An improved nonlinear modelling and identification methodology of a servo-pneumatic actuating system with complex internal design for high-accuracy motion control applications

Precise and smooth motion control of servo-pneumatic actuating systems can be improved only by using appropriate mathematical models and identification procedures. In this paper, an improved modelling and parameter identification methodology of a servo-pneumatic linear drive unit with complex internal design that takes into account the influence of dead volume in terms of system dynamics has been developed. The proposed modelling methodology considers the nonlinear mass flow characteristic, dead zone of the valve, polytropic temperature model, heat transfer and nonlinear friction behavior. Experimental results indicates that a proper identification of the dead volume improves model accuracy in terms of pressure dynamics, piston velocity and piston position and therefore the proposed modelling methodology can be a good starting point to design better motion controllers in the field of servo-pneumatic actuating systems.     

室内环境参数远程监测系统设计

设计了一种可以在室内进行环境参数监测,并且可以远程监测的系统。通过使用灰尘浓度传感器、温湿度传感器、可燃气体传感器和气压传感器实现检测当前环境的各项参数,单片机将采集的数据进行卡尔曼滤波处理之后,得到更加稳定的数据。在数据显示端,通过硬件部分的Wi-Fi模块将数据发送到自组建的网络中,浏览器端编程采用MyEclipse开发环境,运用JavaWeb语言进行Wi-Fi信息抓取和处理,并将数据传输到云端,实现了由数据采集到网络传输,最后到用户可以进行远程监测的可视化系统设计。     

A practical control strategy for servo-pneumatic actuator systems

A practical control strategy with a simple controller structure is proposed for servo-pneumatic cylinder actuator systems. Theoretical analysis reveals that the acceleration of the piston indirectly represents the cylinder chamber pressure difference so it is possible to employ acceleration feedback instead of pressure feedback in the construction of servo-pneumatic actuator control systems. The main features of the control strategy developed in the paper are (1) using acceleration feedback to improve the stability of the system; and (2) introducing time-delay minimisation and optimised null offset compensation to address the problem of time delay and dead zone, which are mainly caused by the compressibility of air and friction. The experimental studies have been conducted using an asymmetric pneumatic cylinder system and the results show that the system performance has been much improved when compared with a conventional PID controller.     

The application of servo-pneumatic drives for flexible mechanical handling techniques

The present article describes the possibilities of pneumatic drives for fast analog valves and intelligent control system applications. Firstly, the necessary components - valves and actuators - are described. In particular rodless cylinders and optimized motors are discussed. Then design and performance of servo-pneumatic cylinder and rotational drives are presented by means of examples tested under practical service conditions. This shows that the combination of servo-pneumatic components and intelligent microelectronic controllers leads to an up to now unknown system behaviour in the field of pneumatics. Finally, practical examples of the field of robotics are introduced, comprising multi-axle robots built up from individual CNC axles. Moreover, an intelligent gripper with integrated slip sensors is described. As a whole, the great opportunities offered by the application of servo-pneumatics are presented.     

测压系统动态特性参数辨识

讨论了实际的带斜坡的阶跃激励信号的上升时间对测压系统参数辨识的影响。推导了其作为输入的二阶系统的时域输出表达式,根据输出表达式在加随机干扰的条件下对系统参数和激励参数进行了辨识,结果表明得到的辨识结果是正确的。     

基于云存储的农业环境参数监控系统设计

农业环境参数对于农业生物生存和发展至关重要,合适的农业环境将会促进农作物更好的生长发育。从农业信息化出发,将云存储技术与物联网技术相结合,实现大面积农田的环境参数的远程监控。采用多节点远距离模式进行环境参数采集,提出主从节点方式进行数据传送,设计与增益天线相结合的433 MHz的无线通信模块进行远距离无线通信,同时采用串口双线制控制的Wi Fi模块实现与云端的数据通信,最后采用改进的加权平均数进行数据处理,提高数据的真实性与可靠性。用户可通过网络实现远程访问,对农业环境参数进行监控和追溯管理。     

The Pachycondyla Apicalis metaheuristic algorithm for parameters identification of chaotic electrical system

Abstract

In this work, the Pachycondyla Apicalis metaheuristic algorithm (API) is used to identify and optimize control parameters for piezoelectric oscillator that exhibits frequency hysteresis behavior under strong excitation when asymmetric period which the bifurcation and chaotic behavior of higher harmonics appear by minimizing errors between actual and evaluated states of the model. In order to investigate the efficiency of the API algorithm, numerical experiments are carried out on the piezoelectric chaotic resonator. The simulation results indicate that the API algorithm can be effective in identifying the unknown parameters for given chaotic systems with high accuracy and low deviations.     

