Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM

Active fault-tolerant control of nonlinear systems with wind turbine application

The thesis concerns the theoretical development of Active Fault-Tolerant Control (AFTC) methods for nonlinear system via T-S multiple-modelling approach. The thesis adopted the estimation and compensation approach to AFTC within a tracking control framework. In this framework, the thesis considers several approaches to robust T-S fuzzy control and T-S fuzzy estimation: T-S fuzzy proportional multiple integral observer (PMIO); T-S fuzzy proportional-proportional integral observer (PPIO); T-S fuzzy virtual sensor (VS) based AFTC; T-S fuzzy Dynamic Output Feedback Control TSDOFC; T-S observer-based feedback control; Sliding Mode Control (SMC). The theoretical concepts have been applied to an offshore wind turbine (OWT) application study. The key developments that present in this thesis are:• The development of three active Fault Tolerant Tracking Control (FTTC) strategies for nonlinear systems described via T-S fuzzy inference modelling. The proposals combine the use of Linear Reference Model Fuzzy Control (LRMFC) with either the estimation and compensation concept or the control reconfiguration concept.• The development of T-S fuzzy observer-based state estimate fuzzy control strategy for nonlinear systems. The developed strategy has the capability to tolerate simultaneous actuator and sensor faults within tracking and regulating control framework. Additionally, a proposal to recover the Separation Principle has also been developed via the use of TSDOFC within the FTTC framework.• The proposals of two FTTC strategies based on the estimation and compensation concept for sustainable OWTs control. The proposals have introduced a significant attribute to the literature of sustainable OWTs control via (1) Obviating the need for Fault Detection and Diagnosis (FDD) unit, (2) Providing useful information to evaluate fault severity via the fault estimation signals.• The development of FTTC architecture for OWTs that combines the use of TSDOFC and a form of cascaded observers (cascaded analytical redundancy). This architecture is proposed in order to ensure the robustness of both the TSDOFC and the EWS estimator against the generator and rotor speed sensor faults.• A sliding mode baseline controller has been proposed within three FTTC strategies for sustainable OWTs control. The proposals utilise the inherent robustness of the SMC to tolerate some matched faults without the need for analytical redundancy. Following this, the combination of SMC and estimation and compensation framework proposed to ensure the close-loop system robustness to various faults.• Within the framework of the developed T-S fuzzy based FTTC strategies, a new perspective to reduce the T-S fuzzy control design conservatism problem has been proposed via the use of different control techniques that demand less design constraints. Moreover, within the SMC based FTTC, an investigation is given to demonstrate the SMC robustness against a wider than usual set of faults is enhanced via designing the sliding surface with minimum dimension of the feedback signals

