Networked Predictive Fuzzy Control of Systems with Forward Channel Delays based on a Linear Model Predictor

This paper presents a novel networked control framework, using fuzzylogic control, for systems with network delays which are known togreatly weaken the control performance of the controlled system. Todeal with the network delays, the predicted differences between thedesired future set-points and the predicted outputs from a modelpredictor are utilized as the inputs of a fuzzy controller, thus aseries of future control actions are generated. By selecting theappropriated control sequence in the plant side, the network delaysare compensated. The simulative results demonstrate that theproposed method can obviously reduce the effect of network delays,and improve the system dynamic performance

A novel robust predictive control system over imperfect networks

This paper aims to study on feedback control for a networked system with both uncertain delays, packet dropouts and disturbances. Here, a so-called robust predictive control (RPC) approach is designed as follows: 1- delays and packet dropouts are accurately detected online by a network problem detector (NPD); 2- a so-called PI-based neural network grey model (PINNGM) is developed in a general form for a capable of forecasting accurately in advance the network problems and the effects of disturbances on the system performance; 3- using the PINNGM outputs, a small adaptive buffer (SAB) is optimally generated on the remote side to deal with the large delays and/or packet dropouts and, therefore, simplify the control design; 4- based on the PINNGM and SAB, an adaptive sampling-based integral state feedback controller (ASISFC) is simply constructed to compensate the small delays and disturbances. Thus, the steady-state control performance is achieved with fast response, high adaptability and robustness. Case studies are finally provided to evaluate the effectiveness of the proposed approach

Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Robust Controller for Delays and Packet Dropout Avoidance in Solar-Power Wireless Network

Solar Wireless Networked Control Systems (SWNCS) are a style of distributed control systems where sensors, actuators, and controllers are interconnected via a wireless communication network. This system setup has the benefit of low cost, flexibility, low weight, no wiring and simplicity of system diagnoses and maintenance. However, it also unavoidably calls some wireless network time delays and packet dropout into the design procedure. Solar lighting system offers a clean environment, therefore able to continue for a long period. SWNCS also offers multi Service infrastructure solution for both developed and undeveloped countries. The system provides wireless controller lighting, wireless communications network (WI-FI/WIMAX), CCTV surveillance, and wireless sensor for weather measurement which are all powered by solar energy

Study on State Predictive Controllers for Networked Control System

When different control components of a closed loop control system are connected through a common network channel then the resulting control system is a Networked Control System. This spatially distributed system has several advantages like reduced system wiring, easy fault detection and maintenance capability.Unfortunately the introduction of communication channel results in several disadvantages like network induced delays and packet dropouts leading to loss of synchronism in the control system. The network induced imperfections causes system instability and complexity for the control engineers to design a suitable controller in order to compensate their effect on closed loop control system. In addition to the complexity in design the network induced imperfections should be measured, analysed by incorporating them in the closed loop control system. The project investigates the problem of network induced time delays in a networked control system by studying the behaviour of network induced time delay in a control system controlled by Linear Quadratic controller or a Pole placement controller using the states obtained from discrete Kalman filter state estimation, which estimates the current state in the presence of state and output noises. Further a control augmentation method is used by incorporating network induced delay in the plant model control vector. The time delayed control vector creates difficulty in designing the controller which is solved by time shifting approach. Further a state predictor is designed by using plant model transition matrix to predict the future states from present and past values of control vector and state estimate. Hence an optimal predictive controller is designed wherein the Linear Quadratic or pole placement controller uses the predictive state obtained from the state predictor to compensate the effect of network induced time delay and improve the control system performance

Performance Analysis of Universal Robot Control System Using Networked Predictive Control

Networked control systems are feedback control systems with system components distributed at different locations connected through a communication network. Since the communication network is carried out through the internet and there are bandwidth and packet size limitations, network constraints appear. Some of these constraints are time delay and packet loss. These network limitations can degrade the performance and even destabilize the system. To overcome the adverse effect of these communication constraints, various approaches have been developed, among which a representative one is networked predictive control. This approach proposes a controller, which compensates for the network time delay and packet loss actively. This paper aims at implementing a networked predictive control system for controlling a robot arm through a computer network. The network delay is accounted for by a predictor, while the potential of packet loss is mitigated using redundant control packets. The results will show the stability of the system despite a high delay and a considerable packet loss. Additionally, improvements to previous networked predictive control systems will be suggested and an increase in performance can be shown. Lastly, the effects of different system and environment parameters on the control loop will be investigated

Predictive state feedback control of network control systems

Networked control systems have gained attention in the recent years due to their widespread applications to various real time systems. Controlling these systems poses several challenges which are currently still being investigated. A study of these issues is provided along with recent proceedings in technology to counter such issues like limited bandwidth, time delays and packet drop-outs. This thesis focuses on the problem of time delays in network control system which can cause instability of closed loop operation of these systems. A guaranteed cost approach is employed to achieve stability along with achieving a certain level of performance as defined by the cost function. A state feedback controller is used and along with it, a predictive control scheme is implemented to design variable gains of the feedback controller depending on the number of packets missed (packet drop-outs) and time delays of the received input sample or state of the plant, both of which can be random but bounded for a given communication channel. The controllers are connected to the plant via the network. They generate the appropriate input for the plant so that delays in the channel will not instabilize the system and thus they comprise the network delay compensator. The controller gains and the observer gain are determined by formulating a linear matrix inequality (LMI) problem and solving this problem by using the Robust Control Toolbox in MATLAB. Further, this technique is implemented on a fictitious system by modelling the networked system with constant delay in SIMULINK and the observer states as well as the plant output are shown to be stable

Predictive controller design for networked systems

This thesis considered the analysis and design of networked control systems with the communication delay and data loss, which are responsible for degradation of the control performance. Predictive control strategy is applied to compensate the communication delay and data loss in the NCS. The stability and the system performance of the close-loop networked control system are analyzed. Also, this control strategy is applied to a DC servo control system with communication delay and data packet loss. The stability of the closed-loop networked predictive control system has been analyzed and the comparison with other existing networked control methods like H_∞ control [20] , Networked predictive control for random network delays in both forward and feedback channels [21] and Model-based control [22]