arXiv subjects

Mohsen Zamani

Publications and source records attributed to Mohsen Zamani.

15 recordsLinked to original sources

arXiv2022

Structural Consensus in Networks with Directed Topologies and Its Cryptographic Implementation

The existing cryptosystem based approaches for privacy-preserving consensus of networked systems are usually limited to those with undirected topologies. This paper proposes a new privacy-preserving algorithm for networked systems with directed topologies to reach confidential consensus. As a prerequisite for applying the algorithm, a structural consensus problem is formulated and the solvability conditions are discussed for an explicitly constructed controller. The controller is then implemented with encryption to achieve consensus while avoiding individual's information leakage to external eavesdroppers and/or malicious internal neighbors.

cs.MA↗

arXiv2021

Optimal Actor-Critic Policy with Optimized Training Datasets

Actor-critic (AC) algorithms are known for their efficacy and high performance in solving reinforcement learning problems, but they also suffer from low sampling efficiency. An AC based policy optimization process is iterative and needs to frequently access the agent-environment system to evaluate and update the policy by rolling out the policy, collecting rewards and states (i.e. samples), and learning from them. It ultimately requires a huge number of samples to learn an optimal policy. To improve sampling efficiency, we propose a strategy to optimize the training dataset that contains significantly less samples collected from the AC process. The dataset optimization is made of a best episode only operation, a policy parameter-fitness model, and a genetic algorithm module. The optimal policy network trained by the optimized training dataset exhibits superior performance compared to many contemporary AC algorithms in controlling autonomous dynamical systems. Evaluation on standard benchmarks show that the method improves sampling efficiency, ensures faster convergence to optima, and is more data-efficient than its counterparts.

cs.LG↗

arXiv2020

Reinforcement Learning Using Expectation Maximization Based Guided Policy Search for Stochastic Dynamics

Guided policy search algorithms have been proven to work with incredible accuracy for not only controlling a complicated dynamical system, but also learning optimal policies from various unseen instances. One assumes true nature of the states in almost all of the well known policy search and learning algorithms. This paper deals with a trajectory optimization procedure for an unknown dynamical system subject to measurement noise using expectation maximization and extends it to learning (optimal) policies which have less noise because of lower variance in the optimal trajectories. Theoretical and empirical evidence of learnt optimal policies of the new approach is depicted in comparison to some well known baselines which are evaluated on an autonomous system with widely used performance metrics.

eess.SY↗

arXiv2019

Considering pricing and uncertainty in designing a reverse logistics network

Companies try to maximize their profits by recovering returned products of highly uncertain quality and quantity. In this paper, a reverse logistics network for an Original Equipment Manufacturer (OEM) is presented. Returned products are selected for remanufacturing or scrapping, based on their quality and proportional prices are offered to customers. A Mixed Integer Non-linear Programming (MINLP) model is proposed to determine the location of collection centers, the optimum price of returned products and the sorting policy. The risk in the objective function is measured using the Conditional Value at Risk (CVaR) metric. CVaR measures the risk of an investment in a conservative way by considering the maximum lost. The results are analyzed for various values of the risk parameters (α, and λ). These parameters indicate that considering risk affects prices, the classification of returned products, the location of collection centers and, consequently, the objective function. The model performs more conservatively when the weight of the CVaR part (λ) and the value of the confidence level α are increased. The results show that better profits are obtained when we take CVaR into account.

q-fin.GN↗

arXiv2018

Secure and Privacy Preserving Consensus for Second-order Systems Based on Paillier Encryption

This paper aims at secure and privacy preserving consensus algorithms of networked systems. Due to the technical challenges behind decentralized design of such algorithms, the existing results are mainly restricted to a network of systems with simplest first-order dynamics. Like many other control problems, breakthrough of the gap between first-order dynamics and higher-order ones demands for more advanced technical developments. In this paper, we explore a Paillier encryption based average consensus algorithm for a network of systems with second-order dynamics, with randomness added to network weights. The conditions for privacy preserving, especially depending on consensus rate, are thoroughly studied with theoretical analysis and numerical verification.

