On-line reinforcement learning for trajectory following with unknown faults

Reinforcement learning (RL) is a key method for providing robots with appropriate control algorithms. Controller blending is a technique for combining the control output of several controllers. In this article we use on-line RL to learn an optimal blending of controllers for novel faults. Since one cannot anticipate all possible fault states, which are exponential in the number of possible faults, we instead apply learning on the effects the faults have on the system. We use a quadcopter pathfollowing simulation in the presence of unknown rotor actuator faults for which the system has not been tuned. We empirically demonstrate the effectiveness of our novel on-line learning framework on a quadcopter trajectory following task with unknown faults, even after a small number of learning cycles. The authors are not aware of any other use of on-line RL for fault tolerant control under unknown faults

Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault tolerant control

Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method

Fractional Order Fault Tolerant Control - A Survey

In this paper, a comprehensive review of recent advances and trends regarding Fractional Order Fault Tolerant Control (FOFTC) design is presented. This novel robust control approach has been emerging in the last decade and is still gathering great research efforts mainly because of its promising results and outcomes. The purpose of this study is to provide a useful overview for researchers interested in developing this interesting solution for plants that are subject to faults and disturbances with an obligation for a maintained performance level. Throughout the paper, the various works related to FOFTC in literature are categorized first by considering their research objective between fault detection with diagnosis and fault tolerance with accommodation, and second by considering the nature of the studied plants depending on whether they are modelized by integer order or fractional order models. One of the main drawbacks of these approaches lies in the increase in complexity associated with introducing the fractional operators, their approximation and especially during the stability analysis. A discussion on the main disadvantages and challenges that face this novel fractional order robust control research field is given in conjunction with motivations for its future development. This study provides a simulation example for the application of a FOFTC against actuator faults in a Boeing 747 civil transport aircraft is provided to illustrate the efficiency of such robust control strategies

Development and Evaluation of an Integral Sliding Mode Fault Tolerant Control Scheme on the RECONFIGURE Benchmark

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.This paper describes the development, application and evaluation of a linear parameter-varying integral sliding mode control allocation scheme to the RECONFIGURE benchmark model to deal with an actuator failure/fault scenario. The proposed scheme has the capability to maintain close to nominal (fault free) load factor control performance in the face of elevator failures/faults, by including a retro-fitted integral sliding mode term and then re-routing (via control allocation) the augmented control signal to healthy elevators without reconfiguring the baseline controller. In order to mitigate any chattering appearing in the elevator demands, the retro-fitted signal is based on a super-twisting sliding mode structure. This produces a control signal which is continuous and does not have the discontinuous switching nature of traditional sliding mode schemes. The scheme is evaluated using an industrial Functional Engineering Simulator developed as part of the RECONFIGURE project. Monte-Carlo campaign results are shown to demonstrate the performance of the proposed scheme.The work in this paper is supported by EU-FP7 Grant (FP7-AAT-2012-314544): RECONFIGUR

Model-Based Parameter Estimation for Fault Detection Using Multiparametric Programming

Fault detection has become increasingly important for improving the reliability and safety of process systems. This paper presents a model-based fault detection methodology for nonlinear process systems. The objective of this work is to detect faults by estimating the model parameters using multiparametric programming. The parameter estimates are obtained as an explicit function of the measurements by using multiparametric programming. The diagnosis of fault is carried out by monitoring the changes in the residual of model parameters. Case studies of fault detection for a single stage evaporator system and quadruple tank system are presented. A number of faulty and fault-free scenarios are considered to show the effectiveness of the presented approach. The proposed approach successfully estimates the model parameters and detects the faults through a simple function evaluation of explicit functions

A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives

During the last decade, due to the increasing importance of reliability and availability, railway industry is making greater use of fault diagnosis approaches for early fault detection, as well as Condition-based maintenance frameworks. Due to the influence of traction drive in the railway system availability, several research works have been focused on Fault Diagnosis for Railway traction drives. Fault diagnosis approaches have been applied to electric machines, sensors and power electronics. Furthermore, Condition-based maintenance framework seems to reduce corrective and Time-based maintenance works in Railway Systems. However, there is not any publication that summarizes all the research works carried out in Fault diagnosis and Condition-based Maintenance frameworks for Railway Traction Drives. Thus, this review presents the development of Health Assessment and Fault Diagnosis in Railway Traction Drives during the last decade

A Hybrid Sensor Fault Diagnosis for Maintenance in Railway Traction Drives

Due to the importance of sensors in railway traction drives availability, sensor fault diagnosis has become a key point in order tomove frompreventivemaintenance to condition-basedmaintenance. Most research works are limited to sensor fault detection and isolation, but only a few of them analyze the types of sensor faults, such as offset or gain, with the aim of reconfiguring the sensor in order to implement a fault tolerant system. This article is based on a fusion of model-based and data-driven techniques. First, an observer-based approach, using a Sliding Mode observer, is utilized for sensor fault reconstruction in real time. Then, once the fault is detected, a timewindowof sensormeasurements and sensor fault reconstruction is sent to the remotemaintenance center for fault evaluation. Finally, an offline processing is carried out to discriminate between gain and offset sensor faults, in order to get a maintenance decision-making to reconfigure the sensor during the next train stop. Fault classification is done by means of histograms and statistics. The technique here proposed is applied to the DC-link voltage sensor in a railway traction drive and is validated in a hardware-in-the-loop platform

Fault-Tolerant Aircraft Control Based on Self-Constructing Fuzzy Neural Networks and Multivariable SMC under Actuator Faults

This paper presents a fault-tolerant aircraft control (FTAC) scheme against actuator faults. Firstly, the upper bounds of the norms of the unknown functions are introduced, which contain actuator faults and model uncertainties. Subsequently, self-constructing fuzzy neural networks (SCFNNs) with adaptive laws are capable of obtaining the bounds. The bound estimation can reduce the computational burden with a lower amount of rules and weights, rather than the dynamic matrix approximation. Moreover, with the aid of SCFNNs, a multivariable sliding mode control (SMC) is developed to guarantee the finite-time stability of the handicapped aircraft. As compared to the existing intelligent FTAC techniques, the proposed method has twofold merits: fault accommodation can be promptly accomplished and decoupled difficulties can be overcome. Finally, simulation results from the nonlinear longitudinal Boeing 747 aircraft model illustrate the capability of the presented FTAC scheme