Cooperative Control Algorithms Research Articles

Immersive Extended Reality and Remote Sensing Technologies, Simulation Modeling and Spatial Data Acquisition Tools, and Cooperative Decision and Control Algorithms in a Real-Time Interoperable Decentralized Metaverse

Immersive Extended Reality and Remote Sensing Technologies, Simulation Modeling and Spatial Data Acquisition Tools, and Cooperative Decision and Control Algorithms in a Real-Time Interoperable Decentralized Metaverse

Cooperative Adaptive Model-Free Control With Model-Free Estimation and Online Gain Tuning.

In this article, a distributed adaptive model-free control algorithm is proposed for consensus and formation-tracking problems in a network of agents with completely unknown nonlinear dynamic systems. The specification of the communication graph in the network is incorporated in the adaptive laws for estimation of the unknown linear and nonlinear terms, and in the online updating of the elements in the main controller gain matrix. The decentralized control signal at each agent in the network requires information about the states of the leader agent, as well as the desired formation variables of the agents in a local coordinate frame. These two sets of variables are provided at each agent by utilizing two recently proposed distributed observers. It is shown that only a spanning-tree rooted at the leader agent is enough for the convergence and stability of the proposed cooperative control and observer algorithms. Two simulation studies are provided to evaluate the performance of the proposed algorithm in comparison with two state-of-the-art distributed model-free control algorithms. With lower control effort as well as fewer offline gain tuning, the same level of consensus errors is achieved. Finally, the application of the proposed solution is studied in the formation-tracking control of a team of autonomous aerial mobile robots via simulation results.

A unified inner‐/outer‐loop distributed design for task‐space robust cooperative tracking of uncertain networked manipulators under directed graphs

AbstractThis paper investigates task‐space cooperative tracking of networked manipulators with an inner‐/outer‐loop closed control architecture, taking effects of uncertain kinematics, dynamics, disturbances, and unavailable task‐space velocities into account. In the existing related works, the developed distributed cooperative control algorithms require either an open control architecture with torque‐based controllers or the combination of inner/outer loop to be stable with the effects of unknown dynamics being neglected. In contrast, in this work we propose a novel distributed control framework such that a distributed outer‐loop adaptive scheme is developed to achieve task‐space robust cooperative tracking with dynamic effects being considered and without modifying the inner control loop. In particular, a distributed estimator is firstly developed to estimate the desired global task. With this estimated information, distributed cooperative algorithms are presented for two types of inner‐loop controllers to ensure task‐space coordinated tracking asymptotically. If the robot has an open control architecture, the torque‐based distributed controller is provided for task‐space cooperative tracking, which can cover existing related results as a special case. Finally, numerical simulations are provided to show the effectiveness of the proposed designs.

Design of Distributed Event-Triggered Average Tracking Algorithms for Homogeneous and Heterogeneous Multiagent Systems

This article addresses the design problem of distributed event-triggered average tracking (DETAT) algorithms for homogeneous and heterogeneous multiagent systems. The objective of the DETAT problem is to develop a group of distributed cooperative control algorithms with event-triggered strategies for agents to track the average of multiple time-varying reference signals. First, for homogeneous linear multiagent systems, based on sampling measurements and model-relied holding techniques, a class of static-gain DETAT algorithms is proposed with a couple of local event-triggered functions for estimators and controllers, respectively. Compared with the existing distributed average tracking (DAT) algorithms, the static-gain DETAT algorithms greatly reduce the cost over communication networks and the frequency of control protocol updates. Second, to reduce the chattering phenomenon caused by nonsmooth items in static-gain algorithms and requirements of the global information of networks, smooth dynamic-gain DETAT algorithms are introduced based on boundary layer approximation methods and self-adaptive principles. Third, for heterogeneous linear multiagent systems, a new algorithm is established by using the output regulation techniques for the heterogeneous DETAT problem. The outputs of heterogeneous agents can ultimately track the average of multiple time-varying reference signals. To the best of our knowledge, it is the first time to study the DETAT problem for heterogeneous multiagent systems. Finally, some examples are presented to show the validity of theoretical results.

