Rapid Load Following of an SOFC Power System via Stable Fuzzy Predictive Tracking Controller

The solid oxide fuel cell (SOFC) is widely accepted for clean and distributed power generation use, but critical operation problems often occur when the stand-alone fuel cell is directly connected to the electricity grid or the dc electric user. In order to address these problems, in this paper, a data-driven fuzzy modeling method is employed to identify the dynamic model of an integrated SOFC/capacitor system. A novel offset-free input-to-state stable fuzzy predictive controller is developed based on the obtained fuzzy model. Both the rapid power load following and safe SOFC operation requirements are taken into account in the design of the closed-loop control system. Simulations are also given to demonstrate the load following control performance of the proposed fuzzy predictive control strategy for the SOFC/capacitor power system.     

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.     

Thermal behavior of high-temperature fuel cells: reliable parameter identification and interval-based sliding mode control

In this contribution, we present interval methods for mathematical modeling, for parameter identification, and for control design of dynamical systems. The corresponding approaches are applied to the thermal subsystem of a high-temperature solid oxide fuel cell (SOFC) which is available as a test rig at the Chair of Mechatronics at the University of Rostock. In practice, most internal parameters of SOFC stack modules cannot be measured directly. Therefore, system characteristics such as heat capacities or internal thermal resistances cannot be identified exactly, but only bounded. For this reason, intervals represent a good first approach to dealing with parameter uncertainty. In the first part of the paper, we present interval methods for the parameter identification aiming at the computation of globally optimal parameterizations. In comparison with classical local optimization procedures, the approximation quality is improved by the presented identification approach. The corresponding bounds for admissible domains are used to design a robust sliding mode control law for arbitrary operating points compensating the impact of disturbances and parameter uncertainties in a reliable way. In the second part of the paper, we show a simple approach to handling non-smoothness appearing in SOFC models based on ordinary differential equations in a verified way. We use a generalized derivative definition for a certain type of non-smooth functions inside the algorithm of the verified solver ValEncIA-IVP to be able to compute solutions to non-smooth initial value problems. The applicability of our method is demonstrated using the designed sliding mode controller.     

Predictive functional control based on fuzzy model forheat-exchanger pilot plant

In this paper, a new method of predictive control is presented. In this approach, a well-known method of predictive functional control is combined with fuzzy model of the process. The prediction is based on fuzzy model given in the form of Takagi-Sugeno type. The proposed fuzzy predictive control has been evaluated by implementation on heat-exchanger plant, which exhibits a strong nonlinear behavior. It has been shown that in the case of nonlinear processes, the approach using fuzzy predictive control gives very promising results. The proposed approach is potentially interesting in the case of batch reactors, heat-exchangers, furnaces, and all the processes that are difficult to model     

模糊广义预测控制及其应用

本文将单变量广义预测控制原理应用到多变量模糊系统,提出了一种基于辨识模糊模型 的多变量预测控制方法.仿真研究表明,该模糊广义预测控制方法适用于工业过程的动态辨 识和控制,交能取得良好的效果.     

Fuzzy Predictive Functional Control in the State Space Domain

In the paper, a well-known predictive functional control strategy is extended to nonlinear processes. In our approach the predictive functional control is combined with a fuzzy model of the process and formulated in the state space domain. The prediction is based on a global linear model in the state space domain. The global linear model is obtained by the fuzzy model in Takagi–Sugeno form and actually represents a model with changeable parameters. A simulation of the system, which exhibits a strong nonlinear behaviour together with underdamped dynamics, has evaluated the proposed fuzzy predictive control. In the case of underdamped dynamics, the classical formulation of predictive functional control is no longer possible. That was the main reason to extend the algorithm into the state space domain. It has been shown that, in the case of nonlinear processes, the approach using the fuzzy predictive control gives very promising results.     

Control‐oriented fault detection of solid oxide fuel cell system unknown input on fuel supply

As the solid oxide fuel cell (SOFC) system work environment is a high‐temperature environment for a long time, it is difficult to obtain the SOFC stack internal state change directly. When the fault occurs, it is difficult to determine where the fault occurs. Moreover, the existing literature ignores the impact of faults, which creates many problems for SOFC system control. Therefore, a state observer‐based fault detection method, which is used to detect the input flow sensor fault and the fuel input fault, is proposed. Their advantage is that they do not need data processing. To realize the fault detection, the observer is used to track the changes of SOFC stack chamber temperature. To obtain the observer estimation parameter, an approach from the actual stack structure parameters is employed to approximate the observer parameters. The results show the proposed fault detect method can judge fuel input fault type quickly and shield the disturbances signals from the sensor effectively. The proposed method also can be used to other operating points or air input fault.     

Stability indices for a self-organizing fuzzy controlled robot: A case study

This study developed a model-free self-organizing fuzzy controller (SOFC) for manipulating multiple-input multiple-output systems. The SOFC has an online learning algorithm that can continually update fuzzy rules during the control process, beginning from an empty rule table. The SOFC was used to control a three-link robot with a complex dynamic model in order to evaluate its applicability. Stability and robustness of the SOFC were demonstrated using a state-space approach. Simulation results confirmed that the control performance of the SOFC outperforms that of the fuzzy logic controller for the control of the robot.     

A Nonsmooth Nonlinear Programming Based Predictive Control for Mechanical Servo Systems with Backlash‐like Hysteresis

In this paper, a multi‐step‐ahead predictive control approach for dynamic systems with preceded backlash‐like hysteresis based on nonsmooth nonlinear programming is proposed. In this approach, a nonsmooth multi‐step‐ahead predictive model is developed for long‐range prediction of the controlled dynamic systems with preceded backlash‐like hysteresis. Then, the predictive control strategy is treated as a problem of on‐line nonsmooth nonlinear programming. Subsequently, the stability of the nonsmooth predictive control system is analyzed and the corresponding stability condition is derived. Afterward, a numerical example and a simulation based on a mechanical servo system are presented, respectively.     

