Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

On Development of 100-Gram-Class Spacecraft for Swarm Applications

A novel space system architecture is proposed, which would enable 100-g-class spacecraft to be flown as swarms (100 s-1000 s) in low Earth orbit. Swarms of Silicon Wafer Integrated Femtosatellites (SWIFT) present a paradigm-shifting approach to distributed spacecraft development, missions, and applications. Potential applications of SWIFT swarms include sparse aperture arrays and distributed sensor networks. New swarm array configurations are introduced and shown to achieve the effective sparse aperture driven from optical performance metrics. A system cost analysis based on this comparison justifies deploying a large number of femtosatellites for sparse aperture applications. Moreover, this paper discusses promising guidance, control, and navigation methods for swarms of femtosatellites equipped with modest sensing and control capabilities

Advances in Control Techniques for Floating Platform Stabilization in the Zero-G Lab

peer reviewedThe study presents a novel control approach for managing floating platforms in the unique environment of a zero-gravity laboratory (Zero-G Lab) of University of Luxembourg. These platforms are pivotal for diverse experiments and technologies in space. Our solution combines Model Predictive Control (MPC) and Proportional-Derivative (PD) control techniques to ensure precise positioning and stability. The MPC algorithm generates optimal trajectories based on predictive platform models, adjusting paths for minimal effort. Augmented by a PD controller using feedback from the Optitrack motion system, real-time adjustments maintain stability by considering platform state, position, and orientation data. Extensive simulations and experiments within the Zero-G Lab demonstrate the effectiveness of our approach. The MPC-PD strategy accurately controls platforms, making them resilient against external disturbances and human interactions. This strategy holds promise for space exploration, microgravity experiments, and beyond, offering adaptable control in zero-gravity conditions

Trajectory optimization using MILP

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2002.Includes bibliographical references (p. 121-129).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.This thesis presents methods for finding optimal trajectories for vehicles subjected to avoidance and assignment requirements. The former include avoidance of collisions with obstacles or other vehicles and avoidance of thruster plumes from spacecraft. Assignment refers to the inclusion of decisions about terminal constraints in the optimization, such as assignment of waypoints to UAVs and the assignment of spacecraft to positions in a formation. These requirements lead to non-convex constraints and difficult optimizations. However, they can be formulated as mixed-integer linear programs (MILP) that can be solved for global optimality using powerful, commercial software. This thesis provides several extensions to previous work using MILP. The constraints for avoidance are extended to prevent plume impingement, which occurs when one spacecraft fire thrusters towards another. Methods are presented for efficient simplifications to complex problems, allowing solutions to be obtained in practical computation times. An approximation is developed to enable the inclusion of aircraft dynamics in a linear optimization, and also to include a general form of waypoint assignment suitable for UAV problems. Finally, these optimizations are used in model predictive control, running in real-time to incorporate feedback and compensate for uncertainty. Two major application areas are considered: spacecraft and aircraft. Spacecraft problems involve minimum fuel optimizations, and include ISS rendezvous and satellite cluster configuration. Aircraft problems are solved for minimum flight-time, or in the case of UAV problems with assignment, waypoint values and vehicle capabilities are included. Aircraft applications include air traffic management and coordination of autonomous UAVs. The results in this thesis provide a direct route to globally-optimal solutions of these non-convex trajectory optimizations.by Arthur George Richards.S.M

