Optimal reset adaptive observer design

A reset adaptive observer (ReAO) is an adaptive observer consisting of an integrator and a reset law that resets the output of the integrator depending on a predefined reset condition. The inclusion of reset elements can improve the observer performance but it can also destroy the stability of the estimation process if the ReAO is not properly tuned. As contribution, a method to optimally tune the parameters and gains of the ReAO is presented. They are optimally chosen by solving the L₂ gain minimization problem, which can be rewritten as an equivalent LMI problem. The effectiveness of the proposed method is checked by simulations comparing the results of an optimal ReAO with an optimal traditional adaptive observer.     

An Improved Extended Active Observer for Adaptive Control of A <Emphasis Type="Italic">n</Emphasis><Emphasis Type="Italic">−</Emphasis>DOF Robot Manipulator

A novel algorithm for simultaneous force estimation and friction compensation of constrained motion of robot manipulators is presented. This represents an extension of the improved extended active observer (IEAOB) algorithm reported earlier and proposes a higher order IEAOB or N?th order IEAOB (IEAOB ?N) for a n?DOF robot manipulator. Central to this observer is the use of extra system states modeled as a Gauss-Markov (GM) formulation to estimate the force and disturbances including robot inertial parameters and friction. The stability of IEAOB ?N is verified through stability analysis. The IEAOB-1 is validated by applying it to a Phantom Omni haptic device against a Nicosia observer, disturbance observer (DOB)/reaction torque observer (RTOB), and nonlinear disturbance observer (NDO), respectively. The results show that the proposed IEAOB-1 is superior to the compared observers in terms of force estimation. Then, the performance of the IEAOB ? N is experimentally studied and compared to the IEAOB-1. Results demonstrate that the IEAOB ? N has an improved capability in tracking nonlinear external forces.     

基于自适应迭代学习算法的一类非线性系统故障检测与估计

针对迭代学习算法在非线性系统故障检测与估计过程中存在估计误差较大和收敛速度较慢等不足的问题,提出了一种基于龙格–库塔故障估计观测器模型的自适应迭代学习算法,有效降低了故障估计误差;并引入H∞性能指标,提高了故障估计观测器的收敛速度.该算法首先设计故障检测观测器对故障进行检测,然后设计故障估计观测器,并将自适应算法与迭代学习策略相结合,使得估计故障逐渐逼近真实故障,从而实现对非线性系统中多种常见故障的精确检测与估计.最后,通过机械臂旋转关节驱动电机的执行器故障仿真验证了所提算法的有效性.     

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.     

一种新的基于自适应观测器中立时滞系统故障估计的设计方法

研究了基于自适应观测器中立时滞系统的故障估计问题. 首先, 本文提出了一种新的快速自适应故障估计算法提升了故障估计的快速性和准确性. 同时, 一个时滞相关的判据用于减少设计过程中的保守性, 特别对于小时滞系统. 然后, 应用线性矩阵不等式技巧, 给出了详细的设计步骤. 最后, 仿真结果验证了所提方法的有效性.     

动力定位船舶自适应滑模无源观测器设计

针对带有模型参数不确定性的动力定位船舶,提出一种动力定位船全速域自适应滑模无源观测器,解决了现有观测器只能应用于低速作业动力定位系统的问题.采用速度估计误差作为滑模面,设计切换自适应律估计模型不确定项上界,保证了观测器增益的有界性和系统鲁棒性.对速度估计回路的无源性进行了分析,并证明了观测器的稳定性.最后利用船舶动力定位系统半实物仿真平台,验证了算法的有效性.     

Noncertainty equivalent adaptive control of robot manipulators without velocity measurements

This paper presents a noncertainty equivalent adaptive motion control scheme for robot manipulators in the absence of link velocity measurements. A new output feedback adaptation algorithm, based on the attractive manifold design approach, is developed. A proportional-integral adaptation is selected for the adaptive parameter estimator to strengthen the passivity of the system. In order to relieve velocity measurements, an observer is designed to estimate the velocities. The controller guarantees semiglobal asymptotic motion tracking and velocity estimation, as well as L_∞ and L₂ bounded parameter estimation error. The effectiveness of the proposed controller is verified by simulations for a two-link robot manipulator and a four-bar linkage. The results are further compared with the earlier certainty-equivalent adaptive partial and full state feedback controller to highlight potential closed-loop performance improvements.     

