Nonlinear Adaptive Observer Research Articles

Comparative analysis of experimental performance and stability of sensorless induction motor drives

This paper compares the experimental performance of three flux and speed observers for speed-sensorless induction motor drives and discusses the cause of their differences. The small signal analysis using the linearized model is carried out to analyze stability. Three methods are generally accepted to be representative candidates for high sensorless performance, namely: 1) rotor-flux model reference adaptive system (MRAS); 2) torque-current MRAS; and 3) adaptive nonlinear flux observer. Many other sensorless methods improved these methods. The paper discusses baseline conditions for the experiments and the stability analysis, which include matched load inertia, specified speed estimator dynamics, and sensorless operation within a speed control loop. For the comparison tests in the paper, the speed estimation dynamics of the methods are the same; this is important for parameter sensitivity. The paper concentrates on the low-speed performance, and all results shown are under sensorless speed control.

ADAPTIVE OBSERVER DESIGN FOR A MARINE PROPELLER

Recently, new control designs and performance monitoring schemes for electrically driven marine propellers have been developed. The new control designs have been shown to increase the thrust capability, reduce mechanical wear and tear, and limit power transients. To achieve optimum performance, these designs rely on knowledge of the friction in the propeller drive system. Since this information not necessarily is available, a nonlinear adaptive observer for estimating the propeller load torque and the coefficient of friction is developed. Experiments are provided to verify the observer performance.

FULLY ADAPTIVE SEMI-ACTIVE CONTROL OF VIBRATION ISOLATION BY MR DAMPER

This paper is concerned with a new fully adaptive control scheme for vibration isolation using a semi-active MR damper installed between ground and first floor, which is composed of two adaptive controllers. One is an adaptive inverse controller which can give necessary input voltage to MR damper so as to generate specified reference damping force to be acted on a controlled structure. The input voltage is decided using a nonlinear adaptive observer with identified model parameters of the MR damper which expresses hysteresis behavior of nonlinear dynamic friction mechanism of the MR fluid. The other is an adaptive reference controller which can match the dynamics of the first floor of structure to a reference dynamics. The proposed fully adaptive approach can cope with uncertainties in both models of MR damper and structure. Theoretical analysis and experimental results are also shown to validate its effectiveness.

Fault diagnosis based on adaptive observer for a class of non-linear systems with unknown parameters

In this paper, the fault diagnosis problem for a class of non-linear systems with uncertainty which depends on states, inputs and unknown constant parameters is discussed. Under some geometric conditions, the system is transformed into two different subsystems. One is not affected by actuator faults, so a non-linear adaptive observer can be designed based on the assumption of the strictly positive realness (SPR). The other whose states can be measured is affected by the faults. Actuator fault diagnosis is based on estimations of both the state and the unknown parameters with good accuracy. Discussions on release of SPR requirement and extension to the sensor fault case are also made. Finally, two examples are given in order to illustrate the applicability of the proposed methods for actuator fault diagnosis and sensor fault diagnosis respectively.

Detecting and diagnosing faults in dynamic stochastic distributions using a rational B-splines approximation to output PDFs

This paper presents a novel approach to detect and diagnose faults in the dynamic part of a class of stochastic systems the Such a group of systems are subjected to a set of crisp inputs but the outputs considered are the measurable probability density functions (PDFs) of the system output, rather than the system output alone. A new approximation model is developed for the output probability density functions so that the dynamic part of the system is decoupled from the output probability density functions. A nonlinear adaptive observer is constructed to detect and diagnose the fault in the dynamic part of the system. Convergency analysis is performed for the error dynamics raised from the fault detection and diagnosis phase and an applicability study on the detection and diagnosis of the unexpected changes in the 2D grammage distributions in a paper forming process is included.

On the Control of a Class of Electromechanical Systems

In this article we propose nonlinear adaptive observers that—identifying the parameters of the mechanical potential energy and the electrical resistance—asymptotically reconstruct the speed of a large class of fully actuated electromechanical systems. The design of the observers is based on the recently proposed technique of Immersion and Invariance and can be used in conjunction with any robustly stabilizing full information controller to achieve (global) asymptotic position regulation of the system measuring only the electrical coordinates and the mechanical position. This property is illustrated with a scheme derived via the Interconnection and Damping Assignment passivity-based control method. The case of a stepper motor is presented

STATE AND PARAMETER ESTIMATION FOR NONLINEAR SYSTEMS

A constructive method is proposed for the design of nonlinear adaptive observers with global convergence for recursive joint estimation of states and parameters. It extends an earlier result to systems with a more general parametrization. The considered nonlinear systems are those typically considered for the design of high gain observers with additional terms involving unknown parameters. A numerical example is presented for illustration.

