Global linearizing control of MIMO microwave-assisted thawing

The application of a global linearizing control algorithm to a multi-input–multi-output (MIMO) microwave thawing process is addressed in this paper. The objective is to control the defrosting time while preventing thermal runaway. A model of the heat transfer with a source term is proposed and is reduced to a system of nonlinear ordinary differential equations using a finite volumes scheme. The control consists of multivariable trajectory tracking, while acting on the microwave power and on the air velocity of a tangential cooling air blast. Experimental and simulation results are discussed in MIMO and single-input–single-output (SISO) configurations.     

微波冷冻干燥升华阶段热失速控制的模拟

采用一种半理论的方法对微波冷冻干燥升华阶段的热失速现象进行控制。一维数值模拟结果表明:这一控制策略在升华阶段热失速的控制中是相当有效的。     

Land cover-based optimal deconvolution of PALS L-band microwave brightness temperatures

An optimal deconvolution (ODC) technique has been developed to estimate microwave brightness temperatures of agricultural fields using microwave radiometer observations. The technique is applied to airborne measurements taken by the Passive and Active L and S band (PALS) sensor in Iowa during Soil Moisture Experiments in 2002 (SMEX02). Agricultural fields in the study area were predominantly soybeans and corn. The brightness temperatures of corn and soybeans were observed to be significantly different because of large differences in vegetation biomass. PALS observations have significant over-sampling; observations were made about 100 m apart and the sensor footprint extends to about 400 m. Conventionally, observations of this type are averaged to produce smooth spatial data fields of brightness temperatures. However, the conventional approach is in contrast to reality in which the brightness temperatures are in fact strongly dependent on land cover, which is characterized by sharp boundaries. In this study, we mathematically deconvolve the observations into brightness temperature at the field scale (500-800 m) using the sensor antenna response function. The result is more accurate spatial representation of field-scale brightness temperatures, which may in turn lead to more accurate soil moisture retrieval.     

一类非线性系统的自适应时延观测器设计

利用输出差分及滑模方法研究一类非线性系统的自适应时延状态观测器设计问题. 首先将系统输出的微分及高阶微分用输出和其延迟测量值的差商来表示, 然后在此基础上构造了一种时延状态观测器, 估计误差的影响可由滑模项来消除. 在系统满足线性参数化的条件下给出了一种自适应时延状态观测器设计. 最后给出仿真研究说明设计方法的有效性.     

Thermal MIMO controller for setpoint regulation and load disturbance rejection

The thermal interaction problem of a multi-input multi-output (MIMO) temperature process is caused by the temperature gradient through the plant. Generally, industrial controllers are used for the decentralized control and do not use a process mathematical model. In many cases, trial-and-error tuning is required for solving the problem. The present paper introduces a MIMO thermal model and the decoupling PID control strategy. The decoupling control and its shortcomings are described from the perspective of the user. An improved strategy based on a robust observer is developed. For industrial implementation, this observer is quite simple because only the first-order model given by the decoupling model is implemented. The program code is simple for the microprocessor of a low-cost commercialized controller. In addition, a hunting rejection method based on the observer is presented. The present paper applies the method to the twin screw extruder in order to verify the effectiveness of the proposed strategy. The proposed method has been successfully to solve the interaction problem for MIMO temperature process.     

Passive sensing experiments and mapping at 3.3 mm wavelength

The ability of a 3.3-mm radiometer to produce thermal images with a 10 arc-minutes resolution on a real-time display has been proved. The influence of the terrain and of the atmosphere on a picture taken from a ground-based antenna has been investigated. The range of zenith sky noise temperatures measured (40–240 degrees K) is so large that a well recognizable object at low sky noise temperature (object on a natural terrain) may disappear completely for a heavy cloud cover. It is shown that the uniquely high angular resolution of this microwave radiometer permits the display of thermograms which approach the visual impression of optical images even under Fresnel conditions.     

