Adaptive Fuzzy Containment Control for Uncertain Nonlinear Multiagent Systems

This paper considers the containment control problem for uncertain nonlinear multiagent systems under directed graphs. The followers are governed by nonlinear systems with unknown dynamics while the multiple leaders are neighbors of a subset of the followers. Fuzzy logic systems (FLSs) are used to identify the unknown dynamics and a distributed state feedback containment control protocol is proposed. This result is extended to the output feedback case, where observers are designed to estimate the unmeasurable states. Then, an output feedback containment control scheme is presented. The developed state feedback and output feedback containment controllers guarantee that the states of all followers converge to the convex hull spanned by the dynamic leaders. Based on Lyapunov stability theory, it is proved that the containment control errors are uniformly ultimately bounded (UUB). An example is provided to show the effectiveness of the proposed control method

An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination

This article reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, task assignment, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations

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

Nonlinear control and synchronization of multiple Lagrangian systems with application to tethered formation flight spacecraft

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 217-228).This dissertation focuses on the synchronization of multiple dynamical systems using contraction theory, with applications to cooperative control of multi-agent systems and synchronization of interconnected dynamics such as tethered formation flight. Inspired by stable combinations of biological systems, contraction nonlinear stability theory provides a systematic method to reduce arbitrarily complex systems into simpler elements. One application of oscillation synchronization is a fully decentralized nonlinear control law, which eliminates the need for any inter-satellite communications. We use contraction theory to prove that a nonlinear control law stabilizing a single-tethered spacecraft can also stabilize arbitrarily large circular arrays of tethered spacecraft, as well as a three-spacecraft inline configuration. The convergence result is global and exponential due to the nature of contraction analysis. The proposed decentralized control strategy is further extended to robust adaptive control in order to account for model uncertainties. Numerical simulations and experimental results validate the exponential stability of the tethered formation arrays by implementing a tracking control law derived from the reduced dynamics.(cont.) This thesis also presents a new synchronization framework that can be directly applied to cooperative control of autonomous aerospace vehicles and oscillation synchronization in robotic manipulation and locomotion. We construct a dynamical network of multiple Lagrangian systems by adding diffusive couplings to otherwise freely moving or flying vehicles. The proposed tracking control law synchronizes an arbitrary number of robots into a common trajectory with global exponential convergence. The proposed control law is much simpler than earlier work in terms of both the computational load and the required signals. Furthermore, in contrast with earlier work which used simple double integrator models, the proposed method permits highly nonlinear systems and is further extended to adaptive synchronization, partial-joint coupling, and concurrent synchronization. Another contribution of the dissertation is a novel nonlinear control approach for underactuated tethered formation flight spacecraft. This is motivated by a controllability analysis that indicates that both array resizing and spin-up are fully controllable by the reaction wheels and the tether motor. This work reports the first propellant-free underactuated control results for tethered formation flight.(cont.) We also fulfill the potential of the proposed strategy by providing a new momentum dumping method. This dissertation work has evolved based on the research philosophy of balancing theoretical work with practicality, aiming at physically intuitive algorithms that can be directly implemented in real systems. In order to validate the effectiveness of the decentralized control and estimation framework, a new suite of hardware has been designed and added to the SPHERES (Synchronize Position Hold Engage and Reorient Experimental Satellite) testbed. Such recent improvements described in this dissertation include a new tether reel mechanism, a force-torque sensor and an air-bearing carriage with a reaction wheel. This thesis also introduces a novel relative attitude estimator, in which a series of Kalman filters incorporate the gyro, force-torque sensor and ultrasound ranging measurements. The closed-loop control experiments can be viewed at ...by Soon-Jo Chung.Sc.D

New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

Fixed-wing UAV tracking of evasive targets in 3-dimensional space

In this thesis, we explore the development of autonomous tracking and interception strategies for single and multiple fixed-wing Unmanned Aerial Vehicles (UAVs) pursuing single or multiple evasive targets in 3-dimensional (3D) space. We considered a scenario where we intend to protect high-value facilities from adversarial groups employing ground-based vehicles and quadrotor swarms and focused on solving the target tracking problem. Accordingly, we refined a min-max optimal control algorithm for fixed-wing UAVs tracking ground-based targets, by introducing constraints on bank angles and turn rates to enhance actuator reliability when pursuing agile and evasive targets. An intelligent and persistent evasive control strategy for the target was also devised to ensure robust performance testing and optimisation. These strategies were extended to 3D space, incorporating three altitude control algorithms to facilitate flexible UAV altitude control, leveraging various parameters such as desired UAV altitude and image size on the tracking camera lens. A novel evasive quadrotor algorithm was introduced, systematically testing UAV tracking efficacy against various evasive scenarios while implementing anti-collision measures to ensure UAV safety and adaptive optimisation improve the achieved performance. Using decentralised control strategies, cooperative tracking by multiple UAVs of single evasive quadrotor-type and dynamic target clusters was developed along with a new altitude control strategy and task assignment logic for efficient target interception. Lastly, a countermeasure strategy for tracking and neutralising non-cooperative adversarial targets within restricted airspace was implemented, using both Nonlinear Model Predictive Control (NMPC) and optimal controllers. The major contributions of this thesis include optimal control strategies, evasive target control, 3D target tracking, altitude control, cooperative multi-UAV tracking, adaptive optimisation, high-precision projectile algorithms, and countermeasures. We envision practical applications of the findings from this research in surveillance, security, search and rescue, agriculture, environmental monitoring, drone defence, and autonomous delivery systems. Future efforts to extend this research could explore adaptive evasion, enhanced collaborative UAV swarms, machine learning integration, sensor technologies, and real-world testing