Overview of Consensus Algorithms in Cooperative Control

In this paper, we study cooperative control problems with a multi-UAV system. Specifically, we propose a c consensus-based cooperative formation control strategy with collision-avoidance capability for a group of multiple unmanned aerial vehicles (UAVs). To cooperatively fly in formation, a consensus-based algorithm and a leader-follower structure are applied to the UAVs. Collisions among the UAVs can occur while they are flying with the cooperative control algorithm. Therefore, we describe how the cooperatively controlled UAVs avoid collisions while they are flying to desired position for formation. The collision-avoidance strategy is based on an artificial potential approach. The convergence is guaranteed even when the cooperative formation control algorithm and the collision-avoidance control algorithm are simultaneously applied to the quadrotors. Finally, the proposed approach is validated by some simulations.

Cooperative control in multi-agent systems involves managing a group of agents that collaborate to accomplish a shared task. This approach has significant potential across various applications, including environmental monitoring, distributed aperture observing, traffic management, and more. A key technology employed in cooperative control is the consensus algorithm, which guides all agents to reach a common decision via communication. This dissertation aims to develop event-triggered consensus algorithms for linear multiagent systems (MASs) under general directed network. Under proposed algorithms, continuous information transmission and control input update are avoided so that unnecessary consumption of communication and computation resources can be reduced. In practical scenarios, agents may lack access to their complete state information as well as the overall network data. Building on the developed event-triggered consensus algorithm, an observer is designed to estimate each agent's state information, while an adaptive control law is utilized to infer the unknown aspects of the network. This allows the consensus algorithm to be entirely dependent on local state information, making it fully distributed. Using a developed extension method, these algorithms are first formulated for strongly connected networks and then adapted for general directed networks that contain a directed spanning tree.

This paper proposes a collaborative control system to be designed for multi-UAV. This makes it easy to perform many tasks at the same time and with high accuracy. Therefore, this cooperative control and guidance subsystems of the aircraft should have robust performance against sensors noise and wind disturbances. Four types of control algorithms were designed for a single Aerosonde UAV autopilot. This is to pick up which control algorithm is the best. As such, this control algorithm is proposed to be designed for the cooperative flight control system. Two classical control algorithms and two intelligent control algorithms have been proposed for the autopilot design of a single Aerosonde UAV. The first classical controller proposed is genetically tuned PID, while the second classical controller proposed is the fractional order PID. The first intelligent controller proposed for autopilot system is the Fuzzy logic controller known as FLC, while the second intelligent controller proposed is the adaptive neuro fuzzy inference system known as ANFIS. The proposed control algorithms have been applied to the nonlinear multivariable system of Aerosonde UAV. The analysis of simulation results assure that ANFIS is the best performance and the most robust control algorithm proposed. As such, ANFIS controller has been selected to be the cooperative flight controller system either in the low-level of a single UAV and in the top-level of multi-UAVs. Sometimes, classical controllers are preferred because of their simplicity in design. If this is the case, the simulation results assure that the genetically tuned fractional order PID controller- which proposed here for the first time with UAVs- is better than genetically tuned PID.

Simultaneous arrival of multiple UAVs is one of the classic applications in cooperative control. In this paper, the expected time of arrival (ETA) is selected as the coordination variable, and the consensus algorithm is used to realize the air aggregation and simultaneous arrival of multiple UAVs. Based on the comprehensive consideration of UAV speed constraints, acceleration constraints and target motion model, according to the different starting point, flight direction and distance, the control algorithm is simulated and verified. The simulation results show that the algorithm is effective, robust and flexible.

With the increasing energy consumption in urban rail transit systems, energy-efficient train operation has been paid significant attention. Considering the application of regenerative braking technology, many studies focus on offline energy-saving train trajectory optimization, in which an accelerating regime is inserted into the train trajectory when there is regenerative braking energy (RBE) that can be utilized. However, in practical operation, train states are dynamic and scheduled trajectory might be useless. This paper proposes a real-time cooperative train control method based on Train-to-Train (T2T) communication technology. According to the train states transmitted through T2T communication, whether there is a train in braking regime is judged. Besides, an accelerating regime is inserted by changing train running modes when there is another train in the braking regime. A cooperative control algorithm is developed to achieve the energy-efficient cooperative control strategy. Besides, simulations based on a real-life metro line demonstrate that the proposed cooperative control method can reduce substation energy consumption and improve the utilization of RBE.

