Sharif Digital Repository

Prevalent deployment of Distributed Energy Resources(DERs) in the demand side of electrical energy grids has several advantages such as greenhouse gases emission reduction, mitigation of power losses in the distribution grids and abatement of the dependency on the wholesale generation companies. At the same time, it paves the way for the advent of local interactive energy trading frameworks. In the following thesis, a distributed data-driven algorithm is developed for market-clearing in a community-based local energy market. The social welfare maximization is considered as the objective of the energy trading. The production and demand of electricity are the decision variables of sellers and... 

This thesis presents a new approach to design structural output feedback controllers with stabilization and pole-placement at desired points on the S plane. It is assumed that the linear time-invariant system has no fixed mode corresponding to the specified control structure, outside the desired region of the closed-loop poles, such that the structural controller design problem is theoretically feasible. To this end, the structural output controller design problem is first transformed into a structurally equivalent problem based on the solution of a set of discrete linear programs. Then, by using the resultant structure, a new method is proposed for designing static and dynamic control loops... 

This thesis addresses the problem of optimal distributed control of unknown interconnected systems. In order to deal with this problem, a data-driven learning framework for finding the optimal centralized and the suboptimal distributed controllers has been developed via convex optimization.First of all, the linear quadratic regulation (LQR) problem is formulated into a nonconvex optimization problem. Using Lagrangian duality theories, a semidefinite program is then developed that requires information about the system dynamics. It is shown that the optimal solution to this problem is independent of the initial conditions and represents the Q-function, an important concept in reinforcement... 

This thesis introduces a distributed backstepping control strategy based on reinforcement learning to achieve fixed-time consensus in high-order nonlinear multi-agent systems. Each agent, whether a leader or a follower, is modeled using a strict feedback approach, incorporating uncertainties as unknown nonlinear functions and disturbance signals across all state variables. The communication network among agents can be either directed or undirected, with a fixed topology. Each follower employs a control law derived from actor-critic networks, utilizing only the outputs from neighboring agents. The proposed method combines virtual and actual backstepping control laws, formulated from two key... 

In this thesis, a new method is proposed to find the class of stabilizing control structures for interconnected systems from a graphtheoretic
perspective. The objective is to classify all possible control structures (static or dynamic), capable of stabilizing the closedloop system. The method can be formulated by using the characteristics of fixed modes of the system for arbitrary control structures.To this end, first, a method is proposed based on solving a set of discrete linear programming to determine whether a system is stabilizable via a decentralized controller. Subsequently, by solving a set of discrete linear feasibility problems, all possible stabilizing controller structures... 

In this thesis, the robust control problem of interconnected systems that are connected to each other with an arbitrary structure is investigated. A new approach based on distributed optimization design is used for solving this problem. This method exploits graph and optimization algorithms. The stability of controlled systems, overcoming convergence issue in high-dimensional problems, and minimum use of the other subsystem information are the primary objectives of this research. In the first step, polytopic uncertainties in the design of the minimum norm controller are formulated using the block diagonal Lyapunov matrix and expressed with semi-definite programs. The goal of this work is... 

This thesis addresses the distributed optimal control problem. In order to solve this problem a suboptimal distributed design approach has been developed using tools from graph theory and optimization theory. The first objective of this thesis is overcoming the non-scalability of distributed control problems for larges -scale systems. The second objective is using minimum information of other subsystems in the design layer. First, the problems of designing a structured controller minimizing the H_2 and H_∞ norms of the closed-loop system have been formulized and stated as semidefinite optimization problems. Then in order to use the fundamental results that links the chordal graphs and... 

This research studied optimal combination therapy of diseases with uncertain evolutionary dynamics. The problem of neutralizing HIV virus in the host’s body was modeled as a convex optimization problem whose objective function is minimizing disturbance effect while the disease dynamics is stable in the presence of polytopic uncertainty.In the first part of this thesis, according to the characteristics of positive systems, the convexity of H2 and H∞ in the presence of polytopic uncertainty was shown. To solve the minimization problem of disturbance effect, an algorithm based on minimizing a cutting plane was used. This algorithm, with solving the minimization problem iteratively in each step... 

Real-Time Bidding (RTB) has revolutionized the online display advertising market in recent years. In RTB market the advertisers are able to use impression-level data, such as user cookies and context information, to bid for each ad impression in the RTB auctions.Therefore, it is important for an advertiser to design an effective bidding strategy. The proposed bidding strategy should consider the target audience of the campaign and the information of each specific ad impression.This thesis is focused on the design of optimized bidding strategies for RTB auctions.By the optimal strategy, we mean the one which maximizes the campaign’s Key Performance Indicator (KPI) in the presence of budget... 

Television, radio, newspaper, magazines, and billboards are among the major channels that traditionally place ads, however, the advancement of the Internet enables users to seek information online. In display and mobile advertising, the most significant technical development in recent years is the growth of Real-Time Bidding (RTB), which facilitates a real-time auction for a display opportunity. Real-time means the auction is per impression and the process usually occurs less than 300 milliseconds before the ad is placed. RTB has fundamentally changed the landscape of the digital media market by scaling the buying process across a large number of available inventories among publishers in an... 

Nowadays, advanced and inexpensive pre-clinical methods for investigating the effects of anti-cancer drugs are expanding. One of the latest three-dimensional laboratory modeling for evaluating the effects of drugs is the use of tumor-on-chip technology, which actually models the physiological system of the body through three-dimensional scaffolds, multicellular cultures, and shaped vascular systems. In this study, three-dimensional culture of cancer cells was performed in the form of spheroids. A chip of U-shaped microstructures with and without gaps was used to trap cells and form cancer spheroids. We simulated the simultaneous effect of drug and oxygen concentration distribution inside the...