Formation Control Research Articles

Distributed Robust UAVs Formation Control Based on Semidefinite Programming

Distributed Robust UAVs Formation Control Based on Semidefinite Programming

Minimum Time Formation Control of AUVs With Smooth Transition in Communication Topology

Minimum Time Formation Control of AUVs With Smooth Transition in Communication Topology

Time-Varying Formation Control of Autonomous Surface Vehicles Based on Affine Observer

Time-Varying Formation Control of Autonomous Surface Vehicles Based on Affine Observer

Adaptive Imune Fuzzy Quasi-Sliding Mode Formation Tracking Control for Wheeled Mobile Robots with Obstacle Avoidance Under Incidence of Uncertainties and Disturbances: An Artificial Immune Systems Inspired Approach

This paper presents an adaptive immune fuzzy quasi-sliding mode kinematic control integrated with a PD dynamic control for the trajectory tracking and the leader-follower formation control by nonholonomic differential-drive wheeled mobile robots under incidence of uncertainties and disturbances. An immune regulation mechanism bio-inspired approach with reaction effect established by novel fuzzy rules set to adjust the control effort adaptively is designed, also using a fuzzy boundary layer and introducing an adaptation law for the immune portion gain online adjustment in such a way that they can also avert parameter drift, dealing with the drawbacks of a classic first-order sliding mode control, suppressing chattering and still maintaining the robustness with no a priori knowledge of the bounds of the disturbances. An obstacle avoidance strategy with a reactive method and variable avoidance radius is also proposed. The stability analysis is performed based on the Lyapunov theory. Simulation results demonstrate the proposed control effectiveness.

Adaptive formation control for obstacle avoidance of USVs with asymmetric input saturation

Due to the complex maritime navigation environment, Unmanned Surface Vessels (USVs) are influenced by unknown nonlinear dynamics arising from external disturbances and internal uncertainties. Achieving effective formation control while maintaining obstacle avoidance performance presents significant challenges. This article proposes a Neural Networks (NNs) adaptive formation Artificial Potential Field (APF) obstacle avoidance control method for multiple USVs. By employing online updates of Radial Basis Function (RBF) NNs technology, the unknown nonlinear dynamics are approximated, thus addressing complex nonlinear dynamics problems. In scenarios involving multiple USVs navigating under high wind and wave conditions, collisions with obstacles frequently occur. To tackle this issue, a leader-follower control strategy is designed that effectively addresses risk assessment and obstacle avoidance under such challenging conditions. Additionally, to account for saturation constraints or potential faults in the controller inputs commonly encountered in engineering applications, it implements an asymmetric auxiliary control system. Furthermore, the Lyapunov stability theorem is utilized to ensure the stability of both the formation control and obstacle avoidance algorithms for multiple USVs. Finally, the effectiveness of the proposed algorithm is validated through simulations.

Distributed Adaptive Formation Control for Fractional-Order Multi-Agent Systems with Actuator Failures and Switching Topologies

In this paper, a class of distributed adaptive formation control problems are investigated for second-order nonlinear fractional-order multi-agent systems with actuator failures and switching topologies. To address these challenges, two adaptive coupling gains based on agents’ position and velocity are incorporated into the control protocol. Using the Lyapunov method along with graph theory and matrix analysis, sufficient conditions for system stability are derived in the presence of actuator failures and switching topologies. The effectiveness of the proposed control protocol is demonstrated through numerical simulations, which show its capability to maintain stable formation control under these challenging conditions.

Characterization and Sources of VOCs during PM2.5 Pollution Periods in a Typical City of the Yangtze River Delta

To investigate the characteristics and sources of volatile organic compounds (VOCs) as well as their impacts on secondary organic aerosols (SOAs) formation during high-incidence periods of PM2.5 pollution, a field measurement was conducted in December 2019 in Hefei, a typical city of the Yangtze River Delta (YRD). During the whole process, the mixing ratios of VOCs were averaged as 21.1 ± 15.9 ppb, with alkanes, alkenes, alkyne, and aromatics accounting for 59.9%, 15.3%, 15.0%, and 9.8% of the total VOCs, respectively. It is worth noting that the contributions of alkenes and alkyne increased significantly during PM2.5 pollution periods. Based on source apportionment via the positive matrix factorization (PMF) model, vehicle emissions, liquefied petroleum gas/natural gas (LPG/NG), and biomass/coal burning were the main sources of VOCs during the research in Hefei. During pollution periods, however, the contribution of biomass/coal burning to VOCs increased significantly, reaching as much as 47.6%. The calculated SOA formation potential (SOAFP) of VOCs was 0.38 ± 1.04 µg m−3 (range: 0.04–7.30 µg m−3), and aromatics were the dominant contributors, with a percentage of 96.8%. The source contributions showed that industrial emissions (49.1%) and vehicle emissions (28.3%) contributed the most to SOAFP during non-pollution periods, whereas the contribution of biomass/coal burning to SOA formation increased significantly (32.8%) during PM2.5 pollution periods. These findings suggest that reducing VOCs emissions from biomass/coal burning, vehicle, and industrial sources is a crucial approach for the effective control of SOA formation in Hefei, which provides a scientific basis for controlling PM2.5 pollution and improving air quality in the YRD region.

Role of silicon in the growth of ZSM-5 synthesized with coal fly ash as raw materials

In this work, single-phase ZSM-5 zeolite was synthesized using solid waste coal fly ash (CFA) as raw material, providing a potential alternative for creating high-value-added products from this waste. X-ray diffraction (XRD), scanning electron microscopy, X-ray fluorescence, Fourier transform infrared spectroscopy, N2 physisorption, and other techniques were used to evaluate the crystallization and morphology of the ZSM-5 obtained. The results indicated that ZSM-5 can be synthesized by the simple method of mixing, stirring, and heating. A well-defined hexagonal shape of ZSM-5 with a specific surface area of 162 m2/g and mesoporous structure was obtained at the temperature of 160 °C during hydrothermal treatment for 72 h. Theoretically, the mechanism of crystal growth of ZSM-5, i.e. the liquid phase nucleation and step growth models, was clarified. These results open the possibility of exploring the crystallization of ZSM-5 and the utilization of CFA. Simultaneously, the critical roles of silicon in framework formation and crystallization control during the formation and growth of zeolites were elucidated.

Consensus formation control of wheeled mobile robots with mixed disturbances under input constraints

This paper addresses the problem of distributed consensus-based formation control for wheeled mobile robots (WMRs) under the influence of mixed disturbances, including both random noise and non-random disturbances. A consensus formation auxiliary subsystem is constructed based on the leader’s position estimated by the distributed estimator. A formation tracking subsystem for each robot is constructed based on the trajectory tracking error method. The above two subsystems are constructed into an extended formation modeling system. Further, a distributed model predictive control (DMPC) is designed to control this system without disturbance, and the controller is solved by means of a general-purpose neural network. A combination of Kalman filter (KF) and extended state observer (ESO) is intended to reduce the effect of both non-random disturbances and random noise, hence increasing the controller’s resilience to disturbances. Moreover, a composite control law is designed to ensure the controller’s effectiveness. Finally, simulation results demonstrate that the proposed control strategy is well-suited to addressing the problem, as it not only achieves accurate formation control but also effectively regulates the robot’s physical constraints while suppressing both non-random disturbances and random noise.

Алгоритмы группового управления подводными подвижными объектами

The problem of formation control is relevant when searching for objects, surveying a given area, and group monitoring. In the underwater environment, this problem is complicated by restrictions on the frequency of receiving navigation data and the speed of information exchange between underwater robots. The purpose of this article is to develop and study group control algorithms that ensure the given formation and the maintenance of this formation during movement. The article provides an overview of the results in the field of group control of underwater vehicles, provides a mathematical model of the object, navigation system, underwater environment and communication system. The equations of kinematics and dynamics of a solid body in three-dimensional space, supplemented by equations of actuators, are used as a mathematical model. Algorithms for the distribution of moving objects in formation and nonlinear control algorithms for moving in a line are proposed. When constructing the devices, it is assumed that there is a group leader who transmits his coordinates to the rest of the underwater robots. Motion control is synthesized by the method of positional trajectory control in the form of a function of external coordinates. A study was carried out using numerical methods, during which the process of the formation and movement along the trajectory described by straight line segments was studied. The influence of errors in the navigation system and the frequency of data updates on the error of maintaining a given position by a separate underwater robot is investigated.

