Operator-Based Robust Nonlinear Control for SISO and MIMO Nonlinear Systems With PI Hysteresis

In this paper, operator based robust nonlinear control for single-input single-output(SISO) and multi-input multi-output(MIMO) nonlinear uncertain systems preceded by generalized Prandtl-Ishlinskii(PI) hysteresis is considered respectively. In detail, by using operator based robust right coprime factorization approach, the control system design structures including feedforward and feedback controllers for both SISO and MIMO nonlinear uncertain systems are given, respectively.In which, the controller design includes the information of PI hysteresis and its inverse, and some sufficient conditions for the controllers in both SISO and MIMO systems should be satisfied are also derived respectively. Based on the proposed conditions, influence from hysteresis is rejected, the systems are robustly stable and output tracking performance can be realized.Finally, the effectiveness of the proposed method is confirmed by numerical simulations.

Apple grading method based on neural network with ordered partitions and evidential ensemble learning

In order to improve the performance of the automatic apple grading and sorting system,in this paper,an ensemble model of ordinal classification based on neural network with ordered partitions and Dempster–Shafer theory is proposed.As a non-destructive grading method,apples are graded into three grades based on the Soluble Solids Content value,with features extracted from the preprocessed near-infrared spectrum of apple serving as model inputs.Considering the uncertainty in grading labels,mass generation approach and evidential encoding scheme for ordinal label are proposed,with uncertainty handled within the framework of Dempster–Shafer theory.Constructing neural network with ordered partitions as the base learner,the learning procedure of the Bagging-based ensemble model is detailed.Experiments on Yantai Red Fuji apples demonstrate the satisfactory grading performances of proposed evidential ensemble model for ordinal classification.

A FIR flter assisted with the predictive model and ELM integrated for UWB-based quadrotor aircraft localization

To improve the accuracy of the Ultra-Wide Band(UWB)based quadrotor aircraft localization,a Finite Impulse Response(FIR)flter aided with an integration of the predictive model and Extreme Learning Machine(ELM)is proposed in this work.The FIR flter estimates the quad-rotor aircraft’s position by fusing the positions measured with the UWB and Inertial Navigation System respectively.When the UWB dada are unavailable,both the ELM and the predictive model are used to provide the measurements,replacing those unavailable UWB data,for the FIR flter.The ELM estimates the measurement via the mapping between the one step prediction of state vector and the measurement built when the UWB data are available.For the predictive model,we mathematically describe the missing UWB data.Then,both the measurements estimated with the ELM and predictive model are employed to estimate the observations via Mahalanobis distance.The test results show that the FIR flter aided by the predictive model/ELM integrated can reduce the Cumulative Distribution Function and position Root Mean Square Error efectively when the UWB is unavailable.Compared with the ELM assisted FIR flter,the proposed FIR flter can reduce the localization error by about 48.59%,meanwhile,the integrated method is close to the method with a better solution.