Cooperative Control Algorithm Research Articles

Unmanned Floating Object Transportation Applying Multiple Dynamic Positioning Tugboats: A Novel Cooperative Control Algorithm

Abstract To promote the automation and intelligence of marine floating object transportation, this paper proposes a cooperative consensus control algorithm for multiple unmanned dynamic positioning (DP) tugboats to transport an unactuated floating structure object using cables. The major concept of present algorithm is to implement a leader-follower formation strategy among distributed tugboats, with the aim of effectively maneuvering the object by adjusting the relative position between the virtual leader and the object in a reasonable manner. A cooperative consensus controller is integrated into the feedback controller of each tugboat local DP system in order to facilitate cooperative coordination among tugboats in a formation. A customized DP system, which incorporates the radial basis function neural network supervisor control into the DP controller design and introduces nonlinear disturbance observer into the low-frequency motion state observer design, is proposed to address cable tension disturbances, and thus, the tracking performance of the DP tugboat is improved. Full-scale nonlinear time domain simulations are performed under variable environmental conditions, verifying the effectiveness of the proposed automatic towing control scheme.

Distributed bounded finite‐time cooperative control algorithm for multiple nonlinear manipulators

AbstractFor the position synchronization control problem of multiple nonlinear manipulator systems, a bounded distributed cooperative controller is designed in this paper. Based on a fixed‐time position observer, a distributed finite‐time synchronization controller is designed by using the saturated control technique and homogenous system theory. Compared with the existing results, the proposed distributed cooperative control algorithm not only guarantees finite‐time convergence but also satisfies the input saturation. Finally, some numerical simulation results are presented to verify the feasibility of the theory results with six‐degree‐of‐freedom robots.

Distributed constrained control for multi‐train systems with multiple cars

AbstractIn practical multi‐train systems, there are four different kinds of necessary nonlinearities cause by the interactive forces between adjacent cars, the nonlinear resistances, the input constraints, and the velocity constraints. However, most of the existing works on distributed control for multi‐train systems only focused one or two kinds of these nonlinearities. To the best of our knowledge, there are only a few works considering three kinds of these nonlinearities. In this paper, the effects of control input and velocity constraints, interactive forces between adjacent cars and the train running resistance on the system are studied, and a distributed cooperative control algorithm based on the state information of adjacent cars is proposed. The analysis is given based on model transformations, appropriate parameter selection and convex analysis. It is proved that all trains finally run in a given formation even when the inputs and velocities of all trains are constrained to lie in some given sets. Numerical simulation examples are presented to demonstrate the theoretical conclusions.

Event-Triggered Cooperative Model-Free Adaptive Iterative Learning Control for Multiple Subway Trains With Actuator Faults.

This article investigates the issue of speed tracking and dynamic adjustment of headway for the repeatable multiple subway trains (MSTs) system in the case of actuator faults. First, the repeatable nonlinear subway train system is transformed into an iteration-related full-form dynamic linearization (IFFDL) data model. Then, the event-triggered cooperative model-free adaptive iterative learning control (ET-CMFAILC) scheme based on the IFFDL data model for MSTs is designed. The control scheme includes the following four parts: 1) the cooperative control algorithm is derived by the cost function to realize cooperation of MSTs; 2) the radial basis function neural network (RBFNN) algorithm along the iteration axis is constructed to compensate the effects of iteration-time-varying actuator faults; 3) the projection algorithm is employed to estimate unknown complex nonlinear terms; and 4) the asynchronous event-triggered mechanism operated along the time domain and iteration domain is applied to lessen the communication and computational burden. Theoretical analysis and simulation results show that the effectiveness of the proposed ET-CMFAILC scheme, which can ensure that the speed tracking errors of MSTs are bounded and the distances of adjacent subway trains are stabilized in the safe range.