Fault Diagnosis for a Class of Nonlinear Systems with Unknown Parameters

In this paper, the fault diagnosis problem for a class of nonlinear systems with unknown parameters which depend on both states and inputs is discussed. Under some geometric conditions, the system is transformed into two different subsystems. One is not affected by actuator faults, so the nonlinear adaptive observer and the parameter adaptive law can be designed based on the assumption of the strictly positive realness (SPR). The other whose states can be measured is affected by the faults. Actuator fault identification is based on estimations of both the state and the unknown parameters with good accuracy. Discussions on release of SPR requirement and extension to sensor fault case are also made. Finally, a series DC motor example is included to illustrate the applicability of the proposed methods.

Adaptive observers for unknown general nonlinear systems

Several neural network (NN) models have been applied successfully for modeling complex nonlinear dynamical systems. However, the stable adaptive state estimation of an unknown general nonlinear system from its input and output measurements is an unresolved problem. This paper addresses the nonlinear adaptive observer design for unknown general nonlinear systems. Only mild assumptions on the system are imposed: output equation is at least C(1) and existence and uniqueness of solution for the state equation. The proposed observer uses linearly parameterized neural networks (LPNNs) whose weights are adaptively adjusted, and Lyapunov theory is used in order to guarantee stability for state estimation and NN weight errors. No strictly positive real (SPR) assumption on the output error equation is required for the construction of the proposed observer.

Remarks on nonlinear adaptive observer design

A solution to the problem of state estimation for systems with unknown parameters is to use some on-line adaptation of the observer parameters. On the basis of various existing results for such adaptive observer designs, a unifying “adaptive observer form” is proposed in this paper. This form indeed emphasizes properties allowing some asymptotic state estimation in spite of unknown parameters, as well as additional properties which further allow parameter estimation. As an example, it is shown how an adaptive observer can be designed for a class of state affine systems.

A Nonlinear Adaptive Observer Based Method for Fault Detection and Isolation

In this paper we propose a new method for fault detection and isolation based on adaptive observers. Each of the adaptive observers estimates the state variables and one of the parameters of the monitored system. The algorithms for both residual generation and evaluation are presented. The proposed method is applicable to all nonlinear dynamic systems for which the required adaptive observers can be designed.

Estimation of turbine torque using an adaptive nonlinear observer for closed-loop control of vehicle automatic transmissions

This paper presents an adaptive sliding observer-based method for the estimation of turbine torque. The estimations of turbine torque can be used in the closed-loop control of vehicle power train for better shift quality. A sliding observer structure including saturation functions and boundary layers is applied to adapt torque converter parameters. The proposed method has been investigated via simulation and laboratory experimental studies. It has been shown via simulation and experiments that the proposed methodology is promising for the estimations of turbine torque since it uses only inexpensive angular velocity measurements and is not sensitive to parametric uncertainties.

Nonlinear Adaptive Observer Based Component Fault Diagnosis of Nonlinear Systems in Closed-Loops

Designing Adaptive observers for MIMO nonlinear time-varying deterministic systems is an open problem. In this paper we give a novel solution to this problem by use of a modified strong tracking filter(STF). First, the modified STF is presented, then some technical points of view to use the STF as a nonlinear adaptive observer are discussed. Later on, by combining with a modified Bayes classification algorithm, the proposed nonlinear adaptive observer is adopted to constitute a fault detection and diagnosis algorithm, and is successfully used for the component fault diagnosis of a continuous stirred tank reactor, a nonlinear deterministic system controlled in closedloops. Computer simulation results demonstrate the effectiveness of the proposed schemes

