Electrical and Electronic Engineering - Research Publications

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    PATIENT-SPECIFIC NEURAL MASS MODELING - STOCHASTIC AND DETERMINISTIC METHODS
    Freestone, DR ; Kuhlmann, L ; Chong, MS ; Nesic, D ; Grayden, DB ; Aram, P ; Postoyan, R ; CooK, MJ ; Tetzlaff, R ; Elger, CE ; Lehnertz, K (WORLD SCIENTIFIC PUBL CO PTE LTD, 2013-01-01)
    Deterministic and stochastic methods for online state and parameter estimation for neural mass models are presented and applied to synthetic and real seizure electrocorticographic signals in order to determine underlying brain changes that cannot easily be measured. The first ever online estimation of neural mass model parameters from real seizure data is presented. It is shown that parameter changes occur that are consistent with expected brain changes underlying seizures, such as increases in postsynaptic potential amplitudes, increases in the inhibitory postsynaptic time-constant and decreases in the firing threshold at seizure onset, as well as increases in the firing threshold as the seizure progresses towards termination. In addition, the deterministic and stochastic estimation methods are compared and contrasted. This work represents an important foundation for the development of biologically-inspired methods to image underlying brain changes and to develop improved methods for neurological monitoring, control and treatment.
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    Extremum Seeking Methods for Online Automotive Calibration
    Manzie, C ; Moase, W ; Shekhar, R ; Mohammadi, A ; Nesic, D ; Tan, Y ; Waschl, H ; Kolmanovsky, I ; Steinbuch, M ; del Re, L (Springer, 2014-01-01)
    The automotive calibration process is becoming increasingly difficult as the degrees of freedom in modern engines rises with the number of actuators. This is coupled with the desire to utilise alternative fuels to gasoline and diesel for the promise of lower CO2 levels in transportation. However, the range of fuel blends also leads to variability in the combustion properties, requiring additional sensing and calibration effort for the engine control unit (ECU). Shifting some of the calibration effort online whereby the engine controller adjusts its operation to account for the current operating conditions may be an effective alternative if the performance of the controller can be guaranteed within some performance characteristics. This tutorial chapter summarises recent developments in extremum seeking control, and investigates the potential of these methods to address some of the complexity in developing fuel-flexible controllers for automotive powertrains.
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    Nonlinear Sampled-Data Systems
    Nesic, D ; Postoyan, R ; Baillieul, J ; Samad, T (Springer, 2014)
    Sampled-data systems are control systems in which the feedback law is digitally implemented via a computer. They are prevalent nowadays due to the numerous advantages they offer compared to analog control. Nonlinear sampled-data systems arise in this context when either the plant model or the controller is nonlinear. While their linear counterpart is now a mature area, nonlinear sampled-data systems are much harder to deal with and, hence, much less understood. Their inherent complexity leads to a variety of methods for their modeling, analysis, and design. A summary of these methods is presented in this entry.
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    Nonlinear Sampled-Data Systems
    Nesic, D ; Postoyan, R ; Baillieul, J ; Samad, T (Springer London, 2015)
    Sampled-data systems are control systems in which the feedback law is digitally implemented via a computer. They are prevalent nowadays due to the numerous advantages they offer compared to analog control. Nonlinear sampled-data systems arise in this context when either the plant model or the controller is nonlinear. While their linear counterpart is now a mature area, nonlinear sampled-data systems are much harder to deal with and, hence, much less understood. Their inherent complexity leads to a variety of methods for their modeling, analysis, and design. A summary of these methods is presented in this entry.
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    Output Feedback Event-Triggered Control
    Abdelrahim, M ; Postoyan, R ; Daafouz, J ; Nesic, D ; Seuret, A ; Hetel, L ; Daafouz, J ; Johansson, KH (Springer, 2016)
    Event-triggered control has been proposed as an alternative implementation to conventional time-triggered approach in order to reduce the amount of transmissions. The idea is to adapt transmissions to the state of the plant such that the loop is closed only when it is needed according to the stability or/and the performance requirements. Most of the existing event-triggered control strategies assume that the full state measurement is available. Unfortunately, this assumption is often not satisfied in practice. There is therefore a strong need for appropriate tools in the context of output feedback control. Most existing works on this topic focus on linear systems. The objective of this chapter is to first summarize our recent results on the case where the plant dynamics is nonlinear. The approach we follow is emulation as we first design a stabilizing output feedback law in the absence of sampling then we consider the network and we synthesize the event-triggering condition. The latter combines techniques from event-triggered and time-triggered control. The results are then proved to be applicable to linear time-invariant (LTI) systems as a particular case. We then use these results as a starting point to elaborate a co-design method, which allows us to jointly construct the feedback law and the triggering condition for LTI systems where the problem is formulated in terms of linear matrix inequalities (LMI). We then exploit the flexibility of the method to maximize the guaranteed minimum amount of time between two transmissions. The results are illustrated on physical and numerical examples.
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    Periodic Event-Triggered Control
    H. Heemels, WPM ; Postoyan, R ; Donkers, MCFT ; Teel, AR ; Anta, A ; Tabuada, P ; Nešić, D ; Miskowicz, M (CRC Press, 2015-11-24)
    This chapter discusses periodic event-triggered control (PETC) strategies, their benefits, and two analysis and design frameworks for linear and nonlinear plants. It focuses on approaches to PETC that include a formal analysis framework, which apply for continuous- time plants and incorporate intersample behavior in the analysis. The chapter explores PETC as a class of event-triggered control (ETC) strategies that combines the benefits of periodic time-triggered control and event-triggered control. In ETC, the control task is executed after the occurrence of an event, generated by some well-designed event-triggering condition, rather than the elapse of a certain fixed period of time, as in conventional periodic sampled-data control. The PETC strategy is based on the idea of having an event-triggering condition that is verified only periodically, instead of continuously as in most existing ETC schemes. There is a strong need for systematic methods to construct PETC strategies that appropriately take into account the features of the paradigm.