134 results about "Distributed model predictive control" patented technology

Disclosed in the present invention is an unmanned aerial vehicle formation cooperative obstacle avoidance method based on distributed model predictive control. The method comprises the following steps: establishing an unmanned aerial vehicle motion model to predict a control state of a single unmanned aerial vehicle; on the basis of the unmanned aerial vehicle motion model and control objectives of reaching a target state and maintaining formation, establishing a cost function; setting a constraint, updating the control state in a prediction time domain under the condition that the cost function is minimum, and obtaining the control state of each unmanned aerial vehicle in a subsequent period of time; and each unmanned aerial vehicle flying in the subsequent period of time on the basis of the control state, so as to realize cooperative obstacle avoidance. The unmanned aerial vehicle formation cooperative obstacle avoidance method based on distributed model predictive control disclosed in the present invention has the advantages of high stability, low operation amount, low delay and the like.

The invention discloses a path planning and hierarchical cooperative control method and system for an unmanned aerial vehicle cluster. The system comprises a path planning module and a formation motion control module. The method comprises the steps that firstly, an improved RRT * algorithm is adopted by a path planning module, through a multi-strategy heuristic node expansion mechanism fusing target bias and artificial potential field guidance and comprehensively considering path length, channel volume and Z-axis height change, a center path and a three-dimensional safe channel which take into account safety and smoothness are planned for an unmanned aerial vehicle cluster; and then, based on the central path, the formation motion control module adopts a distributed model prediction control framework, designs different optimization targets for a navigator and a follower, and solves an optimal control instruction on line, so that a cluster is guided to complete trajectory tracking, collision avoidance among individuals and self-adaptive formation reconstruction in a secure channel. According to the invention, the navigation problem of the unmanned aerial vehicle cluster in a complex obstacle environment is solved, and the path planning efficiency and the robustness of cooperative control are improved.

The invention relates to the field of robot control, and discloses a robot control method and system, and the method comprises the steps: carrying out the fusion sensing of environment data through a multi-modal sensor, and constructing a dynamic environment model through the combination of the clustering segmentation of a dynamic obstacle and the geometric matching of a target cart; generating a Nash equilibrium obstacle avoidance path based on a non-cooperative game framework, and coordinating motion tracks of multiple robots by using distributed model predictive control and an alternating direction multiplier method; a heterogeneous grabbing pose generation network is designed, simulation and real scene feature distribution are aligned through domain adversarial training, and online optimization of a grabbing strategy is achieved in combination with incremental learning; newly-added obstacles and cart deviation are monitored in real time to trigger dynamic re-planning, and the path and the grabbing pose are updated through a closed-loop feedback mechanism. According to the method, the cooperative control robustness in a dynamic environment is improved, the obstacle avoidance efficiency and the grabbing stability are both considered, and the method is suitable for automatic operation in complex scenes such as airports.

The invention provides an energy storage power station and power grid collaborative complementary regulation method and system, and relates to the technical field of smart power grids, comprising the steps of obtaining disturbance components by performing multi-scale decomposition on power grid frequency and voltage data, and constructing a distributed model prediction control framework to optimize energy storage resources in groups; and designing a self-adaptive dynamic programming controller to evaluate the system state and output the optimal charging and discharging power, and responding to the topological change of the power grid in real time. According to the invention, collaborative optimization configuration of energy storage resources can be realized, the power grid stability is improved, the service life of energy storage equipment is prolonged, and the system operation cost is reduced.

The invention relates to the technical field of power supply, and particularly discloses a peak and frequency regulation heat supply method for a multi-element energy storage coupling coal power unit, which is used for solving the problems that only active frequency is optimized, electric heating coordination and charge state constraint are lacked and time delay compensation is insufficient in the prior art. The method provided by the invention comprises the steps of system modeling, controller design, coupling constraint, adaptive parameter adjustment, delay compensation and distributed consensus. According to the method, a dual-domain electric power-thermal interconnection model is constructed, a cooperative controller based on delay compensation distributed model prediction control is designed, electric-thermal cooperative penalty and charge state constraint are introduced into a target function, a prediction window and weight parameters are adaptively adjusted, and network delay is identified and compensated online. And a consensus algorithm is adopted to realize multi-region power-heat collaborative optimization.

