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．     

Generalized set‐theoretic unknown input observer for LPV systems with application to state estimation and robust fault detection

This paper proposes to design an unknown input observer (UIO) for the linear‐parameter‐varying (LPV) system on the basis of the set theory, which is named as the set‐theoretic UIO (SUIO). The advantage of the SUIO consists in that it combines active and passive approaches to obtain robustness in state estimation (SE) and fault detection (FD). The active approach is based on the use of UIO to decouple unknown inputs, while the passive approach is based on the set theory to bound uncertain factors that cannot be actively decoupled. As a result, the effect of both unknown inputs (process disturbances, modeling errors, etc.) and measurement noises can be appropriately handled in the residual signals compared with the standard UIO‐based SE and FD approaches. The design of SUIO can overcome the limitations of the traditional UIO design conditions, which can significantly broaden the application of the UIO‐based SE and FD theory. Moreover, this paper proposes a generalized framework that can provide more flexibility in the design of SUIO guaranteeing their stability by means of a group of matrix inequalities. Because the LPV system uses a collection of online obtainable scheduling variables to embed nonlinearities, the design of SUIO for the LPV system can be used to address the SE and FD problems of nonlinear systems. At the end of this paper, two case studies are used to illustrate the effectiveness of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd.     

A weighted variable gain super-twisting observer for the estimation of kinetic rates in biological systems

The knowledge of kinetic reaction rates is important for monitoring and controlling biotechnological processes. However, the lack of on-line sensors for this purpose and the inherent problems with numerical differentiation make observers indispensable. In this work, we propose the use of a weighted variable gain super-twisting observer (WVGSTO), applicable to a class of second-order nonlinear systems that include a measurable weight on the unmeasured variable and the possibility of bounding the perturbations with measurable functions. This estimation method is illustrated with an academic example and then applied to a fed-batch bioprocess.     

Design of equivalent-input-disturbance estimator using a generalized state observer

In this paper, we present a design method based on the concept of equivalent input disturbance (EID) to reject disturbances for a linear time-invariant system. A generalized state observer (GSO) is used to estimate an EID of the external disturbances, and the pole-assignment algorithm is employed to select the matrices of the GSO. Simulation and experimental results of a rotational speed control system demonstrate the validity of our method.     

Adaptive fuzzy tracking control for a class of uncertain MIMO nonlinear systems using disturbance observer

In this paper,the adaptive fuzzy tracking control is proposed for a class of multi-input and multioutput(MIMO)nonlinear systems in the presence of system uncertainties,unknown non-symmetric input saturation and external disturbances.Fuzzy logic systems(FLS)are used to approximate the system uncertainty of MIMO nonlinear systems.Then,the compound disturbance containing the approximation error and the timevarying external disturbance that cannot be directly measured are estimated via a disturbance observer.By appropriately choosing the gain matrix,the disturbance observer can approximate the compound disturbance well and the estimate error converges to a compact set.This control strategy is further extended to develop adaptive fuzzy tracking control for MIMO nonlinear systems by coping with practical issues in engineering applications,in particular unknown non-symmetric input saturation and control singularity.Within this setting,the disturbance observer technique is combined with the FLS approximation technique to compensate for the efects of unknown input saturation and control singularity.Lyapunov approach based analysis shows that semi-global uniform boundedness of the closed-loop signals is guaranteed under the proposed tracking control techniques.Numerical simulation results are presented to illustrate the efectiveness of the proposed tracking control schemes.     

Magnetic sensor-based simultaneous state and parameter estimation using a nonlinear observer

This paper presents an observer design technique for a newly developed non-intrusive position estimation system based on magnetic sensors. Typically, the magnetic field of an object as a function of position needs to be represented by a highly nonlinear measurement equation. Previous results on observer design for nonlinear systems have mostly assumed that the measurement equation is linear, even if the process dynamics are nonlinear. Hence, a new nonlinear observer design method for a Wiener system composed of a linear process model together with a nonlinear measurement equation is developed in this paper. First, the design of a two degree-of-freedom nonlinear observer is proposed that relies on a Lure system representation of the observer error dynamics. To improve the performance in the presence of parametric uncertainty in the measurement model, the nonlinear observer is augmented to estimate both the state and unknown parameters simultaneously. A rigorous nonlinear observability analysis is also presented to show that a dual sensor configuration is a sufficient and necessary condition for simultaneous state and parameter estimation. Finally, the developed observer design technique is applied to non-intrusive position estimation of the piston inside a pneumatic cylinder. Experimental results show that both position and unknown parameters can be reliably estimated in this application.     

On the estimation problem of a class of continuous bioreactors with unknown input

The estimation problem of substrate–biomass reactors with isotonic or nonisotonic growth is addressed. The unmeasured reactor and time-varying feed substrate concentrations must be estimated from the dilution rate input and biomass measurements. First, the solvability conditions (nonlinear-global observability or detectability) are characterized, finding that: (i) the conditions depend on particular motions and growth mechanisms, and (ii) with isotonic (or nonisotonic) growth, the unknown input–state pair is observable in the classical (or nonstandard) single-valued (S) [or bivalued (B)] sense. Then, a robustly convergent S (or B)-observer is designed accordingly. The S-observer yields the estimate of the only unmeasured input–state trajectory pair associated with the measured input–output signal. The B-observer yields the estimates of the two possible unmeasured input–state trajectory pairs associated with the measured input–output signal, each one satisfying the reactor mass balances. The developments and findings are illustrated with representative (Monod and Haldane) examples through analytic assessment and numerical simulation.     