Nonlinear adaptive observer design based on B-spline neural network

A design approach of adaptive observer for a class of nonlinear systems with unknown time-varying nonlinearity is proposed. The nonlinear adaptive observer is constructed using a linear observer and a nonlinear compensation term. The latter term is determined recursively using a proposed “decon volution procedure”. The determined term is then approximated using a B-spline neural network with state estimate as its input. The convergence of state estimation error and weight error of the network is proved, and a weight updating rule is derived. Other weight updating rules, such as Instantaneous Gradient Descent method and Recursive Least-Squares method, are also discussed. Simulation results show that the proposed approach is effective

Nonlinear adaptive controller for electrodynamic transducers without additional sensor

Nonlinearities inherent in common transducers (woofers, shakers) produce new spectral components (distortion) in the reproduced sound which affect the perceived sound quality and impair active noise and vibration control. Recent research has shown that these nonlinear mechanisms are predictable and a nonlinear control can compensate for these distortions by inverse preprocessing of the electric input signal. Adaptive schemes have been developed to adjust the control parameters to the particular transducer and to cope with parameter uncertainties due to heating and aging. The adaptive controller requires an acoustic or mechanic output signal derived from the transducer. Using an additional sensor increases the costs and is impractical under harsh environment. However, a motional signal can also be derived from the back-induced EMF by monitoring the input current of the transducer only. Following this approach a nonlinear adaptive observer is implemented in a digital control system providing a robust and cost-effective solution for audio applications and active noise control.

A systematic approach to adaptive observer synthesis for nonlinear systems

Geometric techniques of controller design for nonlinear systems have enjoyed great success. A serious shortcoming, however, has been the need for access to full-state feedback. This paper addresses the issue of state estimation from limited sensor measurements in the presence of parameter uncertainty. An adaptive nonlinear observer is suggested for Lipschitz nonlinear systems, and the stability of this observer is shown to be related to finding solutions to a quadratic inequality involving two variables. A coordinate transformation is used to reformulate this inequality as a linear matrix inequality. A systematic algorithm is presented, which checks for feasibility of a solution to the quadratic inequality and yields an observer whenever the solution is feasible. The state estimation errors then are guaranteed to converge to zero asymptotically. The convergence of the parameters, however, is determined by a persistence-of-excitation-type constraint.

Design of a stable adaptive nonlinear observer for an exothermic stirred tank reactor

A stable nonlinear adaptive observer is constructed to estimate reactant composition and uncertain model parameters from temperature measurements in an exothermic stirred tank reactor. The construction is based on an adaptive observer canonical formulation proposed in the literature. Only a very limited class of nonlinear systems are known to be transformable to this canonical form, the exothermic reactor with first order irreversible kinetics however reveals itself to be suitable for the transformation. The utility of the adaptive observer is demonstrated by simulations while implementing feedback linearizing control of the reactor in the presence of state and parametric uncertainties.

Adaptive control of robotic manipulators including motor dynamics

Motor dynamics in a mechanical transmission chain of a robot are common in practice and may significantly affect the dynamic characteristics and stability of a robot. Synthesis of a robust controller for a robot, including motor dynamics, is essentially more complex than that for rigid-body manipulators. The controllers currently in existence require exact knowledge of both the robot and motor dynamics. The acceleration feedback is needed to form a complete state feedback vector for the third-order dynamic system. In this paper, an adaptive controller is proposed, which is able to deal with uncertainties in both the robot and the motor dynamics. An adaptive nonlinear observer is designed to observe the acceleration instead of measuring it by feedback. The closed-loop system is proved to be globally stable in the Lyapunov sense.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Observer-based Nonlinear Adaptive Tracking Control

This paper presents a nonlinear adaptive tracking control scheme. First, the output tracking control scheme is introduced. To increase the robustness of the control system against the uncertainty of the model parameters, a nonlinear adaptive observer which estimates the model parameters is used. The complete concept can be considered as a nonlinear adaptive control scheme, which guarantees the stability of the whole closed-loop system.

Grinding Force Control Using Nonlinear Adaptive Strategy

This paper presents a nonlinear adaptive control scheme for the grinding force control of unround grinding processes. First, a nonlinear control model is introduced. Based on this, a nonlinear controller is derived by using a model following scheme. To increase the robustness of the control system against parameter uncertainty, a nonlinear adaptive observer which estimates the model parameters is used. The complete concept can be considered as a nonlinear adaptive control scheme, which guarantees the stability of the whole closed-loop system. The simulation results show that the controller provides the desired performance.