Adaptive coupled-sliding-variable-based finite-time control of composite formation for multi-robot systems

Adaptive coupled-sliding-variable-based finite-time control of composite formation for multi-robot systems

A novel test set up to study three-dimensional electrokinetic dewatering of dredged soil

The dredged soil obtained from maintenance activities of water bodies has emerged as a potential alternate fill material for infrastructure development. However, dredged soil requires stabilization due to high initial water content, low shear strength and high compressibility. Among several methods, stabilization of dredged soil by using electrokinetics is one of the effective ground improvement techniques that uses electric field to dewater and strengthen the soil. In this context, a series of experiments were conducted on dredged soil by using a combination of electrokinetic treatment with and without 6 kPa seating pressure (viz., low surcharge). A customized and patented electrokinetic dewatering (EKD) test set up was used for the three-dimensional electrokinetic treatment of soil. The potential difference (in the range of 6 V–48 V) within the soil was achieved by inserting stainless steel pipes of 21.4 mm outer diameter, 1.2 mm thickness, and 170 mm length. Two control tests (with and without seating pressure of 6 kPa) also were performed to understand the effectiveness of EKD. From the study, up to 1057% and 427% increase in dewatering was noted in EKD tests due to application of 24 V (optimum voltage noted in EKD tests) as compared to control tests, without and with seating pressure, respectively. Further, seating pressure with EKD resulted in effective control of crack formation in the dredged soil and uniform improvement in shear strength along the depth (up to 95 kPa). The combination of low surcharge with EKD, adopted in the study, is also expected to yield lower differential settlement, and hence better performance of geotechnical structures built on improved dredged soil. The novel 3-dimensional patented EKD test setup with Arduino-programmed automatic water pumping enables collecting and accurately measuring dewatered effluent volume, performing cone penetration tests on undisturbed soil, and collecting soil samples for determination of water content/physiochemical properties from different locations. Overall, the developed EKD setup can be utilized for evaluating the effectiveness and adopting real-time progress management for EKD or other ground improvement methods, and remediation of sludge, mine tailings, dredged sediments, and contaminated soils.

Reinforcement learning-based robust formation control for Multi-UAV systems with switching communication topologies

This paper introduces a novel optimal robust formation method for quadcopter multiple unmanned aerial vehicle (multi-UAV) systems. Firstly, a reinforcement learning (RL) algorithm based on a unique gradient descent training approach is proposed to solve the Hamilton–Jacobi–Bellman (HJB) equation, which can effectively eliminate the requirement of the Persistent Excitation (PE) condition. Secondly, the robustness of the controlled system is emphasized, and an Uncertainty and Disturbance Estimator (UDE) observer is developed to suppress model uncertainty and external disturbances through filtering techniques. Furthermore, a switched sliding mode control technique according to the average dwell time (ADT) is employed to convert switching communication topology between UAVs dynamically, and the stability analysis of the corresponding closed-loop control systems is then performed by the use of Lyapunov analysis. Finally, the simulation examples are provided to verify the effectiveness of the designed control strategy.

Dynamic Optimal Obstacle Avoidance Control of AUV Formation Based on MLoTFWA Algorithm

In addressing the optimal formation obstacle avoidance control problem for Autonomous Underwater Vehicles (AUVs) in environments with unknown and moving obstacles, this paper employs the Modified Fireworks Algorithm based on a Loser Elimination Mechanism (MLoTFWA) and constructs a Distributed Model Predictive Control (DMPC) framework to achieve obstacle avoidance for AUV formations. Initially, a prediction model is established, followed by feedback compensation to mitigate the effects of unknown perturbations. An appropriate fitness function is then formulated, and enhancements such as the loser elimination rule are introduced to optimize the fireworks algorithm. Additionally, the concept of an adaptive DMPC prediction window is proposed to conserve resources. The local and global stability of the DMPC formation control framework is theoretically proven. Simulations verify that the control system based on the DMPC framework ensures safe obstacle avoidance for the formation, maintains formation consistency, and achieves the shortest and smoothest path. The improved fireworks algorithm demonstrates superior performance compared with the original fireworks algorithm and other optimization algorithms. In testing, the improved fireworks algorithm exhibits better adaptability, higher average fitness, and best fitness, along with a significantly faster convergence speed. Compared with the ordinary fireworks algorithm, the convergence speed is reduced by 30%.

Formation Mechanisms and Control Methods of Cyanobacteria Bloom

Formation Mechanisms and Control Methods of Cyanobacteria Bloom

A hybrid formation path planning based on the velocity-virtual spring method in dynamic environments

Purpose The purpose of this study is to propose a path-planning strategy based on the velocity-virtual spring method to realize collision-free tasks in dynamic environments and further improve the effect. Design/methodology/approach By considering factors such as the relative velocity and direction of dynamic obstacles, the repulsive force of the robot is improved, thereby enhancing the adaptability of the strategy and achieving flexible and effective avoidance against dynamic obstacles. The attraction formula has been designed to allow the robot to have better smooth changes and higher gradients near the target, helping robots better reach the target and follow formations. Moreover, to meet the demands of the various stages during the driving process, the null space behavioral control is used to solve multi-task conflict problems and strengthen formation coordination and control. Findings Comparison of the planning path and formation effects through simulation and physical experiments, the results of this study show that the algorithm proposed can successfully maintain formation stability and plan smooth and safe paths in static or dynamic environments. Originality/value This paper proposes a path-planning strategy based on the velocity-virtual spring method to plan collision-free paths for formation in dynamic environments.