Review and Simulation of a Novel Formation Building Algorithm While Enabling Obstacle Avoidance

Technically, a group of more than two wheeled mobile robots working collectively towards a common goal are known as a multi-robot system. An increasing number of industries have implemented multi-robot systems to eliminate the risk of human injuries while working on hazardous tasks, and to improve productivity. Globally, engineers are continuously researching better, simple, and faster cooperative Control algorithms to provide a Control strategy where each agent in the robot formation can communicate effectively and achieve a consensus in their position, orientation and speed. This paper explores a novel Formation Building Algorithm and its global stability around a configuration vector. A simulation in MATLAB? was carried out to examine the performance of the Algorithm for two geometric formations and a fixed number of robots. In addition, an obstacle avoidance technique was presented assuming that all robots are equipped with range sensors. In particular, a uniform rounded obstacle is used to analyze the performance of the technique with the use of detailed geometric calculations.

Distributed Fault-Tolerant Voltage/Frequency Synchronization in Autonomous AC Microgrids

This paper proposes two new distributed fault-tolerant control (FTC) algorithms for the restoration of voltage and frequency in autonomous inverter-interfaced AC microgrids (MGs). The proposed control schemes give fault-tolerance capability to the system against different potential faults in sensors and actuators. In the first algorithm, a distributed adaptive fault-tolerant method is employed to compensate for the potential sensor faults. Then, to address both sensor and actuator faults simultaneously, a distributed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> protocol is employed to guarantee the voltage and frequency synchronization for the closed-loop system. In the design of these algorithms, there is no need to know the amplitude or any quantitative information about fault signals. Additionally, there is no restriction on the number of faulty units. Finally, to evaluate the performance of the proposed distributed cooperative fault-tolerant control algorithms, simulation studies are carried out on a test MG system, including several distributed generation (DG) units in MATLAB/Simulink environment and also the results are compared with several previously-reported methods. Simulation results and comparison with previous works reveal the effectiveness and accuracy of the proposed method in regulating MG voltage and frequency and providing accurate proportional active power-sharing.

Optimal cooperative control for formation flying spacecraft with collision avoidance.

This article investigates optimal cooperative control algorithms for spacecraft formation flying system that can guarantee collision avoidance between spacecrafts. By selecting potential functions to avoid collisions and constructing index cost functions to describe optimal control, the optimal control algorithms based on the state-dependent Riccati equations, which only require local information vectors, are presented that can guarantee the formation spacecraft to track the reference trajectory without collisions and achieve optimal performance states. Finally, the corresponding proof analysis by Lyapunov stability theory shows that the closed-loop system for spacecraft formation flying is asymptotically stable. The simulation results demonstrate the effectiveness of the proposed optimal cooperative control algorithms with desired control objectives, including trajectory tracking, formation optimality, and collision avoidance.

Simulation on the Effect of Gait Variability, Delays, and Inertia with Respect to Wearer Energy Savings with Exoskeleton Assistance.

Exoskeletons are human-robot interfaces that have enormous potential to assist people with everyday tasks. To improve the design of exoskeletons for use in clinical populations, it is important to further our understanding of how exoskeleton design and control parameters lead to sub-optimal effectiveness. Here we simulated the effect of three factors, gait variability, wearer-exoskeleton delays, and exoskeleton inertia, have on the predicted energy assistance provided by an exoskeleton with a finite-state controller trained on a set of stroke survivors' free walking gait data. Results indicate that larger errors between the wearer's desired ankle trajectory and the exo's estimated ankle trajectory result in statistically large reductions in the actual assistance provided. Specifically lags on the order of even 10 ms can illustrate statistically sub-optimal performance. Likewise subjects that exhibit large gait variability will have a statistical reduction in actual assistance. However, reasonably low exoskeleton inertias are not significant as a factor in terms of sensitivity to wearer assistance. Therefore, to improve cooperative control algorithms for exoskeletons and achieve true assistance based on wearer induced motion, this work implies that designers should prioritize minimizing delays and wearers should train to reduce variability in order to maximize energy savings.