Active suspension control of electric vehicle with in-wheel motors

In-wheel motor (IWM) technology has attracted increasing research interests in recent years due to the numerous advantages it offers. However, the direct attachment of IWMs to the wheels can result in an increase in the vehicle unsprung mass and a significant drop in the suspension ride comfort performance and road holding stability. Other issues such as motor bearing wear motor vibration, air-gap eccentricity and residual unbalanced radial force can adversely influence the motor vibration, passenger comfort and vehicle rollover stability. Active suspension and optimized passive suspension are possible methods deployed to improve the ride comfort and safety of electric vehicles equipped with inwheel motor. The trade-off between ride comfort and handling stability is a major challenge in active suspension design. This thesis investigates the development of novel active suspension systems for successful implementation of IWM technology in electric cars. Towards such aim, several active suspension methods based on robust H∞ control methods are developed to achieve enhanced suspension performance by overcoming the conflicting requirement between ride comfort, suspension deflection and road holding. A novel fault-tolerant H∞ controller based on friction compensation is in the presence of system parameter uncertainties, actuator faults, as well as actuator time delay and system friction is proposed. A friction observer-based Takagi-Sugeno (T-S) fuzzy H∞ controller is developed for active suspension with sprung mass variation and system friction. This method is validated experimentally on a quarter car test rig. The experimental results demonstrate the effectiveness of proposed control methods in improving vehicle ride performance and road holding capability under different road profiles. Quarter car suspension model with suspended shaft-less direct-drive motors has the potential to improve the road holding capability and ride performance. Based on the quarter car suspension with dynamic vibration absorber (DVA) model, a multi-objective parameter optimization for active suspension of IWM mounted electric vehicle based on genetic algorithm (GA) is proposed to suppress the sprung mass vibration, motor vibration, motor bearing wear as well as improving ride comfort, suspension deflection and road holding stability. Then a fault-tolerant fuzzy H∞ control design approach for active suspension of IWM driven electric vehicles in the presence of sprung mass variation, actuator faults and control input constraints is proposed. The T-S fuzzy suspension model is used to cope with the possible sprung mass variation. The output feedback control problem for active suspension system of IWM driven electric vehicles with actuator faults and time delay is further investigated. The suspended motor parameters and vehicle suspension parameters are optimized based on the particle swarm optimization. A robust output feedback H∞ controller is designed to guarantee the system’s asymptotic stability and simultaneously satisfying the performance constraints. The proposed output feedback controller reveals much better performance than previous work when different actuator thrust losses and time delay occurs. The road surface roughness is coupled with in-wheel switched reluctance motor air-gap eccentricity and the unbalanced residual vertical force. Coupling effects between road excitation and in wheel switched reluctance motor (SRM) on electric vehicle ride comfort are also analysed in this thesis. A hybrid control method including output feedback controller and SRM controller are designed to suppress SRM vibration and to prolong the SRM lifespan, while at the same time improving vehicle ride comfort. Then a state feedback H∞ controller combined with SRM controller is designed for in-wheel SRM driven electric vehicle with DVA structure to enhance vehicle and SRM performance. Simulation results demonstrate the effectiveness of DVA structure based active suspension system with proposed control method its ability to significantly improve the road holding capability and ride performance, as well as motor performance

Observer based active fault tolerant control of descriptor systems

The active fault tolerant control (AFTC) uses the information provided by fault detection and fault diagnosis (FDD) or fault estimation (FE) systems offering an opportunity to improve the safety, reliability and survivability for complex modern systems. However, in the majority of the literature the roles of FDD/FE and reconfigurable control are described as separate design issues often using a standard state space (i.e. non-descriptor) system model approach. These separate FDD/FE and reconfigurable control designs may not achieve desired stability and robustness performance when combined within a closed-loop system.This work describes a new approach to the integration of FE and fault compensation as a form of AFTC within the context of a descriptor system rather than standard state space system. The proposed descriptor system approach has an integrated controller and observer design strategy offering better design flexibility compared with the equivalent approach using a standard state space system. An extended state observer (ESO) is developed to achieve state and fault estimation based on a joint linear matrix inequality (LMI) approach to pole-placement and H∞ optimization to minimize the effects of bounded exogenous disturbance and modelling uncertainty. A novel proportional derivative (PD)-ESO is introduced to achieve enhanced estimation performance, making use of the additional derivative gain. The proposed approaches are evaluated using a common numerical example adapted from the recent literature and the simulation results demonstrate clearly the feasibility and power of the integrated estimation and control AFTC strategy. The proposed AFTC design strategy is extended to an LPV descriptor system framework as a way of dealing with the robustness and stability of the system with bounded parameter variations arising from the non-linear system, where a numerical example demonstrates the feasibility of the use of the PD-ESO for FE and compensation integrated within the AFTC system.A non-linear offshore wind turbine benchmark system is studied as an application of the proposed design strategy. The proposed AFTC scheme uses the existing industry standard wind turbine generator angular speed reference control system as a “baseline” control within the AFTC scheme. The simulation results demonstrate the added value of the new AFTC system in terms of good fault tolerance properties, compared with the existing baseline system

Measurements, Models, Systems and Design

531 s.

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis