Browsing by Author "Mondal, Sabyasachi"

Now showing 1 - 18 of 18
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    Asymmetric bipartite consensus of nonlinear agents with communication noise
    (Springer, 2023-08-24) Mondal, Sabyasachi; Tsourdos, Antonios
    In this paper, the asymmetric bipartite consensus problem of a nonlinear multi-agent system is solved using Distributed Nonlinear Dynamic Inversion (DNDI) based controller. The application of DNDI is new in the context of asymmetric bipartite consensus, and it inherits all the advantages of NDI and works efficiently to solve the asymmetric bipartite problem. The mathematical details presented provide theoretical proof of its efficiency. A realistic simulation study is performed to establish the claims. The controller’s performance has been tested in the presence of communication noise, and the results are promising.
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    Autonomous addition of agents to an existing group using genetic algorithm
    (MDPI, 2020-12-05) Mondal, Sabyasachi; Tsourdos, Antonios
    This paper presents an idea of how new agents can be added autonomously to a group of existing agents without changing the existing communication topology among them. Autonomous agent addition to existing Multi-Agent Systems (MASs) can give a strategic advantage during the execution of a critical beyond visual line-of-sight (BVLOS) mission. The addition of the agent essentially means that new connections with existing agents are established. It is obvious that the consensus control energy increases as the number of agent increases considering a specific consensus protocol. The objective of this work is to establish the new connections in a way such that the consensus energy increase due to the new agents is minimal. The updated topology, including new connections, must contain a spanning tree to maintain the stability of the MASs network. The updated optimal topology is obtained by solving minimum additional consensus control energy using the Two-Dimensional Genetic Algorithm. The results obtained are convincing
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    Autonomous Architecture for UAV-based Agricultural Survey
    (AIAA, 2020-01-05) Mondal, Sabyasachi; Williamson, Alex; Xu, Zhengjia; Tsourdos, Antonios
    This paper presents the concept of autonomous architecture for UAVs to minimize human involvement for agricultural surveying. Agricultural surveying applications include monitoring crop health and collecting ground truth data for treatment and harvest planning. The proposed architecture can automate the entire surveying process and helps farmers to obtain specific and essential knowledge about the crop more quickly. This architecture helps to increase crop yields while reducing operating costs. The autonomy is achieved by integrating functional modules such as Mission Planning, image processing, task allocation, and communication. This work is focused on describing the mission planning and task allocation since image processing is not within the scope.
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    Autonomous collection of ground truth data by unmanned aerial vehicles instructed using SMS text messages
    (IEEE, 2020-02-17) Williamson, Alex; Mondal, Sabyasachi; Xu, Zhengjia; Tsourdos, Antonios
    This paper describes a solution to increase the efficiency of collecting agricultural ground truth data by the use of one or more off-the-shelf drones to autonomously collect high quality RGB image data at low level, through the incorporation of a bespoke smartphone application that receives routing path-planned location data in the form of Short Message Service (SMS) text messages.
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    Autonomous detect and avoid algorithm respecting airborne right of way rules
    (AIAA, 2024-01-04) Mukherjee, Anurag; Mondal, Sabyasachi; Tsourdos, Antonios
    Robust conflict resolution systems are crucial for BVLOS (beyond visual line of sight) operations of UAVs (Unmanned Aerial Vehicles) in the unsegregated airspace. The present conflict resolution research focus is skewed towards optimal path planning, often ignoring the airborne Right-of-way rules prescribed by the FAA and CAA. Although this approach might result in the most optimal path to resolve the conflict, it can cause confusion among other airspace users if the rules of the air are not obeyed when operating in the vicinity of other aircraft. In the present work, a real-time model predictive control approach is proposed that heavily prioritizes adherence to the prescribed right-of-way rules of the air. The maneuvering limitations of the involved aircraft are also taken into account. Several conflict scenarios were simulated, and the results show that the developed algorithm could resolve all conflicts.
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    Bipartite consensus of nonlinear agents in the presence of communication noise
    (MDPI, 2022-03-18) Mondal, Sabyasachi; Tsourdos, Antonios
    In this paper, a Distributed Nonlinear Dynamic Inversion (DNDI)-based consensus protocol is designed to achieve the bipartite consensus of nonlinear agents over a signed graph. DNDI inherits the advantage of nonlinear dynamic inversion theory, and the application to the bipartite problem is a new idea. Moreover, communication noise is considered to make the scenario more realistic. The convergence study provides a solid theoretical base, and a realistic simulation study shows the effectiveness of the proposed protocol.
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    Bipartite consensus of nonlinear agents with actuator fault
    (IEEE, 2024-05-22) Mondal, Sabyasachi; Tsourdos, Antonios
    This paper introduces a bipartite consensus controller to address the challenge of achieving consensus among nonlinear agents, particularly when actuator faults are present, leading to significant obstacles. To tackle this issue, the controller is developed by adapting the Distributed Nonlinear Dynamic Inversion (DNDI) technique, thereby accommodating the impact of actuator faults. The randomness of the actuator the fault is taken into account to reflect real-world conditions. The the paper also furnishes comprehensive mathematical insights into the convergence of the fault-tolerant controller, establishing a robust theoretical foundation. An extensive array of simulation studies demonstrate that the proposed controller effectively manages actuator faults, leading to the successful attainment of bipartite consensus.
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    The consensus of non-linear agents under switching topology using dynamic inversion in the presence of communication noise and delay
    (Sage, 2021-05-03) Mondal, Sabyasachi; Tsourdos, Antonios
    In this study, a consensus protocol for non-linear multi-agent systems using the non-linear dynamic inversion (NDI) technique is presented. It is named as distributed NDI or DNDI. The agents are considered to be working in a randomly changing environment which is realistic. The randomness in the operating environment is introduced by random switching communication topology, delays, and noise. The consensus protocol is obtained as a closed-form expression, which is a critical requirement for real-time implementation. Also, various cases regarding the communication issues have been considered to study the performance of the DNDI controller. The simulation results are found to be satisfactory.
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    Consensus tracking of nonlinear agents using distributed nonlinear dynamic inversion with switching leader-follower connection
    (MDPI, 2022-12-06) Mondal, Sabyasachi; Tsourdos, Antonios
    In this paper, a consensus tracking protocol for nonlinear agents is presented, which is based on the Nonlinear Dynamic Inversion (NDI) technique. Implementation of such a technique is new in the context of the consensus tracking problem. The tracking capability of nonlinear dynamic inversion (NDI) is exploited for a leader-follower multi-agent scenario. We have provided all the mathematical details to establish its theoretical foundation. Additionally, a convergence study is provided to show the efficiency of the proposed controller. The performance of the proposed controller is evaluated in the presence of both (a) random switching topology among the agents and (b) random switching of leader–follower connections, which is realistic and not reported in the literature. The follower agents track various trajectories generated by a dynamic leader, which describes the tracking capability of the proposed controller. The results obtained from the simulation study show how efficiently this controller can handle the switching topology and switching leader-follower connections.
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    Constrained quasi-spectral MPSP with application to high-precision missile guidance with path constraints
    (American Society of Mechanical Engineers (ASME), 2020-10-15) Mondal, Sabyasachi; Padhi, Radhakant
    This paper extends the recently developed quasi-spectral model predictive static programming (QS-MPSP) to include state and control path-constraints and yet retain its computational efficiency. This is achieved by (i) formulating the entire problem in the control variables alone by (a) converting the system dynamics to an equivalent algebraic constraint and (b) converting the state constraints to equivalent control constraints, both of which is done by manipulating the system dynamics, (ii) representing the control variables in Quasi-spectral form, which makes the number of free-variables independent of time-grids and (iii) using a computationally efficient optimization algorithm to solve this low-dimensional problem. This generic computationally efficient technique is utilized next as an effective lead angle, and lateral acceleration constrained optimal missile guidance to intercept incoming high-speed ballistic targets with high precision successfully. Both of these constraints, as well as near-zero miss-distance, are of high practical significance for this challenging problem. Extensive three-dimensional simulation studies show the effectiveness of the newly proposed constrained QS-MPSP guidance algorithm. Six degrees-of-freedom simulation studies have also been carried out using autopilot in the loop to validate the results more realistically.
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    Dynamic path planning of UAV in three-dimensional complex environment based on interfered fluid dynamical system
    (AIAA, 2024-01-04) Tamanakijprasart, Komsun; Mondal, Sabyasachi; Tsourdos, Antonios
    The difficulties of path planning for unmanned aerial vehicles (UAVs) grow with the increase of static obstacles. Moreover, the presence of dynamic obstacles piles up the computation burden, as the UAVs need to dynamically replan and compute a new path to avoid them within an expanding search space. Existing studies on dynamic path planning have primarily evaluated algorithms in low-fidelity simulations and focused on improving computational efficiency. Nonetheless, these efforts remain insufficient for practical applications due to the limited computing powers of onboard processors, coupled with the significantly more cluttered nature of real-world environments. This paper introduces a dynamic autorouting program featuring the Interfered Fluid Dynamical System (IFDS) for adaptive path planning and a novel safeguarding function to ensure safety distances during obstacle avoidance. The proposed strategy brings a dynamic path planning framework, allowing UAVs to adaptively reroute to avoid areas and obstacles that will change throughout the flight in complex environments.
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    Fault-tolerant consensus of nonlinear agents considering switching topology in the presence of communication noise
    (SAGE, 2022-04-11) Mondal, Sabyasachi; Tsourdos, Antonios
    In this paper, the consensus of nonlinear multi-agent systems (MASs) is discussed, considering actuator fault and switching topology in the presence of communication noise. The actuator fault and communication noise are both considered to be random. The switching of the topologies is considered random as well. These issues are handled by Distributed Nonlinear Dynamic Inversion (DNDI), which is designed for Multi-Agent Systems (MASs) operation. The convergence proof with actuator fault is provided, which shows the robustness of the controller. The simulation results show that DNDI successfully dealt with the actuator fault and communication events simultaneously.
