doc. Hossein Barghi Jond, Ph.D.

This paper presents a novel zone-based flocking control approach suitable for dynamic multi-agent systems (MAS). Inspired by Reynolds behavioral rules for boids, flocking behavioral rules with the zones of repulsion, conflict, attraction, and surveillance are introduced. For each agent, using only bearing and distance measurements, behavioral contribution vectors quantify the local separation, local and global flock velocity alignment, local cohesion, obstacle avoidance and boundary conditions, and strategic separation for avoiding alien agents. The control strategy uses the local perception-based behavioral contribution vectors to guide each agent’s motion. Additionally, the control strategy incorporates a directionally aware obstacle avoidance mechanism that prioritizes obstacles in the agent’s forward path. Simulation results validate the effectiveness of the model in creating flexible, adaptable, and scalable flocking behavior. Asymptotic stability and convergence to a stable flocking configuration for any initial conditions provided the interaction graph is a spanning tree are demonstrated. The flocking model’s reliance on locally sensed bearing and distance measurements ensures scalability and robustness, particularly in scenarios where communication is unreliable or resource-intensive. This makes it well-suited for real-world applications demanding seamless operation in highly dynamic and distributed environments.

Optimal collision-free formation control of the unmanned aerial vehicle (UAV) is a challenge. The state-of-the-art optimal control approaches often rely on numerical methods sensitive to initial guesses. This paper presents an innovative collision-free finite-time formation control scheme for multiple UAVs leveraging the differential flatness of the UAV dynamics, eliminating the need for numerical methods. We formulate a finite-time optimal control problem to plan a formation trajectory for feasible initial states. This optimal control problem in formation trajectory planning involves a collective performance index to meet the formation requirements to achieve relative positions and velocity consensus. It is solved by applying Pontryagin's principle. Subsequently, a collision-constrained regulating problem is addressed to ensure collision-free tracking of the planned formation trajectory. The tracking problem incorporates a directionally aware collision avoidance strategy that prioritizes avoiding UAVs in the forward path and relative approach. It assigns lower priority to those on the sides with an oblique relative approach, disregarding UAVs behind and not in the relative approach. The high-fidelity simulation results validate the effectiveness of the proposed control scheme.

The development, design, examination, and optimization of carbon-free power generation models are essential to achieve a sustainable future with net-zero emissions. This study introduces a novel multigeneration system, uniquely combining a supercritical CO2 Brayton cycle and a transcritical CO2 Rankine cycle, supported by a solar parabolic trough collector. The system integrates a reverse osmosis desalination unit, enabling simultaneous production of clean water, heating, and power. A multi-objective optimization framework is implemented by the NSGA-II algorithm, coupled with the TOPSIS method, to explore and identify optimal operational conditions. The innovation lies in the comprehensive consideration of solar incident angles and their impact on system performance, a rarely addressed aspect in the literature. Detailed thermodynamic analysis highlights system performance, achieving a net power capacity of 1052 kW, freshwater generation of 90.44 m3/h, and hot water generation of 1614 kW. The optimized results demonstrate significant improvements in overall energy (50.28 %) and exergy efficiency (22.31 %), showcasing the system's potential as a benchmark for sustainable, zero-emission energy solutions.

Hydraulic-actuated legs for quadruped robots excel in producing high force and offering precise control. Although the overall efficiency of hydraulic servo systems can be limited by pump and valve losses, the local mechanical efficiency from the actuator to the leg mechanism can be relatively high. This study introduces an optimization driven methodology for designing and validating robotic leg mechanisms using evolutionary algorithms. By solving three distinct optimization problems, the study addresses trajectory tracking accuracy and force transmission efficiency. The resulting design was experimentally validated, demonstrating reliable motion reproduction with minimal deviation and achieving a force transmission efficiency of 94%. These results demonstrate the feasibility of translating optimization outcomes into high-performing physical prototypes, providing a robust framework for future robotic mechanism development.

In a social network, individuals express their opinions on several interdependent topics, and therefore the evolution of their opinions on these topics is also mutually dependent. In this work, we propose a differential game model for the multi-dimensional opinion formation of a social network whose population of agents interacts according to a communication graph. Each individual's opinion evolves according to an aggregation of disagreements between the agent's opinions and its graph neighbors on multiple interdependent topics exposed to an unknown extraneous disturbance. For a social network with strategist agents, the opinions evolve over time with respect to the minimization of a quadratic cost function that solely represents each individual's motives against the disturbance. We find the unique Nash/worst-case equilibrium solution for the proposed differential game model of coupled multi-dimensional opinions under an open-loop information structure. Moreover, we propose a distributed implementation of the Nash/worst-case equilibrium solution. We examine the non-distributed and proposed distributed open-loop Nash/worst-case strategies on a small social network with strategist agents in a two-dimensional opinion space. Then we compare the evolved opinions based on the Nash/worst-case strategy with the opinions corresponding to social optimality actions for non-strategist agents.

A social network population engages in collective actions as a direct result of forming a particular opinion. The strategic interactions among the individuals acting independently and selfishly naturally portray a noncooperative game. Nash equilibrium allows for self-enforcing strategic interactions between selfish and self-interested individuals. This article presents a differential game approach to the opinion formation problem in social networks to investigate the evolution of opinions as a result of a Nash equilibrium. The opinion of each individual is described by a differential equation, which is the continuous-time Hegselmann-Krause model for opinion dynamics with a time delay in input. The objective of each individual is to seek optimal strategies for its own opinion evolution by minimizing an individual cost function. Two differential game problems emerge, one for a population that is not stubborn and another for a population that is stubborn. The open-loop Nash equilibrium actions and their associated opinion trajectories are derived for both differential games using Pontryagin's principle. Additionally, the receding horizon control scheme is used to practice feedback strategies where the information flow is restricted by fixed and complete social graphs, as well as the second neighborhood concept. The game strategies were executed on the well-known Zachary's Karate Club social network and a representative family opinion network. The resulting opinion trajectories associated with the game strategies showed consensus, polarization, and disagreement in final opinions.

This paper studies cooperative control of noncooperative double-integrator multi-agent systems (MASs) with input delay on connected directed graphs in the context of a differential graphical game (DGG). In the distributed DGG, each agent seeks a distributed information control policy by optimizing an individual local performance index (PI) of distributed information from its graph neighbors. The local PI, which quadratically penalizes the agent's deviations from cooperative behavior (e.g., the consensus here), is constructed through the use of the graph Laplacian matrix. For DGGs for double-integrator MASs, the existing body of literature lacks the explicit characterization of Nash equilibrium actions and their associated state trajectories with distributed information. To address this issue, we first convert the N -player DGG with m communication links into m coupled optimal control problems (OCPs), which, in turn, convert to the two-point boundary-value problem (TPBVP). We derive the explicit solutions for the TPBV that constitute the explicit distributed information expressions for Nash equilibrium actions and the state trajectories associated with them for the DGG. An illustrative example verifies the explicit solutions of local information to achieve fully distributed consensus.