An observer looks at the cell temperature in automotive battery packs

The internal temperature of Li-ion batteries for electric or hybrid vehicles is an important factor influencing their ageing. Generally not measured, it can be reconstructed from an external measurement and a model. This paper presents the simplified modelling of heat transfers in a battery module, leading to a Linear Parameter-Varying (LPV) model. Then, a polytopic observer is proposed to estimate the cell temperature and internal resistance, ensuring a tradeoff between the convergence speed and the noise of the estimated states. Experimental results show the good quality of the estimation and the diagnosis potential offered by internal resistance reconstruction.     

Fault estimation and active fault-tolerant control for a class of nonlinear systems with actuator and sensor faults based on unknown input iterative learning

In this paper, fault estimation and active fault-tolerant control are studied for a class of nonlinear systems with simultaneous actuator and sensor faults, as well as unknown external disturbances. Firstly, the state equation of a class of nonlinear systems is transformed into an augmented system state equation by extending the sensor fault as an auxiliary state. Then, a novel fault estimation observer based on iterative learning with unknown inputs is designed to estimate the system state, as well as actuator and sensor faults. Subsequently, by using the fault estimation information, a dynamic output feedback active fault-tolerant control scheme is proposed to compensate for the influence of faults on the system. Lyapunov stability theory is used to prove the stability of the closed-loop system and the convergence of the fault estimation observer. The gain matrices of the fault estimation observer and fault-tolerant controller are obtained by solving linear matrix inequalities. Furthermore, the paper avoids the use of the $$ norm in the convergence proof of the conventional iterative learning algorithm, which reduces the amount of calculation in the derivation process. Finally, the effectiveness and accuracy of the proposed method are verified through simulation of the DC motor angular velocity system.     

Sliding Mode Observer Design for a Parabolic PDE in the Presence of Unknown Inputs

This paper considers observer design for systems modeled by linear partial differential equations (PDEs) of parabolic type, which may be subject to unknown inputs. The system is assumed to have only one spatial dimension, over which it is discretised to obtain what is referred to as the lattice system, which is a set of linear time invariant (LTI) ordinary differential equations (ODEs) having a canonical Toeplitz‐like structure with a specific sparsity pattern. This lattice structure is shown to be particularly appropriate for step‐by‐step sliding mode observer design that can reconstruct the state estimates at the points of discretisation and estimate the unknown input. Simulation results for both stable and unstable PDEs show that accurate state estimates can be provided at the points of discretisation. An approach to reconstruct the unknown input is demonstrated.     

Robust tracking of the heating value in an underground coal gasification process using dynamic integral sliding mode control and a gain-scheduled modified Utkin observer

In this paper, a model-based control and state reconstruction of an underground coal gasification (UCG) process is elaborated. In order to deploy model-based control strategies, a sophisticated model of the UCG process based on partial differential equations is approximated with a nonlinear control-oriented model that adequately preserves the fundamental dynamic characteristics of the process. A robust dynamic integral sliding mode control (DISMC) is designed based on the control-oriented model to track the desired heating value, which is one of the key indicators for evaluating the performance of an UCG process. Unknown states required for the model-based control are reconstructed using a gain-scheduled modified Utkin observer (GSMUO). In order to assess the robustness of the nonlinear control and estimation techniques, the water influx phenomenon is considered as an input disturbance. Moreover, the underlying UCG plant model is subjected to parametric variations as well as measurement noise. In order to guarantee the stability of the overall system, the boundedness of the internal dynamics is also proved. To make a fair comparison, the performance of the proposed controller is compared with an integral sliding mode control (ISMC) and a classical proportional-integral (PI) controller. Simulation results highlight the effectiveness of the proposed control scheme in terms of minimum control energy and improved tracking error. Moreover, the simulation study shows that the combination of DISMC and GSMUO exhibit robustness against an input disturbance, parametric uncertainties and measurement noise.     

Modelling of an interactive system with an agent-based architecture using Petri nets, application of the method to the supervision of a transport system

This article presents an original method using high level Petri nets for the specification and design of interactive systems. We suggest an agent oriented architecture based on the classic components of an interactive application (application, dialogue control, interface with the application). Our approach is validated via the specification and design of a human–machine interface used in the supervision of a land-based transport system (bus/tramway).     

An exploratory architecture using data and knowledge to close the loop between design and Maintenance from a product quality perspective

Products for personalization require frequent dynamic interactions at all stages of the entire lifecycle. Based on this, the researchers have been studying closed-loop product lifecycle management. However, most of them often focus on the strategic and conceptual levels and ignore the technical details of implementation. From a product quality perspective, a three-tier architecture using data and knowledge to close the loop between design, operation, and maintenance (O&M) is proposed to deal with this problem. FMECA automation enabled by industrial knowledge graph and construction of Bayesian network for failure propagation is introduced in detail to enhance efficient integration of design and maintenance phases. A proof-of-concept simulation based on an electromechanical actuator validates the applicability and efficiency of the proposed method, showing a closed-loop optimization of design and O&M services. This article potentially enables manufacturers to implement the linkage of design and O&M service business.