62 results about "Virtual leader" patented technology

The invention provides a zero-sum game-based distributed elastic consensus optimal control method under denial of service attack of a multi-agent system, and the method specifically comprises the steps: firstly, constructing a multi-agent formation model in which a leader and a plurality of followers cooperatively move through graph theory knowledge and a multi-agent second-order state equation; secondly, in order to reduce the influence of denial of service attack on communication topology, a time-varying weight distributed elastic observer is provided to estimate the state of a leader, and the attacked condition of the leader is considered; then, by constructing an augmentation system, a distributed consistency tracking problem with a leader is converted into a local tracking problem between each follower and a virtual leader thereof; and finally, in order to solve the zero-sum game problem, introducing a Hamiltonian-Jacobian-Ansaxophone equation to realize optimal control input under maximum external disturbance, and realizing algorithm design by using single-evaluation reinforcement learning with experience playback and combining a gradient descent method.

The invention provides an unmanned aerial vehicle formation conflict avoidance control method and system and a medium, and the method comprises the steps: introducing a virtual leader-follower-based Stackelberg game architecture, enabling a virtual leader unmanned aerial vehicle to adaptively adjust the trajectory according to the formation state, and enabling the follower unmanned aerial vehicle to maintain the stability of the formation while completing a set tracking task; constructing an obstacle avoidance penalty term based on a speed obstacle method, and embedding the obstacle avoidance penalty term into an optimization objective function of the follower unmanned aerial vehicle to ensure that the follower unmanned aerial vehicle realizes collision avoidance in an obstacle environment; and respectively solving Hamiltonian-Jacobian-Bellman equations corresponding to the leader unmanned aerial vehicle and the follower unmanned aerial vehicle by adopting a single-network ADP algorithm, and generating a controller in real time. According to the invention, the problems of safe obstacle avoidance and tracking control of a multi-unmanned aerial vehicle system with formation stability requirements are effectively solved.

The present disclosure discloses a method and system for formation control for an USV swarm via a CEDRL. The method includes: designing a desired formation pattern based on a formation hierarchical virtual leader strategy, establishing an USV desired location library, and assigning a location index to a desired location of each USV in a formation; updating the desired location of each USV and the corresponding location index via an USV formation local consensus strategy in a case where there is a risk of collision between USVs; and acquiring an actual geolocation of each USV in real time, and adopting a surface vessel control decision-making network to direct the USV toward a latest desired location. An autonomous collaborative formation of a large-scale USV swarm may be realized by the present disclosure.

The invention discloses a multi-robot affine formation path optimization method driven by a virtual navigator, and the method comprises the steps: firstly setting a point set composed of the virtual navigator and followers, enabling the virtual navigator to form a convex hull and meet an affine localization condition, so as to guarantee that the followers are always located in the convex hull; generating a collision-free reference trajectory of the mass center of the virtual navigator based on a fast random tree algorithm; constructing a nonlinear constraint optimization problem NCO which contains linear transformation constraint and meets smoothness, obstacle avoidance, speed and angular speed constraint; and establishing a communication graph of the virtual navigator and the follower, calculating balance stress, designing a smoother follower distributed differential speed control law, and realizing formation path optimization and obstacle avoidance control. Compared with an existing method, the formation path generation method has the advantages that the smooth and stable formation path fitting the reference trajectory can be generated while obstacle avoidance and physical feasibility are guaranteed, and the cooperative motion capability and the control performance of a multi-robot system in a complex environment are improved.

The invention relates to a multi-unmanned aerial vehicle distributed fault-tolerant formation control method and system based on a potential game, belongs to the technical field of unmanned aerial vehicle control, and is used for solving the distributed formation control problem of a multi-quadrotor unmanned aerial vehicle virtual leader-follower system under the conditions of model uncertainty, actuator fault and external disturbance. According to the method, a distributed leader estimator is designed to obtain leader expectation information, a preset performance function is introduced in preset time to constrain position and attitude tracking errors, and a linear parameterized regression matrix and a multi-parameter adaptive law are constructed to realize unified compensation of uncertainty and faults. And the formation robustness is guaranteed by combining a potential game and a dynamic average consistency strategy. According to the invention, it is ensured that the tracking error converges to the preset range within the preset time, and the fault-tolerant capability and stability of the formation system are improved.

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.