eess.SY↗

arXiv2018

Probabilistic Optimal Power Flow Considering Correlation of Wind Farms via Markov Chain Quasi-Monte Carlo Sampling

The probabilistic characteristics of daily wind speed are not well captured by simple density functions such as Normal or Weibull distribuions as suggested by the existing literature. The unmodeled uncertainties can cause unknown influences on the power system operation. In this paper, we develop a new stochastic scheme for the probabilistic optimal power flow (POPF) problem, which can cope with arbitrarily complex wind speed distributions and also take into account the correlation of different wind farms. A multivariate Gaussian mixture model (GMM) is employed to approximate actual wind speed distributions from multiple wind farms. Furthermore, we propose to adopt the Markov Chain Monte Carlo (MCMC) sampling technique to deliver wind speed samples as the input of POPF. We also novelly integrate a Sobol-based quasi-Monte Carlo (QMC) technique into the MCMC sampling process to obtain a faster convergence rate. The IEEE 14- and 118-bus benchmark systems with additional wind farms are used to examine the effectiveness of the proposed POPF scheme.

eess.SY↗

arXiv2018

Subgradient-Free Stochastic Optimization Algorithm for Non-smooth Convex Functions over Time-Varying Networks

In this paper we consider a distributed stochastic optimization problem without the gradient/subgradient information for the local objective functions, subject to local convex constraints. The objective functions may be non-smooth and observed with stochastic noises, and the network for the distributed design is time-varying. By adding the stochastic dithers into the local objective functions and constructing the randomized differences motivated by the Kiefer-Wolfowitz algorithm, we propose a distributed subgradient-free algorithm to find the global minimizer with local observations. Moreover, we prove that the consensus of estimates and global minimization can be achieved with probability one over the time-varying network, and then obtain the convergence rate of the mean average of estimates as well. Finally, we give a numerical example to illustrate the effectiveness of the proposed algorithm.

eess.SY↗

arXiv2018

Structural Controllability of a Consensus Network with Multiple Leaders

This paper examines the structural controllability for a group of agents, called followers, connected to each other based on the consensus law under commands of multiple leaders, which are agents with superior capabilities, over a fixed communication topology. It is proved that the graph-theoretic sufficient and necessary condition for the set of followers to be structurally controllable under the leaders' commands is leader-follower connectivity of the associated graph topology. This shrinks to graph connectivity for the case of solo leader. In the approach, we explicitly put into account the dependence among the entries of the system matrices for a consensus network using the linear parameterization technique introduced in [1].

eess.SY↗

arXiv2018

Constrained Optimal Consensus in Dynamical Networks

In this paper, an optimal consensus problem with local inequality constraints is studied for a network of single-integrator agents. The goal is that a group of single-integrator a gents rendezvous at the optimal point of the sum of local convex objective functions. The local objective functions are only available to the corresponding agents that only need to know their relative distances from their neighbors in order to seek the final optimal point. This point is supposed to be confined by some local inequality constraints. To tackle this problem, we integrate the primal dual gradient-based optimization algorithm with a consensus protocol to drive the agents toward the agreed point that satisfies KKT conditions. The asymptotic convergence of the solution of the optimization problem is proven with the help of LaSalle's invariance principle for hybrid systems. A numerical example is presented to show the effectiveness of our protocol.

math.DS↗

arXiv2017

Constrained Optimal Consensus in Multi-agent Systems with First and Second Order Dynamics

This paper fully studies distributed optimal consensus problem in non-directed dynamical networks. We consider a group of networked agents that are supposed to rendezvous at the optimal point of a collective convex objective function. Each agent has no knowledge about the global objective function and only has access to its own local objective function, which is a portion of the global one, and states information of agents within its neighborhood set. In this setup, all agents coordinate with their neighbors to seek the consensus point that minimizes the networks global objective function. In the current paper, we consider agents with single-integrator and double-integrator dynamics. We further suppose that agents movements are limited by some convex inequality constraints. In order to find the optimal consensus point under the described scenario, we combine the interior-point optimization algorithm with a consensus protocol and propose a distributed control law. The associated convergence analysis based on Lyapunov stability analysis is provided.

