17 results about "Virtual leader" patented technology

The application discloses a method for adaptive secure consensus control of a multi-agent spatio-temporal dynamic system with unknown boundary nonlinearity under hybrid attacks, comprising the following steps: constructing a multi-agent spatio-temporal dynamic system model with unknown boundary nonlinearity and a virtual leader model; establishing a hybrid network attack model containing deception attacks and denial of service (Dos) attacks, and using an adaptive radial basis neural network to approximate the unknown boundary nonlinearity function; defining a consensus error signal to obtain a consensus state error system; designing a composite adaptive neural network secure boundary consensus control scheme, constructing a Lyapunov function for the error system, and obtaining a sufficient condition for the error system to realize mean square secure consensus control. The application effectively solves the problem that the traditional method is difficult to simultaneously cope with complex network attacks and unknown boundary nonlinear disturbances, and significantly improves the security and robustness of the system.

This invention proposes an adaptive virtual impedance droop control method, system, device, and medium, relating to the field of droop control. The method includes: calculating the standardized reactive power of each local node and neighboring node based on the equivalent output impedance and reactive power droop coefficient; generating a correction amount for the virtual impedance based on the accelerated consensus algorithm and the standardized reactive power of each local node and neighboring node; adjusting the virtual impedance in the distributed grid topology according to the correction amount of the virtual impedance to obtain the adjusted dynamic virtual impedance; calculating the virtual impedance voltage drop based on the adjusted dynamic virtual impedance; calculating the feedforward compensation voltage reference value; calculating the common compensation amount of the preset virtual leader node and the local compensation amount of each local node and neighboring node to obtain the total voltage compensation command; adjusting the feedforward compensation voltage reference value according to the total voltage compensation command to obtain a voltage stabilization scheme, using the dynamic virtual impedance as the power distribution scheme.

The present application relates to the technical field of unmanned aerial vehicle control, in particular to a control algorithm for cooperative inspection of unmanned aerial vehicle cluster, the present application is designed by innovative non-central architecture and double-neighbor system, when the node communication loss or failure of unmanned aerial vehicle cluster is up to 30%, it can still maintain more than 92% of task completion rate, fault tolerance is significantly better than prior art; its core only relies on three simple behavior rules, the requirement of single machine computing power and communication bandwidth is very low, it can adapt to low-cost civilian unmanned aerial vehicle platform, so that the cost of large-scale deployment is reduced by about 40%. Combined with dynamic weight adjustment mechanism and virtual leader guidance, the system can not only autonomously avoid dynamic obstacles in complex urban areas, but also accurately follow the preset inspection route, realizing the unity of flexibility and accuracy; and supports multi-hop Ad-hoc communication, maintains stable connection in the area without ground network; fault automatic switching, single machine exit does not affect the overall task; combined with fixed airport, it can realize 7*24 hours normal unmanned inspection.

The present application relates to unmanned vehicle group cooperative control technical field, specifically relates to a kind of unmanned car group LSTM-MPC cooperative formation control method for complex environment in open country, including S1 training LSTM neural network prediction model, S2 realizes the cooperative formation control of virtual leader method based on LSTM-MPC algorithm, S3 is coordinated by virtual leader method, and the control planning of virtual leader car and real following car, the present application adopts LSTM data-driven model, replaces traditional physical model, solves the low state prediction accuracy and trajectory deviation problem caused by model mismatch, reduces the calculation complexity, improves real-time response speed, meets the efficient demand of large-scale vehicle group cooperation.Combined with virtual leader architecture and rolling cycle mechanism, the dynamic self-adaptive adjustment of target trajectory is realized, the instability of formation and cooperative response lag caused by sudden situation such as topographic mutation are avoided, the consistency of mapping is guaranteed, and the defects in the prior art, such as strong model dependence, poor field adaptability, insufficient real-time performance and weak formation cooperative ability, are overcome.

The application discloses a kind of dual unmanned aerial vehicle anti-interference cooperative hoisting control method, system, equipment and medium, comprising: obtaining the actual state information of dual unmanned aerial vehicle and the desired state information of virtual leader;According to desired state information, the desired position information of dual unmanned aerial vehicle is calculated according to pre-set dual-machine cooperative decoupling control rule;With desired position information as input, based on the extended state observer and nonlinear anti-interference controller constructed in advance, the control input information of each unmanned aerial vehicle is output, so that the power system controls dual unmanned aerial vehicle anti-interference cooperative hoisting load according to control input information.The application introduces virtual leader, solves the problem of sling tension imbalance caused by independent trajectory planning;Based on dual-machine cooperative decoupling control rule, the problem of unmeasurable load pose caused by flexible sling is solved, and the extended state observer and nonlinear anti-interference controller are used to realize real-time estimation and active compensation for compound disturbance such as wind disturbance and sudden load.