A consensus algorithm for coordinated control of unmanned surface vehicles (USVs) is studied. The USVs have different driving directions and initial velocities, so the consensus algorithm is needed to coordinate the directions and velocities of the USVs. Firstly, based on the first order linear model of USVs, get the first order nonlinear differential equations of high frequency, and the simulate environment of USVs. Then, based on the Vicsek model, the consensus algorithm in cooperative control of USVs is proposed. Aiming at the problem that USV's angular velocity lags behind the rudder angle, this paper improves the traditional PD control method, and adopts cascade PID control. So, USV can reach the same driving direction in a shorter time. Finally, simulations prove that the algorithm is feasible and compare the difference of efficiency between the PD controller and the cascade PID controller.

The stable operation of the integrated energy system (IES) is affected by the fluctuation of heat or electricity loads, energy or communication problems, and the interaction of different energy networks. To overcome above challenges, firstly, the integrated dynamic models with three-time scales are established for control design, fully considering dynamic behaviors, energy coupling, and constraints of power flow including electrical, heat and gas on network stability. Then, a novel distributed model predictive control (DMPC) consensus algorithm with process control and trajectory control is proposed to achieve stable operation for IES based on second-order cone programming (SOCP). A unified distributed cooperative control mode is formed in IES by implementing the proposed stability control method, which is suitable for different forms of energy. The control design takes into account the different dynamic characteristics of energy equipment and the energy coupling feature, which realizes the multi-scale time cooperation and dynamic process control for IES by tuning the control parameters. The optimal solution is obtained by rolling optimization for achieving frequency/voltage/temperature/pressure/flow regulation and consensus objectives. Finally, the proposed model and method are validated in various simulation scenarios, such as load variation, energy flow variation, nodes removed, and topology change.

General Framework using Affine Transformations to Formation Control Design

Around the cooperative path-following control for the underactuated surface vessel (USV) and the unmanned aerial vehicle (UAV), a logic virtual ship-logic virtual aircraft (LVS-LVA) guidance principle is developed to generate the reference heading signals for the USV-UAV system by using the “virtual ship” and the “virtual aircraft”, which is critical to establish an effective correlation between the USV and the UAV. Taking the steerable variables (the main engine speed and the rudder angle of the USV, and the rotor angular velocities of the UAV) as the control input, a robust adaptive neural cooperative control algorithm was designed by employing the dynamic surface control (DSC), radial basic function neural networks (RBF-NNs) and the event-triggered technique. In the proposed algorithm, the reference roll angle and pitch angle for the UAV can be calculated from the position control loop by virtue of the nonlinear decouple technique. In addition, the system uncertainties were approximated through the RBF-NNs and the transmission burden from the controller to the actuators was reduced for merits of the event-triggered technique. Thus, the derived control law is superior in terms of the concise form, low transmission burden and robustness. Furthermore, the tracking errors of the USV-UAV cooperative control system can converge to a small compact set through adjusting the designed control parameters appropriately, and it can be also guaranteed that all the signals are the semi-global uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed algorithm has been verified via numerical simulations in the presence of the time-varying disturbances.

This paper presents a novel cooperative control algorithm for vehicle-manipulators (VMs) with a human operator. VMs usually operate in unstructured environments, which means that a full automation of the overall system, combing a vehicle and a robotic manipulator is currently very challenging. Therefore, human operator controlled VMs are state-of-the-art. With current developments in autonomous driving, the automation of the vehicle platform is within reach. A cooperative shared control between the autonomous platform and the human controlled manipulator can happen through the coupling motion between the vehicle platform and the manipulator. An autonomous vehicle platform can furthermore be used to support the human operator with the control of the manipulator. However, the future trajectory of the manipulator intended by the human operator is in general not known to the autonomous vehicle. The main question is thus how the autonomous vehicle should act in order to support the human controlled manipulator in following its unknown trajectory. To solve this problem, we propose an approach that characterizes the cooperation and the unknown errors with an algebraic equation. The novel approach is compared to cooperative control methods with known errors of the manipulator, based on the theory of differential games. The benefits of the proposed method are that no sensors for the environment perception and for the state measurements of the manipulator are necessary, which are demonstrated in simulations.

