Detection of asymmetric control valve stiction from oscillatory data using an extended Hammerstein system identification method

The study in this paper is motivated by the detection of control valves with asymmetric stiction resulting in oscillations in feedback control loops. The joint characterization of the control valve and the controlled process is formulated as the identification of a class of extended Hammerstein systems. The input nonlinearity is described by a point-slope-based hysteretic model with two possibly asymmetric ascent and descent paths. An iterative identification method is proposed, based on the idea of separating the ascent and descent paths subject to the oscillatory input and output. The structure of the formulated extended Hammerstein system is shown to be identifiable, and the oscillatory signals in feedback control loops are proved to be informative by exploiting the cyclo-stationarity of these oscillatory signals. Numerical, experimental and industrial examples are provided to illustrate the effectiveness of the proposed identification method.     

State and unknown input estimation for linear discrete-time systems

In this paper, a state and unknown input delayed estimator is designed for discrete-time linear systems even if some well-known matching condition does not hold. This result is obtained using a constructive algorithm that analyzes the state observability and the left invertibility of the systems with unknown inputs and that provides a suitable canonical transformation for the design of the estimator.     

A simple method for detecting valve stiction in oscillating control loops

This paper presents a simple and new method for detecting valve stiction in an oscillating control loop. The method is based on the calculation of areas before and after the peak of an oscillating signal. The proposed method is intuitive, requires very little computational effort, and is easy to implement online. Analytical results are derived to show the theoretical basis of the new method and field results are presented to show its effectiveness on real world control loops.     

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.     

Unbiased minimum-variance state estimation for linear systems with unknown input

The problem of state estimation for a linear system with unknown input, which affects both the system and the output, is discussed in this paper. A recursive optimal filter with global optimality in the sense of unbiased minimum variance over all linear unbiased estimators, is provided. The necessary and sufficient condition for the convergence and stability is also given, which is milder than existing approaches.     

气动控制阀粘滞模型的改进与比较

Choudhury模型和双层二叉树模型在执行阀粘滞特性研究中应用广泛,但各自存在一定的不足。在详细分析各模型不足的基础上,给出改进方案,使得模型输入和输出符合物理特性。原模型以及改进后的模型在Matlab Simulink平台下完成了针对不同参数和输入曲线的粘滞现象的模拟与比较。根据ISA标准,改进后的模型与控制阀实际粘滞特性相符。     

Extension of unbiased minimum-variance input and state estimation for systems with unknown inputs

This paper extends the existing results on joint input and state estimation to systems with arbitrary unknown inputs. The objective is to derive an optimal filter in the general case where not only unknown inputs affect both the system state and the output, but also the direct feedthrough matrix has arbitrary rank. The paper extends both the results of Gillijns and De Moor [Gillijns, S., & De Moor, B. (2007b). Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough. Automatica, 43, 934–937] and Darouach, Zasadzinski, and Boutayeb [Darouach, M., Zasadzinski, M., & Boutayeb, M. (2003). Extension of minimum variance estimation for systems with unknown inputs. Automatica, 39, 867–876]. The resulting filter is an extension of the recursive three-step filter (ERTSF) and serves as a unified solution to the addressed unknown input filtering problem. The relationship between the ERTSF and the existing literature results is also addressed.     

Nonlinear control performance assessment in the presence of valve stiction

Control performance assessment or CPA is a useful tool to establish the quality of industrial feedback control loops, but in practice its usefulness is hampered by nonlinearities in the feedback loop. The Harris index is a popular and easily automated metric used to quantify performance of the loop. However, computing the Harris index simply ignoring common process nonlinearities such as valve stiction will lead to an over-estimate of the index, and consequently a false sense of security. In this paper, we propose two complimentary strategies for accurately assessing the quality of the control performance for loops suffering from modest valve stiction thus avoiding the bias inherent in the standard CPA calculations.     

Detection and estimation of abrupt changes in input or state

Disturbances which may be represented as step changes in the state of a linear, discrete-time, dynamical system are considered. A test for detection of such disturbances is presented. It employs variables computed in fixed-interval optimal smoothing. Several interpretations of the test are offered. The optimal estimate of the state change is shown to be one of the quantities forming the test statistic. Examples show the simplicity and effectiveness of the technique.     

Secure state estimation for cyber-physical systems with unknown input sliding mode observer

In recent years, cyber attacks have posed great challenges to the development of cyber-physical systems. It is of greatsignificance to study secure state estimation methods to ensure the safe and stable operation of the system. This paperproposes a secure state estimation for multi-input and multi-output continuous-time linear cyber-physical systems with sparseactuator and sensor attacks. First, for sparse sensor attacks, we propose an adaptive switching mechanism to mitigate theimpact of sparse sensor attacks by filtering out their attack modes. Second, an unknown input sliding mode observer isdesigned to not only observe the system states, sensor attack signals, and measurement noise present in the system but alsocounteract the effects of sparse actuator attacks through an unknown input matrix. Finally, for the design of an unknowninput sliding mode state observer, the feasibility of the observing system is demonstrated by means of Lyapunov functions.Additionally, simulation experiments are conducted to show the effectiveness of this method.     

