Robust adaptive fuzzy control of twin rotor MIMO system

This paper considers designing an adaptive fuzzy controller to position the yaw and pitch angles of a twin rotor MIMO system (TRMS) in two degrees of freedom. The goal of the controller is to stabilize the TRMS in a desired position or track a specified trajectory. The parameters of the fuzzy controller are updated using the gradient descent algorithm in order to increase its robustness against external disturbances and/or changes in system parameters. Moreover, the stability of the overall closed-loop system is guaranteed based on the Lyapunov stability theory. The proposed controller is applied to a TRMS with heavy cross coupling between its axes. Experimental results show good performance of the proposed controller as compared to the non-adaptive fuzzy and PID controllers, especially when there are system uncertainties and external disturbances.     

Real time adaptive nonlinear model inversion control of a twin rotor MIMO system using neural networks

This paper investigates the development and experimental implementation of an adaptive dynamic nonlinear model inversion control law for a Twin Rotor MIMO System (TRMS) using artificial neural networks. The TRMS is a highly nonlinear aerodynamic test rig with complex cross-coupled dynamics and therefore represents the control challenges of modern air vehicles. A highly nonlinear 1DOF mathematical model of the TRMS is considered in this study and a nonlinear inverse model is developed for the pitch channel of the system. An adaptive neural network element is integrated thereafter with the feedback control system to compensate for model inversion errors. The proposed on-line learning algorithm updates the weights and biases of the neural network using the error between the set-point and the real output. The real-time response of the method shows a satisfactory tracking performance in the presence of inversion errors caused by model uncertainty. The approach is therefore deemed to be suitable to apply real-time to other nonlinear systems with necessary modifications.     

Dynamic modeling,simulation, and MIMO predictive control of a tubular solid oxide fuel cell

Solid oxide fuel cells are a promising option for distributed energy stationary power generation that offers efficiencies up to 50% in stand-alone applications, 70% in hybrid gas turbine applications and 80% in cogeneration. To advance SOFC technology sufficiently for widespread market penetration, the SOFC must demonstrate improved cell lifetime from the status quo. Much research has been performed to improve SOFC lifetime using advanced geometries and materials, and in this research, we suggest further improving lifetime by designing an advanced control algorithm based upon preexisting mechanical stress analysis [1]. Control algorithms commonly address SOFC lifetime related operability objectives using unconstrained, SISO control algorithms that seek to minimize thermal transients. While thermal fatigue may be one thermal stress driver, these studies often do not consider maximum radial thermal gradients or critical absolute temperatures in the SOFC. In addition, researchers often discuss hot-spots as a critical lifetime reliability issue, but as previous stress work demonstrates, the minimum cell temperature is the primary thermal stress driver in tubular SOFCs modeled after the Siemens Power Generation, Inc. design. In this work, we present a dynamic, quasi-two-dimensional model for a high-temperature tubular SOFC combined with ejector and prereformer models. The model captures dynamics of critical thermal stress drivers and is used as the physical plant for closed-loop simulations with a constrained, MIMO model predictive control algorithm. Closed-loop simulation results demonstrate effective load-following, operability constraint satisfaction, and disturbance rejection.     

Unified modeling for digital twin of a knowledge-based system design

While Model-Based Systems Engineering (MBSE) improves the ambiguity problem of the conventional document-based way, it brings management complexity. Faced with the complexity, one of the core issues that companies care about is how to effectively evaluate, predict, and manage it in the early system design stage. The inaccuracy of contemporary complexity measurement approaches still exits due to the inconsistency between the actual design process in physical space and the theoretical simulation in virtual space. Digital Twin (DT) provides a promising way to alleviate the problem by bridging the physical space and virtual space. Aiming to integrate DT with MBSE for the system design complexity analysis and prediction, based on previous work, an integration framework named System Design Digital Twin in 5 Dimensions was introduced from a knowledge perspective. The framework provides services for design complexity measurement, effort estimation, and change propagation prediction. Then, to represent the system design digital twin in a unified way, a modeling profile is constructed through SysML stereotypes. The modeling profile includes System design digital model in virtual space profile, system services profile, relationships profile and digital twin data profile. Finally, the system design of a cube-satellite space mission demonstrates the proposed unfiled modeling approach.     