The invention discloses a multi-cluster four-rotor unmanned aerial vehicle cooperative control method, system, device and medium, and relates to the technical field of sliding mode control, and the method comprises the steps: dividing unmanned aerial vehicles in a multi-cluster four-rotor unmanned aerial vehicle formation into leaders and followers; tracking the virtual leader by using an actual leader to obtain a tracking error, establishing a terminal sliding mode surface according to a dynamic adjustment value of the tracking error, and obtaining control input during convergence; adjusting the state of the follower by using a neighbor state, tracking a sub-cluster leader, and carrying out comprehensive control input according to the state of the follower and a tracking result; the state difference between the follower and the leader is used as a tracking error, a global fast terminal sliding mode surface is constructed according to the tracking error, global control input is obtained by offsetting disturbance for comprehensive control input, and all states are converged to the sliding mode surface within limited time. According to the method, a sliding mode surface structure is redesigned, a nonlinear term about a tracking error dynamic adjustment value is introduced, and it is guaranteed that the system state is rapidly converged within limited time.

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 invention provides an unmanned aerial vehicle cluster formation method, system and device and a storage medium, and aims to solve the problems that formation configuration is carried out again due to different numbers of unmanned aerial vehicle cluster formation flights, the relevance with path points is poor, and formation transformation needs to be realized depending on limitation of a relative angle in formation and the like. The method comprises the following steps: determining a formation pattern required to be realized by an unmanned aerial vehicle cluster according to a formation pattern request of an unmanned aerial vehicle cluster formation task; a formation pattern number corresponding to the formation pattern is set, formation pattern parameters corresponding to the formation pattern number are configured, and the formation pattern parameters comprise position parameters of a real leader or a virtual leader; based on the number of the unmanned aerial vehicles, the formation number of each unmanned aerial vehicle is configured; and based on the position parameters of the real leader or the virtual leader and the formation number of each unmanned aerial vehicle, determining the relative position information of each unmanned aerial vehicle relative to the real leader or the virtual leader under the formation.

The application provides a kind of multi-agent system control method under the condition of having saturation constraint and time delay, it is related to intelligent control technical field, comprising: establishing multi-agent system model, wherein, virtual leader is established between leader and follower agent, follower communicates with virtual leader, virtual leader communicates with leader, on this basis, controller is designed based on saturation constraint and communication time delay, error system between follower agent and leader is constructed, consistency condition is established, and the leader-following consistency of multi-agent system is controlled.The application is aimed at the problem that the dimensions of leader and follower in the model are different, a virtual leader is established between leader and follower agent, and the communication between actual leader and follower is completed.In addition, in view of the actual situation that the inherent properties of agent are limited, the control protocol is adjusted based on saturation constraint information and communication time delay, so that the leader-following consistency of the system is realized.

The application provides a finite time distributed formation method of unmanned surface vehicle cluster system, which can not only guarantee that the unmanned surface vehicle system reaches the expected formation mode in finite time, but also ensure that the system speed and formation error always meet the time-varying constraint requirements during the whole operation period, and the method comprises the following steps: establishing a three-degree-of-freedom unmanned surface vehicle kinematics and dynamics model; approximating the saturation constraint of the system by using a smooth function, and combining a fault model to establish a new system dynamics model subjected to driver faults and saturation constraints; designing a virtual control law, also known as a kinematics guide law; designing a parameterized lane speed of a virtual leader; designing a finite time distributed formation controller; and designing an uncertain compensation adaptive law. According to the needs of the user, the time-varying formation mode and the time-varying constraint function can be designed, the formation form of the multiple unmanned surface vehicles is dynamically adjusted, and meanwhile, it is ensured that the speed and formation tracking error always do not deviate from the specified constraint set.

This disclosure provides a reinforcement learning-driven cooperative formation control method for an air-to-ground unmanned swarm system. A dynamic model of the air-to-ground unmanned swarm system is established; a distributed predefined time observer is constructed to estimate the virtual leader state, providing a state reference for cooperative formation tracking control; the dynamic model of the augmented air-to-ground unmanned swarm system is reconstructed, and a control gain matrix is ​​then constructed. Piecewise constant initial stimuli are applied to the augmented system, and its early operational state data is collected and stored. Then, based on the early operational data of the air-to-ground unmanned system, an initial stable control strategy is obtained using a data-driven method. Finally, based on an offline policy reinforcement learning algorithm and a data-based initial stable control strategy, the optimal formation tracking controller is learned, achieving optimal formation tracking control of the air-to-ground unmanned system. This invention can achieve optimal time-varying formation tracking control of an air-to-ground unmanned swarm system according to complex task requirements, even when the system model is unknown.