math.OC↗

arXiv2017

Distributed Kalman Filter in a Network of Linear Dynamical Systems

This paper is concerned with the problem of distributed Kalman filtering in a network of interconnected subsystems with distributed control protocols. We consider networks, which can be either homogeneous or heterogeneous, of linear time-invariant subsystems, given in the state-space form. We propose a distributed Kalman filtering scheme for this setup. The proposed method provides, at each node, an estimation of the state parameter, only based on locally available measurements and those from the neighbor nodes. The special feature of this method is that it exploits the particular structure of the considered network to obtain an estimate using only one prediction/update step at each time step. We show that the estimate produced by the proposed method asymptotically approaches that of the centralized Kalman filter, i.e., the optimal one with global knowledge of all network parameters, and we are able to bound the convergence rate. Moreover, if the initial states of all subsystems are mutually uncorrelated, the estimates of these two schemes are identical at each time step.

eess.SY↗

arXiv2017

Distributed Convex Optimization in Networks of Agents with Single Integrator Dynamics

In this paper, distributed convex optimization problem over non-directed dynamical networks is studied. Here, networked agents with single-integrator dynamics are supposed to rendezvous at a point that is the solution of a global convex optimization problem with some local inequality constraints. To this end, all agents shall cooperate with their neighbors to seek the optimum point of the networks global objective function. A distributed optimization algorithm based on the interior-point method is proposed, which combines an optimization algorithm with a nonlinear consensus protocol to find the optimum value of the global objective function. We tackle this problem by addressing its sub-problems, namely a consensus problem and a convex optimization problem. Firstly, we propose a saturation protocol for the consensus sub-problem. Then to solve the distributed optimization part, we implement a centralized control law, which yields the optimum value of the global objective function, in a distributed fashion with the help of a distributed estimator. Convergence analysis for the proposed protocol based on the stability theory for time-varying nonlinear systems is included. A simulation example is given at the end to illustrate the effectiveness of the proposed algorithm.

math.OC↗

arXiv2015

On the Reachability of Networked Systems

In this paper, we study networks of discrete-time linear time-invariant subsystems. Our focus is on situations where subsystems are connected to each other through a time-invariant topology and where there exists a base-station whose aim is to control the subsystems into any desired destinations. However, the base-station can only communicate with some of the subsystems that we refer to as leaders. There are no direct links between the base-station and the rest of subsystems, known as followers, as they are only able to liaise among themselves and with some of the leaders. The current paper formulates this framework as the well-known reachability problem for linear systems. Then to address this problem, we introduce notions of leader-reachability and base-reachability. We present algebraic conditions under which these notions hold. It turns out that if subsystems are represented by minimal state space representations, then base-reachability always holds. Hence, we focus on leader-reachability and investigate the corresponding conditions in detail. We further demonstrate that when the networked system parameters i.e. subsystems' parameters and interconnection matrices, assume generic values then the whole network is both leader-reachable and base-reachable.

eess.SY↗

arXiv2014

Zeros of Networked Systems with Time-invariant Interconnections

This paper studies zeros of networked linear systems with time-invariant interconnection topology. While the characterization of zeros is given for both heterogeneous and homogeneous networks, homogeneous networks are explored in greater detail. In the current paper, for homogeneous networks with time-invariant interconnection dynamics, it is illustrated how the zeros of each individual agent's system description and zeros definable from the interconnection dynamics contribute to generating zeros of the whole network. We also demonstrate how zeros of networked systems and those of their associated blocked versions are related.

eess.SY↗

arXiv2013

On the Zero-freeness of Tall Multirate Linear Systems

In this paper, tall discrete-time linear systems with multirate outputs are studied. In particular, we focus on their zeros. In systems and control literature zeros of multirate systems are defined as those of their corresponding time-invariant blocked systems. Hence, the zeros of tall blocked systems resulting from blocking of linear systems with multirate outputs are mainly explored in this work. We specifically investigate zeros of tall blocked systems formed by blocking tall multirate linear systems with generic parameter matrices. It is demonstrated that tall blocked systems generically have no finite nonzero zeros; however, they may have zeros at the origin or at infinity depending on the choice of blocking delay and the input, state and output dimensions.

eess.SY↗