scholarly journals An Internal Model Controller for Three-Phase APF Based on LS-Extreme Learning Machine

2014 ◽  
Vol 8 (1) ◽  
pp. 717-722
Author(s):  
Zhenhua Shao ◽  
Tianxiang Chen ◽  
Li-an Chen ◽  
Hong Tian
Keyword(s):  
Control System ◽  
Least Squares ◽  
Extreme Learning Machine ◽  
Internal Model ◽  
Internal Model Control ◽  
Support Vector ◽  
Three Phase ◽  
Model Control ◽  
Internal Model Controller ◽  
Learning Machine

Aiming at the problem that the three-phase APF’s dynamic model is a multi-variable, nonlinear and strong coupling system, an internal model controller for three-phase APF based on LS-Extreme Learning Machine is studied in this paper. As a novel single hidden layer feed-forward neural networks, extreme learning machine (ELM) has several advantages: simple net structural, fast learning speed, good generalization performance and so on. In order to improve the controller’s dynamic responses, a least squares extreme learning machine for internal model control is proposed. A least squares ELM regression (LS-ELMR) model for the three-phase APFS on-line monitoring was built from external factors with in-out datum. Moreover, the relative stable error is presented to evaluate the system performance and the features for the internal model control system based on extreme learning machine, neural network, kernel ridge regress and support vector machine. The experimental results show that the LS-internal model control system based on extreme learning machine has good dynamic performance and strong filtering result.

Decoupling control of bearingless permanent magnet synchronous motors with LSSVM inverse system method and internal model controller

2020 ◽  
pp. 1-20
Author(s):  
Ke Li ◽  
Feng Ling ◽  
Xiaodong Sun ◽  
Zebin Yang
Keyword(s):  
Control System ◽  
Permanent Magnet ◽  
Internal Model ◽  
Internal Model Control ◽  
Decoupling Control ◽  
Support Vector ◽  
Inverse Models ◽  
Control Scheme ◽  
Model Control ◽  
Internal Model Controller

In this paper, a novel decoupling control scheme combining least squares support vector machines (LSSVM) inverse models and 2-degree-of-freedom (DOF) internal model controllers is employed in the decoupling control system of the bearingless permanent magnet synchronous motor (BPMSM). This scheme can be used to enhance the control properties of high-precision, fast-response, and strong-robustness for the BPMSM system, and effectively eliminate the nonlinear and coupling influence. It introduces LSSVM inverse models into the original BPMSM system to constitute a decoupled pseudo-linear system. In addition, the particle swarm optimization algorithm (PSO) is used to optimize parameters of the LSSVM, which improves its fitting ability and prediction accuracy. What is more, the internal model control scheme is used to design additional closed-loop controllers, thereby improving the robustness of the entire control system. Therefore, this scheme successfully combines the advantages of the LSSVM inverse models and the internal model controller. It can enhance the stability and the static as well as dynamic properties of the whole BPMSM system while independently adjusting the tracking and interference rejection performances. The effectiveness of the proposed scheme has been verified by simulation results at various operations.

Attitude Controller Design of Multiple Independently Targeted Reentry Vehicle Based on Internal Model Control

2013 ◽  
Vol 462-463 ◽  
pp. 809-814
Author(s):  
Fei Zhao ◽  
Fan Li ◽  
Jian Hui Zhao
Keyword(s):  
Control System ◽  
Attitude Control ◽  
Internal Model ◽  
Internal Model Control ◽  
Attitude Dynamics ◽  
Attitude Control System ◽  
Reentry Vehicle ◽  
Model Control ◽  
Internal Model Controller ◽  
Attitude Controller

A Multiple Independently Targeted Reentry Vehicle (MIRV) is a ballistic missile payload containing several warheads each capable of hitting one of a group of targets. In the process of missile flight control, the release of warheads brings about coupling to the missile attitude control system which will lower the flight stability. In order to solve this problem, a missile attitude controller, which combined the α-order integral inverse system with internal model principle, was presented. Firstly, determine the Post Boost Vehicle (PBV) attitude dynamics model. Then, combine the linearization of attitude dynamics equation with feed-forward decoupling method to implement the attitude decoupling. Finally, a two-degree of freedom (TOF) multivariable internal model controller was set up to optimize the control system performance. Simulation results show that the coupling of attitude control system has been eliminated. Compared with the original system, the internal model controller provides the control system better input-tracking performance, robust stability and interference suppression capacity.

