Robust Unknown Input Observer-Based Fault Estimation of Leader–Follower Linear Multi-agent Systems

Helicopter unmanned aerial vehicle (HUAV) are an ideal platform for academic researchs. Abilities of this vehicle to take off and landing vertically while performing hover flight and various flight maneuvers have made them proper vehicles for a wide range of applications. This paper suggests a model-based fault detection and isolation for HUAV in hover mode. Moreover in HUAV, roll, pitch and yaw actuator faults are coupled and affect each other, hence, we need a method that decouples them and also separates fault from disturbance. For this purpose, a robust unknown input observer (UIO) is designed to detect bias fault and also catastrophic fault such as stuck in actuators of HUAV. The robust UIO isolates roll and pitch actuator faults from yaw actuator fault. The novelty of this manuscript is the design of two UIO observers to detect and decouple the faults of helicopter actuators, one for lateral and longitudinal actuators and the other for pedal actuator. Also, the proposed method is compared with extended Kalman filter (EKF). Simulation results show effectiveness of the proposed method for detection and isolation of actuator faults with less number of observers and it is able to decouple fault and disturbance effects.

Fault detection in a three-tank hydraulic system using unknown input observer and extended Kalman filter

This study proposes a fault estimation (FE)‐based fault‐tolerant control (FTC) strategy to maintain system reliability and achieve desirable control performance for a 3‐DOF helicopter system with both actuator drift and oscillation faults and saturation. The effects of the faults and saturation are combined into a composite non‐differentiable actuator fault function, which is approximated by a differentiable function and estimated together with the system state using a non‐linear unknown input observer. An adaptive sliding mode controller based on the estimates is developed to compensate the effects of the faults and saturation. Taking into account the bi‐directional robustness interactions between the FE and FTC functions, an integrated design approach is proposed to obtain the observer and controller gains in a single step, so as to achieve robust overall FTC system performance. In fault‐free cases, the proposed strategy can be considered as a new approach for anti‐windup control to compensate the effect of input saturation. Comparative simulations are provided to verify the effectiveness of the proposed design under different actuator fault scenarios.

Event-triggered robust cooperative output regulation for a class of linear multi-agent systems with an unknown exosystem

Unknown input observer based neuro-adaptive fault-tolerant control for vehicle platoons with sensor fault and output quantization

This paper treats the Fault Estimation (FE) problem using a data-projection method. An on-line fast fault estimation procedure is proposed using input-output data collected on-line and off-line. The model structure of the system (linear, bilinear, ..) is supposed to be known but the model parameters values are not needed. No prior model parameter estimation procedure is required. The method was presented in previous work by the authors for sensor FE. This paper extends the procedure for actuator faults. Simulation results of a separately excited DC motor are presented to illustrate the efficiency of the proposed data-projection method for actuator fault detection and estimation.

UIO-based Fault Estimation for a Class of Time-varying Systems with Event-triggered Transmissions

The reliability and accuracy of the wind conversion system largely depend on the early detection and diagnosis of faults. In this paper, a novel fault estimator for wind turbine pitch and drive train systems is developed. The main objective is to estimate actuator and sensor faults along with the system states while mitigating the impact of process disturbances and noises. To accomplish this, an augmented state is created by combining the states of the system and different faults. Subsequently, an Unknown Input Observer (UIO) is developed to estimate them simultaneously. The UIO matrices are obtained by optimizing a multi-objective function formed by transforming states and faults estimation errors into the frequency domain using a genetic algorithm. Compared with other approaches, particularly H∞, the proposed technique shows great superiority in accurately estimating various actuators and sensors faults.

Robust fault detection for uncertain switched systems

In this study, the problem of fault estimation observer design with finite-time convergence specification for continuous-time dynamic systems subject to external disturbances. First, the unknown input observer is constructed to achieve accurate estimation of the occurred fault and to guarantee robustness against the disturbance. Then a pole placement-based fault estimation observer is constructed using time-delay design such that the fault estimation error converges to zero in finite time. Unlike conventional literatures, the proposed fault estimator with finite-time convergence specification does not contain discontinuous sign function. Finally, simulation results of an aircraft application are presented to illustrate the finite-time convergence of the proposed fault estimation observer.

An integrated fault estimation (FE) and fault-tolerant tracking control (FTTC) strategy is developed for Lipschitz nonlinear multiagent systems subject to actuator faults, external disturbance, and uncertainties. First, for each agent, a corresponding unknown input observer with reduced/full order is constructed to obtain the FE. Then, a state/output feedback FTTC strategy is proposed based on the integral sliding-mode technique and adaptive super-twisting algorithm. The observer and controller gains are obtained simultaneously via H∞ optimization with a linear matrix inequality formulation. Finally, the comparison simulations are provided to demonstrate the effectiveness of the proposed algorithm.

This technical note investigates the positive consensus problem of multiagent systems with directed communication topologies, where all the agents have identical continuous-time positive linear dynamics. Existing works of such a problem mainly focus on the case, where networked communication topologies are of either undirected and incomplete graphs, or strongly connected directed graphs. In contrast to them, we study this problem in which the communication topologies of the multiagent system are described by directed graphs each containing a spanning tree, which is a more general and new scenario due to the interplay between the eigenvalues of the Laplacian matrix and the controller gains. Based on the existing results in spectral graph theory and positive linear systems theory, several necessary, and sufficient conditions on positive consensus of the directed multiagent system are derived through using linear matrix inequality techniques. A primal-dual iterative algorithm is developed for the computation of solutions. Finally, several numerical simulations are provided to illustrate the effectiveness of the proposed theoretical results.

International audience

This paper considers the reduced-order observer design problem for one-sided Lipschitz nonlinear systems with unknown inputs. The systems under consideration are a larger class of nonlinear systems than the well-studied Lipschitz systems and have inherent advantages with respect to conservativeness. For such systems, we propose a reduced-order unknown input observer (UIO) by using the linear matrix inequality (LMI) technique. Sufficient conditions that guarantee the existence of the reduced-order UIO are established and transformed into the format of LMIs via using the matrix generalized inverse technique. Simulation results are finally given to illustrate the effectiveness of the proposed design.

This paper is concerned with the problem of actuator fault estimation (FE) for vehicle active suspension systems. First, the fast FE approach, which combines the output error term with its derivative term in the FE algorithm, is extended to the active suspension system with actuator fault and external disturbance input. Then, considering three typical kinds of actuator faults, i.e., constant gain change fault, drift fault, and stuck fault, genetic algorithm (GA) is employed to optimize the adjustable parameters in the FE algorithm, which are usually determined by trials. Finally, simulation results of FE and fault‐tolerant control (FTC) are presented to illustrate the effectiveness and applicability of the proposed FE method.