Review of advanced guidance and control algorithms for space/aerospace vehicles

The design of advanced guidance and control (G&C) systems for space/aerospace vehicles has received a large amount of attention worldwide during the last few decades and will continue to be a main focus of the aerospace industry. Not surprisingly, due to the existence of various model uncertainties and environmental disturbances, robust and stochastic control-based methods have played a key role in G&C system design, and numerous effective algorithms have been successfully constructed to guide and steer the motion of space/aerospace vehicles. Apart from these stability theory-oriented techniques, in recent years, we have witnessed a growing trend of designing optimisation theory-based and artificial intelligence (AI)-based controllers for space/aerospace vehicles to meet the growing demand for better system performance. Related studies have shown that these newly developed strategies can bring many benefits from an application point of view, and they may be considered to drive the onboard decision-making system. In this paper, we provide a systematic survey of state-of-the-art algorithms that are capable of generating reliable guidance and control commands for space/aerospace vehicles. The paper first provides a brief overview of space/aerospace vehicle guidance and control problems. Following that, a broad collection of academic works concerning stability theory-based G&C methods is discussed. Some potential issues and challenges inherent in these methods are reviewed and discussed. Then, an overview is given of various recently developed optimisation theory-based methods that have the ability to produce optimal guidance and control commands, including dynamic programming-based methods, model predictive control-based methods, and other enhanced versions. The key aspects of applying these approaches, such as their main advantages and inherent challenges, are also discussed. Subsequently, a particular focus is given to recent attempts to explore the possible uses of AI techniques in connection with the optimal control of the vehicle systems. The highlights of the discussion illustrate how space/aerospace vehicle control problems may benefit from these AI models. Finally, some practical implementation considerations, together with a number of future research topics, are summarised

On Development of 100-Gram-Class Spacecraft for Swarm Applications

A novel space system architecture is proposed, which would enable 100-g-class spacecraft to be flown as swarms (100 s-1000 s) in low Earth orbit. Swarms of Silicon Wafer Integrated Femtosatellites (SWIFT) present a paradigm-shifting approach to distributed spacecraft development, missions, and applications. Potential applications of SWIFT swarms include sparse aperture arrays and distributed sensor networks. New swarm array configurations are introduced and shown to achieve the effective sparse aperture driven from optical performance metrics. A system cost analysis based on this comparison justifies deploying a large number of femtosatellites for sparse aperture applications. Moreover, this paper discusses promising guidance, control, and navigation methods for swarms of femtosatellites equipped with modest sensing and control capabilities

Formation Control and Fault Accommodation for a Team of Autonomous Underwater Vehicles

The purpose of this thesis is the development of efficient formation control and fault accommodation algorithms for a team of autonomous underwater vehicles (AUVs). The team of AUVs are capable of performing a wide range of deep water marine applications such as seabed mapping and surveying, oil and gas exploration and extraction, and oil and gas pipeline inspection. However, communication limitations and the presence of undesirable events such as component faults in any of the team members can prevent the whole team to achieve safe, reliable, and efficient performance while executing underwater mission tasks. In this regard, the semi-decentralized control scheme is developed to achieve trajectory tracking and formation keeping while requiring information exchange only among neighboring agents. To this end, model predictive control (MPC) technique and dynamic game theory are utilized to formulate and solve the formation control problem. Moreover, centralized and decentralized control schemes are developed to assess the performance of the proposed semi-decentralized control scheme in the simulation studies. The simulation results verify that the performance of the proposed semi-decentralized scheme is very close to the centralized scheme with lower control effort cost while it does not impose stringent communication requirements as in the centralized scheme. Moreover, the semi-decentralized active fault recovery scheme is developed to maintain a graceful degraded performance and to ensure that the team of autonomous underwater vehicles can satisfy mission objectives when an actuator fault occurs in any of the team members. In this regard, online fault information provided by fault detection and isolation (FDI) modules of each agent and its neighbors are incorporated to redesign the nominal controllers based on the MPC technique and dynamic game theory. Additionally, FDI imperfections such as fault estimation error and time delay are taken into account, and a performance index is derived to show the impact of FDI imperfections on the performance of team members. Moreover, centralized and decentralized active fault recovery schemes are developed to evaluate the performance of the proposed semi-decentralized recovery scheme through comparative simulation studies with various fault scenarios. The comparative simulation studies justify that the proposed semi-decentralized fault recovery scheme meets the design specifications even if the performance of the FDI module is not ideal