A particle swarm optimization (PSO) algorithm which can dynamically adjust learning factors is proposed to solve the problems of low efficiency and unstable operation of traditional industrial robots. The method uses piecewise polynomial interpolation to fit the trajectory, and uses an improved particle swarm algorithm to optimize the trajectory of industrial robots with time as a fitness function. This method effectively combines the piecewise polynomial interpolation function with PSO, avoids the complex process of particle swarm algorithm to construct the adaptation function, and improves the problem that the traditional PSO is more likely to fall into local extreme value in the early stage and convergence speed is slow in the later stage. Through experiments to obtain the motion pose, velocity and acceleration trajectory of each joint of the manipulator, we can know that this method can effectively realize the trajectory optimization of the industrial robot, and ensure the stability of the overall operation while improving the operating efficiency.

To realize the stable tracking control of the optoelectronic stabilized platform system under nonlinear friction and external disturbance, an active disturbance rejection controller (ADRC) with friction compensation is proposed to improve the target tracking ability and anti-disturbance performance. First, a nonlinear LuGre observer is designed to estimate friction behavior and preliminarily suppress the interference of friction torque on the system. Then, an ADRC is introduced to further suppress the residual disturbance after friction compensation, and the stability of the ADRC system is also proved. The effectiveness of this scheme is proved by simulation experiments, and this scheme is compared with conventional ADRC and LuGre friction feedforward compensation. The simulation results show that an ADRC with LuGre friction compensation is better with trajectory tracking performance, which suppresses the influence of disturbance and improves the stability of the optoelectronic stabilized platform system.

In order to determine a way to be able to shorten the smelting time of low-carbon ferrochrome alloys and to strengthen the temperature control capability of the AOD converter during the smelting process, this article establishes a mechanism model of the rate of oxygen supply and carbon content change in the smelting process as well as the temperature of the reaction fluid in the converter. The physical and chemical reactions of the smelting process and the actual smelting data are used as the basis. The temperature and carbon content in the smelting converter are considered as the output quantity and the rate of oxygen supply is considered as the input quantity. An expert internal model control framework is built. In addition, an adaptive parameter adjustment mechanism is added to the control framework, taking into account data such as the maximum gas supply rate and the maximum limiting temperature of the actual production process. This method improves the smelting speed and smelting accuracy compared to the general internal mode control method. Finally, according to this method, with the use of the built low-carbon ferrochrome alloy centralized control system for smelting, the system shortens the smelting time by 12.79% compared with the general method and controls the converter temperature below 1950 K, which achieves the expected goal according to the simulation and actual smelting results.

This article presents a deep neural network model that integrates long short-term memory (LSTM), fuzzy logic, and self-attention mechanism to enhance short-term power load forecasting, particularly in scenarios with unstable data quality. The architecture of the model is designed to harness the LSTM’s proficiency in sequential data analysis, the inherent fault tolerance of fuzzy logic, and the self-attention mechanism’s ability to selectively enhance the significance of pivotal information. In addition, a postcompensation mechanism is incorporated to compensate for the deviation when certain model’s parameters are unavailable. Validated on three datasets, the model demonstrates improved accuracy and superior robustness, even under poor data quality conditions.

This paper proposes an enhanced active disturbance rejection sliding mode tracking differentiator, which ensures the rapid state convergence of optoelectronic mechanical platforms under frictional disturbances by reconstructing the optimal synthesis function into the optimal sliding mode control synthesis function. To cope with the low stability of the active disturbance rejection controller caused by deviations in the prescribed parameters, a globally oriented parameter identification scheme is developed by the Newton–Raphson optimized sparrow search algorithm, so that the globally optimal solution of the controller parameters can be obtained iteratively. Furthermore, disturbance observer is employed to perform real-time estimation of the constructed friction disturbance model and to implement feedback compensation within active disturbance rejection control, thereby enhancing the disturbance rejection capability of the system. The effectiveness of the proposed method on the optoelectronic mechanical platform is validated experimentally, and the system exhibits strong adaptability, particularly in the presence of frictional disturbances.