55 results about "Distributed model predictive control" patented technology

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.

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-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 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 provides an urban power distribution network probabilistic load flow calculation method for large-scale access of demand side resources, and belongs to the technical field of power system power distribution network analysis and optimization. The method comprises the following steps: constructing a dynamic Copula model in combination with space-time sequence analysis, introducing a Markov chain, and carrying out multi-dimensional uncertainty coupling modeling; a probabilistic power flow-multi-objective optimization model is constructed, and energy router cooperative control is carried out based on distributed model predictive control; sparse expansion is carried out by adopting error feedback adaptive sampling and sparse Bayesian learning in combination with polynomial chaos expansion, and probability power flow is calculated through OpenDSS; establishing a three-phase probabilistic power flow model and a DG-EV-DR collaborative probability model; and evaluating the extreme scene risk, and visualizing the result through a digital twin platform. According to the method, the accuracy and practicability of distribution network probabilistic load flow calculation in a demand side resource large-scale access scene are effectively improved, and a risk quantification basis is provided for power grid dispatching.

The invention provides a tunnel robot self-adaptive action control method and system of a multi-level collaborative architecture, and relates to the technical field of tunnel construction.The method comprises the steps that real-time three-dimensional force / torque data of an end effector of a mechanical arm is obtained, denoising and preprocessing are conducted, environment contact rigidity is calculated, and a fractional order impedance model is established; inputting the model to a sliding mode control layer to construct a self-adaptive sliding mode surface and generate a compensation torque; the compensation torque and the real-time contact force are input into a distributed model predictive control layer to dynamically optimize impedance parameters; the optimized parameters and historical data are input to a reinforcement learning layer, global optimization is carried out by using a near-end strategy optimization algorithm, and target impedance parameter correction is generated; and generating a speed control instruction based on the correction, and driving the mechanical arm to complete operation. According to the method, rapid suppression of high-frequency disturbance, accurate adjustment of impedance parameters and long-term adaptive optimization are achieved, and the flexibility, stability and operation efficiency of the mechanical arm in the dynamic tunnel environment are remarkably improved.

The invention discloses a multi-unmanned aerial vehicle formation control method based on distributed model predictive control. The method comprises the following steps: describing a motion law of each unmanned aerial vehicle by adopting a state-space equation; modeling a static obstacle as a geometric body, and setting a minimum safe distance; considering position tracking cost, control energy cost, input change cost and formation cost, and establishing an objective function for each unmanned aerial vehicle; adding a CBF security constraint according to whether the security domain has an obstacle avoidance demand or not; constructing an optimal control problem for each unmanned aerial vehicle; in each control period, the optimal control problem is solved; only the first item in the control sequence is adopted as an actual control instruction; and then the system state is updated based on the state-space equation, and prediction and optimization are carried out again in the next period. According to the invention, the problems of obstacle avoidance and formation keeping when the multi-unmanned aerial vehicle formation passes through the obstacle in a complex environment are effectively solved. The method can be widely applied to the field of unmanned aerial vehicle control.

The invention discloses a distributed model prediction control method for an offshore front-end speed regulation type wind turbine generator, and belongs to the technical field of new energy power generation. According to the method, an overall control task is decomposed into three parallel subsystems including a variable pitch subsystem, a hydraulic variable torque speed regulation subsystem and an excitation subsystem, and local discrete prediction models are established respectively. And in each sampling period, rolling optimization of each subsystem is executed in parallel, and future prediction tracks of the rotating speed and the terminal voltage of the generator are introduced as global consistency variables. And by constructing an augmented Lagrangian cost function based on an alternating direction multiplier method and performing iterative solution, driving each local prediction trajectory to converge to a globally consistent optimal trajectory. And finally, outputting a first instruction of an optimal control sequence obtained after iterative convergence to each execution mechanism, and realizing cooperative control of the three subsystems, thereby rapidly and smoothly satisfying frequency and voltage synchronization conditions at the same time before grid connection, effectively inhibiting coupling interference and wind speed fluctuation, and improving grid connection quality and operation reliability.

The invention discloses an unmanned aerial vehicle cluster collaborative obstacle avoidance method based on distributed model predictive control, and belongs to the technical field of unmanned aerial vehicle cluster collaborative navigation. The method aims to solve the problem that the decision is easy to oscillate due to non-convex obstacle avoidance constraint, difficult solution and information asynchronization of the cluster in a dynamic environment. According to the scheme, each unmanned aerial vehicle constructs a local optimization problem containing a non-convex constraint and a cost function based on the unmanned aerial vehicle and neighbor information in each period; taking the optimal trajectory of the previous period as a reference, and converting constraint linearization and cost function second-order approximation into convex quadratic programming sub-problems; solving and outputting a control sequence within the time constraint; and executing the current control quantity and broadcasting a new predicted trajectory. The method is used for real-time collaborative obstacle avoidance of the unmanned aerial vehicle cluster in a communication limited environment.