Distributed control of uncertain multiagent systems for tracking a leader with unknown fractional‐order dynamics

SummaryThis paper proposes distributed adaptive cooperative control algorithms for second‐order agents to track a leader with unknown dynamics. The models of the followers and the leader are composed of uncertain nonlinear components. The order of the leader's dynamics is unknown and can be fractional. Only the single output information is shared among neighbored agents. To simplify the control design, linearly parameterized neural networks are used to approximate the unknown functions. We first present an adaptive control for leaderless consensus and then extend the method to the tracking problem. Thorough theoretical proofs as well as numerical simulation are included to verify the results. Compared with relevant literature, the new approach applies to a larger variety of systems because (i) knowledge about the structure of leader's model is unnecessary; (ii) the unknown functions in different agents' dynamics can be diverse and arbitrary, in other words, the algorithms apply to heterogeneous agents; (iii) the results can be simply used without parameter calculations.

Cooperative Deterministic Learning-Based Formation Control for a Group of Nonlinear Uncertain Mechanical Systems

This paper addresses the formation control problem for a group of mechanical systems with nonlinear uncertain dynamics under the virtual leader-following framework. New cooperative deterministic learning-based adaptive formation control algorithms are proposed. Specifically, the virtual leader dynamics is constructed as a linear system subject to unknown bounded inputs, so as to produce more diverse reference signals for formation tracking control. A cooperative discontinuous nonlinear estimation protocol is first proposed to estimate the leader's state information. Based on this, a cooperative deterministic learning formation control protocol is developed using artificial neural networks, such that formation tracking control and locally-accurate nonlinear identification with learning knowledge consensus can be achieved simultaneously. Finally, by utilizing the learned knowledge represented by constant neural networks, an experience-based distributed control protocol is further proposed to enable position-swappable formation control. Numerical simulations using a group of autonomous underwater vehicles have been conducted to demonstrate the effectiveness and usefulness of the proposed results.

An Overview of Recent Advances in Fixed-Time Cooperative Control of Multiagent Systems

Fixed-time cooperative control is currently a hot research topic in multiagent systems since it can provide a guaranteed settling time, which does not depend on initial conditions. Compared with asymptotic cooperative control algorithms, fixed-time cooperative control algorithms can achieve better closed-loop performance and disturbance rejection properties. Different from finite-time control, fixed-time cooperative control produces the faster rate of convergence and provides an explicit estimation of the settling time independent of initial conditions, which is desirable for multiagent systems. This paper aims at presenting an overview of recent advances in fixed-time cooperative control of multiagent systems. Some fundamental concepts about finite- and fixed-time stability and stabilization are first recalled with insight understanding. Then, recent results in finite- and fixed-time cooperative control are reviewed in detail and categorized according to different agent dynamics. Finally, this paper raises several challenging issues that need to be addressed in the near future.

Simple synchronization protocols for heterogeneous networks: beyond passivity

Abstract Synchronization among autonomous agents via local interactions is one of the benchmark problems in multi-agent control. Whereas synchronization algorithms for identical agents have been thoroughly studied, synchronization of heterogeneous networks still remains a challenging problem. The existing algorithms primarily use the internal model principle, assigning to each agent a local copy of some dynamical system (internal model). Synchronization of heterogeneous agents thus reduces to global synchronization of identical generators and local synchronization between the agents and their internal models. The internal model approach imposes a number of restrictions and leads to sophisticated dynamical (and, in general, nonlinear) controllers. At the same time, passive heterogeneous agents can be synchronized by a very simple linear protocol, which is used for consensus of first-order integrators. A natural question arises whether analogous algorithms are applicable to synchronization of agents that do not satisfy the passivity condition. In this paper, we study the synchronization problem for heterogeneous agents that are not passive but satisfy a weaker input feedforward passivity (IFP) condition. We show that such agents can also be synchronized by a simple linear protocol, provided that the interaction graph is strongly connected and the couplings are sufficiently weak. We demonstrate how stability of cooperative adaptive cruise control algorithms and some microscopic traffic flow models reduce to synchronization of heterogeneous IFP agents.