An adaptive non-linear observer for the estimation of temperature distribution in the planar solid oxide fuel cell

Minimising the thermal gradients is extremely important in a planar solid oxide fuel cell (SOFC) for improving the cell life. The estimation of the temperature distribution in the cell is necessary to achieve this objective through suitable control, since they are not generally measurable. In this work, we have designed a non-linear adaptive observer for estimating the temperatures inside the hydrogen fed planar SOFC. The observer design is based on a lumped parameter model of the SOFC. The stability of the proposed observer is proven using the Lyapunov function method and is based on the concept of input-to-state stability for cascaded systems. The simulations show that the developed observer can track the temperature and species concentration profiles in the planar SOFC during step changes in the cell current. The adaptive observer presented is valid for a wide operating range, requires fewer variables to be measured, and is robust to fluctuations in the inlet flows.     

德拜媒质微波加热过程的H∞保性能温度跟踪控制

德拜媒质微波加热过程中，由于介电常数具有随温度变化的特性，导致电磁场的空间分布将会产生巨大的变化.若缺乏合理的功率调控策略，将导致燃烧、爆炸等一系列热失控现象.针对上述问题，本文提出一种滚动时域H_∞保性能温度跟踪控制策略，以实现对监测位置的最高温度进行控制.基于微波加热德拜媒质的机理模型，同时考虑跟踪系统稳定性、动态性能和输入约束，以H_∞增益和保性能函数作为性能指标，本文将温度跟踪问题转化为线性矩阵不等式（Linear matrix inequality，LMI）多目标优化问题，使得系统动态性能达到最优.最后以德拜媒质微波加热短波导模型为例，对所提出方法的有效性进行仿真验证.     

适应扰动的无人直升机姿态跟踪控制

无人直升机在实际飞行过程中,会受到阵风等外界因素的干扰,并且模型不确定性也会对控制效果带来不利影响.为应对这些挑战,本文设计了一种基于扩张状态观测器的自抗扰反步控制器.首先,建立了无人直升机姿态动力学模型.随后,引入扩张状态观测器,用以实时观测由外界扰动和模型不确定性组成的总和扰动.观测得到的总和扰动估计值与基于Lyapunov函数的反步法控制器控制算法相结合,用以消除总和扰动的影响,使得无人直升机在各种飞行条件下均能对运动指令进行快速和准确的跟踪.最后,仿真研究和飞行实验验证了该控制律的有效性.与同等条件下的PID控制器相比,该控制律表现出更优的飞行性能.     

Observer gain effect in linear interval observer-based fault detection

An interval observer has been shown to be a suitable passive robust strategy to generate an adaptive threshold to be used in residual evaluation when model uncertainty is located in parameters (interval model). In such an approach, the observer gain plays an important role since it determines the minimum detectable fault for a given type of fault and allows enhancing the observer fault detection properties. The aim of this paper is to analyze the influence of the observer gain on the time evolution of the residual sensitivity to a fault. Thereby, as a result of this sensitivity study, the minimum detectable fault time evolution for a given type of fault and the interval observer fault detection performance could be determined. In particular, three types of faults according to their detectability time evolution are introduced: permanently (strongly) detected, non-permanently (weakly) detected or just non-detected. An example based on a mineral grinding-classification process is used to illustrate the results derived.     

Robust Adaptive Gain Higher Order Sliding Mode Observer Based Control-constrained Nonlinear Model Predictive Control for Spacecraft Formation Flying