The present application relates to a kind of based on distributed model predictive control's electric precipitator cluster energy-saving optimization operation control method and control system, based on electric precipitator cluster establishment distributed linear dynamic model, describe the dynamic response relationship between the dust concentration in each electric chamber in electric precipitator cluster and high voltage power supply secondary voltage;According to the outlet concentration information of electric precipitator cluster, design state observer, estimate the dust concentration in each electric chamber;Based on the estimated state of distributed linear dynamic model and state observer, design the controller based on distributed model predictive control, obtain optimal control strategy;Based on method implementation control system.The present application is convenient for control implementation, get rid of the dependence on historical data;Real-time estimation of unobservable electric chamber dust concentration can be implemented in electric precipitator cluster engineering application;Method ensures accurate regulation for each electric chamber, realizes multi-field collaborative dust removal, efficiently and reasonably allocates energy, saves electric precipitator cluster operating cost.

The invention relates to the field of power systems, and particularly discloses a source network load storage dynamic balancing system and method for a high-proportion new energy power grid. The basic principle of the scheme of the invention is as follows: a regional collaborative optimization layer performs rolling optimization by using a distributed model predictive control algorithm based on wide-area measurement and ultra-short-term predictive data to generate a regional collaborative control target in the form of voltage or reactive power demand; and after receiving the target, each intelligent power converter in the distributed equipment execution layer performs real-time negotiation with adjacent equipment by running a distributed consistency algorithm, autonomously determines respective virtual admittance regulation quantity, and finally quickly adjusts the equivalent admittance of the grid-connected point through a virtual admittance control technology, and outputs the required reactive power. The most core technical effect of the scheme is that the second-level real-time dynamic balancing capability and the safe and stable operation level of the power grid in the high-proportion random new energy access environment are improved.

The application particularly relates to a cooperative string stability model predictive control method based on unmanned aerial vehicles, and belongs to the field of unmanned aerial vehicle control, and comprises the following steps: establishing a discrete time state space model of a following unmanned aerial vehicle; introducing a step factor beta, and establishing a determination relationship between control increments at each step in a future prediction time domain and a current time control increment; based on the state space model and the determination relationship, establishing a local optimization problem of a distributed model predictive controller for the following unmanned aerial vehicle; in a control period, the following unmanned aerial vehicle solves the local optimization problem, obtains an optimal current control increment, updates a current control quantity and outputs the current control quantity to an executing mechanism, and cooperative control of the unmanned aerial vehicle is realized. The application also relates to a cooperative string stability model predictive control system based on unmanned aerial vehicles, an electronic device and a computer readable storage medium. The application adopts a distributed efficient model predictive control strategy, and ensures that different types of unmanned aerial vehicles can maintain the stability and string stability of a queue when flying in formation.

This invention relates to the field of power system operation and control technology, and particularly to a method for enhancing grid frequency resilience and providing power support considering inertia spatial distribution. The method includes: acquiring grid operation information, determining the equivalent inertia index of each node or region and generating an inertia spatial distribution heatmap, identifying local inertia-weak areas and frequency-vulnerable areas; after detecting a disturbance event, combining grid operation information, the inertia spatial distribution heatmap, and grid topology relationships to perform spatiotemporal joint prediction of the post-disturbance frequency evolution process, obtaining the frequency change trend and disturbance propagation path; screening target support resources, and forming a power support strategy through distributed model predictive control, implementing emergency power support, and dynamically adjusting the exit rate and power recovery rate of the target support resources during the recovery phase. This method achieves visualized characterization and weak link location of the uneven spatial distribution of grid inertia, solving the problem of difficulty in detecting local inertia depressions in frequency control.

The application belongs to the field of robots, in order to solve the problems of motion planning scalability, navigation flexibility and system safety of large-scale robot cluster, the application provides a collision risk awareness and high scalability large-scale cluster robot motion planning method and device, comprising: in the macro stage, using a Gaussian mixture model to represent the macro state of the cluster, and planning the transport trajectory of the Gaussian mixture model through model predictive control; in the micro stage, using an artificial potential field method to assign target positions to the robots, and combining a distributed model predictive control to realize the tracking and dynamic obstacle avoidance of the individual to the transport trajectory of the Gaussian mixture model. The application can effectively improve the motion planning performance of the large-scale cluster robot system in a complex environment. In the application, by adjusting the risk acceptance degree, the distance between the robot cluster and the obstacle can be flexibly adjusted, and the balance between safety and motion efficiency is realized.

