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.     

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.     

Iterative identification of Hammerstein systems

Convergences of iterative algorithms have been established for identification of Hammerstein systems in the case that the unknown nonlinearities are odd. Then, the results are further extended to nonsmooth nonlinearities.     

A noniterative neuro-fuzzy based identification method for Hammerstein processes

In this paper, a noniterative identification procedure for neuro-fuzzy based Hammerstein model is presented. The proposed method not only avoids the inevitable restrictions on static nonlinear function encountered by using the polynomial approach, but also overcomes the problems of initialization and convergence of the model parameters, which are usually resorted to trial and error procedure in the existing iterative algorithms used for the identification of Hammerstein model. To construct the neuro-fuzzy based model, a clustering algorithm is presented to estimate the centers and widths of the model, and an analytical solution is developed to calculate the weights of the model in a noniterative manner. Examples are used to illustrate the applicability of the proposed method and a comparison with polynomial approach is made.     

Revisiting Hammerstein system identification through the Two-Stage Algorithm for bilinear parameter estimation

The Two-Stage Algorithm (TSA) has been extensively used and adapted for the identification of Hammerstein systems. It is essentially based on a particular formulation of Hammerstein systems in the form of bilinearly parameterized linear regressions. This paper has been motivated by a somewhat contradictory fact: though the optimality of the TSA has been established by Bai in 1998 only in the case of some special weighting matrices, the unweighted TSA is usually used in practice. It is shown in this paper that the unweighted TSA indeed gives the optimal solution of the weighted nonlinear least squares problem formulated with a particular weighting matrix. This provides a theoretical justification of the unweighted TSA, and also leads to a generalization of the obtained result to the case of colored noise with noise whitening. Numerical examples of identification of Hammerstein systems are presented to validate the theoretical analysis.     

Convergence of the iterative algorithm for a general Hammerstein system identification

The convergence of the iterative identification algorithm for a general Hammerstein system has been an open problem for a long time. In this paper, it is shown that the convergence can be achieved by incorporating a regularization procedure on the nonlinearity in addition to a normalization step on the parameters.     

Identification and control of Hammerstein systems via wireless networks

This article investigates a systematic approach for the identification and control of Hammerstein systems over a physical IEEE 802.11b wireless channel. Three major factors which may affect system stability and stabilisation are concerned: wireless network-induced delays, nonlinearity and model mismatch. First the network-induced delays are characterised by an inverse Gaussian distribution model according to IEEE 802.11b protocol and a model-based compensation method is used to estimate the delayed samples. Then an inverse function of nonlinear part of the identified model is used to attenuate the influence of nonlinearity, while the model mismatch is regarded as disturbance which is then dealt with by H _∞ control approach. A sufficient condition for mean-square asymptotic stability is obtained and expressed by a set of linear matrix inequalities, enabling direct controller design. Finally, numerical simulation examples are used to confirm the efficacy of the proposed approach.     

Recursive identification of Hammerstein systems with application to electrically stimulated muscle

Modeling of electrically stimulated muscle is considered in this paper where a Hammerstein structure is selected to represent the isometric response. Motivated by the slowly time-varying properties of the muscle system, recursive identification of Hammerstein structures is investigated. A recursive algorithm is then developed to address limitations in the approaches currently available. The linear and nonlinear parameters are separated and estimated recursively in a parallel manner, with each updating algorithm using the most up-to-date estimation produced by the other algorithm at each time instant. Hence the procedure is termed the alternately recursive least square (ARLS) algorithm. When compared with the leading approach in this application area, ARLS exhibits superior performance in both numerical simulations and experimental tests with electrically stimulated muscle.     

Identification of an additive nonlinear system and its applications in generalized Hammerstein models

An identification algorithm is developed for a class of nonlinear systems that are multi-input and multi-output in an additive form. The convergence results are achieved and its applications to identification of a generalized Hammerstein system is also discussed.     

Identification of a process with control valve stiction using a fuzzy system: A data-driven approach

Many researchers focus on detecting and modelling the valve stiction because it has undesirable effects on the control loop performance, which consequently results in poor product quality and increased energy consumption. It is difficult to model a process with a sticky valve using the mathematical definition because of its nonlinear properties such as stiction, hysteresis, dead band and dead zone. This work aims to develop and determine the appropriate model of a process with stiction, which can be used in controller design to mitigate the undesirable effect of the stiction. To achieve this goal by mapping the process with valve stiction to a fuzzy system, a dynamic fuzzy model of the plant is derived through an iterative well-developed fuzzy clustering algorithm, which generates suitable antecedent parameters from a set of input–output measurements that are obtained from the control output (OP) and the process output (PV). To determine the consequent parameters, the least square (LS) estimation is applied. The results reveal that the obtained data-driven Takagi–Sugeno-type (TS) fuzzy rule-based model can effectively represent an appropriate model of the process with stiction for different amounts of stiction that are obtained from the simulation and different industrial loops.     

基于全可变气门汽油机进气量测量和控制问题研究

基于全可变气门运动机构的气门参数控制燃烧技术是改善传统SI汽油机燃油经济性,提高动力性和降低排放的最为有效的途径之。但是这也对发动机进气量的测量和控制提出了更高的要求。本文通过仿真和试验相结合的方法,对基于全可变气门机构汽油机进气量测量和控制问题进行了探讨和研究。     

A methodology for control-relevant nonlinear system identification using restricted complexity models

A broadly-applicable, control-relevant system identification methodology for nonlinear restricted complexity models (RCMs) is presented. Control design based on RCMs often leads to controllers which are easy to interpret and implement in real-time. A control-relevant identification method is developed to minimize the degradation in closed-loop performance as a result of RCM approximation error. A two-stage identification procedure is presented. First, a nonlinear ARX model is estimated from plant data using an orthogonal least squares algorithm; a Volterra series model is then generated from the nonlinear ARX model. In the second stage, a RCM with the desired structure is estimated from the Volterra series model through a model reduction algorithm that takes into account closed-loop performance requirements. The effectiveness of the proposed method is illustrated using two chemical reactor examples.     

