Optimal Real Time Estimation Strategies For A Class Of Cyber Physical Systems Using Networked Mobile Sensors And Actuators
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Optimal Real-Time Estimation Strategies for a Class of Cyber-Physical Systems Using Networked Mobile Sensors and Actuators
In this chapter, we described a numerical procedure for optimal sensor-motion scheduling of diffusion systems for parameter estimation. The state of the art problem formulation was presented so as to understand our contribution to the field. The problem was formulated as an optimization problem using the concept of the Fisher information matrix. We then introduced the optimal actuation framework for parameter identification in distributed parameter systems. The problem was reformulated into an optimal control one. Later, using our developed "online" scheme, mobile sensors find an initial trajectory to follow and refine the trajectory as their measurements allow finding a better estimate of the system's parameters. Using the Matlab PDE toolbox for the PDE system simulations, RIOTS_95 Matlab toolbox for solving the optimal path-planning problem and Matlab Optimization toolbox for the estimation of the system's parameters, we were able to solve this parameter identification problem in an interlaced manner successfully. With the help of the Matlab PDE toolbox for the system simulations and RIOTS_95 Matlab toolbox for solving the optimal control problem, we successfully obtained the optimal solutions of all the introduced methods for illustrative examples. We believe, this chapter has for the first time laid the rigorous foundation for real-time estimation for a class of cyber-physical systems (CPS).
Industrial Cyber-Physical Systems
Cyber-physical systems (CPS) are one of the key concepts of Industry 4.0. Despite their great potentials for industrial value creation, there are challenges, such as a significant increase in complexity, as a result of which the development status of Industry 4.0 is behind expectations. This book addresses this issue with the following research design: In addition to providing a comprehensive foundation of industrial CPS and Industry 4.0, four studies are conducted, each consisting of an exploratory research part and a design science research (DSR) part. In doing so, four perspectives are directed at the topic of industrial CPS: A systemic, a stakeholder-centered, an organizational and a holistic. In conclusion, the contributions are integrated in a summary and the artifacts are incorporated into an overarching methodological framework. Thus, theoretical contributions are derived and concrete practical recommendations for the main target groups of organizations, educational institutions and international delegations provided.
Optimal Sensing and Actuation Policies for Networked Mobile Agents in a Class of Cyber-physical Systems
The main purpose of this dissertation is to define and solve problems on optimal sensing and actuating policies in Cyber-Physical Systems (CPSs). Cyber-physical system is a term that was introduced recently to define the increasing complexity of the interactions between computational hardwares and their physical environments. The problem of designing the "cyber'' part may not be trivial but can be solved from scratch. However, the "physical'' part, usually a natural physical process, is inherently given and has to be identified in order to propose an appropriate "cyber'' part to be adopted. Therefore, one of the first steps in designing a CPS is to identify its "physical'' part. The "physical'' part can belong to a large array of system classes. Among the possible candidates, we focus our interest on Distributed Parameter Systems (DPSs) whose dynamics can be modeled by Partial Differential Equations (PDE). DPSs are by nature very challenging to observe as their states are distributed throughout the spatial domain of interest. Therefore, systematic approaches have to be developed to obtain the optimal locations of sensors to optimally estimate the parameters of a given DPS.In this dissertation, we first review the recent methods from the literature as the foundations of our contributions. Then, we define new research problems within the above optimal parameter estimation framework. Two different yet important problems considered are the optimal mobile sensor trajectory planning and the accuracy effects and allocation of heterogeneous sensors. Under the remote sensing setting, we are able to determine the optimal trajectories of remote sensors. The problem of optimal robust estimation is then introduced and solved using an interlaced "online'' or "real-time'' scheme. Actuation policies are introduced into the framework to improve the estimation by providing the best stimulation of the DPS for optimal parameter identification, where trajectories of both sensors and actuators are optimized simultaneously. We also introduce a new methodology to solving fractional-order optimal control problems, with which we demonstrate that we can solve optimal sensing policy problems when sensors move in complex media, displaying fractional dynamics. We consider and solve the problem of optimal scale reconciliation using satellite imagery, ground measurements, and Unmanned Aerial Vehicles (UAV)-based personal remote sensing.Finally, to provide the reader with all the necessary background, the appendices contain important concepts and theorems from the literature as well as the Matlab codes used to numerically solve some of the described problems.