Multi-UAV Cooperative Obstacle Avoidance of 3D Vector Field Histogram Plus and Dynamic Window Approach

In this paper, we propose a fusion algorithm that integrates the 3D vector field histogram plus (VFH+) algorithm and the improved dynamic window approach (DWA) algorithm. The aim is to address the challenge of cooperative obstacle avoidance faced by multi-UAV formation flying in unknown environments. First, according to the navigation evaluation function of the standard DWA algorithm, the target distance is introduced to correct the azimuth. Then, aiming at the problem that the fixed weight mechanism in standard DWA algorithm is unreasonable, we combine the A* algorithm and introduce variable weight factors related to azimuth to improve the target orientation ability in local path planning. A new rotation cost evaluation function is added to improve the obstacle avoidance ability of two-dimensional UAV. Then, 3D VFH+ algorithm is introduced and integrated with improved DWA algorithm to design a distributed cooperative formation obstacle avoidance control algorithm. Simulation validation suggests that compared with the traditional DWA algorithm, the improved collaborative obstacle avoidance control algorithm can greatly optimize the obstacle avoidance effect of UAVs’ formation flight.

Centralized model‐predictive cooperative and adaptive cruise control of automated vehicle platoons in urban traffic environments

AbstractRecent research has demonstrated that cooperative and automated driving functions can improve traffic efficiency and safety. Traffic efficiency can be increased and the overall vehicle energy consumption can be reduced through cooperative driving functions not only on the highway, but also in urban areas. To this end, this paper presents a linear model predictive control (MPC) algorithm for the optimization of velocity trajectories of vehicles in a platoon in an urban traffic environment. A novel centralized approach is implemented to optimize the total energy consumption of all vehicles while improving traffic efficiency. To solve the optimal control problem, for the first time, an additional data‐driven velocity prediction model of the vehicle in front of the platoon is used to consider the influence of realistic prediction errors when evaluating the developed control strategy in a vehicle simulation environment. The simulated total energy consumption is compared with a rule‐based cooperative adaptive cruise control (CACC) algorithm. Furthermore, the traffic density and the platoon string stability, the robustness and computer time of the centralized approach are also included in the evaluation. Simulation results indicate that, considering all evaluation criteria, the centralized cooperative control strategy can improve energy efficiency by up to 15% in the investigated urban scenario.

A platoon-based cooperative optimal control for connected autonomous vehicles at highway on-ramps under heavy traffic

To improve traffic efficiency at highway on-ramps under heavy traffic, this study proposes a platoon-based cooperative optimal control algorithm for connected autonomous vehicles (CAVs). The proposed algorithm classifies CAVs on both mainline and on-ramp into multiple local platoons (LPs) according to their initial conditions (i.e., spacing and speed), which enables the algorithm to adapt to time-varying traffic volume. A distributed cooperative control for multiple LPs is designed which projects on-ramp LPs onto mainline to transform the complex 2-D multi-platoon cooperation problem into a 1-D platoon following control problem. An optimal control is applied to further consider the strict nonlinear safety spacing constraint and state limitations (e.g., maximum speed and acceleration), and an analytical solution to the optimal control is derived based on Pontryagin’s maximum principle. The consensus of intra-platoon and inter-platoon are analyzed, and sufficient conditions of the consensus are mathematically deducted based on Lyapunov stability theorem. Numerical simulations are conducted for different traffic demand levels and demand splits to verify the effectiveness of the proposed algorithm. The sensitivity analysis of maximum platoon sizes for mainline and on-ramp LPs is performed. A comparison with a baseline virtual platooning merging strategy is conducted, and results show that the proposed algorithm could significantly improve the average travel speed and traffic efficiency, and reduce total travel time.

Cooperative distributed model predictive control for robot in-hand manipulation

PurposeThis study aims to process multi-agent system with kinds of limitations and constraints, and consider the robot in-hand manipulation as a problem of coordination and cooperation of multi-fingered hand.Design/methodology/approachA cooperative distributed model predictive control (MPC) algorithm is proposed to perform robot in-hand manipulation.FindingsA cooperative distributed MPC approach is formulated for robot in-hand manipulation problem, which enables address complex limitation and constraint conditions in object motion planning, and realizes tracking trajectory of the object more than tracking position of the object.Originality/valueThis method to implement the moving object task uses the kinematic parameters without the knowledge of dynamic properties of the object. The cooperative distributed MPC scheme is designed to guarantee the movement of the object to a desired position and trajectory at algorithmic level.