The invention provides a multi-mode collaborative lithium battery protection circuit control method. The method comprises the following steps: acquiring voltage, current and surface temperature of a battery pack and a relay state signal in real time, and constructing a standardized time sequence tensor through preprocessing; inverting the core temperature of each single battery based on an electricity-heat-aging coupling model; the aging rate index is dynamically calibrated by combining the joint extended Kalman filtering and the real-time core temperature; a dynamic adjacency matrix is constructed through voltage correlation and temperature gradient, and a topological weakness risk is identified by using a lightweight graph convolutional network; according to the core temperature, the aging index, the risk score and the SOC difference, a protection mode is dynamically switched in a multi-mode logic state machine, and a control instruction is generated; and finally, the time sequence of a power tube is adjusted through PWM phase compensation, and a distributed model prediction control algorithm is adopted to drive an equalization circuit, so that lithium battery system protection with safety, service life and performance collaborative optimization is realized.

The invention discloses a longitudinal-transverse coupling distributed model prediction control method for a multi-queue vehicle system, and the method comprises the steps: constructing an eight-dimensional state space model containing longitudinal and transverse motion parameters, and building an in-queue forward chain type and inter-queue multi-strategy communication topological structure; comprising three strategies of pilot car-tripper car communication, pilot car-pilot car communication and hybrid communication; coupling constraints are defined based on a communication topological structure, and a longitudinal-transverse coupling distributed model is constructed to predict control optimization problems, including local optimization of queues and following vehicles and longitudinal-transverse collaborative optimization of cut-in vehicles; terminal state constraint is introduced to ensure system stability; and solving an optimization control problem for each vehicle node, and adjusting a longitudinal traction torque and a transverse steering angle at the same time to realize cooperative updating of vehicle states. According to the method, through a unified longitudinal-transverse optimization framework and a distributed solution mechanism, vehicle following and lane changing behaviors can be processed at the same time, and the safety, comfort and fuel economy of a multi-queue system are improved.

A kind of comprehensive energy system optimization method and system giving consideration to cost and new energy consumption, comprising: comprehensively considering the characteristics of multi-energy equipment and spot market electricity price mechanism, constructing regional comprehensive energy system model;Based on the regional comprehensive energy system model, a multi-objective optimization scheduling model containing day-ahead, intra-day, real-time multi-time scale and minimizing system total cost and maximizing new energy consumption is established;The day-ahead and intra-day multi-objective optimization scheduling model is solved;Based on the day-ahead and intra-day optimization results, the real-time multi-objective optimization scheduling model is solved by designing a distributed intelligent agent architecture and using a distributed model predictive control algorithm;According to the solving result, the output plan of each unit, the charging and discharging plan of energy storage and the system energy purchase plan are determined to realize optimal scheduling.The present application comprehensively considers operation cost and new energy consumption, and realizes economic and low-carbon operation of regional comprehensive energy system.

This invention belongs to the field of smart agriculture technology and collaborative operation of unmanned systems. It discloses a method and system for mobile intelligent agricultural unmanned swarm operations. The method includes: generating a global verification path that minimizes the sum of the total travel distance and the soil compaction penalty; generating a soil property distribution map and fusing aerial imagery data to obtain a farmland fusion perception map; rasterizing the farmland areas in the fusion perception map, treating each abnormal grid as a task, and assigning bidding weights to agents for tasks based on the suitability of the agents in performing the corresponding tasks; allocating agent tasks with the objective of maximizing the total sum of global bidding weights; and predicting the trajectory of the aerial operation drones using a discrete linear model, with each aerial operation drone solving the optimization problem locally within a distributed model predictive control framework. This invention improves operational efficiency and accuracy.

