ROBUST FAULT DIAGNOSIS FOR HELICOPTER FCS BASED ON LINEAR PARAMETERVARYING ADAPTIVE OBSERVER

Based on the linear parameter-varying （LPV） adaptive observer, the robust fault diagnosis for a class of LPV systems with external disturbances is studied. Since the flight control system （FCS） is nonlinear and time-varying, the LPV technique is used for FCS. And then the adaptive fault estimation algorithm based on the LPV adaptive observer is proposed to estimate the fault. To minimize the effect of disturbances on the fault estimation, the H~ robust performance index is introduced to design the LPV adaptive fault diagnosis observer and the fault estimation algorithm. The result shows that the method has good estimation performance and is robust to external disturbances. The design method is presented in terms of linear matrix inequalities （LMIs）. Finally, a helicopter LPV FCS model with the actuator fault is used to illustrate the effectiveness of the proposed method.

H_∞tracking design for a class of decentralized nonlinear systems via fuzzy adaptive observer

A novel H∞ tracking-based decentralized indirect adaptive output feedback fuzzy controller for a class of uncertain large-scale nonlinear systems is developed. By virtue of the proper filtering of the observation error dynamics, the observer-based decentralized indirect adaptive fuzzy control scheme is presented for a class of large-scale nonlinear systems using the combination of H∞ tracking technique, a fuzzy adaptive observer and fuzzy inference systems. The output feedback and adaptation mechanisms are both robust and implementable indeed owing to their freedom from the unavailable observation error vector. All the signals of the closed-loop largescale system are guaranteed to stay uniformly bounded and the output errors take on H∞ tracking performance. Simulation results substantiate the effectiveness of the proposed scheme.

Adaptive Observer Based Backstepping Controller Design for Dynamic Ship Positioning

Modified adaptive observer based backstepping control system for dynamic positioning of ship is proposed. As an improvement, the adaptive observer takes the first-order wave frequency model and the bias term which represent the slowly varying environmental disturbances and the unmodeled dynamics. Thus, the wave-frequency motions are filtered out, and only the reconstructed low-frequency motions are sent as inputs of the controller. Furthermore, as the ship dynamics parameters are unknown, the adaptive estimation law is designed for both the unknown ship dynamics and the unmeasured state variables. Based on the estimated states and parameters, backstepping controller considering the integral action is designed. Global exponential stability （GES） for the total system is proved using Lyapunov direct method. Simulation results show a good performance of the observer and control system.

A novel robust proportional-integral (PI) adaptive observer design for chaos synchronization

In this paper, chaos synchronization in the presence of parameter uncertainty, observer gain perturbation and exogenous input disturbance is considered. A nonlinear non-fragile proportional-integral （PI） adaptive observer is designed for the synchronization of chaotic systems; its stability conditions based on the Lyapunov technique are derived. The observer proportional and integral gains, by converting the conditions into linear matrix inequality （LMI）, are optimally selected from solutions that satisfy the observer stability conditions such that the effect of disturbance on the synchronization error becomes minimized. To show the effectiveness of the proposed method, simulation results for the synchronization of a Lorenz chaotic system with unknown parameters in the presence of an exogenous input disturbance and abrupt gain perturbation are reported.

Actuator fault diagnosis of time-delay systems based on adaptive observer

A novel approach for the actuator fault diagnosis of time-delay systems is presented by using an adaptive observer technique. Systems without model uncertainty are initially considered, followed by a discussion of a general situation where the system is subjected to either model uncertainty or external disturbance. An adaptive diagnostic algorithm is developed to diagnose the fault, and a modified version is proposed for general system to improve robustness. The selection of the threshold for fault detection is also discussed. Finally, a numerical example is given to illustrate the efficiency of the proposed method.

Adaptive Observer-Based Fault Estimate for Nonlinear Systems

An approach for adaptive observer-based fault estimate for nonlinear system is proposed.H-infinity theory is applied to analyzing the design method and stable conditions of the adaptive observer, from which both system state and fault can be estimated. It is proved that the fault estimate error is related to the given H-infinity track performance indexes,as well as to the changing rate of the fault and the Lipschitz constant of the nonlinear item.The design steps of the adaptive observer are proposed.The simulation results show that the observer has good performance for fault estimate even when the system includes nonlinear terms, which confirms the effectiveness of the method.

Sensor fault diagnosis of time-delay systems based on adaptive observer

Presents a novel approach for the sensor fault diagnosis of time-delay systems by using an adaptive observer technique. The sensor tault is modeled as an additive perturbation described by a time varying function. Systems without model uncertainty are initially considered, followed by a discussion of a general situation where the system is subjected to either model uncertainty or external disturbance. An adaptive diagnostic algorithm is developed to diagnose the fault, and a modified version is proposed for general system to improve robusiness. The stability of fault diagnosis system is proved. Finally, a numerical example is given to illustrate the efficiency of the proposed method.

