Nonlinear observer design for state and disturbance estimation

A new systematic framework for nonlinear observer design that allows the concurrent estimation of the process state variables together with key unknown process or sensor disturbances is proposed. The nonlinear observer design problem is addressed within a similar methodological framework as the one introduced in [N. Kazantzis, C. Kravaris, Nonlinear observer design using Lyapunov's auxiliary theorem, Systems Control Lett. 34 (1998) 241; A.J. Krener, M. Xiao, Nonlinear observer design in the Siegel domain, SIAM J. Control Optim. 41 (2002) 932.] for state estimation purposes only. From a mathematical standpoint, the problem under consideration is addressed through a system of first-order singular PDEs for which a rather general set of solvability conditions is derived. A nonlinear observer is then designed with a state-dependent gain that is computed from the solution of the system of singular PDEs. Under the aforementioned conditions, both state and disturbance estimation errors converge to zero with assignable rates. The convergence properties of the proposed nonlinear observer are tested through simulation studies in an illustrative example involving a biological reactor.     

Control strategy for state and input observer design

In this note, an alternative to classical methods for observer design is discussed, based on a dual control approach: it is indeed highlighted how an observer (at least approximate) can be obtained for a system by designing a control law for an auxiliary copy of this system, so that it tracks the system output. An important ingredient in this approach is the use of high-gain in the control design. The strategy is illustrated by various examples, including the case of unknown input reconstruction.     

An observer design procedure for a class of nonlinear time-delay systems

This paper considers a class of uncertain, nonlinear differential state delayed control systems and presents a reduced-order observer design procedure to asymptotically estimate any vector state functionals. The method proposed involves decomposition of the delayed portion of the system into two parts: a matched and mismatched part. Provided that the rank of the mismatched part is less than the number of the outputs, a reduced-order linear functional observer, with any prescribed stability margin, can be constructed by using a simple procedure. A numerical example is given to illustrate the new design procedure and its features.     

Discrete-time nonlinear observer design using functional equations

The present research work proposes a new approach to the discrete-time nonlinear observer design problem. Based on the early ideas that influenced the development of the linear Luenberger observer, the proposed approach develops a nonlinear analogue. The formulation of the discrete-time nonlinear observer design problem is realized via a system of first-order linear nonhomogeneous functional equations, and a rather general set of necessary and sufficient conditions for solvability is derived using results from functional equations theory. The solution to the above system of functional equations can be proven to be locally analytic and this enables the development of a series solution method, that is easily programmable with the aid of a symbolic software package.     

一类基于观测器的网络控制系统鲁棒控制器设计

研究一类基于状态观测器的网络控制系统鲁棒稳定性和鲁棒控制器设计问题.考虑传感器时钟驱动、控制器和执行器事件驱动以及小于等于一个采样周期的不确定时延,建立了基于状态观测器的网络控制系统增广模型.利用线性矩阵不等式方法推导出该系统鲁棒稳定的条件,进一步给出了鲁棒控制律和状态观测器设计方法.数值仿真算例证明了分析方法和结果的有效性.     

Robust state observer and control design using command-to-state mapping

In this paper, by introducing the concept of command-to-state/output mapping, it is shown that the state of an uncertain nonlinear system can robustly be estimated if command-to-state mapping of the system and that of an uncertainty-free observer converge to each other. Then, a global Jacobian system is defined to capture this convergence property for the dynamics of estimation error, and a set of general stability and convergence conditions are derived using Lyapunov direct method. It is also shown that the conditions are constructive and can be reduced to an algebraic Lyapunov matrix equation by which nonlinear feedback in the observer and its corresponding Lyapunov function can be searched in a way parallel to those of nonlinear control design. Case studies and examples are used to illustrate the proposed observer design method. Finally, observer-based control is designed for systems whose uncertainties are generated by unknown exogenous dynamics.     