The invention relates to the technical field of virtual power plants, in particular to a virtual power plant energy scheduling method and system based on distributed optimization, and the method comprises the steps: constructing a distributed energy node state sensing network, collecting the operation state data and load prediction data of each distributed energy unit in real time, and transmitting the data to a distributed optimization scheduling center; based on an improved distributed model predictive control algorithm, establishing a multi-target energy scheduling optimization model, and decomposing a global optimization problem into local sub-problems of each node; parallel iterative solution of each sub-problem is realized through a distributed collaborative solution mechanism, and a scheduling scheme is dynamically corrected; and based on the real-time operation deviation and the external environment change, adaptively adjusting a scheduling strategy and issuing the scheduling strategy to each energy unit for execution. According to the invention, distributed energy resources are integrated through a distributed optimization architecture, efficient collaborative scheduling of virtual power plant energy is realized, the energy utilization rate is improved, the operation cost is reduced, and the system operation stability and flexibility are enhanced.

The invention relates to the field of intelligent traffic systems, discloses a multi-intelligent-vehicle coupling optimization control method and system based on V2X communication, and aims to solve the problems of information isolated island, local optimization and global target mismatch, response lag, control conflict and the like in multi-vehicle cooperation in the prior art. The method comprises the following steps: receiving dynamic information of an adjacent vehicle and a roadside through V2X, fusing high-precision state data of the vehicle, and constructing a multi-agent state coupling graph containing space-time constraints; generating a local context embedding vector by using graph neural network coding; a distributed model prediction controller is input to solve a quadratic programming problem with hard constraints, an objective function of the quadratic programming problem integrates trajectory tracking, control smoothness and a dynamic safety distance, and consistency constraints are introduced to ensure unification of multi-vehicle cooperative logic; and finally outputting a first-step control instruction to drive a drive-by-wire execution mechanism. According to the invention, safe, efficient and energy-saving non-centralized multi-vehicle cooperative control in a high-density dynamic traffic scene is realized.

This invention provides a distributed model-based collaborative navigation control method for polar icebreaker convoys, comprising: Step S1, acquiring ship parameters of the icebreaker convoy, ice condition data of the navigation area, and the motion state of the lead ship to determine safety constraints; Step S2, establishing a longitudinal dynamic model, including a first resistance model based on ice resistance experienced by the lead ship and a second resistance model based on still water resistance experienced by the following ships; Step S3, constructing a safe distance model based on ship parameters, safety constraints, the first resistance model, and the second resistance model; Step S4, setting a distributed model predictive controller for each following ship, and generating control commands by continuously solving a local optimization problem based on the current state of the ship itself, the motion state of the lead ship, and the assumed trajectories of adjacent ships, while satisfying the safety constraints. The beneficial effect is that this invention can improve the autonomy, safety, and overall escort efficiency of icebreaker convoy navigation in ice-covered areas.

The invention discloses a distributed prediction multi-unmanned aerial vehicle cooperative path planning method based on an improved grey wolf algorithm. According to the method, a layered hybrid architecture and a communication limited adaptation strategy are adopted. And decomposing a global communication limitation and shortest path tradeoff problem into a local problem for analysis. According to the method, a distributed model predictive control (DMPC) architecture and a communication limited adaptation strategy are introduced on the basis of a traditional grey wolf algorithm, rolling optimization is performed on the local trajectory of each unmanned aerial vehicle, a global target is realized by combining cooperative constraints, and information interaction and autonomous decision-making of each member are realized. A simulation experiment verifies that the distributed grey wolf algorithm provided by the invention can smoothly complete a path planning task in a complex communication limited environment, the effective communication coverage rate is improved by 11.8-18.6% compared with other traditional algorithms, the dual goals of path optimization and communication maintenance can be effectively coordinated, and the path planning efficiency is improved. And the collaborative path planning task is effectively completed under the condition that the cost of sacrificing the trade-off path length is low.

This invention proposes a game theory-based distributed drive electric vehicle chassis coordinated control method, including: establishing a dynamic and kinematic model and a two-degree-of-freedom reference model for the distributed drive electric vehicle; in the design of the upper-level coordinated controller, a game-based distributed model predictive control is adopted, with the control objective of active front wheel steering being tracking accuracy, active rear wheel steering being lateral stability, direct yaw moment being yaw stability, and active suspension being ride comfort and rollover stability, while also designing a switching control for ride comfort and rollover stability; furthermore, an additional yaw moment is distributed to each wheel through a lower-level torque distribution controller. This invention solves for the optimal control quantity for each player through a game theory-based distributed model predictive architecture, achieving multi-objective optimization and effectively improving the vehicle's trajectory tracking accuracy, handling stability, rollover stability, and ride comfort.