Tensor networks for MIMO LPV system identification

ABSTRACT

In this paper, we present a novel multiple input multiple output (MIMO) linear parameter varying (LPV) state-space refinement system identification algorithm that uses tensor networks. Its novelty mainly lies in representing the LPV sub-Markov parameters, data and state-revealing matrix condensely and in exact manner using specific tensor networks. These representations circumvent the ‘curse-of-dimensionality’ as they inherit the properties of tensor trains. The proposed algorithm is ‘curse-of-dimensionality’-free in memory and computation and has conditioning guarantees. Its performance is illustrated using simulation cases and additionally compared with existing methods.     

Web-based digital twin modeling and remote control of cyber-physical production systems

The rapid development new generation of information technologies facilitate the emergence of cyber-physical production system (CPPS) which could pave a way to exploring new smart manufacturing solutions. Digital twin (DT) is the technical core for establishing CPPS in the context of industry 4.0. Developing an easy-to-deploy and simple-to-use DT-based CPPS is a critical research gap. In this paper, a systemic framework is proposed to provide guidelines for rapid system configuration and easy runtime of DT-based CPPS by integrating CPS, DT modeling technologies, event-driven distributed cooperation mechanisms, and web technologies. The concept of CPS node (CPSN) for manufacturing resources is established by integrating semantic information model, 3D geometric model and function modules. Various CPSNs are orchestrated as an autonomous CPPS using dynamic resource registration and binding technologies. To achieve easy runtime of DT-based CPPS, event-driven distributed cooperation among CPSNs and web-based remote control of CPPS are proposed respectively. Finally, to verify the feasibility of the proposed framework, a prototype of DT-based CPPS is implemented, based on which an exemplary case is conducted.     

Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems

This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for identification and control of a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems. The proposed controller has the following salient features: 1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically; 2) online learning ability of uncertain MIMO nonlinear systems; 3) fast learning speed; 4) fast convergence of tracking errors; 5) adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; 6) robust control, where global stability of the system is established using the Lyapunov approach. Simulation studies on an inverted pendulum and a two-link robot manipulator show that the performance of the proposed controller is superior.     

基于LSSVM的MIMO系统快速在线辨识方法

针对时变非线性多输入多输出(MIMO)系统在线辨识较困难的问题,提出一种基于最小二乘支持向量机(LSSVM)的快速在线辨识方法。介绍了现有LSSVM增量式和在线式学习算法,并为它引入了一些加速实现策略,得到LSSVM快速在线式学习算法。将MIMO系统分解为多个多输入单输出(MISO)子系统,对每一个MISO利用一个LSSVM在线建模;这些LSSVM执行快速在线式学习算法。数字仿真显示该方法建模速度快,模型预测精度高。     

Subspace system identification for training-based MIMO channel estimation

The application of state-space-based subspace system identification methods to training-based estimation for quasi-static multi-input-multi-output (MIMO) frequency-selective channels is explored with the motivation for better model approximation performance. A modification of the traditional subspace methods is derived to suit the non-contiguous nature of training data in mobile communication systems. To track the time variation of the channel, a new recursive subspace-based channel estimation is proposed and demonstrated in simulation with practical MIMO channel models. The comparison between the state-space-based channel estimation algorithm and the FIR-based Recursive Least Squares algorithm shows the former is a more robust modeling approach than the latter.     

相交轴转子系统当量化建模与扭振响应分析

齿轮联结的相交轴转子系统具有转子不同体、不同速、不同轴线等结构特点,使其动力学建模异于常规转子系统,论文采用当量化建模方法,将相交轴转子系统转化为同转速、同轴线的转子系统并建立动力学模型,通过与已有研究对比验证了文中基于DyRoBeS当量化建模方法的有效性.以相交轴齿轮联结转子系统典型结构——某型直升机尾传动系统为研究对象,建立了当量化转子系统动力学模型,分析了系统各阶扭振临界转速及其组成、联结齿轮啮合刚度对扭振的影响、联结齿轮转动惯量对系统临界转速的影响,结果表明:传动系统第1、2阶临界转速为派生临界转速,其余8阶均源于各组成轴;中、尾减啮合刚度值与扭振的各阶共振峰值存在映射关系,部分啮合刚度值会激发共振极大值,应尽量避免;中、尾减啮合刚度变化会引发系统临界转速的变化,尤其是第1、2阶临界转速;不同阶临界转速对各个联结齿轮转动惯量的敏感性亦不同.论文从转子动力学角度研究了齿轮联结的相交轴转子系统的动力学特性,可为此类转子系统的结构优化设计及运行维护提供理论依据.     