Simulation Research on the Compound Control System of Internal Model Add Feed-Forward Compensator

2014 ◽  
Vol 597 ◽  
pp. 372-375
Author(s):  
Sheng Bo Zhang
Keyword(s):  
Control System ◽  
Internal Model ◽  
Dynamic Performance ◽  
Internal Model Control ◽  
Simulation Research ◽  
Feed Forward ◽  
Online Identification ◽  
Compound Control ◽  
Model Control ◽  
Internal Model Controller

According to the characteristics of the internal model control and feed-forward control and Combining the both advantages, the compound control system of the internal model add feed-forward compensator was designed. In order to improve the dynamic performance of the control system, online identification method is adopted to establish the internal model. The designs of the internal model controller and feed-forward compensator were detailed instructions. The simulation shows that the compound control system have not only good dynamic performance, high tracking precision and strong anti-jamming capability, but also have the change of system parameters with strong robustness.

scholarly journals Quasilinear Extreme Learning Machine Model Based Internal Model Control for Nonlinear Process

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Dazi Li ◽  
Qianwen Xie ◽  
Qibing Jin
Keyword(s):  
Extreme Learning Machine ◽  
Internal Model ◽  
Linear Part ◽  
Nonlinear Coefficient ◽  
Internal Model Control ◽  
Least Square ◽  
Machine Model ◽  
Nonlinear Part ◽  
Model Control ◽  
Learning Machine

A new strategy for internal model control (IMC) is proposed using a regression algorithm of quasilinear model with extreme learning machine (QL-ELM). Aimed at the chemical process with nonlinearity, the learning process of the internal model and inverse model is derived. The proposed QL-ELM is constructed as a linear ARX model with a complicated nonlinear coefficient. It shows some good approximation ability and fast convergence. The complicated coefficients are separated into two parts. The linear part is determined by recursive least square (RLS), while the nonlinear part is identified through extreme learning machine. The parameters of linear part and the output weights of ELM are estimated iteratively. The proposed internal model control is applied to CSTR process. The effectiveness and accuracy of the proposed method are extensively verified through numerical results.

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

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Changjun Guan ◽  
Wen You
Keyword(s):  
Control System ◽  
Time Constant ◽  
Internal Model ◽  
Internal Model Control ◽  
Optimal System ◽  
Melting Time ◽  
Low Carbon ◽  
Carbon Ferrochrome ◽  
Model Control ◽  
Internal Model Controller

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.

The Design of Combustion Control System Based on Fuzzy Immune Internal Model Controller

2011 ◽  
Vol 311-313 ◽  
pp. 2230-2234
Author(s):  
Gui Li Yuan
Keyword(s):  
Control System ◽  
Internal Model ◽  
Control Method ◽  
Fast Response ◽  
Internal Model Control ◽  
Combustion Control ◽  
Control Effect ◽  
Large Delay ◽  
Model Control ◽  
Internal Model Controller

The controlled object of boiler combustion system in power plant is a complex system with nonlinear, timing change, large lagging and multi-variable coupling, and does not have precise mathematical model, so it is difficult to obtain the satisfactory control effect adopting the traditional PID control. Advanced control strategies are adopted to improve the performance of the boiler combustion control system, and it has been more and more the concern of the majority of electricity production enterprises. Internal model control is a very practical control method, and its main characteristic is simple structure, intuitive design and few online adjustment parameter, and easy adjustment policy. And it is especially particularly significant to improve the control effect of large delay system. The internal model control system is used in power station boiler combustion system, it can effectively solve the large delay, large inertia and other shortcomings, but there is the contradiction between the fast response and robustness in internal model control system. The fuzzy immune control has advantages, such as, fast response, fast stable and good robust, etc. The fuzzy immune control is introduced into internal model control system, this paper designs fuzzy immune internal model controller, which integrates speed and robustness of the internal model control. The fuzzy immune internal model control is applied to combustion control system, and we compare it with ordinary internal model control method. The simulation result shows that fuzzy immune internal model control can greatly improve the characteristics of the control system with time delay. And this effectiveness of the fuzzy immune internal model controller has been verified.

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

2021 ◽  
pp. 095440702110314
Author(s):  
Yan Ti ◽  
Kangcheng Zheng ◽  
Wanzhong Zhao ◽  
Tinglun Song
Keyword(s):  
Fractional Order ◽  
Electric Vehicles ◽  
Internal Model ◽  
Rear Wheel ◽  
Internal Model Control ◽  
Steering Angle ◽  
Comparison Results ◽  
Model Control ◽  
Tracking Ability ◽  
Internal Model Controller

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.

Application of an Improved Internal Model Controller Based on the Particle Swarm Optimization in Network Control System

2013 ◽  
Vol 397-400 ◽  
pp. 1137-1144
Author(s):  
Wei Chen ◽  
Wen Bin Wang ◽  
Zhi Kai Zhao ◽  
Zhi Yuan Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Internal Model ◽  
Particle Swarm ◽  
Internal Model Control ◽  
Network Control ◽  
Step Response ◽  
Network Control System ◽  
Swarm Optimization ◽  
Internal Model Controller

Internal Model Control (IMC) is widely used in Network Control System (NCS) with its strong robustness and simple parameter adjustment. But the accurate dynamic inversion of the IMC model is not easy to find out. To solve this problem, an improved Internal Model Controller is designed with a PID controller and feedback loop, then the Particle Swarm Optimization (PSO) is used to optimize all the parameters of the improved controller. At last, simulation results show that the improved Internal Model Controller can maintain the system stability and the performance of the step response is extremely great in terms of rapidity and anti-interference ability, compared with the classic internal model controller, which enables NCS to achieve a better control effect.

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