Fast adaptive fault estimation and accommodation for nonlinear time‐varying delay systems

This paper studies the problem of fault estimation and accommodation for a class of nonlinear time‐varying delay systems using adaptive fault diagnosis observer (AFDO). A novel fast adaptive fault estimation algorithm that does not need the derivative of the output vector is proposed to enhance the performance of fault estimation. Meanwhile, a delay‐dependent criteria is obtained based on free weighting matrix method with the purpose of reducing the conservatism of the AFDO design. On the basis of fault estimation, an observer‐based fault‐tolerant controller is designed to guarantee the stability of the closed‐loop system. In terms of matrix inequality, we derive sufficient conditions for the existence of the adaptive observer and fault‐tolerant controller. Simulation results are presented to illustrate the efficiency of the proposed method. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Unknown-input observation techniques for infiltration and water flow estimation in open-channel hydraulic systems

This paper addresses a problem of state and disturbance estimation for an open-channel hydraulic system. Particularly, a cascade of n canal reaches, joined by gates, is considered. The underlying Saint-Venant system of PDEs is managed by means of a collocation-based finite-dimensional approximation. The resulting nonlinear systems' dynamics are linearized, and an estimation algorithm is designed by combining a conventional linear unknown-input observer (UIO) and a nonlinear disturbance observer (DO) based on the sliding-mode approach. By using measurements of the water level in three points per reach, the suggested algorithm is capable of estimating, both, the time varying infiltration and the discharge variables in the middle point of the reaches. The UIO and DO design procedures are constructively illustrated throughout the paper, and simulation results are discussed to verify their effectiveness.     

Immersion and Invariance-based Sliding Mode Attitude Control of Tilt Tri-rotor UAV in Helicopter Mode

In this paper, we propose an immersion and invariance-based sliding mode controller for a tilt tri-rotor unmanned aerial vehicle subjects to parameter perturbation, unmodeled dynamics, and external disturbances. The control scheme is divided into three parts, including the disturbance observer, the attitude controller, and the control allocation. Firstly, to alleviate the chattering and improve the robustness for attitude control, the observer using immersion and invariance theory is developed to estimate the disturbance. Note that the observer can relax the requirement of disturbance upper bound and guarantee the convergence of the estimation error. Secondly, to improve the dynamic response capability, a sliding mode attitude controller with an adaptive switch function is designed based on the disturbance observer. Thirdly, a hierarchical control allocation algorithm is proposed. The performance improvement is illustrated by comparing with other sliding mode controllers. Simulations and flight experiments are conducted to verify the effectiveness and applicability of the proposed control scheme.

     

A globally exponentially convergent immersion and invariance speed observer for mechanical systems with non-holonomic constraints

The problem of velocity estimation for general, n degrees-of-freedom, mechanical systems, is of great practical and theoretical interest. For unconstrained systems many partial solutions have been reported in the literature. However, even in this case, the basic question of whether it is possible to design a globally convergent speed observer remains open. In this paper, an affirmative answer to the question is given for general mechanical systems with knon-holonomic constraints, by proving the existence of a 3n−2k+1-dimensional globally exponentially convergent speed observer. An observer for unconstrained mechanical systems is obtained as a particular case of this general result. Instrumental for the construction of the speed observer is the use of the Immersion and Invariance technique, in which the observer design problem is recast as a problem of rendering attractive and invariant a manifold defined in the extended state-space of the plant and the observer.     

Fault estimation for T-S fuzzy Markovian jumping systems based on the adaptive observer

The adaptive fault estimation problem is studied for a class of stochastic Markovian jumping systems (MJSs) with time delays and nonlinear parameters. By means of Takagi-Sugeno fuzzy models, the dynamics of observer error generator and the fuzzy error dynamical system are constructed. Based on the selected Lyapunov-Krasovskii functional framework, the adaptive fault estimation algorithm is proposed to enhance the rapidity and accuracy performance of fault estimation. In terms of linear matrix inequalities techniques, a sufficient condition on the existence of the adaptive observer is presented and proved. Moreover, the presented results are also extended to multiple time-delayed nonlinear MJSs. A numerical example is given at last to illustrate the effectiveness of the proposed approach.     

Backstepping control with speed estimation of PMSM based on MRAS

This paper presents a backstepping control method with speed estimation of permanent magnet synchronous motor (PMSM) based on model reference adaptive system (MRAS). First, a comprehensive dynamical model of PMSM in d–q axis and its space state equations are established. Next, using Lyapunov stability theorem, based on the backstepping control theory, the PMSM rotor speed and current backstepping controllers are designed. Furthermore, using Popov stability theory, based on MRAS, the PMSM rotor speed observer is designed. Finally, Matlab/Simulink simulation results show that the backstepping control and speed observer are effective and feasible.     

