Online Rotating PI Controller for NCS Over Communication Constraints

A new method for controlling the plant in networked control systems (NCSs) is proposed. Network time delay and packet loss are two major drawbacks in data communication networks which make NCSs unstable. Unlike previous related research works, this new proposed rotating PI controller based method has the advantage of considering time delay and packet loss effects simultaneously. Time delay is estimated online and then used for tuning the PI controller by rotating the phase plane, while packet loss sequences are modeled by Markov chain. This novel method improves the performance compared to other methods, especially when packets are dropped consecutively and network time delays are large. In fact, the results show that with the network time delay as large as 600 ms, and packet loss occurring evenly, the index of the rotating PI controller performance will be improved by approximately two and a half times compared to the performance index of classical Smith predictor. This ratio will be improved by approximately eight and a half times compared to the performance index of PI controller. Furthermore, in the case that the packet loss occurs consecutively, the results show a ratio improvement of approximately three and ten for our suggested method in comparison to Smith predictor and PI controller, respectively

Application of intelligent signal processing to dynamic measurement systems

A new method for dynamic measurement is presented. A feature extractor and two-layer artificial neural network is used to predict the final value of a sensor's response while it is still in oscillation. The method permits arbitrary inputs and initial conditions and does not make any assumptions about the model of the sensor. It also copes with non-linearity defects in primary sensors. Introducing a pre-processor as a feature extraction block before the neural network decreases the effect of noise and dramatically reduces the required number of neurones. This, in turn, reduces the complexity of computation and speeds up the real-time measurement. One important advantage of the proposed method is that it can be used in situations where the input function is an impulse, i.e. the transducer senses the measurand for only a very short time interval. This method also allows the possibility of using some features of the sensor signal, such as frequency, that are rarely used in other methods, despite them having a unique relation with the steady state value of the signal. Amplitude noise also has less effect on these characteristics. In addition dynamic neural networks are used in a novel way to cancel the interference signals. The proposed methods are established by theoretical analysis and justified by means of both simulation and measurements on real data.</p