This work deals with the development of a decentralized optimal control algorithm, along with a robust observer, for the relative motion control of spacecraft in leader-follower based formation. An adaptive gain higher order sliding mode observer has been proposed to estimate the velocity as well as unmeasured disturbances from the noisy position measurements. A differentiator structure containing the Lipschitz constant and Lebesgue measurable control input, is utilized for obtaining the estimates. Adaptive tuning algorithms are derived based on Lyapunov stability theory, for updating the observer gains, which will give enough flexibility in the choice of initial estimates. Moreover, it may help to cope with unexpected state jerks. The trajectory tracking problem is formulated as a finite horizon optimal control problem, which is solved online. The control constraints are incorporated by using a nonquadratic performance functional. An adaptive update law has been derived for tuning the step size in the optimization algorithm, which may help to improve the convergence speed. Moreover, it is an attractive alternative to the heuristic choice of step size for diverse operating conditions. The disturbance as well as state estimates from the higher order sliding mode observer are utilized by the plant output prediction model, which will improve the overall performance of the controller. The nonlinear dynamics defined in leader fixed Euler-Hill frame has been considered for the present work and the reference trajectories are generated using Hill-Clohessy-Wiltshire equations of unperturbed motion. The simulation results based on rigorous perturbation analysis are presented to confirm the robustness of the proposed approach.      

Robust observer design for nonlinear uncertain systems with LQ regulators

For a class of uncertain systems with linear nominal dynamics and nonlinear uncertainties, it has been shown (Katayama and Sasaki 1987) that linear quadratic (LQ) state feedback regulators can be used to provide robust asymptotic stability. In this paper, we study the combined observer-controller design problem, based on the linear state feedback regulator proposed by Katayama and Sasaki (1987), so that only output feedback is needed. Both full-order and reduced-order observers are considered. For the full-order observer, we propose an algorithm to synthesize the robust observer gain matrix. It is shown that with the observer it is still possible to achieve robust asymptotic stability. For the reduced-order observer, some conditions are derived to guarantee the robust asymptotic stabilizability of the uncertain systems. The trade-off between the magnitudes of controller and observer gains is clear in our approach. An example is used to illustrate the design process of the robust controller with full-order as well as reduced-order observers.     

A novel neural internal model control for multi-input multi-output nonlinear discrete-time processes

An internal model-based neural network control is proposed for unknown non-affine discrete-time multi-input multi-output (MIMO) processes in nonlinear state space form under model mismatch and disturbances. Based on the neural state-space model built for an unknown nonlinear MIMO state space process, an approximate internal model and approximate decoupling controllers are derived simultaneously. Thus, the learning of the inverse process dynamics is not required. A neural network model-based extended Kalman observer is used to estimate the states of a nonlinear process as not all states are accessible. The proposed neural internal model control can work for open-loop unstable processes with its closed-loop stability derived analytically. The application to a distributed thermal process shows the effectiveness of the proposed approach for suppressing nonlinear coupling and external disturbances and its feasibility for the control of unknown non-affine nonlinear discrete-time MIMO state space processes.     

Inter-comparing and improving land surface temperature estimates from passive microwaves over the Jiangsu province of the People’s Republic of China

In the recent years, the FengYun-3B passive microwave observations (FY3B) have been used to study global and regional soil moisture estimates, and land surface temperature (LST) has been an important input in the retrieval model. Previous studies proposed a linear model to derive LST from Ka-band brightness temperatures (H09 approach) which were routinely masked based on open water data. This study aimed to further understand microwave emission under such challenging open water conditions and developed the first data-driven approach to correct for such open water conditions. The H09 was first applied to the FY3B observations, and then inter-compared against MODIS (Moderate-resolution Imaging Spectro-radiometer), MERRA (Modern Era Retrospective analysis for Research and Applications) and ground observations over the Jiangsu province in the People’s Republic of China. Several freshwater bodies are located within this region, and the Pacific Ocean bounds at the east, this makes it an excellent region for the study of open water impact on the passive microwave observations. As in previous studies, the quality of the FY3B LST based on the H09 approach dropped over the regions with high open water. While the MERRA and MODIS LST products maintained an average correlation of about 0.95, the FY3B LST product had an average correlation of 0.85 with drops to about 0.65 over the open water regions. Additional comparisons also revealed biases over the open water regions within the passive microwave product. An open water metric, which is based on the percentage of a pixel and its surrounding pixels covered by water was developed to quantify the impact of open water. Based on a pixel-wise linear relationship between the MERRA LST and FY3B Ka-band brightness temperature, spatially variable slopes and intercepts were obtained as a function of open water to produce a correction for open water in the H09 which leads to the HG18 approach. Validations with both the MODIS LST product and the ground observations showed that there was a significant reduction in both the absolute bias (MODIS: 2 K, in situ: 10 K) and the standard errors (MODIS: 0.8 K, in situ: 1 K) using the HG18 approach. This opens up a new window towards further improving and understanding LST retrievals from passive microwave products.     

