In this brief, a precise angular tracking control strategy using nonlinear predictive optimization control (POC) approach is address. In order to deal with the model uncertainty and noise interference, a online Hammerstein-model-based POC is designed using online estimated parameters and model residual. Above all, a rate-dependent Duhem model is used to describe the nonlinear sub-model of the whole Hammerstein architecture for depicting multi-valued mapping nonlinear characteristic. Then, predictive output of angular deflection is obtained by Diophantine function based on linear submodel. Subsequently, the iterative control value depends on estimated parameters through data-driven is acquired. Later, based on the cost function, the iteratively optimization control quantity is fed back to the electromagnetic driven deflection micromirror (EDDM) system on the basis of Hammerstein architecture. It should be stressed that the control value is determined by real-time update model residual and defined cost function. Moreover, the stability of POC strategy is proposed. In addition, experimental result is proposed to validate the effectiveness of the control technique adopted in this paper.

The electromagnetic driven micro-mirror (EDMM) is contaminated with noise measured by piezoresistive sensors located in the basement of the chip during the process of angle positioning. In this paper, a novel residual weight adaptive filter (RWAF) for EDMM is proposed to deal with the problem of noise pollution and eliminating outliers. Above all, the so-called Hammerstein architecture embedded with rate-dependent Duhem sub-model is developed to describe the dynamic performance for EDMM. Then, the residual prediction generated by the random variable is taken as the sample to be estimated, and the confidence interval estimation of the given confidence degree is determined by combining the residual variance. Subsequently, the upper and lower limits of confidence intervals are used as threshold to reduce model error via application of generalized gradient that is added in the Kalman gain for compensating the nonlinear part to improve filtering accuracy. In addition, convergence analysis of the novel algorithm is analyzed using martingale convergence theorem. Finally, conducted quantitative comparisons with unscented Kalman filter (UKF) and extended Kalman filter (EKF) reveal advantages of the proposed RWAF in noise attenuation and state reconstruction.

In this paper, a precise angle tracking control strategy of electromagnetic driven scanning micro-mirror (EDSM) using online iterative optimization control (OIOC) scheme is addressed. Considering modeling difficulty caused by nonlinear hysteresis and complex dynamic characteristics, a simplified model (SM) is applied to describe the essential motion trait of EDSM approximately, more than that, the predictive model (PM) is developed based on the SM to improve the accuracy of the model. As for the omitted section caused by unmolded nonlinear and dynamic factor with unmatched interference (e.g., oscillation, nonlinear hysteresis and stochastic noise), that’s all being introduced into the angular deviation compensator (ADC) once again. Subsequently, a compensating structure SM+ADC is acquired to describe the dynamic performance and to design angular tracking control. Due to the nonlinear performance of multi-valued mapping, the generalized gradient is applied to optimize the control variables located in the nonlinear interval aiming to suppress the model deviation and eliminate unmatched interference. Then, a new iterative optimization control OIOC $+$ ADC is obtained on the basis of compensation structure and generalized gradient. Besides, by designing Lyapunov function, the proposed control design incorporated by the relationship between optimized step-size and optimization gradient that needs to be satisfied guarantees the high accuracy positioning and stability. Conducted quantitative experiment results are provided to validate the effectiveness of the proposed control strategy in precise angle tracking to the fast dynamic system.

A novel fuzzy sliding mode control (FSMC) strategy is proposed to enhance the robustness and stability of position control for underactuated quadrotor unma

The Notice of The State Council on Printing and Distributing the Development Plan for a New Generation of Artificial Intelligence has put forward development goals i.e., accelerating the intelligent upgrading of industries, promoting new manufacturing models such as remote diagnosis and operation and maintenance services, and promoting the integrated innovation of artificial intelligence with various industries. It clearly pointed out that Artificial Intelligence for IT Operations (AIOps), as an innovative application of big data and artificial intelligence technologies in the upgrade of traditional management and automation models, aims to address the challenges of efficiency and stability in the digital transformation of enterprises. Under the background of this plan, the core courses in the undergraduate and postgraduate stages of the electrical major, i.e., Principles of Automatic Control (PAC) and Status Identification and Fault Prediction of Electrical Equipment (SIFPEE), both have the defect of chain reinforcement and optimization as well as the problem of deep disconnection in terms of theoretical foundation supporting practical application and technical progressive complementary influence. Therefore, how to form a collaborative path of basic theory to advanced application for the electrical major based on two courses (PAC+SIFPEE) and promote the comprehensive development of students in the fields of intelligent control and predictive maintenance is the main content of this study.