Robust fault-tolerant control using an accurate emulator-based identification technique

A robust fault-tolerant control scheme using an accurate and robustly identified model of a system operating in a closed loop is proposed. A robust identification of the system and the associated Kalman filter is proposed by the novel use of an emulator, which is a transfer function connected to the input, output or both, to mimic the likely operating scenarios, both normal and abnormal. From the emulator-generated data, a set of perturbed models are generated, which plays a crucial role in ensuring robustness of the identified model and subsequently that of the controller. The prediction error method is employed to identify the perturbed models and a robust optimal model is obtained as a best fit to the perturbed models. A robust Kalman filter-based state feedback controller is obtained from the parameterisation of all stabilising controllers generated using the emulator-perturbed models. It is shown theoretically that the proposed robust controller designed using the emulator-perturbed robustly identified model is superior to the conventional robust controller designed from the identified nominal model, and a significant improvement in robustness is confirmed using illustrative examples. A fault-tolerant control system is developed by exploiting the key properties of the freely available Kalman filter.     

遥控模型直升机分通道模型辨识方法研究

设计了嵌入式控制系统,研究了小型遥控直升飞机在悬停状态的模型辨识方法.按照遥控直升机的4个控制输入(纵向周期变距、横向周期变距、总距、以及尾距),直升机的运动可分解为俯仰、倾斜、升降和航向4个通道,分别采用分通道飞行实验数据,通过Matlab辨识工具,得到了遥控直升机在悬停状态下各通道的单榆入单输出控制模型.飞行实验数据的验证结果表明,分通道数学模型可以很好地反映相应通道的动态特性,对实现遥控直升机自动驾驶控制具有重要的应用价值.     

Real-time compliance control of an assistive joint using QNX operating system

An assistive robot is a novel service robot, playing an important role in the society. For instance, it can amplify human power not only for the elderly and disabled to recover/rehabilitate their lost/impaired musculoskeletal functions but also for healthy people to perform tasks requiring large forces. Consequently, it is required to consider both accurate position control and human safety, which is the compliance. This paper deals with the robot control compliance problem based on the QNX real-time operating system. Firstly, the mechanical structure of a compliant joint on the assistive robot is designed using Solidworks. Then the parameters of the assistive robot system are identified. The software of robot control includes data acquisition and processing, and control to meet the compliance requirement of the joint control. Finally, a Hogan impedance control experiment is carried out. The experimental results prove the effectiveness of the method proposed.     

Fault diagnosis of an industrial gas turbine prototype using a system identification approach

In this work, a model-based procedure exploiting analytical redundancy for the detection and isolation of faults on a gas turbine simulated process is presented. The main point of the paper consists of exploiting an identification scheme in connection with dynamic observer or filter design procedures for diagnostic purposes. Thus, black-box modelling and output estimation approaches to fault diagnosis are in particular advantageous in terms of solution complexity and performance achieved. Moreover, the suggested scheme is especially useful when robust solutions are considered for minimising the effects of modelling errors and noise, while maximising fault sensitivity. In order to experimentally verify the robustness of the solution obtained, the proposed FDI strategy has been applied to the simulation data of a single-shaft industrial gas turbine plant in the presence of measurement and modelling errors. Hence, extensive simulations of the test-bed process and Monte Carlo analysis are the tools for assessing experimentally the capabilities of the developed FDI scheme, when compared also with different data-driven diagnosis methods.     

Application of neural networks for system identification of an adsorption column

This work illustrates the use of neural networks for system identification of the dynamics of a distributed parameter system, an adsorption column for waste-water treatment of water containing toxic chemicals. System identification of this process is done from simulated data for this work. The inputs to the networks include the state of the column at a given point in time and the system input, the velocity. The network predicts the change in the state over a period of time based on these inputs. Recurrent networks were found to be capable of simulating the whole operation of the column from an initial state of zero concentrations throughout the column, and thus predicting the complete breakthrough curves. The feasibility of system identification of this process has been established using synthetic noisy data, which indicates that the same can be performed from experimental data when all the required measurements are available.     

小波神经网络在飞控系统辨识中的应用研究

选择以sigmoid函数为基础的小波基波函数构造了一个小波神经网络,利用小波网络对复杂的飞控系统对象进行在线辨识研究,仿真结果表明小波神经网络基本满足某型飞机飞控系统在线辨识的要求。     

Nonlinear system identification and control of chemical processes using fast orthogonal search

The use of the fast orthogonal search (FOS) method is presented for model estimation and control of nonlinear chemical processes. FOS provides a nonlinear approximation used in an inner-loop that allows for simpler linear control methods to be used as an outer-loop controller. It is a straightforward, simple-to-use method for linearization of systems based on orthogonal system identification. The control concept is derived from the method of inverse dynamic control (IDC). The novel combination of this with the FOS method of system identification results in a very efficient and effective method of control. The method is demonstrated and tested on two nonlinear chemical process control simulations and the results are shown to compare very favourably to published results on the same problems.     

不同阶分数阶混沌系统的同步与参数辨识

针对不确定分数阶混沌系统的同步和参数辨识问题,提出一种新的方法,即用不同阶分数阶系统来同步和参数辨识.利用主动控制和预控制量方法,基于分数阶混沌系统稳定性理论和自适应控制理论,设计控制器,实现不同阶分数阶混沌系统之间的同步和参数辨识.理论和仿真结果实现了不同阶Chen 系统间的同步和辨识,表明了该方法的有效性.