ANN Modified Design Model to Adjust Field Current of D.C. Motor

This work is concerned with designing an adjusted field current of D.C.motors to obtain constant speed, based on ANN. The design is employed by usingtraining model with supervised manner with back-propagation algorithm.MATLAB neural network tool box is used for training purpose.The feed-forward neural network (FFNN) and learning capabilities offers apromising way to solve the problem of system non-linearity, parameter variationson unexpected load excisions associated with D.C. motor drive system.The proposed ANN controller model is implemented with a control dc motordrive system in the laboratory. The laboratory test results validate the efficacy ofthe based controller model for a high performance dc motor drive

Modelling of Dynast Simulator to Controlling Speed in a DC Motor using Proportional Integral Derivative Controller

Modelling of DYNAST simulator to controlling speed in a DC motor using proportional integral derivative controller is work aimed at designing, analyzing, and modeling of a DC motor speed control using Proportional Integral Derivatives Controller (PID). It entails how a PID controller can be used to achieve a desired speed response in a dc motor speed control. It presents a detailed simulation of how this PID controller and technology is added to the DC motor speed control with their varying parameters for desired response. Using the Dynast Shell Simulator shows the hardware and it sub system in their simulated platform without the use of complex equations to achieve your aim. This paper relates the place of PID technology in the present control discipline, how it reduces and eliminates errors in the control processes using Dynast Shell Simulator, it came up with a  designs of dc motor speed control with and without PID. The work demonstrated that with proportional band at kp=100, an ki=200, the controller output is proportional to the error or a change in measurement (depending on the controller).

Mathematical model of thyristor’s system control of DC motor with independent excitation

The result of the research in this article is a mathematical model of thyristor’s system control of DC motor with independent excitation, which includes containing controlled three-phase rectifier. The nonlinear characteristics of transformer’s magnetization was being taken into account. Tyristors’ work in shown by means of a scheme of an ideal ke

Control-Scheduling Codesign for NCS based Fuzzy Systems

In the present paper, a fuzzy codesign approach is proposed to deal with the controller and scheduler design for a networked control system which is physically distributed with a shared communication network. The proposed fuzzy controller is applied to generate the control with different sampling-actuation periods, the configuration supposes a strict actuation period disappears the jitter. The proposed fuzzy scheduling is designed to select the sampling-actuation period. So, the fuzzy codesign reduces the rate of transmission when the system is stable through the scheduler while the controller adjusts the control signal. The fuzzy codesign guarantees the stability of all the system if the network uncertainties do not exceed an upper bound and is a low computational cost method implemented with an embedded system. An unstable, nonlinear system is used to evaluate the proposed approach and compared to a hybrid control, the results show greater robustness to multiple lost packets and time delays much larger than the sampling period. (This paper is an extension of [20]. Reprinted (partial) and extended, with permission based on License Number 4275590998661 IEEE, from "Electrical Engineering, Computing Science and Automatic Control, 2017 14th International Conference on"

Stability and implementation of model based predictive networked control system

Digital control systems that have computer nodes which communicate over a data loss and random delay prone common network are called Networked Control System (NCS). In a typical NCS, the sensor, controller and the actuator nodes reside in different computers and communicate with each other over a network. Random delays and data loss of the communication network can endanger the stability of the NCS and retransmission of data is not feasible in control applications since it adds delay to the system. The aim of this thesis is to verify that the distributed NCS method called Model Based Predictive Networked Control System (MBPNCS) can be implemented using an observer and that it can control an open loop unstable plant. MBPNCS compensates for missed and late data by implementing an intelligent predictive control scheme based on a model of the plant. MBPNCS does not use retransmission and does not guarantee timely delivery of data packets to each computer node since this solution is not feasible on every control application and every communication medium. Instead, MBPNCS offers a control solution that can work under random network delay and data loss by the use of a predictive architecture that predicts plant state estimates and respective control signals from actual plant states. In this thesis, MBPNCS is described along with an introduction to a theoretical stability criterion. This is followed by an implementation of MBPNCS with two different plants. First, MBPNCS is implemented with an observer based DC motor plant to demonstrate the system’s efficiency with an observer. Next, MBPNCS is implemented with an inverted pendulum to demonstrate the system’s efficiency with an open loop unstable plant. Finally, two separate MBPNCS’s are implemented over a common network to demonstrate the systems efficiency and feasibility in industrial applications. The results show that considerable improvement over performance is achieved with respect to an event based networked control system

