Summary This article considers the identification problems of nonlinear systems with scarce measurements by using the instrumental variable technique. When the product of the instrumental matrix and the information matrix is a nonsingular matrix and the weak persistent excitation condition about the instrumental vector is true, the obtained parameter estimates can be unbiased consistent estimates. The key is how to choose the instrumental variables. Difficulty arises in that the system outputs are unavailable. By applying the negative gradient search, a recursive instrumental variable‐based gradient algorithm is derived to estimate the parameters of the nonlinear systems with missing observed data. Moreover, the multi‐innovation identification theory is introduced to further improve the parameter estimation accuracy. The simulation results illustrate that the proposed methods are effective.

According to the operational task of the palletizing robot in the intelligent manufacturing system, the layout and control process of the palletizing system are first designed. And then, the Smart Component of RobotStudio is used to design the system signal with I/O control logic, conveyor control logic, and a suction cup tool. Finally, the rationality, efficiency, and convenience of the function of the palletizing robot control system are verified by building a palletizing simulation system.

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In this paper, the Monte Carlo simulation for sapphire in wet etching is optimized, which improves the accuracy and efficiency of simulated results. Firstly, an eight-index classification method is proposed to classify the kinds of surface atoms, which can make assigned removal probabilities more accurately for surface atoms. Secondly, based on the proposed classification method of surface atoms, an extended removal probability equation (E-RPE) is proposed, which makes the errors between simulated and experimental rates smaller and greatly improves the accuracy of the simulated result of the etch rate distribution under the experimental condition (H2SO4:H3PO4 = 3:1, 236 °C). Thirdly, a modified removal probability equation (ME-RPE) considering the temperature dependence is proposed based on the error analysis between the simulated and experimental rates under different temperature conditions, which can simulate etch rates under the different temperature conditions through a group of optimized energy parameters and improve the simulation efficiency. Finally, small errors between the simulated and experimental rates under the different temperature conditions (H2SO4:H3PO4 = 3:1, 202 °C and 223 °C) verify the validity of the ME-RPE for temperature change. The optimization methods for the Monte Carlo simulation of sapphire in wet etching proposed in this paper will provide a reference for the simulation of other crystal materials.