The application discloses a kind of multi-unmanned aerial vehicle navigation method, system, equipment and medium based on distributed model predictive control and virtual pipeline, and the method comprises the following steps: constructing virtual pipeline, forming the flyable area of unmanned aerial vehicle group;In each sampling period, each unmanned aerial vehicle generates the assumed trajectory in prediction time domain based on its current state using its dynamic model, and broadcasts to neighbor unmanned aerial vehicle;Obstacle intrusion judgment and virtual pipeline dynamic processing, construct linear separation hyperplane, generate local convex safety sub-pipeline constraint;Inter-machine collision risk judgment is carried out and inter-machine collision avoidance constraint is dynamically activated;Set target cost function, and construct optimization problem in combination with the above constraint;The optimal prediction control input sequence of current time is obtained by solving, and its first control instruction is executed;The above process is repeatedly executed until all unmanned aerial vehicles pass through virtual pipeline.The application can simultaneously solve multiple challenges such as dynamic environment adaptability, calculation real-time and trajectory smoothness.

The application discloses a kind of time delay conditions under heterogeneous commercial vehicle fleet multi-objective optimization control method and device, comprising: S1 establishes the dynamics model of each car in commercial vehicle queue and queue system model;S2 based on distributed model predictive control method designs queue controller, establishes cost function and constraint condition, defines sub-predictive optimization problem for each vehicle, designs follow car error, economy and comfort cost function, reduces follow car error in heterogeneous vehicle queue driving, reduces energy consumption, improves comfort, improves queue comprehensive performance;S3 by integrating buffer and delay compensator on controller, add delay compensation link for control system, improve the control effect of heterogeneous vehicle fleet under delay condition, realize the stable control of heterogeneous queue under non-ideal communication condition;S4 solves target function using simulated annealing and particle swarm optimization algorithm, solves the problem of nonlinear discontinuous target value in target function according to probability method, reduces operation time, improves solving efficiency.

The embodiment of the invention discloses an underactuated unmanned ship obstacle avoidance method based on distributed model predictive control, and the method comprises the steps: building a cost function considering the dynamic obstacle avoidance cost through a kinematics model and a dynamics model of a single-propeller single-rudder underactuated unmanned ship; and establishing an objective function and a constraint function based on the cost function, solving the objective function, and obtaining the optimal control input of the unmanned ship to realize obstacle avoidance of the underactuated unmanned ship. According to the method, the dynamic obstacle information (including the neighbor unmanned surface vehicle) is fully utilized, so that the optimal control input of the unmanned surface vehicle is obtained, the unmanned surface vehicle can have high obstacle avoidance precision when executing a task, and the autonomous obstacle avoidance capability of a multi-surface vehicle system in a complex environment is effectively improved.

The invention relates to the technical field of battery energy storage, in particular to a lithium battery energy storage cluster dynamic health weight networking balance control method and system, a health weight networking is constructed in an energy storage cluster, and single batteries are connected in parallel through a DC-DC converter to realize bidirectional power flow; constructing a health degree evaluation model based on a topological graph neural network, and constructing a battery topological relation graph by taking a single battery as a node and DC-DC converter connection as an edge; acquiring voltage, current, temperature and circulating historical data of the single battery; generating a health degree evaluation result based on the evaluation model; constructing a health degree weight vector through a homotopy path optimization algorithm, wherein the health degree weight vector comprises a capacity contribution rate, a power contribution rate and an aging rate; based on the health weight network and the health degree weight vector, a rated current set value of each single battery is obtained through distributed model prediction control optimization, dynamic balance of the single batteries in the cluster is achieved, the battery health state evaluation precision is remarkably improved, and the overall service life of the energy storage cluster is prolonged.

The invention discloses a self-adaptive coordination control method for a photovoltaic energy storage system. The method comprises the following steps: constructing a self-adaptive decision-making mechanism based on deep reinforcement learning; carrying out state estimation on the key state of the system by fusing wavelet packet transformation and an adaptive neural fuzzy inference network; the system is decomposed into a photovoltaic subsystem, an energy storage subsystem and a load subsystem by adopting distributed model predictive control, each subsystem operates a local MPC, and global adaptive coordination is carried out through collaborative optimization; self-adaptive adjustment is carried out on the control parameters; according to the method, DRL training is optimized by using transfer learning, a fuzzy rule is adaptively updated by adopting fuzzy C-means clustering, and a control model and a strategy are dynamically updated. According to the method, deep reinforcement learning is adopted as a core decision framework, distributed model predictive control and collaborative optimization are adopted, and wavelet packet transformation and an adaptive neural fuzzy system are fused; parameter divergence and system instability are avoided, and the coordination control performance of the photovoltaic energy storage system is improved.