The invention relates to the technical field of on-load voltage regulation, in particular to a transformer on-load voltage regulation control method. According to the technical scheme, the transformer on-load voltage regulation control method comprises the following steps that S1, a transformer cluster running in parallel is decomposed into distributed intelligent nodes, and each node executes local voltage prediction and constraint condition management; s2, adopting a distributed model prediction control framework, and cooperatively solving a voltage regulation optimization target through an edge calculation unit; s3, a harmonic disturbance signal with a preset amplitude is injected into the transformer, equivalent impedance is dynamically identified based on response data, and virtual impedance compensation is triggered; and S4, establishing a hierarchical cooperative control architecture, and realizing clock synchronization, dynamic weight distribution and multi-objective optimization decision among nodes. Through a cooperative mechanism of distributed predictive control and dynamic impedance compensation, the response speed and the regulation precision of the on-load voltage regulation system to the power grid voltage fluctuation are remarkably improved, and the voltage out-of-limit problem under the complex working conditions of new energy grid connection, load sudden change and the like is effectively solved.

The invention provides a brushless motor multi-axis cooperative control method and system for an industrial robot, and relates to the technical field of control, and the method comprises the steps: processing the real-time parameters of a brushless motor of each joint axis through constructing an extended dynamic model, and obtaining a temperature drift coefficient and a load inertia parameter; a self-adaptive pre-compensation model, a neural network compensator and a recurrent neural network prediction model are combined, a torque control instruction is output, a three-phase current prediction value is calculated, a distributed model prediction controller is adopted to generate an optimal switching sequence, multi-axis cooperative control is achieved, and the system stability and the response speed can be effectively improved.

The invention discloses a self-adaptive scheduling method for a communication unmanned aerial vehicle group, and relates to the field of unmanned aerial vehicle control, and the method comprises the steps: collecting water surface dynamic data in real time through a multi-mode sensing module, and constructing a water surface dynamic digital twinborn model; based on output data of the water surface dynamic digital twinning model, generating an electromagnetic environment simulation map by using a time-space map convolutional network; identifying the edge of a strong interference area in the electromagnetic environment simulation map, deploying an expandable intelligent reflector array, and dynamically optimizing the phase configuration of an intelligent reflector unit through a deep reinforcement learning algorithm; dividing the unmanned aerial vehicle group into a communication subgroup and a monitoring subgroup according to the spatial distribution of the strong interference area; the monitoring subgroup scans the strong interference area, obtains local fluctuation parameters, and feeds back data to the digital twinborn model in real time; and performing formation optimization on the communication subgroups by adopting a distributed model predictive control framework, solving a multi-target optimization problem, and generating an unmanned aerial vehicle position adjustment instruction.

The invention discloses a distributed virtual power plant game optimization scheduling method, and belongs to the field of optimization scheduling. According to the method, aiming at the characteristics that the distributed power supplies are distributed dispersedly and have randomness and volatility when the large-scale distributed power supplies are accessed to the network, multi-virtual power plant coordinated scheduling under the non-cooperative game is reasonably carried out, the economic benefit of multi-virtual power plant operation is improved, and energy utilization is fully optimized; considering the influence of the renewable energy prediction error along with the time scale, and establishing a multi-virtual power plant multi-time scale optimization scheduling model based on distributed model prediction control; taking a single virtual power plant as a whole to participate in the electricity market, and establishing a multi-virtual power plant non-cooperative game model; the two-way auction mechanism is applied to the electricity market, and the two-way auction process is analyzed through a non-cooperative game model; by considering distributed virtual power plant optimization scheduling under the competition relationship, the operation economy and transaction electric quantity are optimized, and flexible interaction and reasonable electric energy distribution between virtual power plants are realized.

