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.     

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.     

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.     

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.

     

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.     

Solvability conditions of problem of synthesizing proportional-integral control of a linear MIMO system

The solvability conditions and just the solution of the problem of the regular and irregular proportional-integral (PI) control are found in accordance with the properties of invariant zeros of a multi-input multioutput (MIMO) system. It is proved that the problem of synthesizing the control of the MIMO system is solvable if and only if the pair of matrices (A, B) that describes a control plant is controllable and the matrix B_L^⊥AC_R^⊥ (where B_L^⊥ is the left zero divisor of the matrix B and C_R^⊥ is the right zero divisor of the output matrix C) has a complete row rank.     

Multi-periodic repetitive control system: a Lyapunov stability analysis for MIMO systems

A multi-input–multi-output (MIMO) repetitive control problem of tracking and disturbance rejection is considered when both reference and disturbance signals are finite linear combinations of periodic but not necessarily sinusoidal signals. Lyapunov stability analyses under a positive real condition (and a natural relaxation) and exponential stability under a strict positive real condition are provided together with bounds on the induced L ₂ and RMS gains of the closed loop system. It is shown that similar Lyapunov stability results apply when the plant is a positive real state-delay system. Extension of the analyses to a class of non-linear systems is discussed and indicates a good degree of robustness in the design.     

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     

六旋翼飞行器姿态控制系统优化设计

六旋翼飞行器的控制系统具有欠驱动、强耦合、非线性等特点,针对控制系统中的姿态控制易受系统内部参数变化和外部未知条件干扰的问题,提出了一种基于指数收敛的干扰观测器的控制方法。为了提高系统的响应速度和鲁棒性,在干扰观测器的基础上和自适应滑模控制器相结合,并采用边界层法,降低控制系统的抖振。通过Lyapunov稳定性定理证明了飞行器控制系统是稳定且指数收敛的。仿真结果表明:和传统的干扰观测器相比,所设计控制器对六旋翼无人飞行器的姿态控制具有更快的响应速度,提高了干扰抑制能力和系统稳定性。     

基于单变量与多变量系统的模型预测控制研究

为充分验证模型预测控制算法在解决工业过程中单变量与多变量系统控制问题的先进性,研究了以单容水箱与精馏塔为典型系统的模型预测控制算法的实现过程.结合上述实际系统,阐述模型预测控制算法中预测模型、滚动优化以及反馈校正环节的具体含义以及理论推导过程.利用西门子PCS7提供的集散控制系统(DCS)为平台,对单变量与多变量系统进行模型预测控制系统设计与算法开发,从仿真与实际控制两方面体现模型预测控制算法的先进性与实用性.     

BLDC伺服系统控制系统建模与仿真

在分析了永磁无刷直流电机（BLDC）基本运行原理的基础上,建立了BLDC的数学模型,并在MATLAB7.1环境下搭建了伺服系统的双闭环控制系统仿真模型,实验结果表明,该模型在BLDC伺服系统的控制中性能稳定可靠,有较高的实用价值。     

基于智能采集模块的MIMO控制系统的设计

介绍了多输入多输出(multi-input and multi-output,MIMO)计算机过程控制系统的硬件组成及功能,给出了系统硬件设计的核心部件智能采集模块的应用及系统的工作原理.结合实际系统运用开发的应用软件进行调试及性能测试,表明基于智能采集模块设计的多变量计算机控制系统能满足性能要求,且实现了各变量之间的弱关联.该系统的设计为多变量解耦算法的研究,冗错技术,整体性及先进高级控制算法模拟研究提供实验平台,填补了该方面的空白,具有一定的创新价值.     

Time-varying sliding mode control for a class of uncertain MIMO nonlinear system subject to control input constraint

To solve the regulator problem of a class of uncertain MIMO nonlinear systems subject to control input constraint, three types of time-varying sliding mode control laws are proposed. The sliding surfaces pass the initial value of the system at the initial time, and are shifted/rotated towards the predetermined ones. The controller parameters are optimized by genetic algorithm (GA). Lyapunov method is adopted to prove the stability and robustness to the parameter uncertainties and external disturbance. By me...     

基于PC/104总线的电磁型转子自动平衡控制系统

为解决工业现场对转子自动平衡控制系统体积、可靠性及响应速度等实际要求,构建了基于PC/104总线的双盘自由旋转电磁型转子自动平衡控制系统,并给出了可相对转轴正反两个方向自由转动的双平衡盘移动控制策略.该系统可在工业现场狭小空间内就地安装,防尘抗震.实验结果表明,平衡系统移动迅速、准确,缩短了动平衡时间,提高了控制精度;该研究使转子自动平衡系统更加实用化,拓宽了其应用前景.     

A multiscale empirical modeling framework for system identification

A multiscale system identification methodology is presented and discussed, that extends, in a systematic way, the classical board of single-scale system identification tools to a multiscale context. The proposed approach is built upon a wavelet-based multiscale decomposition in a receding horizon sliding window that always includes the last measured values, in order to make it adequate for on-line use. Several examples are presented that illustrate different features of the multiscale modeling framework, such as its improved ability to perform prediction in output variables having most of its energy concentrated at intermediate or coarser time scales when compared to input variables, and its intrinsic smoothing capability.     

Integration of support vector regression and annealing dynamical learning algorithm for MIMO system identification

This paper presents a robust approach to identify multi-input multi-output (MIMO) systems. Integrating support vector regression (SVR) and annealing dynamical learning algorithm (ADLA), the proposed method is adopted to optimize a radial basis function network (RBFN) for identification of MIMO systems. In the system identification, first, SVR is adopted to determine the number of hidden layer nodes, the initial structure of the RBFN. After initialization, ADLA with nonlinear time-varying learning rate is then applied to train the RBFN. In the ADLA, the determination of the learning rate would be an important work for the trade-off between stability and speed of convergence. A computationally efficient optimization method, particle swarm optimization (PSO) method, is adopted to simultaneously find optimal learning rates. Due to the advantages of SVR and ADLA (SVR-ADLA), the proposed RBFN (SVR-ADLA-RBFN) has good performance for MIMO system identification. Two examples are illustrated to show the feasibility and superiority of the proposed SVR-ADLA-RBFNs for identification of MIMO systems. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.