Lipschitz非线性系统观测器设计新方法

考虑了非线性项满足Lipschitz条件的非线性系统观测器设计问题, 利用Lyapunov方法给出了新的判断观测误差稳定性的条件, 并由所给的条件通过求解线性矩阵不等式来设计观测器. 通过算例与其它方法进行的比较, 说明了所提方法的有效性.     

基于奇异观测器的连续系统传感器故障估计

研究连续系统的传感器故障估计问题,提出一种奇异观测器来进行故障估计,所提方法不需要假设故障、故障导数和干扰的上界已知.通过HB∞性能指标抑制了干扰对故障估计的影响;采用线性矩阵不等式来获得奇异观测器的增益阵;利用Lyapunov泛函得到了观测误差动态系统的鲁棒渐近稳定性;最后通过飞行器仿真实例验证了所提方法的有效性.     

非方广义系统的未知输入有限时间观测器设计

研究了含未知输入的非方广义系统的有限时间输入解耦观测器设计问题,在一定条件下基于非方广义系统的结构特征,引入一个输入-状态对的非奇异转换,把含未知输入的非方广义系统等价地转化为输入已知的正常状态空间系统．用传统的设计正常状态空间系统观测器的方法去构造含未知输入的非方广义系统的未知输入观测器,并给出了观测器存在的充分条件,由此得出了有限时间观测器的设计步骤．     

Partial state observers for linear systems with unknown inputs

We consider the problem of constructing partial state observers for discrete-time linear systems with unknown inputs. Specifically, for any given system, we develop a design procedure that characterizes the set of all linear functionals of the system state that can be reconstructed through a linear observer with a given delay. By treating the delay as a design parameter, we allow greater flexibility in estimating state functionals, and are able to obtain a procedure that directly produces the corresponding observer parameters. Our technique is also applicable to continuous-time systems by replacing delayed outputs with differentiated outputs.     

Simultaneous state and input estimation of hybrid systems with unknown inputs

This paper addresses the problem of the simultaneous state and input estimation for hybrid systems when subject to input disturbances. The proposed algorithm is based on the moving horizon estimation (MHE) method and uses mixed logical dynamical (MLD) systems as equivalent representations of piecewise affine (PWA) systems. So far the MHE method has been successfully applied for the state estimation of linear, hybrid, and nonlinear systems. The proposed extension of the MHE algorithm enables the estimation of unknown inputs, or disturbances, acting on the hybrid system. The new algorithm is shown to improve the convergence characteristics of the MHE method by reducing the delay of convergent estimates, while assuring convergence for every possible sequence of input disturbances. To ensure convergence the system is required to be incrementally input observable, which is an extension to the classical incremental observability property.     

Extended active observer for force estimation and disturbance rejection of robotic manipulators

The current control methods applied to robotic manipulators either require full state and force measurements, or use the state and force estimation in the absence of any kind of disturbance. As an alternative approach, a new adaptive motion control approach for robotic manipulators extending the existing active observer for simultaneous inertial parameters and force estimation is proposed. The scheme provides accurate force and full state estimation in the presence of robot inertial parameter variations and measurement noise, both subsequently used in the design of a controller. Since the proposed method relies mainly on the position of the plant, it significantly reduces the difficulty and cost of implementation. The velocity, parameter and force signals are estimated from the position. The approach is applied to a typical two-degree-of-freedom (2DOF) robotic manipulator through computer simulation. The results are encouraging and demonstrate the noise rejection ability of the scheme.     

基于双曲正切函数的二阶时变参数扩张状态观测器

当传统扩张状态观测器(ESO)的状态初值与系统的状态初值相差较大时,普遍存在微分峰值现象.为了消除这种现象,本文给出了用双曲正切非线性函数构造ESO的一般形式,并且用Lyapunov函数证明了二阶ESO的误差系统为渐近稳定.然后又利用双曲正切函数自身的饱和特性,设计出一种时变ESO,可以实现微分峰值的有效抑制.最后,把这种ESO的仿真结果与经典ESO的仿真结果进行对比,表明这里提出的ESO能够有效抑制微分峰值现象,并可以获得系统状态变量和非线性扰动的精确估计.     