The invention discloses a power system load frequency control method considering disturbance compensation and a related device, and belongs to the technical field of power system regulation and control. The method comprises the following steps: respectively carrying out frequency modulation characteristic analysis on a speed regulator, a prime mover and a generator-load of the power system, and establishing a subsystem nominal model of the multi-region interconnected power system; designing a disturbance observer to observe external unknown disturbance to obtain a disturbance estimation value, and further obtaining a subsystem load frequency control model containing disturbance compensation information; based on a distributed model prediction control strategy, a distributed model prediction controller considering disturbance compensation is designed according to the subsystem load frequency control model containing the disturbance compensation information; when a certain area in the interconnected power system is disturbed, the frequency deviation of each sub-area is adjusted to be zero through a distributed model prediction controller considering disturbance compensation, and it is ensured that power exchange between connecting lines in the sub-areas and the interconnected areas of the connecting lines is kept in a zero steady state.

The invention discloses a canal system gate scheduling optimization method based on distributed model predictive control and a one-dimensional hydrodynamic model, and the method comprises the steps: collecting the hydrodynamic data, irrigation demands and canal system characteristics of a canal system, and taking the data as basic data; on the basis of the basic data, various disturbance conditions of the canal system are simulated, and corresponding hydrodynamic response data are obtained; constructing a one-dimensional hydrodynamic model based on the Saint-Venant equation; based on the one-dimensional hydrodynamic model and the hydrodynamic response data, constructing a distributed model prediction control architecture, and predicting the hydrodynamic data of the canal system; based on the prediction result, taking the minimum water level deviation and the minimum control cost as objective functions, and performing dynamic scheduling on the canal system; and performing feedback updating on a dynamic scheduling result of the channel system based on a dynamic rolling optimization mechanism. The method is suitable for multi-channel pool distributed water flow regulation and control, and particularly has important application value in efficient utilization of water resources in an irrigation area, flood scheduling and irrigation canal system optimization management.

The invention discloses a multi-robot probability trajectory generation method based on distributed model predictive control, and relates to the technical field of multi-robot systems, and the method constructs collision avoidance constraints based on a safety corridor with time perception. Uncertainty introduced by state estimation noise and motion interference is fully considered in construction of collision avoidance constraint, probability collision avoidance constraint is converted into deterministic constraint of mean value and covariance of robot states, and robustness of collision avoidance is ensured; meanwhile, in order to solve the problem of deadlock possibly occurring in a multi-robot system, a warning tape mechanism is introduced into probability constraint for avoiding collision and is combined with a right hand rule, and robot trajectory planning is dynamically adjusted; finally, the probabilistic collision constraint avoiding and deadlock preventing mechanisms are integrated into a distributed model predictive control framework, so that a locally optimal collision-free trajectory is generated.

The invention relates to the technical field of intelligent traffic and vehicle control, in particular to an intelligent networked commercial vehicle fleet collaborative lane changing system and an optimization method, and provides a control strategy for intelligent networked commercial vehicle fleet collaborative lane changing by combining a V2X technology with a distributed model predictive control (DMPC) algorithm. The method comprises the following steps: introducing a timing interval strategy in a V2X environment to realize real-time information interaction so as to dynamically adjust a vehicle interval, predicting a motorcade lane changing track by using a DMPC algorithm, and converting a track optimization problem into a linear quadratic programming solving problem through a quadratic programming method so as to obtain a motorcade lane changing track with an optimal control sequence, so that the aim of dynamically adjusting the vehicle interval is fulfilled. Therefore, cooperative control over lane changing of the motorcade is achieved, and the overall stability, safety and lane changing efficiency of the motorcade are improved.

