Robust fault-tolerant leader-follower control of four-wheel-steering mobile robots using terminal sliding mode

This paper addresses the actuator failure compensation problem of non-linear fourwheel-steering mobile robots based on vehicle kinematics, undergoing both known and unknown failures causing degenerated steering performance or wheels stuck at some observable angles. Terminal sliding mode control technique is used to guarantee the tracking stability infinite time with the presence of actuator fault. Simulation results are given to illustrate the effectiveness of the proposed control scheme. © Institution of Engineers Australia 2012.

A fault detection technique for dynamical systems

This paper shows how a functional observer can be utilized to detect faults in LTI MIMO systems. The fault detection technique is designed so that the functional observer based fault indicator asymptotically converges to a fault indicator that can be derived based on the nominal system. The asymptotic value of the proposed fault indicator is not dependent on the functional observer parameters; moreover, by choosing appropriate functional observer parameters the convergence rate of the fault indicator can be altered. Observability of the system is not a requirement for the design of the fault detection scheme.

Fault detection for dynamical systems using first-order functional observers

This paper reports a new result on the fault detection of dynamical systems by employing only first-order functional observers. Indeed, we show that fault detection can be achieved by utilizing first-order functional observers. The advantages for having such simple structured observers are obvious from the economical and practical points of view as significant cost saving can be achieved. We derive existence conditions and an algorithm for the generation of residual signals to detect faults using firstorder functional observers. Two numerical examples are given to illustrate the proposed fault detection scheme. In one of the examples, a two-area interconnected power system with reheat thermal turbines is considered where only a first-order functional observer is designed to detect faults in the power system.

A novel model of sensitization and oral tolerance to peanut protein

The prevalence of food allergic diseases is rising and poses an increasing clinical problem. Peanut allergy affects around 1% of the population and is a common food allergy associated with severe clinical manifestations. The exact route of primary sensitization is unknown although the gastrointestinal immune system is likely to play an important role. Exposure of the gastrointestinal tract to soluble antigens normally leads to a state of antigen-specific systemic hyporesponsiveness (oral tolerance). A deviation from this process is thought to be responsible for food-allergic diseases. In this study, we have developed a murine model to investigate immunoregulatory processes after ingestion of peanut protein and compared this to a model of oral tolerance to chicken egg ovalbumin (OVA). We demonstrate that oral tolerance induction is highly dose dependent and differs for the allergenic proteins peanut and OVA. Tolerance to peanut requires a significantly higher oral dose than tolerance to OVA. Low doses of peanut are more likely to induce oral sensitization and increased production of interleukin-4 and specific immunoglobulin E upon challenge. When tolerance is induced both T helper 1 and 2 responses are suppressed. These results show that oral tolerance to peanut can be induced experimentally but that peanut proteins have a potent sensitizing effect. This model can now be used to define regulatory mechanisms following oral exposure to allergenic proteins on local, mucosal and systemic immunity and to investigate the immunomodulating effects of non-oral routes of allergen exposure on the development of allergic sensitization to peanut and other food allergens.

Online motor fault detection and diagnosis using a hybrid FMM-CART model

In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.