In this study, an improved cooperative integrated guidance and control (IGC) design method is proposed based on distributed networks to address the guidance and control problem of multiple interceptor missiles. An IGC model for a leading interceptor is constructed based on the relative kinematic relations between missiles and a target and the kinematic equations of the missiles in a pitch channel. The unknown disturbances of the model are estimated using a finite-time disturbance observer (FTDO). Then, the control algorithm for the leading interceptor is designed according to the disturbance estimation and nonsingular fast dynamic surface sliding mode control (SMC). To enhance the rate of convergence of the cooperative control commands for the interceptors, an improved cooperative control strategy is proposed based on the leader-follower distributed network. Consequently, the two velocity components of the interceptor in the pitch channel can be obtained, which are subsequently converted to the total velocity and flight path angle commands of the interceptor using kinematic relations. The control algorithm for the following interceptor is similarly designed using an FTDO and dynamic surface SMC. The effectiveness of the improved distributed cooperative control strategy for multiple interceptors is validated through simulations.

Contemporary traffic light control 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.

Image-guided radiation therapy (IGRT) involves two main procedures, performed in different rooms on different days: (1) treatment planning in the simulator room on the first day, and (2) radiotherapy in the linear accelerator room over multiple subsequent days. Both the simulator and the linear accelerator include CT imaging capabilities, which enables both treatment planning and reproducible patient setup, but does not provide good soft tissue contrast or allow monitoring of the target during treatment. We propose a cooperatively-controlled robot to reproducibly position an ultrasound (US) probe on the patient during simulation and treatment, thereby improving soft tissue visualization and allowing real-time monitoring of the target. A key goal of the robotic system is to produce consistent tissue deformations for both CT and US imaging, which simplifies registration of these two modalities. This paper presents the robotic system design and describes a novel control algorithm that employs virtual springs to implement guidance virtual fixtures during "hands on" cooperative control.

This paper uses a line-following approach to study the longitudinal and lateral problems in vehicle platooning. Under this setup, we assume that inter-vehicle distance sensing is unreliable and propose a cooperative control strategy to render the platoon less vulnerable to these sensing difficulties. The proposed control scheme uses the velocity of the predecessor vehicle, communicated through a Vehicle-to-Vehicle technology, to avoid significant oscillations in the local speed provoked by tracking using unreliable local distance measurements. We implement the proposed control algorithm in the RUPU platform, a low-cost experimental platform with wireless communication interfaces that enable the implementation of cooperative control schemes for mobile agent platooning. The experiments show the effectiveness of the proposed cooperative control scheme in maintaining a suitable performance even when subject to temporal distortions in local measurements, which, in the considered experimental setup, arise from losing the line-of-sight of the local sensors in paths with closed curves.

Aiming at the performance bottleneck problem and insufficient scalability caused by consensus algorithm in supply chain financing application scenarios, this paper proposes a Multi-Raft consensus algorithm for improving the Raft consensus algorithm. In the Multi-Raft consensus algorithm, we introduce the contractor election algorithm. Contractor nodes are selected by the KNN algorithm in machine learning. The optimized Multi-Raft consensus algorithm is applied to the Easy-Visible supply chain financing platform for testing. By analyzing the experimental results, it can be seen that the optimized Multi-Raft consensus algorithm has significant improvements in throughput, delay and scalability compared with the original Raft consensus algorithm. The research in this paper solves the problems of performance bottleneck and low scalability of the consensus algorithm of the alliance chain in practical applications. A solution is proposed for the large-scale implementation of the enterprise-level alliance chain. It has certain engineering value and application value.