Network Predictive Control Algorithm Research Articles

Networked Multi Pipeline Pressure Synchronous Coordinated Predictive Control based on the Model Free Adaptive Method

In order to achieve the rapid and accurate synchronous coordination of multi pipeline pressure systems using networked control system theory, an adjacent cross-coupling structure is adopted to improve the synchronisation performance. Considering the possible limitations of network communication, a networked predictive control algorithm based on model free adaptive control is proposed to compensate the delay caused by network. The above two strategies are combined to form the networked multi pipeline pressure synchronous coordination predictive control systems. The simulation experiments of synchronous coordinated control of three pipeline pressure systems are carried out on Simulink platform. The simulation results show that the proposed control scheme can compensate the network delay, and improve the dynamic and synchronous performance. The effectiveness of the control scheme is verified.

Neural Network Predictive Control Method of Sand Mixing Device

In the process of oil extraction, fracturing technology has been widely used as a viable means to increase oil and gas permeability, and sand mixing device is one of the important devices in fracturing process. Based on the working process of the second-stage sand agitator tank, this study establishes the kinetic equations of the mixing process of the second-stage sand mixing device, proposes a network predictive control algorithm based on BP neural network, analyzes its dynamic matrix predictive control structure and apply it to predictive control of the agitator tank of blender. The simulation experiment proves that the neural network control algorithm can effectively improve the control quality of the mixing device of the blender and improve the sand mixing effects.

Robust Tracking Control of Networked Control Systems With Communication Constraints and External Disturbance

This paper addresses the problem of robust tracking control of networked control systems with communication constraints (network-induced delay, packet dropouts, and packet disorder) and external disturbance. A novel networked predictive control algorithm based on k -order adaptive discrete-time sliding-mode control ( k -ADSMC) is proposed. The ADSMC, which can self-adapting adjust sliding-mode parameters and ensure faster convergence, is designed based on the error and error difference of tracking output. Furthermore, k -ADSMC is designed, which uses the estimated disturbance as weight factors and synthesizes the sliding- mode values of all k times, in order to improve the robust of tracking control. Finally, the stability of the system is proved, and a dc motor servo system is utilized to verify the effectiveness of the proposed method.

Semi-active control of structures using neuro-predictive algorithm for MR dampers

A semi-active control method for a seismically excited nonlinear benchmark building equipped with a magnetorheological (MR) damper is presented and evaluated. A linear quadratic Gaussian (LQG) controller is designed to estimate the optimal control force. The required voltage for the MR damper to produce the control force estimated by LQG controller is calculated by a neural network predictive control algorithm (NNPC). The LQG controller and the NNPC are linked to control the structure. The coupled LQG and NNPC system are then used to train a semi-active neuro-controller designated as SANC, which produces the necessary control voltage that actuates the MR damper. The effectiveness of the NNPC and SANC is illustrated and verified using simulated response of a 3-story full-scale, nonlinear, seismically excited, benchmark building excited by several historical earthquake records. The semi-active system using the NNPC algorithm is compared with the performances of passive as well as active and clipped optimal control (COC) systems, which are based on the same nominal controller as is used in the NNPC algorithm. The results demonstrate that the SANC algorithm is quite effective in seismic response reduction for wide range of motions from moderate to severe seismic events, compared with the passive systems and performs better than active and COC systems. Copyright © 2008 John Wiley & Sons, Ltd.

DESIGN AND IMPLEMENTATION OF NETWORKED PREDICTIVE CONTROL SYSTEMS

This paper discusses the design and implementation of networked predictive control systems. A networked predictive control algorithm is proposed to compensate the network delay and achieve desired control performance of networked control systems. Two schemes of networked control systems are presented: one is the off-line simulation scheme and the other is a real-time application. Based on a networked control system test rig, the networked predictive control algorithm is implemented and applied to a practical servo control system. The simulation and practical experiment results illustrate the efficiency and feasibility of the proposed networked predictive control algorithm and simulation schemes.