State estimation and unknown input reconstruction via both reduced-order and high-order sliding mode observers

This paper considers the problems of the simultaneous estimation of the system states and the unknown inputs for linear systems when the so-called observer matching condition is not satisfied. An auxiliary output vector is introduced so that the observer matching condition is satisfied with respect to it. A high-order sliding mode observer is considered to get the exact estimates of both the auxiliary outputs and their derivatives in a finite time based on the system measured outputs. After this, a reduced-order observer is constructed by using the estimated auxiliary outputs as the new system outputs. The reduced-order observer is able to asymptotically estimate the system states without suffering the influence of the unknown inputs. A kind of unknown input reconstruction method based on both the state and the auxiliary output derivative estimates is developed. Finally, a numerical simulation example is given to illustrate the effectiveness of the proposed methods.     

A method for unbiased parameter estimation by means of the equation error input covariance

A method for obtaining an unbiased estimate of the finiteq-dimensional parameter vector defining a time-invariant linear dynamical system in the presence of noise is described. The system is excited by a stationary mean-square bounded process. The method is based on anr geq qparameter "equation error" and is presented in continuous time. The equation error input covariance (EEIC) is equated to zero, and the resulting single linear equation havingr > qunknown parameters provides a necessary condition for their unique identification. From it,r - 1additional independent equations are generated. The resultingrlinear independent equations provide the unbiased estimate of the parameter vector in which the excessr - qcomponents vanish. The method does not require the identification of the noise statistics, and it can be applied without a priori assumption of the order of the system's numerator and denominator. Performance of the method is illustrated by simulated examples demonstrating the convergence of the parameter estimate in on-line recursive identification both in open and closed loop.     

Quasi-Newton method based control design for unknown nonlinear systems with input constraints

Science China Information Sciences -     

Estimation of valve stiction in control loops using separable least-squares and global search algorithms

This contribution presents a new procedure for quantifying valve stiction in control loops based on global optimisation. Measurements of the controlled variable (PV) and controller output (OP) are used to estimate the parameters of a Hammerstein system, consisting of a connection of a two-parameter stiction model and a linear low-order process model. As the objective function is non-smooth, gradient-free optimisation algorithms, i.e., pattern search (PS) methods or genetic algorithms (GA), are used for fixing the global minimum of the parameters of the stiction model, subordinated with a least-squares estimator for identifying the linear model parameters. Some approaches for selecting the model structure of the linear model part are discussed. Results show that this novel optimisation-based technique recovers accurate and reliable estimates of the stiction model parameters, dead-band plus stick band (S) and slip jump (J), from normal (closed-loop) operating data for self-regulating and integrating processes. The robustness of the proposed approach was proven considering a range of test conditions including different process types, controller settings and measurement noise. Numerous simulation and industrial case studies are described to demonstrate the applicability of the presented techniques for different loops and for different amounts of stiction.     

控制阀粘滞特性的频域检测方法研究

控制阀粘滞特性是导致控制回路振荡的主要原因之一,对控制阀粘滞特性进行了详细分析,在此基础上使用离散傅里叶变换分析振荡的偏差信号,提出了一种频域分析、适用范围较广的控制阀粘滞特性检测方法。通过仿真研究,表明了该方法的有效性。     

Modified UBB-based input estimation method for nonlinear manoeuvring target tracking problem

The input detection and estimation methods in the manoeuvring target tracking (MTT) application need algorithms for manoeuvring detection and covariance resetting. This algorithm causes an improper delay in target states tracking. In this paper, for solving this problem, unknown but bounded approach for uncertainties modelling is used and a different state space model is developed. In this model, target acceleration is treated as an augmented state in the corresponding state equation. By using interval mathematics, the linearisation error is bounded by an ellipsoidal set and considered in the model development. In augmented state equations, the MTT problem converted to non-manoeuvring target tracking problem. Therefore, the set membership filter is rearranged and used for simultaneous target state and manoeuvre estimation. Furthermore, estimated convex set boundedness is analysed and an upper bound for the estimation error is calculated. The theoretical development of the proposed method is verified with numerical simulations, which contain examples of tracking various manoeuvring targets. The simulation result of the proposed method is compared with traditional input estimation methods. The comparison shows the acceptable performance of the proposed method in the simultaneous estimation of the target acceleration and state vector for the manoeuvring and non-manoeuvring scenarios.     

Detection of stiction in flow control loops based on fuzzy clustering

In the presence of stiction, the control valves may present an oscillatory behaviour that affects the regulatory control performance, thereby causing a loss of product quality and increasing energy consumption. Detection of stiction in the early phase is a crucial key for process control to avoid major disruptions to the plant operations. In this paper, a novel technique based on a well-developed fuzzy clustering approach is proposed. Based on a dramatic change of the slope of the lines obtained from successive cluster centres in the presence of stiction, a new performance index to distinguish the cause of oscillation is proposed. The simulation, experimental and industrial results are provided.     

A novel approach to the design of unknown input observers

A novel state estimator design scheme for linear dynamical systems driven by partially unknown inputs is presented. It is assumed that there is no information available about the unknown inputs, and thus no prior assumption is made about the nature of these inputs. A simple approach for designing a reduced-order unknown input observer (UIO) with pole-placement capability is proposed. By carefully examining the dynamic system involved and simple algebraic manipulations, it is possible to rewrite equations eliminating the unknown inputs from part of the system and to put them into a form where it could be partitioned into two interconnected subsystems, one of which is directly driven by known inputs only. This makes it possible to use a conventional Luenberger observer with a slight modification for the purpose of estimating the state of the system. As a result, it is also possible to state similar necessary and sufficient conditions to those of a conventional observer for the existence of a stable estimator and also arbitrary placement of the eigenvalues of the observer. The design and computational complexities involved in designing UIOs are greatly reduced in the proposed approach