Coprime-factorized Model Predictive Control for Unstable Processes with Delay

This paper presents coprime-factorized model predictive control. The main idea of the proposed approach is in process-output prediction based on a coprime-factorized process model. The proposed approach provides a framework to design the control for a wide range of processes such as: higher order, phase non-minimal, unstable and also multivariable. In the paper the coprime-factorized predictive design methodology was studied and implemented on unstable processes with a time-delay which are very difficult to control. The proposed methodology leads to a simple analytical control law, which results in much better performance than previously known control methods.     

PEMFC的模糊辨识和非线性预测控制

质子交换膜燃料电池(PEMFC)是21世纪最有前景的发电技术之一。该文针对PEMFC等一类具有严重非线性的复杂被控对象,提出一种基于模糊模型的非线性预测控制算法对PEMFC系统进行建模与控制。在建模与控制过程中,采用模糊聚类和线性辨识方法在线建立PEMFC控制系统的T-S模糊预测模型,然后基于分支定界法的基本原理对控制量进行离散寻优,从而实现PEMFC的非线性预测控制。仿真和实验结果证明了该算法的有效性和优越性。     

直线电机的非参数模型直接自适应预测控制

将基于紧格式线性化的非参数模型直接自适应预测控制方法应用到直线电机速度和位置控制中.控制器的设计是直接基于伪偏导数的估计和预报,而伪偏导数信息则足通过参数估计算法和预报算法利用I/O数据在线导出.仿真演示了该方法对电机这种不确知动态非线性系统的有效性和抗干扰能力.     

PF-MPC: Particle filter-model predictive control

In this article, a new model predictive control approach to nonlinear stochastic systems will be presented. The new approach is based on particle filters, which are usually used for estimating states or parameters. Here, two particle filters will be combined, the first one giving an estimate for the actual state based on the actual output of the system; the second one gives an estimate of a control input for the system. This is basically done by adopting the basic model predictive control strategies for the second particle filter. Later in this paper, this new approach is applied to a CSTR (continuous stirred-tank reactor) example and to the inverted pendulum. These two examples show that our approach is also real-time-capable.     

包含执行器动力学的子空间预测动态控制分配方法

针对具有冗余执行机构的过驱动系统的最优控制分配问题, 基于数据驱动的子空间辨识方法和预测控制理论, 提出了一种考虑执行器动力学特性的动态控制分配新方法. 在考虑范数有界不确定性的在线子空间辨识的基础上, 对执行器动力学特性进行不确定性建模, 再结合预测控制理论进行动态控制分配. 从而将执行机构的动力学建模、控制量最优分配和执行机构控制律的设计包含在一个子系统框架内, 对执行机构的模型不确定性具有更好的鲁棒性. 最后给出仿真实例验证了算法的有效性.     

Multi-step prediction error approach for controller performance monitoring

Performance monitoring of model predictive control (MPC) systems has received a great interest from both academia and industry. In recent years some novel approaches for multivariate control performance monitoring have been developed without the requirement of process models or interactor matrices. Among them the prediction error approach has been shown promising, but it is based on single-step prediction and may not be compatible with the MPC objective that is based on multi-step prediction. This paper develops a multi-step prediction error approach for performance monitoring of model predictive control systems, and demonstrates its application in a real industrial MPC performance monitoring and diagnosis problem.     

基于移动公网的变频器无线监控系统设计与实现

针对电动机变频控制系统,提出了基于移动公网的无线监控系统设计思路和实现方案,阐述了无线数据终端的应用与系统集成方法。同时,为了实现对变频器的远程闭环控制,引入预测控制思想,有效解决了无线网络控制中的数据丢包和延迟问题。经实际测试,证明了所设计系统的可靠性和可用性。     

A recursive modified partial least square aided data-driven predictive control with application to continuous stirred tank heater

In this paper, a data-driven predictive control strategy for nonlinear system is proposed and testified on a continuous stirred tank heater (CSTH) benchmark. A recursive modified partial least square (RMPLS) algorithm is employed to regress the local linear model. The algorithm of locally weighted projection regression (LWPR) is then leveraged to build the predictive model, based on which a novel data-driven predictive control strategy is put forward. The proposed predictive controller has the ability to deal with changing working conditions, benefiting from the incremental learning ability of RMPLS and LWPR. The performance of the proposed control strategy is demonstrated with the CSTH while the superiority is illustrated by comparison with an existing model-free adaptive control approach.     

Disturbance compensation incorporated in predictive control system using a repetitive learning approach

In this paper, a disturbance compensation scheme is incorporated into a predictive control scheme using a repetitive learning approach. It has the following contributions. First, based on the assumption of the presence of both state and output disturbances, a predictive control algorithm is derived. Secondly, to estimate the disturbances, two feedforward disturbance learning schemes are proposed. Thirdly, the rigid mathematic proof is given to guarantee the convergence of the tracking error under the proposed disturbance learning laws used in conjunction with the predictive controller formulated. Finally, simulation results are provided to illustrate the good performance achievable by the proposed control law.     

A nonlinear model predictive control system based on Wiener piecewise linear models

In this paper a nonlinear model predictive control (NMPC) based on a Wiener model with a piecewise linear gain is presented. This approach retains all the interested properties of the classical linear model predictive control (MPC) and keeps computations easy to solve due to the canonical structure of the nonlinear gain. Some guidelines for the identification of the nominal model as well as the uncertainty bounds are discussed, and two examples that show the possibility of application of this control scheme to real life problems are presented.