Genetic algorithm-based identification of transfer function parameters for a rectangular flexible plate system

This paper focuses on an identification technique based on genetic algorithms (GAs) with application to rectangular flexible plate systems for active vibration control. A real coded GA with a new truncation-based selection strategy of individuals is developed, to allow fast convergence to the global optimum. A simulation environment characterizing the dynamic behavior of a flexible rectangular plate system is developed using the central finite difference (FD) techniques. The plate thus developed is excited by a uniformly distributed random disturbance and the input–output data of the system acquired is used for black-box modeling the system with the GA optimization using an autoregressive model structure. Model validity tests based on statistical measures and output prediction are carried out. The prediction capability of the model is further examined with unseen data. It is demonstrated that the GA gives faster convergence to an optimum solution and the model obtained characterizes the dynamic system behavior of the system well.     

基于混合PSO-SQP 算法同时实现多变量的结构和参数辨识

研究了一种基于混合粒子群优化算法和序列二次规划算法对系统进行辨识的新方法．该方法将典型数学模型相互组合构成系统模型．首先将系统结构辨识问题转化为组合优化问题;然后利用混合PSO-SQP同时实现系统的结构辨识和参数辨识．为了进一步说明该混合算法的有效性,与标准PSO算法和惯性权值逐减PSO算法进行对比．仿真结果表明,给出的混合算法是有效的,辨识精度高,并具有良好的实用性．     

一种新型的非线性系统模型参数辨识方法

针对传统模型参数辨识方法和遗传算法用于模型参数辨识时的缺点,提出了一种基于微粒群优化(PSO)算法的模型参数辨识方法,利用PSO算法强大的优化能力,通过对算法的改进,将过程模型的每个参数作为微粒群体中的一个微粒,利用微粒群体在参数空间进行高效并行的搜索来获得过程模型的最佳参数值,可有效提高参数辨识的精度和效率.     

变异量子粒子群优化算法在系统辨识中的应用

为了增加全局搜索能力,避免陷入局部最小,在量子粒子群优化算法（QPSO）中引入变异机制,即基于QPSO的特点,用Cauchy分布分别对全局最优和所有个体极值的平均值进行变异。该算法称为带变异算子的量子粒子群优化算法（MQPSO）。通过对一典型的大海捞针类（NiH）问题的试验,证明了MQPSO在全局优化和快速收敛能力上有较大的提高。在此基础上将该算法应用于系统参数辨识中,辨识结果表明该方法具有参数辨识精度高,抗噪声能力强,对输入信号通用性强,也适用于非线性系统参数辫识,具有重要的工程应用价值。     

A VSS identification scheme for time-varying parameters

Based on variable structure system theory and sliding mode, we develop an identification scheme suitable for time-varying parameters. The new identification scheme, working in closed-loop, addresses several key issues in system identification simultaneously: unstable process, highly nonlinear and uncertain dynamics, fast time varying parameters and rational nonlinear in the parametric space. Other important issues associated with identification, such as the persistent excitation property and insensitivity to measurement noise, are also discussed. A robotic manipulator is used as the illustrative example and the basis for comparison.     

基于高冲击激励的加速度计参数辨识的研究

加速度计的动态校准在国内外计量领域越来越受到重视,参考国内外最新的加速度计动态校准方法,主要阐述了基于高冲击激励下加速度计动态特性参数辨识的问题。该方法根据加速度计的物理结构建立了其状态空间模型,利用外差式激光干涉仪测量并经过相应处理得到了输入加速度计的激励信号。利用得到的输入-输出数据,通过最小化其状态空间模型的预测误差序列得到了被校加速度计的动态特性的参数,通过在不同的冲加速度峰值下进行了试验并比较表明该方法的有效性。     

Iris sensor identification in multi-camera environment

Large-scale identity projects such as the Unique Identification Authority of India (UIDAI) comprise of multiple individual organizations, which may use different sensors for enrolling the individuals while the data obtained at the time of verification can be collected from a different sensor. In such multi-camera scenario, it is imperative to perform image-based iris sensor identification. In this research, we propose an efficient algorithm to identify the sensor from which the iris image is captured. The proposed algorithm is the amalgamation of SVM fitness function based Bacteria Foraging (BF) feature selection and fusion of multiple features such as Block Image Statistical Measure (BISM), High Order Wavelet Entropy (HOWE), Texture Measure (TM), Single-level Multi-orientation Wavelet Texture (SlMoWT), and Image Quality Measures (IQM). The selected features are then given input to a supervised classification algorithm for iris sensor identification. The second contribution of this research is developing two sets of multisensor iris image databases that, in total, contain 6000 images with over 150 subjects. The results show that the proposed sensor classification algorithm is computationally very fast and yields an accuracy of over 99% on multiple databases.     