The invention discloses a distributed model predictive control unmanned cluster navigation method based on homotopy perception. The method comprises the following steps of: planning a global path and optimizing a local track; an optimal path planning algorithm based on homotopy perception is designed by utilizing a topological structure of an environment and space-time conflicts between intelligent agents, each intelligent agent in an unmanned cluster adopts a trajectory optimization method based on model predictive control, a dynamic feasible and collision-free trajectory is generated, the motion trajectory of the intelligent agent is optimized, and the optimal path planning algorithm is realized. The global coordination of the agents in the unmanned cluster and the dynamic adaptability of local motion are improved; and performing real-time dynamic adjustment on behaviors of the agents in the unmanned cluster globally and locally through an online re-planning strategy. By adopting the technical scheme of the invention, the cooperative trajectory planning capability of the unmanned cluster in an obstacle dense environment can be improved.

The invention belongs to but is not limited to the technical field of collaborative region search, and discloses an air-ground unmanned cluster collaborative search path planning method based on a learning wolf pack algorithm, which combines distributed model predictive control (DMPC) with a distributed constraint optimization problem (DCOP) framework, constructs a global search objective function of a finite time domain, and performs collaborative search on the air-ground unmanned cluster collaborative search path planning based on the learning wolf pack algorithm. A DCOP model for regional search path planning is constructed; the learning type wolf pack algorithm LWPA is utilized, hierarchical learning is carried out through a reinforcement learning mechanism, algorithm parameters are dynamically adjusted, and therefore effective balance between local fine search and global exploration is achieved. The method provided by the invention has good adaptability, robustness and expandability, and effectively improves the efficiency and accuracy of target search.

The invention discloses a multi-vehicle queue cooperative control method based on dynamic model predictive control and a medium. The method comprises the following steps: establishing a vehicle dynamics model, a vehicle motion state equation, a communication topological structure and a queue state space model; establishing a two-stage control model which comprises a vehicle cut-in optimization model and a distributed model prediction controller; the vehicle cut-in optimization model comprises a target function and a queue stability constraint; the objective function comprises an adjustment cost function during cut-in; the optimization direction is to minimize the adjustment cost; solving and obtaining the optimal cut-in time and position of the cut-in vehicle; and dynamically controlling the vehicle by using the distributed model predictive controller. According to the cut-in vehicle control model, the energy consumption in the cut-in process is remarkably reduced while the cut-in efficiency and the safety are guaranteed, the stability and the cooperative capability of a queue system are enhanced, and an efficient and reliable solution is provided for multi-queue control in an intelligent traffic system.

The invention provides a heterogeneous automatic driving vehicle formation environment-friendly driving method, and relates to the technical field of fleet control, and the method comprises the steps: building a vehicle formation longitudinal dynamics system, a fuel consumption model and a PLF communication topological structure based on a heterogeneous vehicle formation of a front vehicle-navigator following type PLF communication topology; establishing a distributed model prediction control algorithm of the heterogeneous vehicle formation; the algorithm specifically comprises an optimal fuel consumption driving control algorithm for a pilot vehicle and a distributed control algorithm for each following vehicle in a motorcade. Setting constraint conditions for the objective function of the open-loop optimal control problem; an OSQP mathematical solver is adopted to solve a fuel optimization problem for a continuous quadratic programming algorithm SQP, namely, an optimal fuel consumption driving control algorithm, the optimal driving speed and driving acceleration of the pilot vehicle are obtained, a predicted value input by control is applied to update the vehicle state at the (k + 1) th moment, and the following vehicle reaches the optimal fuel consumption according to the driving of the pilot vehicle, so that the optimal driving speed and driving acceleration of the pilot vehicle are achieved. And the purposes of optimal fuel consumption and environmental protection are achieved.

A self-triggering unmanned vehicle distributed formation control method based on a double-triggering mechanism belongs to the field of unmanned vehicle distributed cooperative formation control, and comprises the following steps: S1, constructing a multi-agent consistency cooperative control framework; s2, constructing an intelligent vehicle cooperative control system driven by a dynamic situation field; s3, carrying out communication topology modeling between the vehicles; s4, a self-triggering mechanism is introduced, and correct triggering of communication is ensured by designing a double-triggering threshold function; s5, establishing a prediction model of the vehicle formation; and S6, the distributed model prediction controller controls accurate implementation of a formation transformation strategy. According to the invention, through combination of a self-triggering mechanism and distributed model prediction control, the control efficiency and precision of the unmanned vehicle formation are significantly improved; compared with the traditional method, the communication frequency is reduced, and the communication burden is reduced by 5%; when the vehicle runs on a curve, the vehicle track error is reduced and is 4% more accurate than that of a traditional method.