Collision-Free Distributed Control for Multiple Quadrotors in Cluttered Environments With Static and Dynamic Obstacles

To ensure robust flight of multiple quadrotors in unknown environments, this letter proposes a distributed cooperative control algorithm with a separation-merge mechanism to solve the problem of safe flight and formation reconfiguration. The separation-merge framework integrates the obstacle avoidance mechanism, inter-robot collision avoidance mechanism, and formation reconfiguration mechanism, and couples them all to the control layer. Obstacle detection mechanism and velocity adjustment mechanism are designed based on the obstacle avoidance behavior of the pigeons. And a rotational potential energy is introduced to solve the case of local minima and oscillations where the rotation angle can be changed in real-time according to the relative position. Inspired by spring damping system, a collision avoidance mechanism is designed with the help of Hooke's law with damping. A formation reconfiguration mechanism is designed based on consensus theory, which enables multiple quadrotors to quickly return to the prescribed formation after obstacle avoidance. The proposed method is integrated into a multi-quadrotor system and its effectiveness is verified in the visualization simulator and outdoor experiments.

Research on a Cooperative Adaptive Cruise Control (CACC) Algorithm Based on Frenet Frame with Lateral and Longitudinal Directions.

Research on the cooperative adaptive cruise control (CACC) algorithm is primarily concerned with the longitudinal control of straight scenes. In contrast, the lateral control involved in certain traffic scenes such as lane changing or turning has rarely been studied. In this paper, we propose an adaptive cooperative cruise control (CACC) algorithm that is based on the Frenet frame. The algorithm decouples vehicle motion from complex motion in two dimensions to simple motion in one dimension, which can simplify the controller design and improve solution efficiency. First, the vehicle dynamics model is established based on the Frenet frame. Through a projection transformation of the vehicles in the platoon, the movement state of the vehicles is decomposed into the longitudinal direction along the reference trajectory and the lateral direction away from the reference trajectory. The second is the design of the longitudinal control law and the lateral control law. In the longitudinal control, vehicles are guaranteed to track the front vehicle and leader by satisfying the exponential convergence condition, and the tracking weight is balanced by a sigmoid function. Laterally, the nonlinear group dynamics equation is converted to a standard chain equation, and the Lyapunov method is used in the design of the control algorithm to ensure that the vehicles in the platoon follow the reference trajectory. The proposed control algorithm is finally verified through simulation, and validation results prove the effectiveness of the proposed algorithm.

A Cooperative Control Algorithm for Line and Predecessor Following Platoons Subject to Unreliable Distance Measurements

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.

Third-order consensus for robust distributed formation control of double integrator vehicles

This paper presents the development of a robust distributed and cooperative control algorithm for formation tracking by teams of vehicles modeled as double integrators, and exemplifies its use through the application to multirotor vehicles. To achieve coordination between the vehicles in a distributed manner, in the sense that only local information is either exchanged or perceived, consensus-based protocols are considered. Consensus protocols for agents modeled with up to three integrators are presented, and the third-order protocol is analyzed under any time-invariant sensing or communication topology. The analysis seeks to determine exact bounds on the coupling gains of the protocol that lead to convergence. To that end, an extension of the Routh–Hurwitz criterion to polynomials with complex coefficients is used, leading to novel necessary and sufficient conditions for convergence. Moreover, these novel bounds turn out to be a generalization of the ones described in the literature for second-order consensus, as the latter can be recovered as a special case. The effect of disturbances acting upon the agents is also analyzed and related to their ability to achieve robust consensus in the sense that bounded disturbances do not lead to instability. Further results are derived for the case of constant disturbances, a special case that is particularly relevant for formation control. The third-order consensus protocol is then explored to incorporate integral action in a formation tracking controller used for double integrator vehicles and thereby enable constant disturbance rejection. Finally, experiments resulting from the application of the proposed control algorithms to multirotor vehicles are presented, in order to validate the analysis and demonstrate the usefulness of this approach.

