Adaptive fault-tolerant control of nonlinear uncertain systems: an information-based diagnostic approach

This paper presents a unified methodology for detecting, isolating and accommodating faults in a class of nonlinear dynamic systems. A fault diagnosis component is used for fault detection and isolation. On the basis of the fault information obtained by the fault-diagnosis procedure, a fault-tolerant control component is designed to compensate for the effects of faults. In the presence of a fault, a nominal controller guarantees the boundedness of all the system signals until the fault is detected. Then the controller is reconfigured after fault detection and also after fault isolation, to improve the control performance by using the fault information generated by the diagnosis module. Under certain assumptions, the stability of the closed-loop system is rigorously investigated. It is shown that the system signals remain bounded and the output tracking error converges to a neighborhood of zero.     

Sensor bias fault isolation in a class of nonlinear systems

This note presents a robust fault isolation scheme for a class of nonlinear systems with sensor bias type of faults. The proposed fault diagnosis architecture consists of a fault detection estimator and a bank of isolation estimators, each corresponding to a particular output sensor. Based on the class of nonlinear systems and sensor bias faults under consideration, the stability and learning properties of the fault isolation estimators are obtained, adaptive thresholds are derived for the isolation estimators, and fault isolability conditions are rigorously investigated, characterizing the class of nonlinear faults that are isolable by the proposed scheme. A simulation example is used to illustrate the effectiveness of the sensor bias fault isolation methodology.     

Cooperative Constrained Control of Distributed Agents With Nonlinear Dynamics and Delayed Information Exchange: A Stabilizing Receding-Horizon Approach

This paper addresses the problem of cooperative control of a team of distributed agents with decoupled nonlinear discrete-time dynamics, which operate in a common environment and exchange-delayed information between them. Each agent is assumed to evolve in discrete-time, based on locally computed control laws, which are computed by exchanging delayed state information with a subset of neighboring agents. The cooperative control problem is formulated in a receding-horizon framework, where the control laws depend on the local state variables (feedback action) and on delayed information gathered from cooperating neighboring agents (feedforward action). A rigorous stability analysis exploiting the input-to-state stability properties of the receding-horizon local control laws is carried out. The stability of the team of agents is then proved by utilizing small-gain theorem results.      

Model-free fault detection: A spectral estimation approach based on coherency functions

This paper presents a model?free fault detection technique based on the use of a specific spectral analysis tool, namely, squared coherency functions. The fault?free dynamic behaviour of the plant considered is described by a stochastic linear state equation, where the stochastic part is due to unpredictable external disturbances. A fault is assumed to be a nonlinear dynamic perturbation of the linear plant dynamics. The detection of the fault is achieved by on?line monitoring the estimates of a squared coherency function that is sensitive to the occurrences of non-linear events affecting the plant dynamics. A theoretical analysis of the fault-detectability issue is made and an original algorithm for a low?bias estimation of the squared coherency function is exploited to minimize the false-alarm rate. Finally, experimental results obtained by using real data concerning the three-tank benchmark problem are reported, showing the effectiveness of the proposed methodology.     

Silane-Mediated Expansion of Domains in Si-Doped κ-Ga2O3 Epitaxy and its Impact on the In-Plane Electronic Conduction

Unintentionally doped (001)-oriented orthorhombic κ-Ga₂O₃ epitaxial films on c-plane sapphire substrates are characterized by the presence of ≈ 10 nm wide columnar rotational domains that can severely inhibit in-plane electronic conduction. Comparing the in- and out-of-plane resistance on well-defined sample geometries, it is experimentally proved that the in-plane resistivity is at least ten times higher than the out-of-plane one. The introduction of silane during metal-organic vapor phase epitaxial growth not only allows for n-type Si extrinsic doping, but also results in the increase of more than one order of magnitude in the domain size (up to ≈ 300 nm) and mobility (highest µ ≈ 10 cm²V⁻¹s⁻¹, with corresponding lowest ρ ≈ 0.2 Ωcm). To qualitatively compare the mean domain dimension in κ-Ga₂O₃ epitaxial films, non-destructive experimental procedures are provided based on X-ray diffraction and Raman spectroscopy. The results of this study pave the way to significantly improved in-plane conduction in κ-Ga₂O₃ and its possible breakthrough in new generation electronics. The set of cross-linked experimental techniques and corresponding interpretation here proposed can apply to a wide range of material systems that suffer/benefit from domain-related functional properties.     

Physically accurate nonlinear models for fault detection and diagnosis: the case of a power plant

This paper deals with two main contributions. The first is the definition of an accurate nonlinear model of a section of a real 320 MW power plant. Within the framework of this global method, the second contribution is the modelling of the most common faults that may occur in plants like the one considered in the paper. All these models allow the simulation of the system both under normal working conditions and under anomalous conditions due to the occurrence of one of the faults modelled. The simulated model can be integrated with the automation system of the plant and used in real time, thus providing the plant technicians with crucial information on the plant behaviour, for instance, fault detection and diagnosis can be accomplished in a natural way. Simulation results and comparisons with real data show the effectiveness of the proposed approach.     

High-Gain Adaptive Control: A Derivative-Based Approach

In this work, we propose an adaptive scheme which is a counterpart of existing high gain control techniques based on control Lyapunov functions. Given a control Lyapunov function, the main idea is that of tuning the feedback gain according to a suitably-chosen Lyapunov time-derivative. The control gain is not monotonically non-decreasing as in existing techniques, but it is increased or decreased depending on the imposed derivative, thus avoiding the well-known issue of actuator over-exploitation. We are able to show robust convergence of the proposed adaptive control scheme as well as other interesting properties. For instance, it is possible to guarantee an a-priori given upper bound for the transient mode of behavior during adaptation. Furthermore, if the control Lyapunov function is designed based on an optimal control problem, then the control action is nominally optimal, precisely it yields the optimal trajectory for any initial condition, if the actual plant matches the nominal system.     

A neural state estimator with bounded errors for nonlinear systems

A neural state estimator is described, acting on discrete-time nonlinear systems with noisy measurement channels. A sliding-window quadratic estimation cost function is considered and the measurement noise is assumed to be additive. No probabilistic assumptions are made on the measurement noise nor on the initial state. Novel theoretical convergence results are developed for the error bounds of both the optimal and the neural approximate estimators. To ensure the convergence properties of the neural estimator, a minimax tuning technique is used. The approximate estimator can be designed offline in such a way as to enable it to process on line any possible measure pattern almost instantly