The invention discloses a multi-vehicle-queue cooperative control method based on distributed model predictive control, and the method comprises the following steps: constructing a multi-vehicle-queue system and a dynamic model, building a one-way communication topological structure, defining the coupling constraint of vehicles and queues based on the communication topological structure, and carrying out the prediction of the multi-vehicle-queue cooperative control. Constructing a distributed model predictive control optimization problem and a cost function, wherein the distributed model predictive control optimization problem comprises a vehicle node local optimization problem, a queue global optimization problem and a multi-target optimization problem; and solving an optimization control problem for each vehicle node, and adjusting the control input and the motion state of each vehicle. According to the technical scheme, dynamic synchronization and stable control between vehicles and between queues are achieved by means of one-way communication topology design, introduction of coupling constraints inside the queues and between the queues, construction of a local optimization problem and combination of a global coordination mechanism, and the cooperative control performance of a multi-vehicle queue system in a complex dynamic environment can be effectively improved.

The invention belongs to the technical field of unmanned aerial vehicle cooperative crossing, and relates to a multi-unmanned aerial vehicle cooperative crossing method and device. The method comprises the following steps: acquiring scene information of cooperative crossing of multiple unmanned aerial vehicles, and obtaining a data set, a model of a crossing frame and a dynamic model of each unmanned aerial vehicle; according to the dynamic model of each unmanned aerial vehicle, constructing a model prediction control framework of each unmanned aerial vehicle; acquiring a historical trajectory sequence of each unmanned aerial vehicle to obtain a trained neural network, and embedding the trained neural network into a model prediction control framework to form a distributed model prediction control framework of multiple unmanned aerial vehicles; and performing path prediction planning on the current unmanned aerial vehicle and other unmanned aerial vehicles according to the distributed model prediction control framework of the current unmanned aerial vehicle, the model of the crossing frame and the kinetic model of the current unmanned aerial vehicle to obtain the real-time position of each unmanned aerial vehicle so as to perform multi-unmanned aerial vehicle cooperative crossing. According to the invention, cooperative crossing can be realized under the condition that multiple unmanned aerial vehicles do not communicate with each other.

The invention relates to an information physics multi-agent cooperative control method in a mixed traffic flow environment, and belongs to the technical field of intelligent traffic, and the method comprises the following steps: S1, at the upstream section of an intersection, according to the driving direction and dynamic state of vehicles, taking a first vehicle reaching the boundary of a lane changing area as a trigger vehicle, detecting the vehicles in a preset time window, dividing the triggered vehicle and all vehicles in front of the triggered vehicle within the detection range into the same target vehicle set; s2, for the target vehicle set, constructing a 0-1 integer programming model, and optimizing an organization scheme of a hybrid queue; s3, on the basis of the optimal formation scheme, constructing a vehicle and hierarchical control framework which comprises two parts of cloud deep reinforcement learning DRL and vehicle-end distributed model prediction control DMPC; the cloud DRL outputs a reference action; and the vehicle end DMPC takes the reference action and state output by the cloud end DRL as a target, and realizes cooperative passing of the whole motorcade while satisfying local constraints.

