Electrical and Electronic Engineering - Research Publications
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ItemInternet of Things for Structural Health Monitoring(CRC Press, 2016-05)The Internet revolution led to the interconnection between people at an unprecedented scale and pace. The ability of the sensor networks to send data over the Internet further enhanced the scope and usage of the sensor networks. The Internet uses unique address to identify the devices connected to the network. Structural Health Monitoring (SHM) implies monitoring of the state of the structures through sensor networks in an online mode and are pertinent to aircraft and buildings. SHM can be further divided into two categories: global health monitoring and local health monitoring. Continuous online SHM would be an ideal solution. SHM is performed by using acoustic sensors, ultrasonic sensors, strain gauges, optical fibers, and so on. Video cameras can also be used for SHM. SHM can be achieved in real-time and rich analytics. With the advent of smart sensors—sensors with programmable microprocessors, memory, and processing—has reduced load of central data processing, communication overhead while proving continuous SHM status.
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ItemPATIENT-SPECIFIC NEURAL MASS MODELING - STOCHASTIC AND DETERMINISTIC METHODS(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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ItemExtremum Seeking Methods for Online Automotive Calibration(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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ItemNonlinear Sampled-Data Systems(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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ItemNetworked control systems: Emulation-based design(Springer, 2008-12-01)A common approach to the implementation of digital systems is through the emulation of idealized continuous-time blocks in order to be able to leverage the rich expanse of results and design tools available in the continuous-time domain. The so-called sampled-data systems are now commonplace in practice and rely upon results that ensure that many properties of the nominal continuous-time system, including notions of stability, are preserved under sampling when certain conditions are verified. In analogy with (fast) sampled-data design, this chapter explores an emulation-based approach to the analysis and design of networked control systems (NCS). To that end, we survey a selection of emulation-type NCS results in the literature and highlight the crucial role that scheduling between disparate components of the control systems plays, above and beyond sampling. We detail several different properties that scheduling protocols need to verify together with appropriate bounds on inter-transmission times such that various notions of input-output stability of the nominal network-free system are preserved when deployed as an NCS.
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ItemModel predictive control for nonlinear sampled-data systems(SPRINGER-VERLAG BERLIN, 2007-01-01)The topic of this paper is a new model predictive control (MPC) approach for the sampled-data implementation of continuous-time stabilizing feedback laws. The given continuous-time feedback controller is used to generate a reference trajectory which we track numerically using a sampled-data controller via an MPC strategy. Here our goal is to minimize the mismatch between the reference solution and the trajectory under control. We summarize the necessary theoretical results, discuss several aspects of the numerical implemenation and illustrate the algorithm by an example.
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ItemSampled-data control of nonlinear systems(SPRINGER-VERLAG BERLIN, 2006-01-01)This chapter provides some of the main ideas resulting from recent developments in sampled-data control of nonlinear systems. We have tried to bring the basic parts of the new developments within the comfortable grasp of graduate students. Instead of presenting the more general results that are available in the literature, we opted to present their less general versions that are easier to understand and whose proofs are easier to follow. We note that some of the proofs we present have not appeared in the literature in this simplified form. Hence, we believe that this chapter will serve as an important reference for students and researchers that are willing to learn about this area of research.
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ItemSampled-data control of nonlinear systems: An overview of recent results(SPRINGER-VERLAG, LONDON LTD, 2001-01-01)Some recent results on design of controllers for nonlinear sampled-data systems are surveyed.
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ItemNonlinear Sampled-Data Systems(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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ItemOutput Feedback Event-Triggered Control(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.