Advantage of cooperative traffic light control algorithms

Based on the presentation at ITS Europe Congress Glasgow 2016. Contemporary traffic light control (TLC) systems rely on sensors for detection of traffic which are costly in purchase, installation and maintenance. Emerging cooperative technology offers an attractive alternative where only one road side unit per intersection is required, instead of several infrastructure sensors per lane. However, studies showed that traffic control with cooperative detection requires a penetration rate of at least 20% to function effectively. To show the potential of cooperative traffic control, this study presents three algorithms: (i) the SWARM control algorithm, which is designed to work with very low penetration rates; (ii) an extension to the adaptive control algorithm, ImFlow, which uses cooperative data for enhanced queue modelling; and (iii) an ImFlow extension to stabilise green planning to enable green light optimal speed advice. The results from micro-simulation show a 7.8% improvement for stops and delay time over traditional adaptive control for SWARM, and 14.9% for Cooperative ImFlow. Adding planning stabilisation reduced the average perceived change for end users from 9.0-2.3%, without performance loss for the overall traffic flow. This shows the large potential of cooperative traffic control.

Formations for Resilient Robot Teams

All cooperative control algorithms for robot teams assume the ability to communicate without considering the possibility of a malicious or malfunctioning robot that may either communicate false information or take wrong actions. This paper addresses the development of formations that enable resilience, the ability to achieve consensus, and to cooperate in the presence of malicious or malfunctioning robots. Specifically, we use the notion of robust graphs to build resilient teams, and focus on the problem of designing robot formations with communication graphs (each edge models a bidirectional communication link) that are robust. We present algorithms to build robust graphs. Given a set of robots and the maximum number of malicious or malfunctioning robots, we are able to 1) state if it is possible to build a resilient team; 2) say what the proximity relationships that enable communication ought to be; 3) construct elemental resilient graphs; and 4) develop a framework for composing resilient teams to build larger resilient teams. We illustrate these algorithms by constructing resilient robot formations in the plane.

Experimental Validation of Distributed Cooperative Control of Multiple Mobile Robots via Local Information Exchange

In this paper, we present the experimental testing results of distributed cooperative control algorithms for multiple mobile robots with limited sensing/communication capacity and kinematic constraints. Rendezvous and formation control problems are considered, respectively. To deal with the inherent kinematic constraints with robot model, the input/output linearization via feedback is used to convert the nonlinear robot model into a linear one, and then the distributed cooperative control algorithms are designed via local information exchange among robots. Extensive experiments using Quanser's QBot2 mobile robot platforms are conducted to validate the effectiveness of the proposed distributed cooperative control algorithms. Specifically, the robot's onboard Kinect vision sensor is applied to solve the localization problem, and the information exchange is done through an ad-hoc peer-to-peer wireless TCP/IP connection among neighboring robots. Collision avoidance problem is also addressed based on the utilization of fuzzy logic rules.

Which impedance strategy is the most effective for cooperative object manipulation?

PurposeThe purpose of this paper is to present a comparison study of cooperative object manipulation control algorithms. To this end, a full comprehensive survey of the existing control algorithms in this field is presented.Design/methodology/approachCooperative manipulation occurs when manipulators are mechanically coupled to the object being manipulated, and the manipulators may not be treated as an isolated system. The most important and basic impedance control (IC) strategies for an assumed cooperative object manipulation task are the Augmented Object Model (AOM) control and the multiple impedance control (MIC) which are found based on the IC, where the former is designed based on the object movement, and the latter is designed based on the whole robot movement. Thus, the basis of these two algorithms are fully studied.FindingsThe results are fully analyzed, and it is practically verified that the MIC algorithm has the better performance. In fact, the results reveal that the MIC system could successfully perform the object manipulation task, as opposed to the AOM controller: for the same controller gains, the MIC strategy showed better performance than the AOM strategy. This means that because there is no control on the robot base with the AOM algorithm, the object manipulation task cannot be satisfactorily performed whenever the desired path is not within the robot work space. On the other hand, with the MIC algorithm, satisfactory object manipulation is achieved for a mobile robotic system in which the robot base, the manipulator endpoints and the manipulated object shall be moved.Practical implicationsA simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.Originality/valueThe basis of these two aspects or these two algorithms is fully studied and compared which is the foundation of this paper. For this purpose, a case study is considered, in which a space free-flying robotic system, which contains two 2-degrees of freedom planar cooperative manipulators, is simulated to manipulate an object using the above control strategies. The system also includes a rotating antenna and camera as its third and fourth arm. Finally, a simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.