The application discloses a kind of cluster unmanned plane fixed time quick adaptive fault-tolerant cooperative control method, first, design a kind of elasticity distributed fixed time observer based on topology switching signal, for accurately estimating virtual leader state and weakening the influence of denial-of-service attack.Second, by combining power integrator technology with topology switching signal, for follower unmanned plane affected by unknown actuator fault, construct fixed time adaptive fault-tolerant cooperative control protocol, so that the cluster unmanned plane system can still realize trajectory tracking in fixed time under the condition of actuator fault and denial-of-service attack coexistence.Finally, by radial basis function neural network, online approximation of composite nonlinear disturbance is realized, more accurate cooperative control is realized.The application can be applied to cluster unmanned plane formation fault-tolerant cooperative control under actuator fault and denial-of-service attack.

The application relates to a speed planning method, system, medium, computer program product, electronic equipment and mobile tool of a guide vehicle, wherein the method comprises the following steps: step 101, receiving guide vehicle target information, the guide vehicle target information comprising a first distance between the guide vehicle and a guide vehicle target; step 102, mapping the position of a virtual leader aircraft on the driving path in front of the guide vehicle according to the first distance, the distance between the virtual leader aircraft and the guide vehicle being a second distance; the second distance is negatively correlated with the first distance; step 103, judging whether the second distance is greater than a preset following distance; step 104, if the second distance is greater than the preset following distance, increasing the guide speed of the guide vehicle according to a preset value; and step 105, if the second distance is less than the preset following distance, reducing the guide speed according to a preset value.

This invention relates to a multi-agent cluster cooperative control method based on reinforcement learning, comprising: obtaining normalized global state information based on a first normalized relative distance and a first normalized relative velocity between an agent and a virtual leader point, and a second normalized relative distance and a second normalized relative velocity between an agent and neighboring agents; calculating the individual reward of each agent based on an individual reward function, and obtaining the total reward for a preset interval based on the individual rewards of all agents; training an Actor-Critic network model based on the normalized global state information at the current moment, the actions performed by the agents, the total reward for the preset interval, and the normalized global state information at the current moment, to obtain a trained Actor-Critic network model; and deploying the trained Actor network onto the agents to obtain the final acceleration command to control the agent's movement. This invention has the advantages of high sample utilization, strong generalization, excellent cooperative effect, and stable training.

The present application relates to the technical field of ship control, in particular to a ship fin stabilizer variable gain robust control method and system. It includes: constructing a three-layer nested working condition judgment model based on real-time navigation data, sequentially evaluating the roll energy, resonance risk and actuator capacity to determine whether to start control. The concept of virtual leader is introduced to construct a dynamic topology to coordinate the cooperative control of multiple fin stabilizer units. A multi-objective optimization algorithm is used to set the variable gain robust control parameters to balance the roll reduction effect, energy consumption and robustness. During the control process, a three-level abnormality fusing mechanism is implemented: fine-tune the parameters when there is a slight abnormality, backtrack the working condition judgment when there is a moderate abnormality, and roll back to the historical stable strategy when there is a serious abnormality. Through performance evaluation and intermittent freezing mechanism, the parameter optimization is suspended when the working condition is stable to reduce energy consumption and equipment wear and tear, and quickly recover the response when there is a mutation. The method realizes complete control from working condition identification, cooperative control, parameter optimization to safety guarantee.

The application discloses a kind of distributed time-varying state safety constraint control methods of multi-flexible manipulator system, process as follows: construct the dynamics model of 1 N Flexible manipulator with asymmetric time-varying state safety constraints and input quantization;Select the desired signal as a virtual leader, construct a multi-flexible manipulator system;Design trajectory planning function, construct desired tracking signal for each follower manipulator;Consider unknown time-varying disturbance, construct N Disturbance observer;Construct an asymmetric time-varying barrier Lyapunov function based on angle tracking error and convert it to symmetric form through coordinate transformation;Based on distributed observer, construct a distributed adaptive boundary controller;Solve the parameters of the distributed controller;Apply distributed time-varying state safety constraint control to the multi-flexible manipulator system.The application realizes distributed time-varying tracking and vibration control of multi-flexible manipulator system in the presence of asymmetric time-varying state safety constraints and input quantization.

