The paper aims to revive the interest in bioimpedance analysis for pain studies in communicating and non-communicating (anesthetized) individuals for monitoring purpose. The plea for exploitation of full potential offered by the complex (bio)impedance measurement is emphasized through theoretical and experimental analysis. A non-invasive, low-cost reliable sensor to measure skin impedance is designed with off-the-shelf components. This is a second generation prototype for pain detection, quantification, and modeling, with the objective to be used in fully anesthetized patients undergoing surgery. The 2D and 3D time–frequency, multi-frequency evaluation of impedance data is based on broadly available signal processing tools. Furthermore, fractional-order impedance models are implied to provide an indication of change in tissue dynamics correlated with absence/presence of nociceptor stimulation. The unique features of the proposed sensor enhancements are described and illustrated here based on mechanical and thermal tests and further reinforced with previous studies from our first generation prototype.

Many processes in industry are highly-coupled Multiple-Input Multiple-Output (MIMO) systems. In this paper, a methodology, based on the Kissing Circle (KC) tuning method, is proposed to tune a fractional-order PI controller for these types of systems. The KC method relies on frequency domain specifications and emphasizes improving robustness. The method does not require a model, a single sine test suffices to obtain the controller parameters. Hence, the method can be categorized as an auto-tuner. For comparison, an integer-order PI is tuned with the same requirements. To evaluate and analyze the performance of both controllers an experimental test bench is used, i.e. a landscape office lighting system. A direct low-order discretization method is used to implement the controller in a real process. Both controllers are subjected to simulation experiments to test the performance in time and frequency domain and they are subjected to process variations to evaluate their robustness. The fractional controller manages to control a process that is susceptible to 85% variation in time constant mismatch as opposed to 79% for the integer-order controller. An Integer Absolute Error evaluation of experimental results show that the fractional-order PI controller and integer-order PI controller have similar control performance, as expected from the frequency domain analysis. As model uncertainty can add up in MIMO systems, improved robustness is crucial and with this methodology the control performance does not deteriorate. Moreover, a decrease in power consumption of 6% is observed.

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