Small Noise May Diversify Collective Motion in Vicsek Model

Natural systems are inextricably affected by noise. Within recent decades, the manner in which noise affects the collective behavior of self-organized systems, specifically, has garnered considerable interest from researchers and developers in various fields. To describe the collective motion of multiple interacting particles, Vicsek et al. proposed a well-known self-propelled particle (SPP) system, which exhibits a second-order phase transition from disordered to ordered motion in simulation; due to its non-equilibrium, randomness, and strong coupling nonlinear dynamics, however, there has been no rigorous analysis of such a system to date. To decouple systems consisting of deterministic laws and randomness, we propose a general method which transfers the analysis of these systems to the design of cooperative control algorithms. In this study, we rigorously analyzed the original Vicsek model under both open and periodic boundary conditions for the first time, and developed extensions to heterogeneous SPP systems (including leaderfollower models) using the proposed method. Theoretical results show that SPP systems switch an infinite number of times between ordered and disordered states for any noise intensity and population density, which implies that the phase transition indeed takes a nontraditional form. We also investigated the robust consensus and connectivity of these systems. Moreover, the findings presented in this paper suggest that our method can be used to predict possible configurations during the evolution of complex systems, including turn, vortex, bifurcation and flock merger phenomena as they appear in SPP systems.

Dynamic Characteristics in Cooperative Admittance Control of a Multi-Finger-Arm Robot with Finger Manipulability Control

Cooperative control algorithms for a finger-arm robot composed of a 3-DOF finger and 6-DOF arm have previously been proposed based on the movement of the human hand-arm system. In these algorithms, a finger-arm robot completes a constrained task by integrating admittance and impedance control with manipulability control of the finger. The finger manipulability is controlled both by an approximate global search, i.e., the top search method, and by a local optimization method. In this paper, these methods are developed further by relating them to dynamic movement. We also discuss the feasibility of virtual dynamics and clarify the dynamic characteristics in terms of the control algorithms. The dynamic characteristics are verified with virtual external force as shown in experiments with a redundant multi-finger-arm robot. Experimental results are presented for a multi-finger-arm robot to simultaneously achieve manipulability control and admittance control in a three-dimensional space. The developed methods responded to more dynamic movement.

Low-cost Multi-UAV Technologies for Contour Mapping of Nuclear Radiation Field

Low cost UAVs are becoming more and more popular in both research and practical applications, and it leads to a new, potentially significant service product known as UAV-based personal remote sensing (PRS). Multi-UAV system with advanced cooperative control algorithms has advantages over single UAV system, especially in time urgent tasks such as detecting nuclear radiation before deploying the salvage. This paper considers two scenarios for nuclear radiation detection using multiple UAVs, of which contour mapping of the nuclear radiation is simulated. Then, for real applications, this paper presents a low-cost UAV platform with built-in formation flight control architecture together with a formulated standard flight test routine. Three experimental formation flight scenarios that imitate the nuclear detection missions are prepared for contour mapping of nuclear radiation field in 3D space.

Mapping and Tracking

This article presents the implementation of decentralized data fusion (DDF) and cooperative control algorithms on an unmanned aerial system (UAS).We conduct a number of demonstrations with a pair of unmanned aerial vehicles (UAVs) performing an information-gathering mission, and we show that significant benefits can be achieved by enabling cooperation through the sharing of information between members of the team. The objective is to utilize the UAV team to estimate the position and velocity of a number of ground-based features. The UAVs are given some prior knowledge of the feature states and are required to gather further information above a predefined threshold. This situation models a scenario where initial information is made available from an external source (e.g., a high-flying UAV or satellite imagery), which then prompts the start of the feature-localizationmission.