Fault diagnosis based on high-gain observer with an update law for a class of nonlinear systems

In this paper, by using the well-known high-gain observer design, an update law for the gain and an adaptive estimation of parameters, a new method of fault diagnosis for a class of nonlinear systems is presented. Without resort to any transformation for the parameters, the estimation errors of the states and the parameters are guaranteed to be globally exponentially convergent by a persistent excitation condition. Compared to the existing results, it can be applied to nonlinear systems with nonlinear terms admitting an incremental rate depending on the measured output. A case study further verifies the validity of the proposed research.     

Dynamic scaling and observer design with application to adaptive control

A constructive algorithm for designing an observer for a class of nonlinear systems is presented. We follow the invariant manifold based approach which allows one to shape the dynamics of the estimation error. However, this shaping relies on the solution of a partial differential equation, which becomes difficult for multi-output systems. In this paper we remove this restriction by adding to the reduced-order observer an output filter and a single dynamic scaling parameter. We show that this method can be applied to systems with unknown parameters, leading to a new class of adaptive controllers. As an application, we consider two examples: an induction motor with unknown load and a longitudinal controller for an aircraft with unknown aerodynamic properties.     

基于条件高斯分布的未知输入与状态估计算法

针对未知输入同时存在于系统方程和测量方程的直接馈通线性随机系统, 提出了一种同时估计未知输入 和状态的算法. 首先, 通过将未知输入模型描述为有限方差的高斯分布, 利用条件高斯分布的性质, 推导出新的滤波 算法, 以同时得到未知输入估计和状态估计. 其次, 证明了当未知输入的方差趋于无穷大时, 本文提出的算法等价于 已有的递归三步滤波算法. 最后, 分析了本文算法的渐进稳定性条件, 结果表明, 与已有算法相比, 本文的算法适用 范围更广.     

Robust adaptive observer for nonlinear systems with unmodeled dynamics

Unmodeled dynamics exist in almost all applications of observers due to the impossibility of using exact and detailed models. It is highly desired that the observers can dominate the effects of unmodeled dynamics independently to prevent the state estimations from diverging and to get the precise estimations. Based on adaptive nonlinear damping, this paper presents a robust adaptive observer for multiple-input multiple-output nonlinear systems with unknown parameters, uncertain nonlinearities, disturbances and unmodeled dynamics. The observer only has one adaptive parameter no matter how high the order of the system is and how many unknown parameters there are. With the proposed observer, neither estimating the unknown parameters or solving linear matrix inequalities is needed. It is shown that the state estimation error is uniformly bounded and can be made arbitrarily small.     

Consensus of multiagent systems based on disturbance observer

The objectives of this work are the development and design of disturbance observers (DO’s) for a team of agents that accomplish consensus on agents’ states in the presence of exogenous disturbances. A pinning control strategy is designed for a part of agents of the multiagent systems without disturbances, and this pinning control can bring multiple agents’ states to reaching an expected consensus value. Under the effect of the disturbances, nonlinear disturbance observers are developed for disturbances generated by an exogenous system to estimate the disturbances. Asymptotical consensus of the multiagent systems with disturbances under the composite controller can be achieved. Finally, by applying an example of multiagent systems with switching topologies and exogenous disturbances, the design of the parameters of DO’s are illuminated.     

An adaptive observer framework for accurate feature depth estimation using an uncalibrated monocular camera

This paper presents a novel solution to the problem of depth estimation using a monocular camera undergoing known motion. Such problems arise in machine vision where the position of an object moving in three-dimensional space has to be identified by tracking motion of its projected feature on the two-dimensional image plane. The camera is assumed to be uncalibrated, and an adaptive observer yielding asymptotic estimates of focal length and feature depth is developed that precludes prior knowledge of scene geometry and is simpler than alternative designs. Experimental results using real camera imagery are obtained with the current scheme as well as the extended Kalman filter, and performance of the proposed observer is shown to be better than the extended Kalman filter-based framework.