一种水下机器人传感器故障诊断与容错控制方法

采用自适应滤波器ＦＩＲ 对水下机器人进行在线自适应建模,并利用ＬＭＳ算法来调节滤波器的权系数．通过对滤波器权系数和误差信号平方的分析,实时检测出传感器的故障,并应用ＦＩＲ 滤波器输出替代故障传感器信号,实现传感器故障情形下水下机器人容错控制．应用该方法对Ｏｕｔｌａｎｄ１０００水下机器人传感器的故障进行检测和容错,实验结果表明所提故障检测方法准确可靠,具有较好的容错效果．

     

基于降阶卡尔曼滤波器的水下机器人滑模容错控制

针对水下机器人执行器时变、非线性故障,提出一种基于降阶卡尔曼滤波器的故障估计和滑模容错控制方法.用降阶卡尔曼滤波器估计水下机器人故障解耦子系统的状态,受故障的影响,子系统状态可测.由估计的状态和测量的状态可进一步得到水下机器人执行器的故障信息.滑模容错控制器根据所估计的执行器故障调整控制器的输出以实现容错控制.仿真结果验证了所提出的故障辨识与容错控制算法的有效性.     

旋翼飞行机器人故障诊断与容错控制技术综述

对故障诊断和容错技术的发展过程进行了简要概述，以旋翼飞行机器人为研究对象，在分析了旋翼飞行机器人故障诊断与容错控制特点的基础上，介绍了当前国内外在该领域的研究进展和主要方法．最后，总结了该领域待解决的难点问题，并指出了该研究领域的发展趋势．     

水下机器人推力器容错控制技术的研究

为保证自主式水下机器人在高压、可见度差的未知海洋环境下顺利完成作业任务， 必然要求水下机器人具有容错控制能力．本文主要以哈尔滨工程大学研制的“智水Ⅲ” 型水下机器人为对象，探讨水下机器人推力器容错控制技术．文中先给出在正常情况下水下 机器人的解耦控制器及推力分配，然后给出了在推力器出现故障时的容错控制策略．仿真结 果表明，该控制器及容错控制策略都能达到很好的控制精度．     

基于ODDD水下机器人故障诊断方法

近年来数据挖掘技术的快速发展使得利用水下机器人作业过程中积累的大量数据进行故障诊断成为可能;基于数据挖掘的故障诊断技术能够从数据中获取潜在的诊断知识;针对水下机器人推进器系统数据特征,提出一种基于聚类和距离的离群点检测方法(outlier detection based on dbscan and distance,ODDD);首先,对数据进行粗聚类,然后采用剪枝规则进行离群点检测,来实现故障诊断;仿真实验结果表明算法能够实现水下机器人快速有效的故障检测.     

水下机器人传感器容错控制技术的研究

在分析水下机器人的解耦控制器及传感器系统的基础上，探讨了传感器出现故障时采用以滑模观测器输出代替传感器输出的容错控制（又叫取代控制）策略．对于传感器出现的无法修复性故障，这种策略最小化了其带给机器人的损伤．对于传感器出现的跳变故障，则在跳变故障的时间内采用取代控制．仿真结果表明了方法的有效性．     

水下机器人多传感器并发故障检测方法

针对水下机器人多传感器并发故障检测问题,提出了一种小波分析和神经网络相结合的故障特征提取 方法,将小波多分辨率分解后的细节系数进行小波重构,对重构后的细节系数进行融合得到整体高频细节信息量作 为一类故障特征值;同时,基于改进的Elman 网络建立水下机器人的全阶状态观测器模型,模型输出与传感器测量 值之间的差值作为另一类故障特征值．为进行水下机器人多传感器并发故障定位,提出了一种模糊加权属性信息融 合方法,将两类故障特征值的重要度与可信度进行模糊合成转换,基于转换结果将各故障特征值加权融合,进行水 下机器人多传感器并发故障定位．水下机器人实验样机的水池实验结果验证了本文所提方法的可行性和有效性．     

水下机器人的神经网络自适应控制

研究了水下机器人神经网络直接自适应控制方法,采用Lyapunov稳定性理论,证明了存在有界外界干扰和有界神经网络逼近误差条件下,水下机器人控制系统的跟踪误差一致稳定有界.为了进一步验证该水控制方法的正确性和稳定性,利用水下机器人实验平台进行了动力定位实验、单自由度跟踪实验和水平面跟踪实验等验证实验.     

水下机器人传感器故障诊断

在分析水下机器人传感器故障形式的基础上,对传感器可能出现的三种故障形式分别给出了相应的诊断方法,即通过传感器长时间采集不到数据来诊断传感器信息保持不变的故障,线性平滑滤波解决传感器输出振荡,小波变换检测传感器信息的突变．海中试验验证了本方法的可行性．     

可重构模块机器人分散容错控制

针对可重构模块机器人的执行器故障,提出一种基于自适应模糊系统的分散被动容错控制方法.该方法不需要机器人动力学模型与模块之间的信息交换,模块控制器分别采用间接和直接自适应方法设计,自适应参数的更新律基于Lyapunov稳定性理论设计,保证了系统的稳定性和H∞跟踪性能.数值仿真结果表明了所提出方法的有效性.     