平流层飞艇热性能分析软件开发

为减少平流层飞艇的设计周期和设计试验经费,清晰准确地反映出平流层飞艇空间轨迹和温度场分布以及各种不同因素对轨迹和温度场分布的影响,自主开发了平流层飞艇热性能分析软件.通过建立平流层飞艇运动和热特性模型,利用VC++编程环境,对平流层飞艇在不同输入状态下的上升、浮空、下降过程中的轨迹和温度进行数值仿真,并利用图形显示界面进行显示.软件设计包括参数输入、中间计算和性能分析三个部分.软件将输出结果存储到ACCESS数据库中,增强了数据的可读性和交互性.从软件的运行结果反映出该软件涵盖面广、数据可靠、操作方便、界面生动等特点,达到预期设计目标.     

Online exact differentiation and notion of asymptotic algebraic observers

In recent years, the availability of computer-based methods has created a revival of interests in exploring algebraic methods in nonlinear context. This paper proposes a new approach to algebraic nonlinear observer design. After giving the notion of algebraic observability, and based on a novel algorithm of exact differentiation, the formulation of the nonlinear observer is realized via the construction of a set of linear time-varying differentiators. An example of a chemical reaction is given to show the effectiveness of our approach.     

Nonlinear observer of the thermal loads applied to the helium bath of a cryogenic Joule–Thompson cycle

In this paper, a nonlinear observer of the thermal loads applied to the helium bath of a cryogenic refrigerator is proposed. The thermal loads represent a time-varying thermal disturbance expected to take place in future tokamaks refrigerators such as those used in the cooling systems for the International Thermonuclear Experimental Reactor (ITER) or the Japan Torus-60 Super Advanced (JT-60SA). The proposed observer can serve as a monitoring tool for cryogenic operators and/or in observer-based advanced control strategies. The observer is based on a part of the nonlinear model of the refrigerator. The paper details how the physical model of the Joule–Thompson cycle is obtained and the structure of the observer and validates its performance using experimental data.     

Eigenstructure assignment design for proportional-integral observers: the discrete-time case

A complete parametric design approach for proportional-integral observers of multivariable discrete-time linear systems is proposed based on an eigenstructure assignment technique. Complete parameterizations for all the observer gains as well as the eigenvector matrix of the observer system matrix are established in terms of three sets of design parameters that satisfy three basic and simple constraints. The proposed approach provides all the degrees of freedom and has great potential in applications. An illustrative example shows the effect of the proposed approach.     

Decision tree and first-principles model-based approach for reactor runaway analysis and forecasting

Decision trees (DTs) are effective in extracting linguistically interpretable models from data. This paper shows that DTs can also be used to extract information from process models, e.g. they can be used to represent homogenous operating regions of complex process. To illustrate the usefulness of this novel approach a detailed case study is shown where DTs are used for forecasting the development of runaway in an industrial, fixed bed, tube reactor. Based on first-principles knowledge and historical process data the steady-state simulator of the tube reactor has been identified and validated. The runaway criterion based on Ljapunov's indirect stability analysis has been applied to generate a data base used for DT induction. Finally, the logical rules extracted from the DTs are used in an operator support system (OSS), since they are proven to be useful to describe the safe operating regions. A simulation study based on the dynamical model of the process is also presented. The results confirm that by the synergistic combination of a DT based on expert system and the dynamic simulator a powerful tool for runaway forecasting and analysis is achieved and it can be used to work safe operating strategies out.