Comparison of Adaptive Simulation Observation Experiments of the Heavy Rainfall in South China and Sichuan Basin

This study examines the effectiveness of adaptive observation experiments using the ensemble transformation sensitivity(ETS) method to improve precipitation forecasts during heavy rainfall events in South China and the Sichuan Basin. High-resolution numerical models are employed to simulate adaptive observations. By identifying the sensitive areas of key weather system positions 42 hours before heavy rainfall events, the adaptive observations improve the prediction of jet streams, strong winds, and shear lines, which are essential for accurate heavy rainfall forecasting. This improvement is reflected in both the precipitation structure and location accuracy within the verification region. In South China, targeted observations enhance rainfall predictions by improving water vapor transport. In the Sichuan Basin, adaptive observations refine water vapor transport and adjust vortex dynamics. This research highlights the importance of accurately predicting shear lines and jet streams for forecasting heavy rainfall in these areas. Overall, this study found that adaptive observation enhances the precipitation forecast skills of the structure and location for heavy rainfall in South China and the Sichuan Basin, emphasizing their potential utility in operational numerical weather prediction.

Observer Based Control for a Class of Systems with Output Deadzone Nonlinearity

In this paper, a combination of model based adaptive design along with adaptive linear output feedback controller is used to compensate for robotic manipulator with output deadzone nonlinearity. The deadzone dynamics are utilized to adaptively estimate the deadzone parameter and a switching function is designed to eliminate the error produced in the adaptive observer dynamics. The overall design of the closed loop system ensures stability in the BIBO criterion.

Robust Control of Robotic Manipulators in the Task-Space Using an Adaptive Observer Based on Chebyshev Polynomials

In this paper,an adaptive observer for robust control of robotic manipulators is proposed.The lumped uncertainty is estimated using Chebyshev polynomials.Usually,the uncertainty upper bound is required in designing observer-controller structures.However,obtaining this bound is a challenging task.To solve this problem,many uncertainty estimation techniques have been proposed in the literature based on neuro-fuzzy systems.As an alternative,in this paper,Chebyshev polynomials have been applied to uncertainty estimation due to their simpler structure and less computational load.Based on strictly-positive-rea Lyapunov theory,the stability of the closed-loop system can be verified.The Chebyshev coefficients are tuned based on the adaptation rules obtained in the stability analysis.Also,to compensate the truncation error of the Chebyshev polynomials,a continuous robust control term is designed while in previous related works,usually a discontinuous term is used.An SCARA manipulator actuated by permanent magnet DC motors is used for computer simulations.Simulation results reveal the superiority of the designed method.

Robust adaptive observer for nonlinear time-delay systems with disturbances and uncertainties

A method of designing robust adaptive observer for nonlinear continuous-time systems with time-delays is presented.It is assumed that the nonlinear functions of the system satisfy Lipschitz condition.The observer is derived considering plant disturbances,structured parameter uncertainties and unknown parameters.Like any other adaptive observer for nonlinear systems with ideal working conditions,it also simultaneously estimates both the states and unknown parameters.This property is very much useful for many applications like fault diagnosis.The convergence of the state errors and estimated unknown parameters are analysed by H_(∞)-control.The sufficient conditions for the existence of the observer are determined and expressed in linear matrix inequality form.With the help of a numerical example,the effectiveness of the proposed observer is demonstrated.

On Fractional Order Adaptive Observer

This article derives a new scheme to an adaptive observer for a class of fractional order systems. Global asymptotic convergence for joint state-parameter estimation is established for linear time invariant single-input single-output systems. For such fractional order systems, it is proved that all the signals in the resulting closed-loop system are globally uniformly bounded, the state and parameter estimation errors converge to zero. Potential applications of the presented adaptive observer include online system identification, fault detection, adaptive control of fractional order systems, etc. Numerical simulation examples are presented to demonstrate the performance of the proposed adaptive observer.

Adaptive Observer for Dynamic Voltage Restorer with Optimized Proportional Integral Gains

An algorithm based on a sliding-mode adaptive observer is proposed for the effective control of dynamic voltage restorers(DVRs).Three single-phase voltage source converter-based topologies are implemented for the DVR.In this control,frequency adaption is considered for estimating the phase angle,system frequency,and fundamental component of the disturbed input voltage signals.The estimated fundamental component of the supply voltage is used to generate the DVR reference load voltage.The phase jump in the supply voltage is also considered for DVR compensation studies along with voltage sag,swell,and voltage distortions.For this purpose,the gains of the proportional integral(PI)controllers used in this control algorithm are estimated using a nature-inspired optimization approach for the desired response.A moth flame optimization algorithm is implemented for PI controller gain tuning owing to the advantage of finding the best solution by each moth’s search and updating it.Through Matlab simulation and hardware testing,the performance of the DVR with an adaptive observer is found to be satisfactory for supply voltage sag,swell,phase jump,and voltage distortions.

Universal Adaptive Observerfor Nonlinear Time varying Deterministic Systems

Designing adaptive observers for MIMO nonlinear time varying deterministic systems is an open problem .Inthis papera novelsolutiontothis problem is given byuse ofa “strongtrackingfilter(STF)”.First,the STFis outlined,then sometechnicalpoints ofview to usethe STFas an adaptive observer are discussed .Finally, two typicalexamples are presentedtoillustrate the effectiveness ofthe proposed approach.

