This paper presents a comparison between some well-known control schemes such as feedback, feedback plus feed-forward, cascade and cascade plus feed-forward for controlling a third-order process. The controller applied in various control schemes is a PID controller that has been tuned using Ziegler Nichols (ZN) and relay auto-tuning (RA) methods. The comparative analysis is based upon various performance measures such as rise time (t r), settling time (t s), maximum overshoot (M p), steady-state error (e ss), integral of absolute error (IAE), integral of square error (ISE), integral of time square error (ITSE), and integral of time absolute error (ITAE). Simulation results show that the RA method provides superior performance in case of feedback plus feed-forward and cascade control schemes. On the other hand, the ZN method proves to be better in case of cascade plus feed-forward control scheme.
In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.
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