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.     

A coevolutionary algorithm for tyre model parameters identification

The problem of tyre model coefficients identification using minimum test data is studied in this work. To obtain these tyre model parameters, an intense research effort by the automotive community has been made and there are different methods to fit the values of these parameters. This problem is addressed in this work through a coevolutionary algorithm that interactively searches the optimum tyre model parameters and new test data in disagreement with the tyre model. The algorithm is composed of two stages: the estimation phase, which finds out the tyre model parameters which can predict actual tyre test data, and the exploration phase, which finds out new test data which have the most disagreement with the response of the current model. The feasibility of the methodology is demonstrated comparing the obtained results with other known techniques.     

含齿隙双电机伺服系统未知参数的两阶段辨识

针对含齿隙的双电机伺服系统,提出一种两阶段辨识法辨识双电机系统未知参数以及齿隙非线性环节的死区参数.针对电机转动惯量、等效粘性摩擦系数以及齿隙大小未知的情况,运用Legendre多项式将双电机伺服系统中的齿隙模型分段化,根据最小二乘法辨识双电机伺服系统参数.最后,通过仿真验证两阶段辨识法的有效性,验证结果表明,两阶段辨识法能够较准确地辨识电机参数和齿隙参数.     

Application of a Bayesian hierarchical model to system identification of structural parameters

System identification (SI) is a key step in the process of evaluating the status or condition of physical structures and of devising a scheme to sustain their structural integrity. SI is typically carried out by updating the current structural parameters used in a computational model based on the measured responses of the structure. In the deterministic approach, SI has been conducted by minimizing the error between calculated responses (using the computational model) and measured responses. However, this brought about unexpected numerical issues such as the ill-posedness of the inverse problem, which likely results in non-uniqueness of the solutions or non-stability of the optimization operation. To address this issue, Bayesian updating enhanced with an advanced modeling technique such as a Bayesian network (BN) was introduced. However, it remained challenging to construct the quantitative relations between structural parameters and responses (which are placed in conditional probability tables: CPTs) in a BN setting. Therefore, this paper presented a novel approach for conducting the SI of structural parameters using a Bayesian hierarchical model (BHM) technique. Specifically, the BHM was integrated into the Bayesian updating framework instead of utilizing a BN. The primary advantage of the proposed approach is that it enables use of the existing relations between structural parameters and responses. This can save the computational effort needed to construct CPTs to relate the parameter and response nodes. The proposed approach was applied to two experimental structures and a realistic soil-slope structure. The results showed that the proposed SI approach provided good agreement with actual measurements and also gave relatively robust estimation results compared to the traditional approach of maximum likelihood estimation. Hence, the proposed approach is expected to be utilized to address SI problems for complex structural systems and its computational model when integrated with a statistical regression approach or with various machine learning algorithms.     

A Galerkin discretisation-based identification for parameters in nonlinear mechanical systems

In the paper, a new parameter identification method is proposed for mechanical systems. Based on the idea of Galerkin finite-element method, the displacement over time history is approximated by piecewise linear functions, and the second-order terms in model equation are eliminated by integrating by parts. In this way, the lost function of integration form is derived. Being different with the existing methods, the lost function actually is a quadratic sum of integration over the whole time history. Then for linear or nonlinear systems, the optimisation of the lost function can be applied with traditional least-squares algorithm or the iterative one, respectively. Such method could be used to effectively identify parameters in linear and arbitrary nonlinear mechanical systems. Simulation results show that even under the condition of sparse data or low sampling frequency, this method could still guarantee high accuracy in identifying linear and nonlinear parameters.     

Neural network for system identification

A double-hidden layer neural network is proposed to identify a non-linear dynamic system. The structure of this neural network is derived from the Kolmogorov theorem. Each node of the network corresponds to an unknown non-linear function to be estimated. An elementary function is designed to be a constituent of an arbitrary continuous function. The difficulty of function estimation is solved by estimating the weightings of the elementary functions. Two algorithms are applied to estimate the network weightings. The weightings of the upper hidden layer are estimated by the least squares method. On the other hand, the recursive prediction error algorithm is applied to estimate the parameters of the lower hidden layer. The simulation studies show that the proposed neural network can model the dynamics of a general non-linear system.