The invention relates to the technical field of robot welding, in particular to an intelligent path planning system and optimization method for large structure robot welding, by collecting three-dimensional point cloud data of a workpiece and temperature field and stress field data in the welding process, a geometry-heat-force multi-field coupling model is established, and a machine learning algorithm is adopted for parameter correction; based on the model, a deep learning network is adopted to identify welding seam topological features and generate a path constraint parameter set; welding path nodes are optimized by using a hybrid optimization strategy of reinforcement learning and a multi-target genetic algorithm; monitoring the shape of a molten pool through a visual sensing system, and dynamically adjusting welding parameters in combination with a model predictive control algorithm; a distributed model predictive control algorithm is adopted to coordinate the motion trails of the multiple robots; and finally, constructing a welding process knowledge graph to realize continuous optimization of the system. The problems of inaccurate welding deformation prediction and poor path planning adaptability in the prior art are solved, and the welding quality and the production efficiency of the large structural part are remarkably improved.

The invention discloses a multi-agent formation obstacle avoidance method based on distributed model predictive control, which obtains an optimal control sequence of a system by solving an optimization problem in a limited predictive time domain so as to realize online closed-loop control of the system in the whole control time domain. A leader agent moves according to a preset track, other agents consider consistency performance indexes between the leader agent and other agents in a neighborhood, a control target is constructed, then a target function is constructed based on the control target, and the position and speed consistency performance indexes serve as the target function. The collision avoidance requirements and the obstacle avoidance requirements between the intelligent agents are constructed into constraint conditions; according to the method, an obstacle avoidance problem is converted into an optimization problem of a target function, and then a distributed model prediction control method is used for solving the optimization problem to obtain an optimal control sequence, so that an expected formation obstacle avoidance function is realized, the system control performance is improved, the calculation complexity is reduced, and the method has relatively good practicability.

The invention discloses a multi-unmanned aerial vehicle navigation method, system and device based on distributed model predictive control and a virtual pipeline and a medium, and the method comprises the steps: constructing the virtual pipeline, and forming a flight area of an unmanned aerial vehicle group; in each sampling period, each unmanned aerial vehicle generates an assumed trajectory in a prediction time domain by using a kinetic model of the unmanned aerial vehicle based on the current state of the unmanned aerial vehicle, and broadcasts the assumed trajectory to the neighbor unmanned aerial vehicle; obstacle intrusion judgment and virtual pipeline dynamic processing are carried out, a linear separation hyperplane is constructed, and local convex safety sub-pipeline constraints are generated; carrying out inter-machine collision risk judgment and dynamically activating inter-machine collision avoidance constraints; setting a target cost function, and constructing an optimization problem in combination with the constraints; solving to obtain an optimal predictive control input sequence at the current moment, and executing a first control instruction of the optimal predictive control input sequence; and the process is repeatedly executed until all the unmanned aerial vehicles pass through the virtual pipeline. According to the method, multiple challenges of dynamic environment adaptability, calculation real-time performance, track smoothness and the like can be solved at the same time.

The invention discloses a distributed model prediction control method for an unmanned aerial vehicle formation under a non-continuous communication condition. The method comprises the following steps: establishing an unmanned aerial vehicle kinematics model and a trajectory tracking error model; simulating an independent random data loss condition of communication between unmanned aerial vehicles, and establishing a navigator and follower formation control target based on a navigation-following formation mode; the invention relates to a distributed model prediction controller design of an unmanned aerial vehicle formation under a non-continuous communication condition. A navigator unmanned aerial vehicle trajectory tracking controller, a follower unmanned aerial vehicle cooperative controller and a terminal feedback controller are included. Establishing a predicted value of a follower unmanned aerial vehicle cooperative controller for a reference trajectory of a navigator unmanned aerial vehicle and a predicted value of a reference trajectory of a neighbor node unmanned aerial vehicle in a prediction time domain; and solving the rolling optimization problem of each unmanned aerial vehicle at the current moment to obtain an optimal solution. According to the invention, under the condition of non-continuous communication, when data loss is caused, cooperative control can still be carried out among the unmanned aerial vehicles.

