     

Sensorless adaptive output feedback control of wind energy systems with PMS generators

This paper addresses the problem of controlling wind energy conversion (WEC) systems involving permanent magnet synchronous generator (PMSG) fed by IGBT-based buck-to-buck rectifier–inverter. The prime control objective is to maximize wind energy extraction which cannot be achieved without letting the wind turbine rotor operate in variable-speed mode. Interestingly, the present study features the achievement of the above energetic goal without resorting to sensors of wind velocity, PMSG speed and load torque. To this end, an adaptive output-feedback control strategy devoid of any mechanical sensor is developed (called sensorless), based on the nonlinear model of the whole controlled system and only using electrical variables measurements. This control strategy involves: (i) a sensorless online reference-speed optimizer designed using the turbine power characteristic to meet the maximum power point tracking (MPPT) requirement; (ii) a nonlinear speed regulator designed by using the backstepping technique; (iii) a sensorless interconnected adaptive state observer providing online estimates of the rotor position as well as speed and load/turbine torque. The proposed output-feedback control strategy is backed by a formal analysis showing that all control objectives are actually achieved. Several simulations show that the control strategy enjoys additional robustness properties.     

Set theoretic approach to fault-tolerant control of linear parameter-varying systems with sensor reintegration

In this paper, we present a fault-tolerant control scheme for linear parameter-varying systems that utilises multiple sensor switching to compensate for sensor faults. The closed-loop scheme consists of an estimator-based feedback tracking controller and sensor-estimate switching strategy which allows for the reintegration of previously faulty sensors. The switching mechanism tracks the transitions from faulty to healthy behaviour by means of set separation and pre-computed transition times. The sensor-estimate pairings are then reconfigured based on available healthy sensors. Under the proposed scheme, preservation of closed-loop system boundedness is guaranteed for a wide range of sensor fault situations. An example is presented to illustrate the performance of the fault-tolerant control strategy.     

Fault-tolerant sensor integration for micro flow-sensor arrays and networks

Distributed micro flow-sensor arrays and networks (DMFSA/N), built from collections of spatially scattered, cooperating intelligent and redundant micro flow-sensor nodes, can improve the accuracy and reliability of system. However, it is unrealistic to expect all the sensor nodes and communication links in the system to function properly all the time. This paper is based on an earlier research, in which a DMFSA/N with a cluster architecture and fault-tolerant time-out Protocol (FTTP) was developed. In this paper, a fault-tolerant sensor integration algorithm (FTSIA) is proposed and evaluated in simulations. Experimental results showed that the FTSIA could always give reliable results even when certain portion of the sensors yielded faulty information. Furthermore, the results from FTSIA were significantly more accurate than the mean of sensor readings (popularly applied in industry) if some of the sensors produced faulty readings. Finally, the application of the proposed FTSIA is illustrated by measuring flow pressure using a pressure sensor array of eight sensors.     

最优鲁棒容错控制新方法及其在飞行控制中的应用*

本文研究了一类参数不确定且传感器失效的多变量系统的容错控制问题，提出了一种基于模型参数的最优鲁棒容错控制器新的设计方法，该方法可用于解决部分传感器失效时的最优容错控制，并给出了某国产飞机纵向飞行控制的仿真结果。     

考虑传感器故障的导弹姿态控制系统主动容错控制研究

针对导弹姿态控制系统惯性传感器故障,提出了基于信号重构的主动容错控制方法.分别利用基于梯形算法的数值积分器和有限时间收敛微分器对姿态角信号和角速率信号进行重构,当在线诊断出姿态角或角速率传感器故障时,以重构信号代替故障信号进行反馈控制来实现系统的主动容错控制.在建立导弹姿态控制系统模型并采用次最优控制方法设计输出反馈控制器的基础上,对所设计的主动容错控制方法进行仿真,仿真结果表明该方法是有效的.     