Real-time Topography and Hamaker Constant Estimation in Atomic Force Microscopy Based on Adaptive Fading Extended Kalman Filter

In this study, a novel technique based on adaptive fading extended Kalman filter for atomic force microscopy is proposed to directly estimate the topography of a sample surface without needing any control system. While in conventional imaging techniques, the scanning speed or the bandwidth is limited due to a relatively large settling time, the method proposed in this research is able to address this issue and estimate the topography throughout transient oscillation of the microcantilever. With this aim, an estimation process using an adaptive fading extended Kalman filter (augmented with forgetting factor) as the system observer is designed and coupled with the system dynamics to obtain sample topography. Obtained results demonstrate that the sample height is estimated by this algorithm with high accuracy and a relatively high scanning speed. Moreover, the observer is able to identify the topography and Hamaker constant simultaneously. Therefore, the presented approach can compensate for the low scanning speed of the classical imaging method as well as eliminate the need for a closed-loop controller.

     

Neural observer-based adaptive compensation control for nonlinear time-varying delays systems with input constraints

This paper describes a neural network state observer-based adaptive saturation compensation control for a class of time-varying delayed nonlinear systems with input constraints. An advantage of the presented study lies in that the state estimation problem for a class of uncertain systems with time-varying state delays and input saturation nonlinearities is handled by using the NNs learning process strategy, novel type Lyapunov-Krasovskii functional and the adaptive memoryless neural network observer. Furthermore, by utilizing the property of the function tan h²(?/?)/?, NNs compensation technique and backstepping method, an adaptive output feedback controller is constructed which not only efficiently avoids the problem of controller singularity and input saturation, but also can achieve the output tracking. And the proposed approach is obtained free of any restrictive assumptions on the delayed states and Lispchitz condition for the unknown nonlinear functions. The semiglobal uniform ultimate boundedness of all signals of the closed-loop systems and the convergence of tracking error to a small neighborhood are all rigorously proven based on the NN-basis function property, Lyapunov method and sliding model theory. Finally, two examples are simulated to confirm the effectiveness and applicability of the proposed approach.     

Adaptive nonlinear observer for state and unknown parameter estimation in noisy systems

This paper proposes a novel adaptive observer for Lipschitz nonlinear systems and dissipative nonlinear systems in the presence of disturbances and sensor noise. The observer is based on an H_∞ observer that can estimate both the system states and unknown parameters by minimising a cost function consisting of the sum of the square integrals of the estimation errors in the states and unknown parameters. The paper presents necessary and sufficient conditions for the existence of the observer, and the equations for determining observer gains are formulated as linear matrix inequalities (LMIs) that can be solved offline using commercially available LMI solvers. The observer design has also been extended to the case of time-varying unknown parameters. The use of the observer is demonstrated through illustrative examples and the performance is compared with extended Kalman filtering. Compared to previous results on nonlinear observers, the proposed observer is more computationally efficient, and guarantees state and parameter estimation for two very broad classes of nonlinear systems (Lipschitz and dissipative nonlinear systems) in the presence of input disturbances and sensor noise. In addition, the proposed observer does not require online computation of the observer gain.     

Near optimal interval observers bundle for uncertain bioreactors

In this paper we design an interval observer for the estimation of unmeasured variables of uncertain bioreactors. The observer is based on a bounded error observer, as proposed in [Lemesle, V., & Gouzé, J.-L. (2005). Hybrid bounded error observers for uncertain bioreactor models. Bioprocess and Biosystems Engineering, 27, 311-318], that makes use of a loose approximation of the bacterial kinetics. We first show how to generate guaranteed upper and lower bounds on the state, provided that known intervals for the initial condition and the uncertainties are available. These so-called framers depend on a tuning gain. They can be run in parallel and the envelope provides the best estimate. An optimality criterion is introduced leading to the definition of an optimal observer. We show that this criterion provides directly a gain set containing the best framers. The method is applied to the estimation of the total biomass of an industrial wastewater treatment plant, demonstrating its efficiency.     

An improved multiple‐state observer of Boolean control networks

This paper explores the issue of state estimation for Boolean control networks (BCNs), and a kind of improved multiple‐state observer is proposed. The improved multiple‐state observer can be described by means of a specific BCN that overcomes the difficulty of the existing multiple state observers where it is difficult to find a general expression for the observer gain matrix. Next, based on the states that can possibly generate the output and those that are observed by the designed observer in current time step, an adaptive algorithm that completes the design of the multiple‐state observer is provided to update the observer states, and which makes the state estimation of Boolean control networks feasible. Finally, an example is presented to illustrate the effectiveness of the obtained results.