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    Handover prediction for aircraft dual connectivity using model predictive control
    (IEEE, 2021-03-17) Mondal, Sabyasachi; Al-Rubaye, Saba; Tsourdos, Antonios
    Providing connectivity to aircraft such as flying taxis is a significant challenge for tomorrow’s aviation communication systems. One major problem is to provide ground to air (G2A) connectivity, especially in the airport, rural and sub-rural areas where the number of radio ground stations is not adequate to support the data link resulting in frequent interruption. Hence, effective handover decision-making is necessary to provide uninterrupted services to aircraft while moving from one domain to another. However, the existing handover decision is not efficient enough to solve the aircraft connectivity in such airspace. To overcome this problem, a prediction based optimal solution to handover decision making (handover prediction) would be appropriate to provide seamless dual connectivity to aircraft. In this paper, the handover prediction problem is formulated as a constrained optimization problem in the framework of the model for predictive control (MPC). The cost function and the constraints are derived in terms of dual connectivity variables over the prediction horizon. This problem is solved using a two-dimensional genetic algorithm (2D-GA) to obtain the predictive optimal handover solution. Simulation results show that the proposed dual connectivity handover can significantly improve the handover success probability. Finally, our results show that network densification and predictive control model have improved aircraft performance.
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    Neuro-adaptive augmented distributed nonlinear dynamic inversion for consensus of nonlinear agents with unknown external disturbance
    (Nature, 2022-02-07) Mondal, Sabyasachi; Tsourdos, Antonios
    This paper presents a novel neuro-adaptive augmented distributed nonlinear dynamic inversion (N-DNDI) controller for consensus of nonlinear multi-agent systems in the presence of unknown external disturbance. N-DNDI is a blending of neural network and distributed nonlinear dynamic inversion (DNDI), a new consensus control technique that inherits the features of Nonlinear Dynamic Inversion (NDI) and is capable of handling the unknown external disturbance. The implementation of NDI based consensus control along with neural networks is unique in the context of multi-agent consensus. The mathematical details provided in this paper show the solid theoretical base, and simulation results prove the effectiveness of the proposed scheme.
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    Optimal topology for consensus using genetic algorithm
    (Elsevier, 2020-05-08) Mondal, Sabyasachi; Tsourdos, Antonios
    In the Multi-Agent Systems (MAS), graph network topologies play a crucial role in building consensus among the connected agents. Consensus may be achieved on many network graphs using distributed control theory. However, the optimal network topology is not addressed in most of the literature, which is an important part of building stable consensus among networked agents. In this paper, the optimal topology is obtained irrespective of the agent dynamics by using two-dimensional Genetic Algorithm (GA), which is a new approach in this context. Simulation result for agents with first, and second-order linear dynamic is obtained. These results show that the proposed method achieves consensus using the optimal network topology satisfactorily.
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    Real-time collision avoidance trajectory planner using generalized vector explicit guidance
    (AIAA, 2023-01-19) Subies Hueso, Josep; Mondal, Sabyasachi; Tsourdos, Antonios; Chadwick, Andrew
    In this paper, a new real-time collision avoidance scheme is proposed using Generalized Vector Explicit Guidance (GENEX) law, an optimal guidance algorithm that can simultaneously achieve design specifications on miss distance and final UAV–target relative orientation. Low computation, closed-form expression, and optimal derivation are the key features that make it a potential candidate for real-time applications. The guidance law has been tested through simulations for the two-dimensional scenario, and the results are very satisfactory.
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    Real-time path planning considering static and dynamic obstacles
    (AIAA, 2024-01-04) Subies Hueso, Josep; Mondal, Sabyasachi; Tsourdos, Antonios
    This paper introduces a real-time path planning strategy that effectively navigates around both static and dynamic obstacles. The approach combines the principles of Generalised Explicit Vector (GENEX) and Inverse Proportional Navigation (IPN), established algorithms in missile guidance known for their advantageous features such as low computation and closedform expressions. Leveraging these attributes, the proposed strategy addresses path planning challenges involving static and dynamic obstacles. The performance of the combined algorithm is assessed through a comprehensive simulation study in both 2D and 3D scenarios, considering multiple static and dynamic obstacles.
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    Two-dimensional quantum genetic algorithm: application to task allocation problem
    (MDPI, 2021-02-10) Mondal, Sabyasachi; Tsourdos, Antonios
    This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabular form. The performance of 2D-QGA is compared to two-dimensional GA (2D-GA), which is used to solve two-dimensional problems as well. The comparison study is performed by applying both the algorithm to the task allocation problem. The performance of 2D-QGA is better than 2D-GA while comparing execution time, convergence iteration, minimum cost generated, and population size.