Fuzzy adaptive finite-time formation control of unmanned ground vehicles with performance and feasibility constraints

This paper investigates the problem of fuzzy adaptive finite-time formation tracking control for unmanned ground vehicles (UGVs) systems with two different constraints. By utilizing the distance tracking error between the actual trajectory and the desired trajectory of each UGV, as well as the constraints of bearing angle, to achieve the performance and feasibility constraints, respectively. Furthermore, the fuzzy logic systems (FLSs) are used to approximate unknown nonlinear functions. Due to the limitations of the field of view and communication distance of UGVs, the controller is designed by using finite time stability theory and universal barrier function. Under the proposed control scheme, the stability of the closed-loop system can be ensured, feasibility and performance constraints are also achieved. Finally, the effectiveness of the proposed control design is verified through simulation results.

Sublinear message bounds of authenticated implicit Byzantine agreement

This paper studies the message complexity of authenticated Byzantine agreement (BA) in synchronous, fully-connected distributed networks under an honest majority. We focus on the so-called implicit Byzantine agreement problem where each node starts with an input value and at the end a non-empty subset of the honest nodes should agree on a common input value by satisfying the BA properties (i.e., there can be undecided nodes)3. We show that a sublinear (in n, number of nodes) message complexity BA protocol under honest majority is possible in the standard PKI model when the nodes have access to an unbiased global coin and hash function. In particular, we present a randomized Byzantine agreement algorithm which, with high probability achieves implicit agreement, uses O˜(n) messages, and runs in O˜(1) rounds while tolerating (1/2−ϵ)n Byzantine nodes for any fixed ϵ>0, the notation O˜ hides a O(polylogn) factor4. The algorithm requires standard cryptographic setup PKI and hash function with a static Byzantine adversary. The algorithm works in the CONGEST model and each node does not need to know the identity of its neighbors, i.e., works in the KT0 model. The message complexity (and also the time complexity) of our algorithm is optimal up to a polylog n factor, as we show a Ω(n) lower bound on the message complexity. We further extend the result to Byzantine subset agreement, where a non-empty subset of nodes should agree on a common value. Lastly, we analyze several relevant results that follow from the construction of the main result.To the best of our knowledge, this is the first sublinear message complexity result of Byzantine agreement. A quadratic message lower bound is known for any deterministic BA protocol (due to Dolev-Reischuk [JACM 1985]). The existing randomized BA protocols have at least quadratic message complexity in the honest majority setting. Our result shows the power of a global coin in achieving significant improvement over the existing results. It can be viewed as a step towards understanding the message complexity of randomized Byzantine agreement in distributed networks with PKI.

Reinforcement learning intermittent optimal formation control for multi-agent systems with disturbances

Abstract This paper investigates disturbance-resistant intermittent event-triggered optimal formation control problems of second-order multi-agent systems by using the reinforcement learning method, which takes into account the influence of network damage including denial-of-service (DoS) and deception attacks, stochastic noises, and unknown external disturbances. Firstly, we propose a novel disturbance observer based on adaptive control to estimate unknown external disturbances under an event-triggered mechanism. Secondly, by use of estimation of disturbances, an innovative intermittent event-triggered optimal formation algorithm is given. By applying theories such as Lyapunov stability and stochastic stability, sufficient conditions are derived to guarantee that all agents achieve the desired formation in mean square sense. Additionally, in the model-free case, the optimal controller is solved using the least squares method, which is computationally less complex than some existing approaches. Finally, the theoretical results are effectively validated through simulation examples.

Fault‐tolerant formation control of multi‐agent systems with multiple leaders based on intermediate variable estimator

AbstractThis article investigates the distributed fault‐tolerant formation control problem of a multi‐leader system with process faults and system uncertainties. This study guarantees all the followers to achieve a specified time‐varying formation and track the combination of multiple leaders. By treating faults as a special type of states, an enhanced system is constructed. Furthermore, an intermediate variable observer is designed, but the observer matching condition is not necessary. The observer is used to estimate faults and system uncertainties, for reconstructing the formation controller. The observer gains are determined by solving linear matrix inequalities. Subsequently, controller gains are designed based on Lyapunov theorem and adaptive techniques. Finally, the proposed method is verified through a simulation example.