In order to deal with a system with both randomness and uncertainty, chance theory has been built, and uncertain random variable has been proposed as a generalization of random variable and uncertain variable. Correspondingly, as a generalization of both stochastic process and uncertain process, this paper will propose an uncertain random process. In addition, some special types of uncertain ra...

A Robust Markov Decision Process (RMDP) is a sequential decision making model that accounts for uncertainty in the parameters of dynamic systems. This uncertainty introduces difficulties in learning an optimal policy, especially for environments with large state spaces. We propose two algorithms, RTD-DQN and Deep-RoK, for solving large-scale RMDPs using nonlinear approximation schemes such as d...

To evaluate the performance and optimize systems under uncertainty, two main avenues have been suggested in the literature: stochastic analysis and optimization describing the uncertainty probabilistically and robust optimization describing the uncertainty deterministically. Instead, we propose a novel paradigm which leverages the conclusions of probability theory and the tractability of the ro...

In this paper a new iterative approach to probabilistic robust controller design is presented, which is applicable to any robust controller/filter design problem that can be represented as an LMI feasibility problem. Recently, a probabilistic Subgradient Iteration algorithm was proposed for solving LMIs. It transforms the initial feasibility problem to an equivalent convex optimization problem,...

This paper introduces a robust task-space control scheme for a robotic system with an adaptive observer. The proposed approach does not require the availability of the system states and an adaptive observer is developed to estimate the state variables. These estimated states are then used in the control scheme. First, the dynamic model of a robot is derived. Next, an observer-based robust contr...

supply chain network design decisions are among the strategic decisions of supply chain management which play significant role on the efficient performance of the supply chain. however, there are two challenging factors which may have great impact on the supply chain performance. these factors are on the one hand disruptions and their attendant damages and on the other hand uncertain nature of ...

This thesis applies recent advances in the field of robust optimization to the optimal control of multiclass queueing networks. We develop models that take into account the uncertainty of interarrival and service time in multiclass queueing network problems without assuming a specific probability distribution, while remaining highly tractable and providing insight into the corresponding optimal...

The urban waste collection is one of the major municipal activities that involves large expenditures and difficult operational problems. Also, waste collection and disposal have high expenses such as investment cost (i.e. vehicles fleet) and high operational cost (i.e. fuel, maintenance). In fact, making slight improvements in this issue lead to a huge saving in municipal consumption. Some inci...

A recent sequence of works, initially motivated by the study of the nonlocal properties of entanglement, demonstrate that a source of information-theoretically certified randomness can be constructed based only on two simple assumptions: the prior existence of a short random seed and the ability to ensure that two black-box devices do not communicate (i.e. are non-signaling). We call protocols ...

In order to detect targets upon sea surface or near it, marine radars should be capable of distinguishing signals of target reflections from the sea clutter. Our proposed method in this paper relates to detection of dissimilar marine targets in an inhomogeneous environment with clutter and non-stationary noises, and is based on adaptive thresholding determination methods. The variance and t...