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 invention relates to the field of artificial intelligence, and provides a control method for bipartite consistency of a multi-agent system based on a virtual leader, a control device for bipartite consistency of the multi-agent system based on the virtual leader, electronic equipment and a computer readable storage medium. The method comprises the following steps: setting a virtual leader; constructing a fixed time containment feedback control protocol of each agent; constructing a bipartite consistency error of each agent according to a fixed time containment feedback control protocol; and according to the bipartite consistency error, determining the convergence time upper bound of the multi-agent system for realizing the bipartite consistency within the fixed time based on the fixed time containment feedback control protocol. According to the method and the device, the technical effects of ensuring the convergence speed and the communication robustness of the system and saving communication resources can be realized.

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 formation control method and device for realizing multi-target tasks by cooperation of a UAV cluster. The formation control method comprises the following steps: acquiring initial information of multiple target tasks and basic information of a UAV cluster for executing the multiple target tasks; obtaining cluster grouping data of the UAV cluster when executing the multiple target tasks according to the initial information; obtaining task allocation data of each cluster group according to the cluster grouping data and the initial information; obtaining a reference path of each UAV cluster according to the task allocation data, the cluster grouping data and the initial information; setting a virtual leader based on the reference path, and obtaining expected position and speed data of each UAV for different target tasks according to the reference path and the virtual leader; and obtaining a formation shape of each UAV after reaching a corresponding expected position at a corresponding expected speed according to the basic information of the UAV cluster, the expected position and speed data of each UAV.

The invention discloses a distributed commercial load cluster information security convergence system oriented to denial of service network attack. The system obtains a temperature track of a virtual leader air conditioner when a follower air conditioner is subjected to DoS attack through a preset track evolution model of the virtual leader air conditioner; and a distributed tracking control law of each follower air conditioner is constructed through an auxiliary sliding mode function, the distributed tracking control law is combined with a thermodynamic model, the temperature trajectory of the follower air conditioner is controlled, and the temperature trajectory of the virtual leader air conditioner when the follower air conditioner is subjected to DoS attack is tracked. The method can ensure that the air conditioner load cluster can stably track the temperature track of the virtual leader air conditioner.

The application discloses a multi-unmanned system formation control method based on specified time convergence, and belongs to the field of unmanned system control. The application solves the problem that multi-unmanned systems cannot converge to a common target state at a specified time under arbitrary initial states and dynamically changing environments. The method comprises the following steps: step one, establishing a multi-unmanned system kinematics model containing position, velocity and control input information; step two, regarding each unmanned system as a follower, taking the tracking error of each follower relative to a target position and the tracking error of each follower relative to a target velocity as state variables in a multi-unmanned system formation model of actual followers-virtual leaders, and then designing a formation controller according to the state variables and a reference differential convergence function; and step three, designing a final formation controller of the multi-unmanned system with disturbance based on a sliding mode control and the formation controller designed in step two. The method can be applied to multi-unmanned system formation control.

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 invention provides a multi-quad-rotor unmanned aerial vehicle distributed anti-collision optimal formation control method based on reinforcement learning, and the method specifically comprises the steps: firstly constructing a multi-unmanned aerial vehicle distributed optimal formation model through graph theory knowledge and an unmanned aerial vehicle dynamics model; according to different distances between the unmanned aerial vehicle and the obstacle, the flight area is divided into three parts, and the speed is limited after the unmanned aerial vehicle detects the obstacle, so that the anti-collision function of the unmanned aerial vehicle is realized; secondly, in order to convert a distributed formation control problem into a trajectory tracking problem, a distributed observer is designed to estimate the state of a virtual leader; then, a newly defined value function is corrected through a Lyapunov-like control barrier function, so that anti-collision distributed optimal formation control is realized; and finally, designing a new Hamiltonian-Jacobian-Bellman equation by using the corrected value function, and determining optimal control input by using single-evaluation reinforcement learning in combination with a gradient descent method and a parallel learning algorithm.

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 embodiment of the invention provides a quad-rotor unmanned aerial vehicle formation control scheme based on a full-drive system method, and aims at solving the problems that a traditional nonlinear controller is complex in design and limited in coordination performance. The method comprises the steps of establishing a six-degree-of-freedom dynamics mathematical model of multiple unmanned aerial vehicles, constructing an all-drive error dynamic model of the unmanned aerial vehicles in a layered manner, and respectively designing a horizontal and height-attitude controller based on an all-drive system method. A simulation test on a MATLAB / SIMULINK platform shows that the unmanned aerial vehicle formation can accurately track the virtual navigator and stably maintain a preset formation configuration, the precision is high, the convergence speed is high, and the method has remarkable theoretical value and engineering application prospects.

