arXiv subjects

Mohammad Hayajneh

Publications and source records attributed to Mohammad Hayajneh.

3 recordsLinked to original sources

arXiv2023

Consensus controller with safety guarantee: an application to the kinematic bicycle model

This paper proposes a consensus controller for multi-agent systems that can guarantee the agents' safety. The controller, built with the idea of output prediction and the Newton-Raphson method, achieves consensus for a class of heterogeneous nonlinear systems. The Integral Control Barrier Function is applied in conjunction with the controller, such that the agents' states are confined within pre-defined safety sets. Due to the dynamically-defined control input, the resulting optimization problem from the barrier function is always a Quadratic Program, despite the nonlinearities that the system dynamics may have. We verify the proposed controller using a platoon of autonomous vehicles modeled by kinematic bicycles. A convergence analysis of the leader-follower consensus under the path graph topology is conducted. Simulation results show that the vehicles achieve consensus while keeping safe inter-agent distances, suggesting a potential in future applications.

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arXiv2022

Deep transfer learning for system identification using long short-term memory neural networks

Recurrent neural networks (RNNs) have many advantages over more traditional system identification techniques. They may be applied to linear and nonlinear systems, and they require fewer modeling assumptions. However, these neural network models may also need larger amounts of data to learn and generalize. Furthermore, neural networks training is a time-consuming process. Hence, building upon long-short term memory neural networks (LSTM), this paper proposes using two types of deep transfer learning, namely parameter fine-tuning and freezing, to reduce the data and computation requirements for system identification. We apply these techniques to identify two dynamical systems, namely a second-order linear system and a Wiener-Hammerstein nonlinear system. Results show that compared with direct learning, our method accelerates learning by 10% to 50%, which also saves data and computing resources.

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arXiv2013

Closed-Form Rate Outage Probability for OFDMA Multi-Hop Broadband Wireless Networks under Nakagami-m Channels

Rate outage probability is an important performance metric to measure the level of quality of service (QoS) in the 4th Generation (4G) broadband access networks. Thus, in this paper, we calculate a closed form expression of the rate outage probability for a given user in a down-link multi-hop OFDMA-based system encountered as a result of links channel variations. The channel random behavior on different subcarriers allocated to a given user is assumed to follow independent non-identical Nakagami-m distributions. Besides the rate outage probability formulas for single hop and multi-hop networks, we also derive a novel closed form formulas for the moment generating function, probability distribution function (pdf), and the cumulative distribution function (cdf) of a product of independent non-identical Gamma distributed random variables (RVs). These RVs are functions of the attainable signal-to-noise power ratio (SNR) on the allocated group of subcarriers. For single-hop scenario, inspired by the rate outage probability closed formula, we formulate an optimization problem in which we allocate subcarriers to users such that the total transmission rate is maximized while catering for fairness for all users. In the proposed formulation, fairness is considered by guaranteeing a minimum rate outage probability for each admitted user

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