Optimal System for Improved Internal Model Control of Argon-Oxygen Decarburization Process Based on the Piecewise Linear Model and Time Constant of Filter Optimization

This paper presents an improved internal model control system to raise the efficiency of refining low-carbon ferrochrome. This control system comprises of a piecewise linearized transfer function and an improved internal model controller based on optimized time constant of the filter. The control system is mainly used to control the oxygen supply rate during the argon-oxygen refining for controlling the smelting temperature. The regulatory performance and servo of two closed-loop control schemes are compared between the improved internal model controller based on the optimized filter time 0000-0002-7606-6546and the internal model controller based on the fixed filter time constant. The simulation analysis shows that the piecewise linearized model and the optimization of the time constant of the filter improves the response time, stability, and anti-interference ability of the controller. Then, the proposed improved internal model controller is used to adjust the gas supply flow in 5 ton AOD furnace to control the smelting temperature. Ten production tests performed the effectiveness of the controlling refining optimal system. The analysis of the experimental data shows that the improved internal model control system can shorten the melting time and improve the melting efficiency. Thus, the application of the improved internal model control system in low-carbon ferrochrome refining is an interesting potential direction for future research.

Internal model control for rocket launcher position servo system based on improved wavelet neural network

This paper proposes an improved wavelet neural network-internal model controller (WNN-IMC) for the rocket launcher position servo system. Due to complex nonlinearities and uncertainties of external disturbances in the rocket launcher position servo system, it is vitally challenging to establish its accurate model by the mechanical modeling technique. A wavelet neural network (WNN) identification method is proposed to determine the system mathematical model through test datum, which optimized by the hybrid algorithm of differential evolution (DE) and particle swarm optimization (PSO). Then, the proposed method is applied to identify the semi-physical simulation platform of the rocket launcher velocity servo system. The results demonstrate that the validity of the DEPSO-WNN method is better than that of the WNN and PSO-WNN methods. Finally, compared with the WNN-IMC controller and the ADRC controller, the effectiveness of the improved WNN-IMC controller is verified by the semi-physical simulation experiments.

A novel IMC-FOF design for four wheel steering systems of distributed drive electric vehicles

To improve handling and stability for distributed drive electric vehicles (DDEV), the study on four wheel steering (4WS) systems can improve the vehicle driving performance through enhancing the tracking capability to desired vehicle state. Most previous controllers are either a large amount of calculation, or requires a lot of experimental data, these are relatively time-consuming and laborious. According to the front and rear wheel steering angle of DDEV can be distributed independently, a novel controller named internal model controller with fractional-order filter (IMC-FOF) for 4WS systems is proposed and studied in this paper. The IMC-FOF is designed using the internal model control theory and compared with IMC and PID controller. The influence of time constant and fractional-order parameters which is optimized using quantum genetic algorithms (QGA) on tracking ability of vehicle state are also analyzed. Using a production vehicle as an example, the simulation is performed combining Matlab/Simulink and CarSim. The comparison results indicated that the proposed controller presents performance to distribute the front and rear wheel steering angle for ensuring better tracking capability to desired vehicle state, meanwhile it possesses strong robustness.

Advanced Controllers Implementation for Speed Control Analysis of Two Mass Drive System

The speed control of two mass drive systems is presented in this paper. For the purpose of analysis, PI, Linear Matrix Inequality controller and Internal Model Controller are chosen. Tuning of PI controller is achieved by Zeigler Nichols method, whereas the LMI and IMC are adapted with the conventional method of tuning. The various simulations are carried out in order to analyze the performance index of these controllers and to fix the best controller, and the results are compared

Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform

In this article, we designed and implemented neural controllers to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The objective was to evaluate the implementation and performance of neural control algorithms in a low-cost hardware. In a first phase, we designed two classical controllers with the objective to provide the training data for the neural controllers. After, we identified several neural models of the levitation system using Nonlinear AutoRegressive eXogenous (NARX)-type neural networks that were used to emulate the forward dynamics of the system. Finally, we designed and implemented three neural control structures: the inverse controller, the internal model controller, and the model reference controller for the control of the levitation system. The neural controllers were tested on a low-cost Arduino control platform through MATLAB/Simulink. The experimental results proved the good performance of the neural controllers.