The application discloses a full-drive heterogeneous multi-intelligent system preset time cooperative fault-tolerant method, which comprises the following steps: S1, a virtual unmanned aerial vehicle is constructed as a virtual leader, and information interaction characteristics among the leader and followers and among the followers are established by using graph theory knowledge; S2, a point is randomly selected on the ground to establish a three-dimensional rectangular coordinate system, and unmanned ground vehicle full-drive system model and unmanned aerial vehicle full-drive system model are established; finally, a full-drive system model of the heterogeneous multi-intelligent system under fault is obtained; S3, based on the interaction characteristics of individuals in the heterogeneous multi-intelligent system, a distributed preset time observer is constructed; and S4, preset time cooperative formation control of the position of the unmanned ground vehicle and the unmanned aerial vehicle follower under the influence of the actuator fault is realized by means of a decentralized fault-tolerant formation control ware. The application improves the system's maneuvering response speed and the robustness and fault tolerance of the formation system.

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 invention discloses an unmanned vehicle cluster hierarchical adaptive fault-tolerant formation tracking control method based on reinforcement learning, and the method employs a hierarchical control fault-tolerant formation tracking control strategy, and the superior control enables a virtual follower to track a virtual leader according to a formation shape; the lower-level control is used for controlling each actual follower to track the corresponding virtual follower by the unmanned vehicle based on a control law of reinforcement learning, so that adaptive fault-tolerant control without system model data is realized under the condition that the unmanned vehicle has an actuator fault and a process fault to cause uncertain change of a system model; and a dynamic fault detection mechanism based on a Hamilton-Jacobi-Bellman equation residual error is constructed, so that a new round of learning process is triggered. According to the method, a hierarchical control and reinforcement learning method is adopted, so that the fault of the actual follower unmanned vehicle only affects the tracking performance of the actual follower unmanned vehicle and does not affect other actual follower unmanned vehicles, and dynamic online estimation and optimal control of the system can be realized when the system model change caused by the fault is unknown.

The invention discloses an unmanned aerial vehicle distributed consistency formation control method introducing an attention mechanism, and belongs to the technical field of unmanned aerial vehicle formation control, and the method comprises the following steps: (1) constructing an unmanned aerial vehicle kinetic equation; (2) carrying out mathematical description on the unmanned aerial vehicle information transmission topology; (3) constructing a dynamic model of the unmanned aerial vehicle when the unmanned aerial vehicle moves in a three-dimensional space; (4) establishing a distributed consistency formation control method according to whether the virtual leaders of the formation are considered or not; and (5) introducing the attention mechanism into the distributed consistency formation control method. According to the method, a distributed consistency method with a virtual leader is selected as a basis for realizing unmanned aerial vehicle formation flight control, a mathematical relationship of communication topology among the unmanned aerial vehicles is established, and attention weight influence factors among the unmanned aerial vehicles are calculated by introducing an attention mechanism and aiming at measurement errors of flight states of the unmanned aerial vehicles, so that the unmanned aerial vehicle formation flight control is realized. And the influence of different neighbors on the machine is adjusted in real time, so that the rapidity and accuracy of formation aggregation are improved.

The application provides a vehicle lane changing assistance method, system and medium based on a virtual leader follower. The method comprises: receiving a lane changing instruction input by a user, acquiring a front road image and vehicle state information of the vehicle; determining a target lane and a lane center line of the target lane according to the lane changing instruction and the front road image; generating a virtual leader according to the vehicle state information and the lane center line of the target lane; and controlling driving of the vehicle according to the virtual leader to change the vehicle from a current lane to the target lane. The method of the application can accurately complete vehicle lane changing and improve the safety of vehicle lane changing.

Disclosed techniques include a computer-assisted device having an input control, a functional structure, and a processing system. The functional structure is configured to include a repositionable structure, and the repositionable structure is configured to support an instrument. The processing system is configured to receive a movement command from the input control, update a pose of a proxy based on the movement command and a proxy constraint, and cause the functional structure to move based on the updated pose of the proxy. In some embodiments, the input control controls a pose of a virtual leader device. The processing system updates a pose of a proxy based on the pose of the virtual leader device, updates a pose of a virtual follower device based on the updated pose of the proxy, and causes the functional structure to move based on the pose of the virtual follower device.