Aiming at the characteristics of strong non‐linearity and large inertia in the reaction process of a selective catalytic reduction (SCR) denitrification system, a predictive control algorithm based on a back propagation neural network optimized by genetic algorithm (GA‐BP) and particle swarm optimization (PSO) is proposed. First, the prediction model of a SCR denitrification system is established by GA‐BP. Second, output feedback and bias correction are used to reduce the prediction error. Third, the optimal inlet ammonia concentration is obtained by PSO. At the same time, in order to solve the problems of high dimension, large noise, and strong coupling in the original data of the SCR system in the process of establishing the prediction model, the least absolute shrinkage selection operator (LASSO) algorithm and the local outlier factor (LOF) detection algorithm are used to screen important variables and samples in the original data set of the SCR system to remove redundant variables and outliers. Finally, the simulation results show that the prediction model has good prediction accuracy and that the proposed predictive control method can achieve accurate control of ammonia injection concentration. This method improves the denitrification efficiency and reduces the NOx emission concentration, which can provide good guidance for on‐site production.

Parameter identification of the dynamic model of collaborative robots is the basis of the development of collaborative robot motion state control, path tracking, state monitoring, fault diagnosis, and fault tolerance systems, and is one of the core contents of collaborative robot research. Aiming at the identification of dynamic parameters of the collaborative robot, this paper proposes an identification algorithm based on weighted least squares and random weighted particle swarm optimization (WLS-RWPSO). Firstly, the dynamics mathematical model of the robot is established using the Lagrangian method, the dynamic parameters of the robot to be identified are determined, and the linear form of the dynamics model of the robot is derived taking into account the joint friction characteristics. Secondly, the weighted least squares method is used to obtain the initial solution of the parameters to be identified. Based on the traditional particle swarm optimization algorithm, a random weight particle swarm optimization algorithm is proposed for the local optimal problem to identify the dynamic parameters of the robot. Thirdly, the fifth-order Fourier series is designed as the excitation trajectory, and the original data collected by the sensor are denoised and smoothed by the Kalman filter algorithm. Finally, the experimental verification on a six-degree-of-freedom collaborative robot proves that the predicted torque obtained by the identification algorithm in this paper has a high degree of matching with the measured torque, and the established model can reflect the dynamic characteristics of the robot, effectively improving the identification accuracy.

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In the emergency rescue and disposal of social public emergencies, supply transportation effectively provides a strong supply foundation and realistic conditions. The trajectory tracking control of emergency supplies transportation robot is the key technology to ensure the timeliness of transportation. In this paper, the emergency supplies transportation robot is taken as the research object, based on Koopman operator theory, combined with radial basis function (RBF) neural network disturbance observer and adaptive prediction horizon event-triggered model predictive control (APET-MPC) algorithm to investigate the purely data-driven trajectory tracking control problem of emergency supplies transportation robot when the model parameters and models are unknown. Firstly, the Koopman operator is used to establish a high-dimensional linear model of the robot. Secondly, the RBF neural network disturbance observer is designed to estimate the disturbance during the robot operation and compensate it to the controller. Thirdly, APET-MPC is used to optimize the trajectory tracking control of the emergency supplies transportation robot to reduce computational complexity. Finally, the performance of the proposed trajectory tracking controller is verified by Carsim/ Simulink joint simulation. The simulation results show that the model established by Koopman operator theory can achieve the high accuracy approximation of the robot. Compared with the MPC trajectory tracking controller, the APET-MPC trajectory tracking controller based on RBF neural network disturbance observer (RBF-APET-MPC) improves the tracking accuracy of the robot and reduces the total triggering times of the system by more than 50%.