Constant-gain nonlinear adaptive observers revisited: an application to chemostat systems

This study deals with constant-gain adaptive observers for nonlinear systems,for which relatively few solutions are available for some particular cases.We introduce an asymptotic observer of constant gain for nonlinear systems that have linear input.This allows the observer design to be formulated within the linear matrix inequality paradigm provided that a strictly positive real condition bet ween the inpu t disturbance and the output is fulfilled.The proposed observer is t hen applied to a large class of nonlinear chemos tat dynamical systems that are widely used in the fermentation process,cell cultures,medicine,etc.In fact,under standard practical assumptions,the necessary change of the chemos tat state coordinates exis ts,allowing use of the const ant-gain observer.Finally,the developed theory is illustrated by estimating pollutant concentration in a Spirulina maxima wastewater treatment facility.

Robust Adaptive Gain Higher Order Sliding Mode Observer Based Control-constrained Nonlinear Model Predictive Control for Spacecraft Formation Flying

This work deals with the development of a decentralized optimal control algorithm, along with a robust observer,for the relative motion control of spacecraft in leader-follower based formation. An adaptive gain higher order sliding mode observer has been proposed to estimate the velocity as well as unmeasured disturbances from the noisy position measurements.A differentiator structure containing the Lipschitz constant and Lebesgue measurable control input, is utilized for obtaining the estimates. Adaptive tuning algorithms are derived based on Lyapunov stability theory, for updating the observer gains,which will give enough flexibility in the choice of initial estimates.Moreover, it may help to cope with unexpected state jerks. The trajectory tracking problem is formulated as a finite horizon optimal control problem, which is solved online. The control constraints are incorporated by using a nonquadratic performance functional. An adaptive update law has been derived for tuning the step size in the optimization algorithm, which may help to improve the convergence speed. Moreover, it is an attractive alternative to the heuristic choice of step size for diverse operating conditions. The disturbance as well as state estimates from the higher order sliding mode observer are utilized by the plant output prediction model, which will improve the overall performance of the controller. The nonlinear dynamics defined in leader fixed Euler-Hill frame has been considered for the present work and the reference trajectories are generated using Hill-Clohessy-Wiltshire equations of unperturbed motion. The simulation results based on rigorous perturbation analysis are presented to confirm the robustness of the proposed approach.

An adaptive load torque observer using hyperstability for PMSM servo system

An adaptive load torque observer is presented to compensate the torque ripple in PMSM servo system. A simple adaptive scheme is derived using Popov ＇ s hyperstability theory. The torque ripple detected by the observer is compensated by a feed forwarding equivalent current which gives fast response. The noisy current information is exempt from the observer to avoid its deterioration to the quality of the observer. The speed measurement delay is considered by using observed speed sinee the instantaneous velocity can＇t be estimated precisely at low speed because of too few position pulses from the absolute encoder during one time interval. Simulation and experimental results demonstrate that the proposed method can improve the dynamic performance of PMSM servo system satisfyingly.

Improved estimation of rotor position for sensorless control of a PMSM based on a sliding mode observer

This work proposes a new strategy to improve the rotor position estimation of a permanent magnet synchronous motor(PMSM) over wide speed range. Rotor position estimation of a PMSM is performed by using sliding mode observer(SMO). An adaptive observer gain was designed based on Lyapunov function and applied to solve the chattering problem caused by the discontinuous function of the SMO in the wide speed range. The cascade low-pass filter(LPF) with variable cut-off frequency was proposed to reduce the chattering problem and to attenuate the filtering capability of the SMO. In addition, the phase shift caused by the filter was counterbalanced by applying the variable phase delay compensation for the whole speed area. High accuracy estimation result of the rotor position was obtained in the experiment by applying the proposed estimation strategy.

Enhanced adaptive nonlinear extended state observer for pure feedback systems with matched and mismatched disturbances

In this paper, an enhanced adaptive nonlinear extended state observer (EANESO) for single-input single-output pure feedback systems in the presence of external time-varying disturbances is proposed. In this paper, a nonlinear system with matched and mismatched disturbances is considered. The conventional extended state observer (ESO) can only be applied to systems that are in the form of integral chains. Moreover, this method has limitations in the face of mismatched disturbances. In the presence of time-varying disturbances, the traditional ESOs cannot estimate the disturbances accurately. To overcome this limitation, an EANESO is proposed in this paper. The main idea is to design the nonlinear ESO (NESO) to estimate the states of the system and multiple disturbances simultaneously. The observer gains are considered time-varying and adjusted with adaptation laws to improve the estimation accuracy and overcome the peaking phenomenon. Next, the proposed controller is designed based on output feedback to eliminate the effects of multiple disturbances and stabilize the closed-loop system. Subsequently, the stability analysis of the closed-loop system and convergence of the observer error are discussed. Finally, the proposed method is applied to the inverted pendulum system. The simulated results show good performance of the proposed method as compared with a recently published scheme in the related literature.

Model-driven full system dynamics estimation of PMSM-driven chain shell magazine

Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.