Implementation of Model Based Networked Predictive Control System

Networked control systems are made up of several computer nodes communicating over a communication channel, cooperating to control a plant. The stability of the plant depends on the end to end delay from sensor to the actuator. Although computational delays within the computer nodes can be made bounded, delays through the communication network are generally unpredictable. A method which aims to protect the stability of the plant under communication delays and data loss, Model Based Predictive Networked Control System (MBPNCS), has previously been proposed by the authors. This paper aims to demonstrate the implementation of this type of networked control system on a non-real-time communication network; Ethernet. In this paper, we first briefly describe the MBPNCS method, then discuss the implementation, detailing the properties of the operating system, communications and hardware, and later give the results on the performance of the Model Based Predictive Networked Control System implementation controlling a DC motor. This work was supported in part by the Scientific and Technological Re search Council of Turkey, project code 106E155

Implementation of a distributed control system using real-time operating system

In a typical distributed control system, computer nodes communicate through a common communication channel that introduces data loss and random delays. Supplying a generic solution to these constraints is hard due to the complexity and large variety of possibilities that may affect these constraints in real life applications. In a modern communication network, if data is corrupted during transmission, it can be resent. However, it is not feasible to retransmit in control applications; if the packet contents correspond to measured plant outputs, then the most recent data should be measured and sent instead, or if the packet contents correspond to a control signal and the retransmission would cause the control signal to be applied late to the plant, it would be better to recalculate the signal and send it again. This thesis is an attempt to implement a distributed control system design method, Model Based Predictive Networked Control System (MBPNCS), which accepts the fact that arbitrary delay and data loss may happen. The MBPNCS method approaches the problem by using a plant model to predict a predefined number of future states of the plant and respective control signal for each, to compensate for the possible delay and data loss that can take place during the communication between nodes. In this work, after previous works have been examined, predictive control method that is used in the implementation is introduced. Design and implementation of the methodology is explained in detail and results of the tests are presented

Control over imperfect networks: model based predictive networked control systems

Networked control systems are digital control systems in which the functionality of the sensor, control and actuator reside in physically different computer nodes which communicate over a network. Although it is necessary to put an upper bound to the latency from sensing to actuation in a digital control system, the performance of a basic networked control system is threatened by the data loss and unpredictable delays of the communication network. We have proposed model based predictive networked control systems (MBPNCS) in which such losses are compensated by using a model of the plant within the controller node which, based on the actual or estimated state of the plant, makes a series of control estimates into the future, and sends all of them to the actuator node at once. This way the stability of the plant is maintained even under communication delay and data loss. The system is designed to eliminate the necessity of acknowledgments signifying the success of transmission, since such signals are, in general, also unreliable. In this paper we describe MBPNCS, then introduce a stability criterion. This is followed by computer simulations and experiments involving the speed control of a DC motor

Model Based Predictive Networked Control Systems

Networked control systems where the sensors, controller and actuators of a digital control system reside on different computer nodes linked by a network, aim to overcome the disadvantages of conventional digital control systems at the application level, such as difficulty of modification, vulnerability to electrical noise, difficulty in maintenance and upgrades. However random communication delay and loss on the network may jeopardize stability since the communication delay decreases the phase margin of the control system and data loss can be considered as noise. In this project, we propose a novel networked control method where satisfactory control is possible even under random delay and data loss. We keep a model of the plant inside the controller node and use it to predict the plant states into the future to generate corresponding control outputs. At every sampling period the states of the model are reset to the actual or predicted states of the plant. The ambiguity of plant state during periods of total communication loss are also addressed. The proposed model based predictive networked control system architecture is first verified by simulation on the model of a DC motor under conditions of data loss and noise. Then experiments are repeated on a dedicated test platform using a physical DC motor. Results show that significant amounts of data loss and delay can be tolerated in the system before performance starts to degrade