Thruster fault diagnosis and accommodation for open-frame underwater vehicles

This paper introduces a novel thruster fault diagnosis and accommodation system (FDAS) for open-frame underwater vehicles. Basically, the FDAS is a control allocator, but this primary function is enhanced with the ability of automatic thruster fault detection and accommodation. The proposed FDAS consists of two subsystems: a fault diagnosis subsystem (FDS) and a fault accommodation subsystem (FAS). The FDS uses fault detector units (FDUs), associated with each thruster, to monitor their state. Robust and reliable FDUs are based on integration of self-organising maps and fuzzy logic clustering methods. These units are able to detect internal and external faulty states of thrusters. The FAS uses information provided by the FDS to accommodate faults and perform an appropriate control reallocation. A control energy cost function is used as the optimisation criteria. The FAS uses weighted pseudo-inverse to find the solution of the control allocation problem, which minimise this criteria. Two approximations (truncation or scaling) can be used to ensure feasibility of the solution. The proposed FDS is evaluated with data obtained during test trials. The feasible region concept, related with the problem of thruster velocity saturation, is developed in order to provide geometrical interpretation of the control allocation problem. The proposed FDAS is implemented as a Simulink model (ROV simulator), in order to evaluate its performance in different faulty situations.     

Neuro-control of unmanned underwater vehicles

Unmanned underwater vehicles (UUVs) typically operate in uncertain and changing environments. Since the dynamics of UUVs are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions, a high-performance control system of a UUV is needed to have the capacities of learning and adaptation to the variations in the UUV's dynamics. This paper presents the utilization of an adaptive neuro-control scheme as a controller for controlling a UUV in six degrees of freedom. No prior offline training phase and no explicit knowledge of the structure of the vehicle are required, and the proposed scheme exploits the advantages of both neural network control and adaptive control. Asymptotic convergence of the UUV's tracking errors and stability of the presented control system is guaranteed on the basis of the Lyapunov theory. In this paper, neural network architectures based on radial basis functions and multilayer structures have been used to evaluate the performance of the adaptive controller via computer simulation.     

Depth control of autonomous underwater vehicles using indirect robust control method

In this article, we propose a robust depth control design scheme for autonomous underwater vehicles (AUVs) in the presence of hydrodynamic parameter uncertainties and disturbances. The controller is designed via a new indirect robust control method that handles the uncertainties by formulating the uncertainty bounds into the cost functional and then transforming the robust control problem into an equivalent optimal control problem. Both robust asymptotic stability and optimality can be achieved and proved with this new formulation. The θ-D method is utilised to solve the resultant nonlinear optimal control problem such that an approximate closed-form feedback controller can be obtained and thus is easy to implement onboard without intensive computation load. Simulation results demonstrate that robust depth control is accomplished under the system parameter uncertainties and disturbances with small control fin deflection requirement.     

液位传感器容错控制方法研究

传统的PID(比例-积分-微分)控制法在液位过程控制中占据主要地位,但由于液位控制系统,特别是其系统传感器容易发生故障,使得PID控制法具有抗故障能力不足的缺陷,对生产生活造成重大损失,因此需采用容错控制方法使得液位传感器发生故障时仍能较为正常运行,提出状态观测器估计传感器液位故障值的容错控制方法,使状态观测器对故障状态产生观测跟踪,将故障状态作用于原控制器,并使原控制器弃置已经存在故障的传感器信号,使得故障值被抵消。     

Implicit fault-tolerant control: application to induction motors

In this paper we propose an innovative way of dealing with the design of fault-tolerant control systems. We show how the nonlinear output regulation theory can be successfully adopted in order to design a regulator able to offset the effect of all possible faults which can occur and, in doing so, also to detect and isolate the occurred fault. The regulator is designed by embedding the (possible nonlinear) internal model of the fault. This idea is applied to the design of a fault-tolerant controller for induction motors in presence of both rotor and stator mechanical faults.     

Distributed adaptive fault-tolerant control against actuator faults and lossy interconnection links

This paper presents an adaptive method to solve the robust fault-tolerant control （FTC） problem for a class of large scale systems against actuator failures and lossy interconnection links. In terms of the special distributed architectures, the adaptation laws are proposed to estimate the unknown eventual faults of actuators and interconnections, constant external disturbances, and controller parameters on-line. Then a class of distributed state feedback controllers are constructed for automatically compensating the fault and disturbance effects on systems based on the information from adaptive schemes. On the basis of Lyapunov stability theory, it shows that the resulting adaptive closed-loop large-scale system can be guaranteed to be asymptotically stable in the presence of uncertain faults of actuators and interconnections, and constant disturbances. The proposed design technique is finally evaluated in the light of a simulation example.     

An adaptive fuzzy design for fault-tolerant control of MIMO nonlinear uncertain systems

This paper presents a novel control method for accommodating actuator faults in a class of multiple-input multiple-output (MIMO) nonlinear uncertain systems.The designed control scheme can tolerate both the time-varying lock-in-place and loss of effectiveness actuator faults.In each subsystem of the considered MIMO system,the controller is obtained from a backstepping procedure;an adaptive fuzzy approximator with minimal learning parameterization is employed to approximate the package of unknown nonlinear functions in each design step.Additional control effort is taken to deal with the approximation error and external disturbance together.It is proven that the closed-loop stability and desired tracking performance can be guaranteed by the proposed control scheme.An example is used to show the effectiveness of the designed controller.