This invention belongs to the field of unmanned surface navigation technology, and particularly relates to a method, system, and medium for pre-defined time-based cooperative formation control of multiple unmanned surface vessels (USVs) with error constraints. The method includes: first, establishing kinematic and dynamic models of the USVs; then, combining virtual structure and leader-follower methods to establish a virtual leader-follower formation control framework, and designing a pre-defined time-based disturbance observer to observe disturbances; finally, designing a pre-defined time function and a pre-defined performance inequality to design a pre-defined time-based formation control protocol. The controller designed in this invention enables USVs to perform formation tasks within a preset time. Furthermore, the pre-defined time-based disturbance observer observes and compensates for external disturbances, exhibiting strong robustness and anti-interference capabilities. Compared to existing finite-time formation controllers, the controller of this invention does not exhibit chattering and its convergence performance is independent of the initial state; compared to fixed-time formation controllers, the system convergence time can be manually set, improving formation control efficiency.

This invention discloses an adaptive fault-tolerant control method and system for a multi-flexible robot system with preset indices, belonging to the field of fault-tolerant control technology for control systems. The method includes: establishing a dynamic model of the multi-flexible robot and a virtual leader model containing hub angle position, elastic vibration, and total displacement; constructing an intermittent actuator fault model including the number of faults, intervals, and unknown times; approximating system uncertainties using a radial basis function neural network; defining the tracking error and reconstructing the error dynamic equation, introducing a strictly decreasing function to construct a preset index set of boundary error state variables; converting constrained errors into unconstrained variables using a strictly increasing function; designing an adaptive fault-tolerant controller based on the unconstrained variables, and using a projection operator to update the estimated parameters online. This invention achieves consistent angular position tracking and suppression of elastic vibration, with the error converging to a predefined residual set, requiring no prior fault information, thus improving the system's robustness and engineering practicality.

This invention discloses an aircraft formation control method based on distributed observation and time-varying communication, belonging to the field of aircraft control. It employs a Markov process to model the stochastic switching characteristics of the communication topology, designs distributed position and velocity observers for each aircraft, and estimates the virtual leader state through neighbor information interaction. An error-coupled sliding mode surface is constructed, and a distributed control input integrating collaborative correction and sliding mode robust compensation is designed to drive the aircraft to track the desired formation state. This invention does not rely on complete leader information and can still ensure formation stability even when communication links switch or model disturbances occur. It has low computational overhead and is suitable for multi-agent cooperative formation scenarios such as UAVs and unmanned surface vessels.

This invention discloses an adaptive boundary constraint fault-tolerant control method and system for multiple flexible Timoshenko manipulators, belonging to the field of flexible manipulator control technology. The method includes: establishing a dynamic model incorporating elastic vibration, shear vibration, and hub angular motion; constructing a virtual leader to provide the desired trajectory; establishing an intermittent fault model for the end effector and hub actuators; approximating the unknown functions of the system using a neural network; defining boundary state variables and boundary constraints; designing a boundary fault-tolerant controller that integrates fault compensation, adaptive approximation, and constraint satisfaction; and using a projection operator to ensure bounded parameter estimation and online updates. This invention can achieve consistent angular position tracking of multiple manipulators under conditions of intermittent actuator failure, model uncertainty, and constrained states, effectively suppressing elastic and shear vibrations, ensuring that boundary states do not exceed limits, and improving system robustness, safety, and engineering practicality. It is applicable to fields such as precision manufacturing, medical, and aerospace.

The present application discloses a coordinated control method for energy storage of a multi-energy complementary distributed microgrid, and relates to the technical field of microgrids. The coordinated control method includes: collecting and preprocessing renewable energy and energy storage unit data, monitoring, in real time, and predicting the load change of the microgrid and the fluctuation of a power generation capacity of the renewable energy, and generating short-term charging and discharging plans; and by means of a virtual leader algorithm, adjusting power distribution proportions of energy storage units in real time and dynamically adjusting charging and discharging rates of the energy storage units, and after the charging and discharging rates are adjusted, synchronizing voltages and frequencies of the different energy storage units. The accuracy of power and load prediction is improved; and the power distribution proportions and the charging and discharging rates of the energy storage units are dynamically adjusted.

The application discloses a path planning method for a UAV swarm in a complex environment based on reinforcement learning, comprising the following steps: step 1, obtaining a converged proximal policy network; step 2, obtaining an expected path of a virtual leader composed of multiple waypoints; step 3, calculating the probability of the UAV swarm being detected by a radar cluster system and the number of failed UAVs newly added to the UAV swarm at each waypoint, and determining whether the UAV swarm is evenly flown at each waypoint; and step 4, obtaining an expected path of each UAV. The application adopts a proximal policy optimization algorithm to construct a reinforcement learning path planning framework, simultaneously considers a composite reward function of path length, the probability of being detected by a radar and the probability of single-machine failure, and can obtain a stable and high-quality strategy in a complex three-dimensional environment and a high-dimensional state space, thereby reducing the risk of falling into a local optimum based on an artificial rule or a single cost function method.