The invention relates to an airport snow sweeper cooperative formation control method, electronic equipment and a medium, and the method comprises the steps: building a vehicle transverse and longitudinal coupling kinematic model in an airport snow removal environment based on environmental parameters, the vehicle transverse and longitudinal coupling kinematic model comprises transverse and longitudinal physical quantity cross terms, enabling the longitudinal speed to affect the transverse posture change, and enabling the longitudinal speed to affect the transverse posture change; the transverse path characteristics and the control input restrict the longitudinal motion; constructing a formation constraint model based on the vehicle transverse and longitudinal coupling kinematics model; constructing a distributed communication topology model based on a directed graph and an adjacent matrix; constructing a single vehicle local optimization control problem model; and iteratively solving the single vehicle local optimization control problem model based on a path guiding type distributed model predictive control algorithm to obtain the optimal control input of each vehicle. According to the method, control input can be optimized while accurate path tracking of the snow sweeper is ensured, energy consumption and cost are reduced, and collaboration, safety and efficiency of snow removal operation of an airport can be improved.

The invention discloses a multi-unmanned aerial vehicle integrated planning and control method based on distributed model predictive control. The method comprises the following steps: S1, judging an unmanned aerial vehicle communication topology mechanism in an offline stage, determining an expected target point of each unmanned aerial vehicle, initializing a predictive state value of each unmanned aerial vehicle, and setting controller parameters; s2, the front end performs unmanned aerial vehicle positioning and local map construction according to the sensor data, and searches a reference trajectory according to the local map; s3, the rear end uses an IDMPC controller to solve according to the error between the measured value and the predicted value, and an unmanned aerial vehicle input optimal value is obtained; s4, the solved optimal input is converted into unmanned aerial vehicle attitude control quantity input through differential flatness, and unmanned aerial vehicle formation trajectory tracking is achieved. According to the method, quick response and anti-disturbance capability under the high frequency of 100Hz are realized, and the superiority of the method in the aspects of success rate, calculation efficiency and anti-disturbance performance is verified through a simulation experiment.

The present disclosure provides a cooperative distributed model predictive control (Co-DMPC)-based chassis multi-agent system (MAS) cooperative control method for autonomous vehicles, a controller, and a storage medium. A distributed state-space equation with state coupling and control input coupling characteristics is established. Meanings and transformation methods of predicted trajectories, assumed trajectories, and optimal trajectories of the states and control inputs are designed, providing a communication basis for information exchange between the agents. In order to coordinate the global performance indexes of a vehicle, a local agent optimization problem considering cost coupling is established, and the influence of the cooperative relationship on the control effect is quantitatively analyzed through adaptive weight coefficients. A method of performing a plurality of iterations within a unit sampling time is adopted, and iteration errors are utilized to enable the controller to achieve a balance between solution accuracy and efficiency.

The invention discloses a multi-underwater robot dynamic formation cooperation method based on distributed model prediction control. The method comprises the following steps: establishing an underwater robot dynamic model; designing a distributed model predictive control architecture; making a dynamic formation strategy; an information interaction mechanism based on communication constraints; performing interference compensation and robust control; and setting an objective function and constraint conditions. According to the invention, efficient and stable dynamic formation collaborative operation of multiple underwater robots can be realized in a complex underwater environment. The method has a good application prospect in tasks such as marine environment monitoring, resource exploration, underwater rescue and the like.

The invention relates to a USV formation keeping method based on passive positioning under a communication limited condition, and belongs to the field of unmanned surface vessel formation. The method comprises the following steps: establishing a circular USV formation positioning model under a passive condition based on a three-point three-angle measurement method, and adopting an improved least square filtering algorithm and introducing a distributed state estimation mechanism to enable USV to realize self-positioning; a leader-follower model and a virtual structure method are combined, a virtual-leader-follower circular formation model is established based on a passive condition, and an overall formation virtual navigation structure is set during USV formation movement to guide formation movement; and a distributed state estimation mechanism is fused into a distributed model prediction control strategy, so that each USV follows the track of the USV corresponding to the USV in the virtual navigation structure, and real-time rolling optimization control is performed on the track of the USV in each time step. According to the invention, the robustness of formation control in a communication limited scene can be enhanced.