Distributed and Scalable Cooperative Formation of Unmanned Ground Vehicles Using Deep Reinforcement Learning

Cooperative formation control of unmanned ground vehicles (UGVs) has become one of the important research hotspots in the application of UGV and attracted more and more attention in the military and civil fields. Compared with traditional formation control algorithms, reinforcement-learning-based algorithms can provide a new solution with a lower complexity for real-time formation control by equipping UGVs with artificial intelligence. Therefore, in this paper, a distributed deep-reinforcement-learning-based cooperative formation control algorithm is proposed to solve the navigation, maintenance, and obstacle avoidance tasks of UGV formations. More importantly, the hierarchical triangular formation structure and the newly designed Markov decision process for UGV formations of leader and follower attributes make the control strategy learned by the algorithm reusable, so that the formation can arbitrarily increase the number of UGVs and realize a more flexible expansion. The effectiveness and scalability of the algorithm is verified by formation simulation experiments of different scales.

Fixed-Time Output Feedback Distributed Cooperative Event-Triggered Control for Multiple Surface Vessels With Prescribed Performance Constraints

This paper studies the distributed cooperative control problem for multiple surface vessels (MSVs) subject to unknown environmental disturbances, model uncertainties, unavailable velocities and prescribed performance constraints. Firstly, a fixed-time extended state observer (FxESO) is designed to provide the estimations of velocities and lumped disturbances (including unknown environmental disturbances and model uncertainties). Secondly, to improve the convergence performance of the MSVs, a hyperbolic cosecant prescribed performance function is incorporated into the cooperative control algorithm. Thirdly, a fixed-time event-triggered control law with prescribed performance constraint is applied to cooperative control based on a fixed-time nonsingular terminal sliding mode manifold (FxNTSMM), and the cooperative errors can converge within fixed time. Finally, by employing Lyapunov function theory, the stability of the closed-loop system is analyzed. Simulation results are given to demonstrate the effectiveness of the proposed control scheme.

Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning

Multi-cell cooperative control can be competent for the current increasingly complex biomedical experiments, greatly improving the efficiency of cell manipulation experiments. At present, this kind of multi-cell cooperative control algorithm is becoming more and more important. In this study, holographic optical tweezers are used to capture multiple cells, and a cell manipulation controller is designed based on the Fuzzy Brain Emotional Learning (FBEL) neural network. Firstly, the dynamic model of trapping yeast cells by optical tweezers is analyzed. The distance between the trap position and the cell position is constrained to avoid cell detachment due to the trap moving too fast. Then, the design cell manipulation controller is relied upon to realize single transport trajectory tracking control. Finally, a multi-cell cooperative control algorithm is designed, and combined with the cell manipulation controller, a multi-cell cooperative controller based on the FBEL neural network is formed. The error between the cell position and the desired trajectory is the input of the multi-cell cooperative controller. The output of the multi-cell cooperative controller is the optical trap position, which is used to realize the cooperative control of multiple cells by holographic optical tweezers. The simulation results showed that the multi-cell cooperative controller based on the FBEL neural network can effectively control multiple yeast cells and quickly converge the cell formation, while ensuring a higher control accuracy than other traditional cell manipulation controllers. It provides a new solution for the efficient and precise automatic manipulation of multiple cells.

Dynamic game coverage control algorithms for multiple mobile agents through virtual repulsive force

To improve the coverage efficiency of mobile agents, the related information of the agents is generally required partially or completely during the coverage control process, which may lead to a dramatic control cost and energy consumption increase. How to balance the improvement in coverage rate and the reduction of control cost is an important issue in the coverage control area of mobile agents. This paper addresses the dynamic cooperative game coverage control algorithm design problem of a multi-agent system under an incomplete information condition. First, by separating the moving multi-agent system into several subgroups, where each agent can only obtain the position information of the neighbor agents, the coverage control problem of the moving multi-agent system is transformed into a dynamic cooperative game coverage problem of moving multi-groups under incomplete information conditions. Then, the cooperative game rules of the subgroups are presented, and a virtual repulsive force-based dynamic coverage game decision strategy of the agent system is developed to compute the new candidate positions of the agents. Third, the moving multi-agent system is considered a rigid virtual structure, and the coverage control problem of the system is turned into a new one with the virtual structure as a reference frame. Thus, the displacement path planning design of the coverage control for a multi-agent system is simplified. Simulation results reveal that the dynamic cooperative game coverage algorithm based on the virtual repulsive force can effectively realize the coverage control requirement of the moving agent system under the incomplete information condition and can remarkably reduce the cost of coverage control. In addition, compared with several existing coverage algorithms, using the dynamic cooperative game coverage algorithm proposed in this paper requires fewer moving times of agents while obtaining a higher coverage rate.