Model based predictive networked control systems /

Advantages of networked control systems (NCS) are very diverse and NCS’s address many of the demands of industrial development. As more and more sophisticated problems arise, networked control systems will not only become a convenience or an advantage but they will become an indispensable necessity. However usage of networked control systems introduces different forms of timedelay uncertainty in closed-loop system dynamics. These time delays are caused by the time sharing of the communication medium as well as computation time necessary for control algorithms and digital to analog conversions and have a destabilizing effect on system performance. Computational power of computers has increased dramatically; networks speed has also increased. Although both the network and computer architectures have tended to improve throughput over time, their real-time characteristics have not evolved to match the requirements from a control point of view. New control methodologies that cope with these factors and even take advantage of them are emerging. This work first examines some current methods in design and implementation of networked control systems that try to improve existing methods. Then a novel networked control system architecture that runs under non ideal network conditions with packet loss and noise is introduced. The proposed network control system architecture uses a model to predict the plant states into the future and generate corresponding control signals, then an array of the predicted control signals is sent to the actuator node side of the NCS rather than a single control signal like in basic networked control systems. This array of signals can control the plant if they are applied consecutively with sampling time intervals. However this is not the case under ideal conditions, where the network is lossless. Only the first control signal in each array is applied to the plant as a newer packet arrives every sampling period. The remainder of the array of predicted control signals is only used when packet loss occurs. This approach enables the system to be controlled in a pre-simulated manner and stability can be maintained even with high packet loss probabilities. Synchronization of the network elements becomes a major problem in this approach since models are involved. Synchronizing the actuator and controller nodes is done by an algorithm that can identify control signal arrays that have trustable information. Also the controller has a distributed architecture; some parts of the controller are implemented in the sensor node. This is to ensure that sensor to controller synchronization is not broken. The proposed model based predictive networked control system architecture was tested on a DC motor. The effects of packet loss were examined to reveal that the packet loss does not cause destabilization of the system, when packet loss occurs and the control packet cannot be sent to the actuator node, which prevents the changes in reference from being applied to the plant. The overall effect is the retardation of the response of the plant to the reference. Effects of noise are also examined. Under low packet loss conditions noise does not have an unusual effect on the system but when packet loss increases noise cannot be tolerated because the feedback loop is interrupted due to packet loss. Finally a method for determining the number of predictions to be made at the controller node (the prediction horizon) is suggested. The systems settling time is examined and the settling time is taken as the basis for the prediction horizon. The transmission of a single array of control signals from the controller node to the actuator node will enable the system to reach the desired reference. However this approach is only valid for open loop stable systems

Development of a novel system to measure and calculate tooth movements for studying the properties of the periodontal ligament

Motion analysis techniques have been widely used in biomechanics for measuring large-scale motions such as gait, posture etc, but have not yet been significantly explored for measuring smaller movements such as tooth movement under load. In principle, very accurate measurements could be possible and this could provide a valuable tool in many engineering applications. The aim of this study was to develop a novel system to measure and calculate tooth movements with 6DOF in 3D space for studying the properties of the periodontal ligament. The Qualisys ProReflex-MCU120 motion capture system has been developed to measure micro-movements. The calibration frame was designed and made for the system calibration. The system accuracy was 1.17%, 1.67% and 1.31% for diamond markers 1.81%, 2.37% and 1.39% for spherical markers in x, y and z directions in the range of 20 - 200pm. These results demonstrated that the system is accurate enough to measure small-scale movements. To measure tooth movement, two retroreflective marker clusters, two pointers and one plane for the pointer calibration were created. The two marker clusters were fixed on the measured tooth and the reference tooth for measurements. The pointer was used to identify the three landmarks for identifying anatomical coordinate system of tooth. Data analysis software was developed and evaluated for calculating tooth movement in 6DOF. In the data analysis software, the three coordinate systems method was used with transformation matrices to give 6DOF results. The evaluation results of a complete system were 3.2%, 2.8% and 2.4% for rotations 5.3%, 7.7% and 4.7% for translations in x, y and z directions in the term of accuracy. For producing tooth measurements, loading devices and loading control system were designed and tested. The experiments were carried out on human volunteers in clinical setting. Loads of 0.196N, 0.294N and 0.49N were separately applied to the measured tooth in the buccal direction and the intrusive direction for 10s and 30s, respectively. The experimental results demonstrated that, with the buccal loading, the tooth translations were 32pm for load of 0.294N, and 41pm for load of 0.49N in the y direction of the anatomical coordinate system, tooth rotations were 0.09 and 0.07 for load of 0.294N, and 0.1 and 0.06 for load of 0.49N in the x and z directions with the intrusive loading, tooth movements were 37pm, 15pm and 54pm for load of 0.196N, and 140pm, 51pm and 25pm for load of 0.49N in the x, y and z directions, tooth rotations were 0.04 , 0.07 and 0.2 for load of 0.196N, and 0.26 , 0.195 and 0.35 for load of 0.49N about the x, y and z directions. Overall, a novel system of measuring and calculating tooth movement has been developed. It could be useful in applications in many other engineering fields