分布式无线传感器网络容错事件边界检测

事件监测是无线传感器网络的一种重要应用。针对该应用中软故障节点提供的错误数据会降低监测的准确性的问题,提出了一种分布式的容错事件边界检测算法。节点只需与邻节点交换一次传感数据,通过简单地计算识别故障;正常的事件节点利用统计比较的方法判断其是否处于事件边界,边界宽度可根据网络用户的要求调节。该算法执行时所需的通信量小,计算复杂度低,时延小,对大规模网络具有很好的可扩展性。仿真结果表明即使节点故障率很高,应用该算法仍可以获得很好的检测效果。     

A novel fault-tolerant sensor system for sensor drift compensation

The conventional fault-tolerant sensor systems would fail when outputs from incorporated sensors are either noisy or drifting. This paper presents a novel real-time fault compensation method, which uses state estimation and compensation techniques, that the sensor system can perform robust measurements even when outputs from every incorporated sensor are noisy and drifting. In a simulation example, the proposed design can detect and correct the sensor errors (dc bias and drift) in real time. For the dc bias, the minimum detectable offset value is 0.1, which is the same as the standard deviation of the sensor noise. The compensated sensor output is biased at values smaller than 0.02. For the sensor drifts, the proposed method can compensate drifts for the change rate of drifts up to four times faster than that of the signal to be measured. The highest change rate of drifts, that can be compensated by this method, is determined by the standard deviation of the sensor noise.     

Fault-tolerant control of variable speed limits for freeway work zone using likelihood estimation

Freeway work zone with lane closure can lead to disruption to local traffic and cause significant impacts on mobility, safety and environmental sustainability. To mitigate traffic congestion near work zone area, many variable speed limits (VSL) control approaches have been developed. However, VSL control system, as a critical transportation management system, is prone to the occurrence of traffic sensor faults. Faulty sensors can cause great deviations of traffic measurements and system degradation. Therefore, this study aims to develop a fault-tolerant VSL control strategy for freeway work zone with the consideration of the mainline sensor fault and ramp sensor fault. To analyze the traffic dynamics near work zone area, a traffic flow model has been built first. Then a sliding mode controller in the previous study has been utilized for VSL control. In addition to the traffic states estimated by a Kalman filter, two observers have been developed to provide analytical redundancy of traffic states estimation. By comparing the logarithm of the likelihood estimations from the Kalman filter and two observers, a fault diagnosis scheme has been designed to detect and identify the faults of mainline sensors and ramp sensors. Then the VSL controller can be reconfigured accordingly in case of sensor faults. The proposed system is implemented and evaluated under a realistic freeway work zone environment using traffic simulator SUMO. The results demonstrate that the developed system can accurately detect and identify the sensor faults in real time. Consistent improvements of mobility, safety and sustainability are also achieved under fault-free and sensor faults scenarios.     

永磁同步电机电流传感器故障诊断及容错控制

电流传感器对永磁同步电机矢量控制系统的稳定性和安全性十分重要.为实现电流传感器的故障诊断及其容错控制，提出了一种简单的逻辑判断策略，该策略基于αβ相电流构造3个故障判断因子以实现故障判断，一相电流传感器故障时，根据基尔霍夫定律重构电流；两相以上故障时，基于李亚普诺夫稳定性定理设计自适应反推观测器进行电流估计，以确保系统稳定运行.在低速和高速运行状态下分别进行了仿真和实验，证明了该策略能够快速诊断故障、准确判断故障相，并基于逻辑判断策略稳定地重构电流，具有较高的可行性和可靠性.     

Using a New Model of Recurrent Neural Network for Control

This paper shows the results obtained in controlling a mobile robot by means of local recurrent neural networks based on a radial basis function (RBF) type architecture. The model used has a Finite Impulse Response (FIR) filter feeding back each neuron's output to its own input, while using another FIR filter as a synaptic connection. The network parameters (coefficients of both filters) are adjusted by means of the gradient descent technique, thus obtaining the stability conditions of the process. As a practical application the system has been successfully used for controlling a wheelchair, using an architecture made up by a neurocontroller and a neuroidentifier. The role of the latter, connected up in parallel with the wheelchair, is to propagate the control error to the neurocontroller, thus cutting down the control error in each working cycle.