Icing tolerance envelope protection based on variable-weighted multiple-model predictive control

Multiple-model predictive control(MMPC) is a fundamental icing tolerance envelope protection(ITEP) design method that can systematically handle nonlinear and time-varying constraints. However, few studies have addressed the envelope protection failure that results from the inaccurate prediction of multiple linear predictive models when actual conditions deviate from design conditions. In this study, weights that vary with icing conditions and flight parameters are considered to develop an effective and reliable envelope protection control strategy. First, an ITEP structure based on variable-weighted MMPC was implemented to improve the protection performance with condition departure information. Then, a variable-weighted rule was proposed to guarantee the stability of variable-weighted MMPC. A design approach involving a variable-weighted function that uses icing conditions and flight parameters as arguments was also developed with the proposed rules. Finally, a systematic ITEP design method on variable-weighted MMPC was constructed with additional design criteria for other normal control parameters.Simulations were conducted, and the results show that the proposed method can effectively enhance ITEP performance.

Geometrical entropy approach for variable structure multiple-model estimation

The variable structure multiple-model（VSMM） estimation approach, one of the multiple-model（MM） estimation approaches, is popular in handling state estimation problems with mode uncertainties.In the VSMM algorithms, the model sequence set adaptation（MSA） plays a key role.The MSA methods are challenged in both theory and practice for the target modes and the real observation error distributions are usually uncertain in practice.In this paper, a geometrical entropy（GE） measure is proposed so that the MSA is achieved on the minimum geometrical entropy（MGE） principle.Consequently, the minimum geometrical entropy multiple-model（MGEMM） framework is proposed, and two suboptimal algorithms, the particle filter k-means minimum geometrical entropy multiple-model algorithm（PF-KMGEMM） as well as the particle filter adaptive minimum geometrical entropy multiple-model algorithm（PF-AMGEMM）, are established for practical applications.The proposed algorithms are tested in three groups of maneuvering target tracking scenarios with mode and observation error distribution uncertainties.Numerical simulations have demonstrated that compared to several existing algorithms, the MGE-based algorithms can achieve more robust and accurate estimation results when the real observation error is inconsistent with a priori.

Quick identification of guidance law for an incoming missile using multiple-model mechanism

A guidance law parameter identification model based on Gated Recurrent Unit(GRU)neural network is established. The scenario of the model is that an incoming missile(called missile)attacks a target aircraft(called aircraft) using Proportional Navigation(PN) guidance law. The parameter identification is viewed as a regression problem in this paper rather than a classification problem, which means the assumption that the parameter is in a finite set of possible results is discarded. To increase the training speed of the neural network and obtain the nonlinear mapping relationship between kinematic information and the guidance law parameter of the incoming missile, an output processing method called Multiple-Model Mechanism(MMM) is proposed. Compared with a conventional GRU neural network, the model established in this paper can deal with data of any length through an encoding layer in front of the input layer. The effectiveness of the proposed Multiple-Model Mechanism and the performance of the guidance law parameter identification model are demonstrated using numerical simulation.

Feedback structure based entropy approach for multiple-model estimation

The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.

Automated postoperative blood pressure control

It is very important to maintain the level of mean arterial pressure （MAP）. The MAP control is applied in many clinical situations, including limiting bleeding during cardiac surgery and promoting healing for patient＇ s post-surgery. This paper presents a fuzzy controller-based multiple-model adaptive control system for postoperative blood pressure management. Multiple-model adaptive control （MMAC） algorithm is used to identify the patient model, and it is a feasible system identification method even in the presence of large noise. Fuzzy control （FC） method is used to design controller bank. Each fuzzy controller in the controller bank is in fact a nonlinear proportional-integral （PI） controller,whose proportional gain and integral gain are adjusted continuously according to error and rate of change of error of the plant output, resulting in better dynamic and stable control performance than the regular PI controller, especially when a nonlinear process is involved. For demonstration, a nonlinear, pulsatile-flow patient model is used for simulation, and the results show that the adaptive control system can effectively handle the changes in patient＇s dynamics and provide satisfactory performance in regulation of blood pressure of hypertension patients.

Dataset of temperature and precipitation over the major Belt and Road Initiative regions under different temperature rise scenarios

Changes in temperature and precipitation have a profound effect on the ecological environment and socioeconomic systems.In this study,we focus on the major Belt and Road Initiative(BRI)regions and develop a dataset of temperature and precipitation at global temperature rise targets of 1.5°C,2°C,and 3°C above pre-industrial levels under the Representative Concentration Pathway(RCP)8.5 emission scenario using 4 downscaled global model datasets data at a fine spatial resolution of 0.0449147848°(~5 km)globally from EnviDat.The temperature variables include the daily maximum(Tmax),minimum(Tmin)and average(Tmp)surface air temperatures,and the diurnal temperature range(DTR).We first evaluate the performance of the downscaled model data using CRU-observed gridded data for the historical period 1986-2005.The results indicate that the downscaled model data can generally reproduce the pattern characteristics of temperature and precipitation variations well over the major BRI regions for 1986-2005.Furthermore,we project temperature and precipitation variations over the major BRI regions at global temperature rise targets of 1.5°C,2°C,and 3°C under the RCP8.5 emission scenario based on the dataset by adopting the multiple-model ensemble mean.Our dataset contributes to understanding detailed the characteristics of climate change over the major BRI regions,and provides data fundamental for adopting appropriate strategies and options to reduce or avoid disadvantaged consequences associated with climate change over the major BRI regions.The dataset is available at https://doi.org/10.57760/sciencedb.01850.

Center-Distance Continuous Probability Models and the Distance Measure

In this paper, a new statistic model named Center-Distance Continuous Probability Model (CDCPM) for speech recognition is described, which is based on Center-Distance Normal (CDN) distribution. In a CDCPM, the probability transition matrix is omitted, and the observation probability density function (PDF) in each state is in the form of embedded multiple-model (EMM) based on the Nearest Neighbour rule. The experimental results on two giant real-world Chinese speech databases and a real-world continuous-manner 2000 phrase system show that this model is a powerful one. Also,a distance measure for CDCPMs is proposed which is based on the Bayesian minimum classification error (MCE) discrimination.