Design and Testing of Cooperative Motion Controller for UAV-UGV System

Unmanned ground vehicles (UGVs) and quadrotor unmanned aerial vehicles (UAVs) can work together to solve challenges like intelligent transportation, thanks to their excellent performance complements in perception, loading, and endurance. This study presents a UAV-UGV system cooperative control mechanism. To achieve collaborative trajectory tracking, the leader-follower strategy based on a centralized control structure is firstly established in conjunction with the application scenario. The fuzzy robust controller is created to control the quadrotor UAV and improve attitude stability. Meanwhile, the UGV's controller uses the pure pursuit algorithm and a proportional integral derivative (PID) controller. In order to evaluate the cooperative control strategy and algorithm, the UAV-UGV experimental platform is set up based on the QDrone and QCar, and the experimental results show the viability of the suggested plan.

Cooperative Constrained Control of Autonomous Vehicles With Nonuniform Input Quantization

Responsive and highly controlled autonomous vehicles (AVs) on the road easily form platoons with small inter-vehicle gaps to increase traffic throughput and reduce energy consumption. Unlike human drivers, AVs typically employ discrete quantization signals and only generate finite discrete acceleration. Finite discrete acceleration easily causes unstable inter-vehicle gaps and inter-vehicle collisions, especially when there are inaccurate quantization parameters due to quantizer aging. Based on the backstepping technique, this paper proposes the cooperative constrained control algorithm under unknown quantization parameters for AVs with nonuniform input quantization and motion uncertainties. The control laws of each AV in the platoon only depend on the state information of its consecutive AVs. The position trajectories of consecutive AVs are constructed as inter-vehicle collision-free constraints to avoid emergency braking and inter-vehicle collisions. In addition, the consistent characteristic of two types of nonuniform quantizers (i.e., logarithmic quantizers and hysteresis quantizers) is proposed, and unknown disturbances in vehicle motion are compensated. The Lyapunov based local stability and string stability are proved. Numerical simulation shows that the control algorithm is applicable to AVs with either logarithmic quantizers or hysteresis quantizers. Compared with the previous control algorithms without considering input quantization and inter-vehicle collision avoidance, the control algorithm proposed reduces inter-vehicle spacing errors, avoids high acceleration, and avoids inter-vehicle collisions.

Distributed Cooperative Control of Multiple UAVs in the Presence of Actuator Faults and Input Constraints

A distributed cooperative control algorithm of multiple unmanned aerial vehicles (UAVs) with directed topology is investigated in this brief. To overcome the actuator faults and external disturbance, incremental sliding mode formation controller is developed to realize the distributed coordination of the UAVs system, where an adaptive mechanism is utilized to deal with the chattering problem. Moreover, to reduce the adverse influence of input saturation, an auxiliary system is introduced to regulate the control signals. It is shown by Lyapunov method that the stability of the proposed algorithm can be guaranteed. A numerical example is given to illustrate that all follower UAVs can track the leader UAV.

Double-layer AGC frequency regulation control method considering operating economic cost and energy storage SOC consistency

Aiming at the problem of power grid frequency regulation caused by the large-scale grid connection of new energy, this paper proposes a double-layer automatic generation control (AGC) frequency regulation control method that considers the operating economic cost and the consistency of the state of charge (SOC) of the energy storage. At the regional control level, an economically optimized dynamic frequency regulation responsibility distribution between the unit and the energy storage is realized, and the idle time of energy storage is fully used for SOC management to effectively suppress the fluctuation of the energy storage SOC. At the energy storage station level, the cooperative control algorithm is used to ensure that the output and SOC of each energy storage unit are consistent, respectively. The effectiveness of the method is verified by establishing the dynamic model of the unit-storage combined frequency regulation of the regional power grid for simulation and comparison experiments. The method proposed in this paper considers the influence of different disturbance conditions on the AGC frequency regulation responsibility distribution between the unit and the energy storage and fully uses the frequency regulation characteristics of the energy storage's rapid and flexible response. It effectively improves the service life of energy storage and the comprehensive operation efficiency of the system while optimizing the frequency regulation operation cost, improving the frequency regulation performance, and increasing the frequency regulation compensation income.