Ioannis Tzortzis

University of Cyprus · Department of Electrical and Computer Engineering

We analyze the per unit-time infinite horizon average cost Markov control model, subject to a total variation distance ambiguity on the controlled process conditional distribution. This stochastic optimal control problem is formulated as a minimax optimization problem in which the minimization is over the admissible set of control strategies, while...

We analyze the infinite horizon minimax discounted cost Markov Control Model (MCM), for a class of controlled process conditional distributions, which belong to a ball, with respect to total variation distance metric, centered at a known nominal controlled conditional distribution with radius R\in[0,2], in which the minimization is over the control...

This paper develops a Linear Quadratic Regulator (LQR), which is robust to disturbance variability, by using the total variation distance as a metric. The robust LQR problem is formulated as a minimax optimization problem, resulting in a robust optimal controller which in addition to minimizing the quadratic cost it also minimizes the level of dist...

The main objective of active fault diagnosis is the design of separating input signals that enhance the detection and isolation of faults in modern technological systems. A major consideration when evaluating active fault diagnosis methods is robustness in the presence of modeling uncertainties. The presence of modeling inaccuracies will typically...

The linear quadratic tracking control problem is studied for a class of discrete-time uncertain Markov jump linear systems with time-varying conditional distributions. The controller is designed under the assumption that it has no access to the true states of the Markov chain, but rather it depends on the Markov chain state estimates. To deal with...

In this paper, we study the robust linear quadratic regulator (LQR) problem for a class of discrete-time dynamical systems composed of several uncertain players with unknown or ambiguous distribution information. A distinctive feature of the assumed model is that each player is prescribed by a nominal probability distribution and categorized accord...

This work is devoted to the development of a distributionally robust active fault diagnosis approach for a class of nonlinear systems, which takes into account any ambiguity in distribution information of the uncertain model parameters. More specifically, a new approach is presented using the total variation distance metric as an information constr...

This paper studies the infinite horizon average cost Markov control model subject to ambiguity on the controlled process conditional distribution. The stochastic control problem is formulated as a minimax optimization in which, (i) the existence of optimal policies is established through a pair of canonical dynamic programming equations derived for...

Modelling Population Dynamics (MPD) app utilizes mathematical models to capture the natural evolution of different population groups by any set of given characteristics such as age, sex, education level, and employment, based on available demographic data.

The control-coding (CC) capacity of dynamical decision models (DMs) is defined as the maximum amount of information transfer per unit time from its inputs to its outputs, called CC rate R in bits/second, which is operational with the aid of a controller-encoder and a decoder, as in Shannon's mathematical theory of communication over noisy channels,...

This paper develops a robust LQG approach applicable to non-homogeneous Markov jump linear systems with uncertain transition probability distributions. The stochastic control problem is formulated using (i) minimax optimization theory, and (ii) a total variation distance metric as a tool for codifying the level of uncertainty of the jump process. B...

The main objective of active fault diagnosis is the design of auxiliary input signals that enhance the detection and isolation of faults in modern technological systems. A major consideration when evaluating active fault diagnosis methods is robustness in the presence of modeling uncertainties. The presence of modeling inaccuracies will typically c...

This paper addresses the problem of controlling a Markov chain so as to minimize the long-run expected average cost per unit time when the invariant distribution is unknown but we know it belongs to a given uncertain set. The mathematical model used to describe this set is the total variation distance uncertainty. We show that the equilibrium contr...

One of the fundamental and most challenging problems in system biology is the reconstruction of gene regulatory networks from input-output data based on non-linear differential equations. This paper presents an approach to estimate the unknown nonlinearities and to identify the true network that generated the data, based on an error filtering learn...

The aim of this paper is to approximate a finite-state Markov process by another process with fewer states, called herein the approximating process. The approximation problem is formulated using two different methods. The first method, utilizes the total variation distance to discriminate the transition probabilities of a high dimensional Markov...

We study finite alphabet channels with Unit Memory on the previous Channel Outputs called UMCO channels. We identify necessary and sufficient conditions, to test whether the capacity achieving channel input distributions with feedback are time-invariant, and whether feedback capacity is characterized by single letter, expressions, similar to that o...

We show that stochastic dynamical control systems are capable of information transfer from control processes to output processes, with operational meaning as defined by Shannon. Moreover, we show that optimal control strategies have a dual role, specifically, i) to transfer information from the control process to the output process, and ii) to stab...

This paper addresses the optimality of stochastic control strategies based on the infinite horizon average cost criterion, subject to total variation distance ambiguity on the conditional distribution of the controlled process. This stochastic optimal control problem is formulated using minimax theory, in which the minimization is over the control...

The aim of this paper is to address optimality of stochastic control strategies via dynamic programming subject to total variation distance ambiguity on the conditional distribution of the controlled process. We formulate the stochastic control problem using minimax theory, in which the control minimizes the pay-off while the conditional distributi...

We address optimality of stochastic control strategies for infinite-horizon Markov decision problems with discounted pay-off, when the controlled process conditional distribution belongs to a ball of radius R with respect to total variation distance, centered at the nominal conditional distribution. The stochastic control problem is formulated usin...

In this paper, we investigate the problem of aggregating a given finite-state Markov process by another process with fewer states. The aggregation utilizes total variation distance as a measure of discriminating the Markov process by the aggregate process, and aims to maximize the entropy of the aggregate process invariant probability, subject to a...

The aim of this paper is to investigate extremum problems with pay-off being the total variational distance metric defined on the space of probability measures, subject to linear functional constraints on the space of probability measures, and vice-versa; that is, with the roles of total variational metric and linear functional interchanged. Utiliz...

The aim of this paper is to investigate extremum problems with pay-off the total variational distance metric subject to linear functional constraints both defined on the space of probability measures, as well as related problems. Utilizing concepts from signed measures, the extremum probability measures of such problems are obtained in closed form,...

The aim of this paper is to address optimality of stochastic control strategies via dynamic programming subject to total variational distance uncertainty on the conditional distribution of the controlled process. Utilizing concepts from signed measures, the maximization of a linear functional on the space of probability measures on abstract spaces...

The aim of this paper is to address optimality of control strategies for stochastic discrete time control systems subject to conditional distribution uncertainty. This type of uncertainty is motivated from the fact that the value function involves expectation with respect to the conditional distribution. The issues which will be discussed are the f...

The aim of this paper is to address optimality of control strategies for stochastic control systems subject to uncertainty and ambiguity. Uncertainty corresponds to the case when the true dynamics and the nominal dynamics are different but they are defined on the same state space. Ambiguity corresponds to the case when the true dynamics are defined...

This paper presents another application of the results in, where existence of the maximizing measure over the total variation distance constraint is established, while the maximizing pay-off is shown to be equivalent to an optimization of a pay-off which is a linear combination of L₁ and L_∞ norms. Here emphasis is geared towar...

In this paper, it is demonstrated that the Theory of Linear Quadratic is applicable in deriving optimum immigration policies, while maintaining population and immigration levels close to certain pre-specified reference trajectories. An already existed dynamic population model found in literature and statistical data obtained from Cyprus Statistics,...

This paper is concerned with dynamical population models obtained from short and long-term changes in size and age composition due to demographic processes such as births, deaths, migration, etc. Both deterministic and stochastic models are presented. The parameters which are embedded in the models may be either unavailable or noisy, therefore syst...