A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach

Virtual commissioning is a key technology in Industry 4.0 that can address issues faced by engineers during early design phases. The process of virtual commissioning involves the creation of a Digital Twin—a dynamic, virtual representation of a corresponding physical system. The digital twin model can be used for testing and verifying the control system in a simulated virtual environment to achieve rapid set-up and optimization prior to physical commissioning. Additionally, the modular production control systems, can be integrated and tested during or prior to the construction of the physical system. This paper describes the implementation of a digital twin emulator of an automated mechatronic modular production system that is linked with the running programmable logic controllers and allow for exchanging near real-time information with the physical system. The development and deployment of the digital twin emulator involves a novel hybrid simulation- and data-driven modeling approach that combines Discrete Event Simulation and Agent Based Modeling paradigms. The Digital Twin Emulator can support design decisions, test what-if system configurations, verify and validate the actual behavior of the complete system off-line, test realistic reactions, and provide statistics on the system’s performance.

     

基于灰色预测理论的转子系统振动主动控制

以灰色预测控制理论为基础,采用现代控制理论中的二次型优化原理,以控制力和响应加权最小为目标函数,设计了两种基于灰色预测理论的转子系统振动主动控制方案——灰色 GM(1,1)预测优化控制方案和灰色 Verhuslt 预测优化控制方案.并将该两种方案分别应用于带电磁阻尼器转子轴承系统的转子振动主动控制中,通过数值仿真验证了两种控制方法的有效性,并对两种方法的控振效果进行了比较.     

Nonlinear black-box modeling in system identification: a unified overview

A nonlinear black-box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area, with structures based on neural networks, radial basis networks, wavelet networks and hinging hyperplanes, as well as wavelet-transform-based methods and models based on fuzzy sets and fuzzy rules. This paper describes all these approaches in a common framework, from a user's perspective. It focuses on what are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques. It is pointed out that the nonlinear structures can be seen as a concatenation of a mapping form observed data to a regression vector and a nonlinear mapping from the regressor space to the output space. These mappings are discussed separately. The latter mapping is usually formed as a basis function expansion. The basis functions are typically formed from one simple scalar function, which is modified in terms of scale and location. The expansion from the scalar argument to the regressor space is achieved by a radial- or a ridge-type approach. Basic techniques for estimating the parameters in the structures are criterion minimization, as well as two-step procedures, where first the relevant basis functions are determined, using data, and then a linear least-squares step to determine the coordinates of the function approximation. A particular problem is to deal with the large number of potentially necessary parameters. This is handled by making the number of ‘used’ parameters considerably less than the number of ‘offered’ parameters, by regularization, shrinking, pruning or regressor selection.     

System identification and on-line robust control of a multivariable system

System identification and on-line robust control have been developed for a multi-variable system with dead times. For system identification, a modified Astrom and Hagglund' s autotuning method is applied to obtain the transfer function matrix. An accurate transfer function matrix can be obtained using the proposed method. However, if the system has noises, an accurate transfer function matrix may not be obtained even if a relay with hysteresis is used. Modelling error is unavoidable. An on-line robust control based on a stability index is proposed to improve the performance of the control system     

Application of system identification in a practical active noise control system

Noise pollution has become increasingly severe around the world due to fast urbanization. How to soundproof windows from outside noise is of significant interest for both academia and industry. This paper reports an experimental implementation of normalized minmax active noise control (ANC) algorithm on an open window system, where identifying the model of acoustic sound paths plays a central role. By doing this, traffic noise is attenuated by the ANC system, leading to a relatively quiet indoor environment, while the natural lighting and ventilation functions of a window are remained. Our experiments show that an average of 19 dB(A) noise reduction is achieved.     

Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification

In this paper, a new methodology for identifying multiple inputs multiple outputs Hammerstein systems is presented. The proposed method aims at incorporating the impulse response of the system into a least-squares support vector machine (LS-SVM) formulation and therefore the regularisation capabilities of LS-SVM are applied to the system as a whole. One of the main advantages of this method comes from the fact that it is flexible concerning the class of problems it can model and that no previous knowledge about the underlying non-linearities is required except for very mild assumptions. Also, it naturally adapts to handle different numbers of inputs/outputs and performs well in the presence of white Gaussian noise. Finally, the method incorporates information about the structure of the system but still the solution of the model follows from a linear system of equations. The performance of the proposed methodology is shown through three simulation examples and compared with other methods in the literature.