91 results about "Fuzzy logic system" patented technology

The invention relates to an optimal safe formation collision avoidance control method for multiple unmanned surface vessels under DoS attack. The method comprises the following steps: constructing an input saturation model; constructing a multi-unmanned surface vessel dynamic model with unknown nonlinear terms; in the obstacle avoidance control design, an integral multiplication tangent obstacle Lyapunov function is designed by using the tangent obstacle Lyapunov function; establishing an attack compensator; carrying out approximation on the unknown nonlinear term by utilizing a fuzzy logic system to obtain an approximation result; combining the approximation result with an attack compensator, constructing a fuzzy state observer with switching gain, and obtaining an observation error; an optimal performance index function is designed according to the consistency formation error, and then a Bellman residual error is constructed; and constructing a weight updating law, and designing an optimal safe formation collision avoidance controller based on a judgment-execution structure according to the weight updating law and an integral multiplication tangent obstacle Lyapunov function. According to the technical scheme, the problem that collaborative optimization of obstacle avoidance and formation control is difficult is solved.

According to the cooperative control method and system of the non-linear multi-agent system, under the condition that communication link faults are considered, a non-linear dynamic model of each single-connecting-rod mechanical arm is established, and unknown uncertain non-linear dynamic states in the single-connecting-rod mechanical arms are fuzzified through a fuzzy logic system; a mixed event triggering mechanism is introduced, so that state information of a leader is estimated, and the communication frequency is effectively reduced; and constructing a virtual controller and an actual controller by utilizing a backstepping method based on the estimated state information, so as to control the position of the following mechanical arm to be synchronous with the expected trajectory of the leading mechanical arm under the condition of being influenced by the communication link fault. According to the method, the state of the multi-single-connecting-rod mechanical arm system can be kept consistent and stable tracking control can be achieved under the condition that communication link faults and nonlinear uncertainty exist without global information.

The invention discloses a cross-subject electroencephalogram emotion recognition method and system based on adaptive fuzzy domain confrontation. The method comprises the steps that target domain electroencephalogram signal features to be recognized are acquired; to-be-identified target domain electroencephalogram signal features are input into the self-adaptive fuzzy domain adversarial networks in the trained source domain network branches, electroencephalogram signal feature samples are mapped into fuzzy member vectors through a fuzzy encoder, and corresponding fuzzy feature embedding is generated; a task classifier is used for carrying out emotion classification on fuzzy feature embedding; and obtaining a final emotion classification result according to the emotion classification result of each source domain network branch. The method aims to describe the uncertainty of emotions by introducing a fuzzy logic system, so that the robustness, generalization ability and recognition accuracy of an electroencephalogram emotion recognition model in a cross-subject scene are improved.

The invention relates to the technical field of virtual reality interaction, and particularly discloses a multi-modal fused virtual exhibition hall immersive interaction system, which comprises the following steps of: acquiring real-time multi-modal interaction data of a user and performing synchronous preprocessing; inputting the orientation semantic words into an interval type-2 fuzzy logic system to generate a three-dimensional space semantic membership field; according to the gesture pointing vector and the hand shaking amplitude, a pointing conical area is constructed, two-dimensional Gaussian distribution is established in a layered mode, and a gesture pointing probability field is generated; taking a semantic membership field, a gesture pointing probability field and a gaze point Gaussian kernel density field as independent evidences, introducing a wall boundary and a floor channel as spatial topology constraints, and fusing by adopting an evidence theory combination rule to generate a three-dimensional intention probability distribution field; carrying out gravity center defuzzification processing on the distribution field to extract a navigation target area, and planning a navigation path by taking the current position of the user as a starting point and taking a nearest floor channel entrance as a passing point; according to the method, the problems of multi-mode fuzzy intention understanding and space adaptation are solved.

The invention discloses a two-way output consistency control method for a heterogeneous multi-agent system, and the method comprises the following steps: 1, constructing state and output equations of a follower agent and a leader agent, calculating an output tracking error ei (k), and constructing an augmented system state space model and a linear control input ui (k); 2, designing a distributed bipartite observer for each follower agent to enable an output tracking error ei (k) to be zero all the time, and replacing a leader agent state of the augmented system state space model with a state of the distributed bipartite observer; 3, defining a Q function, solving the Q function to obtain an optimal control strategy, and introducing a Q-learning algorithm to obtain an optimal control gain only depending on an input state of the augmented system state space model; 4, introducing a fuzzy logic system approximation Q function to enable a matrix phi i to meet a full-rank condition, and updating a fuzzy weight by minimizing a Bellman residual error to obtain an optimal control gain of the input state of the augmented system state space model; and updating the control gain by adopting a gradient descent method, and calculating linear control input ui (k) by utilizing the updated control gain.

The invention relates to an elastic control method of a high-order nonlinear system under pulse false information injection attack. The method comprises the following steps: acquiring a system output signal and a control input signal of a current control period, and a state estimation value vector and a fuzzy weight vector estimation value of a previous control period; inputting a system output signal and a control input signal of a current control period, and a state estimation value vector and a fuzzy weight vector estimation value of a previous control period into a nonlinear state observer based on a fuzzy logic system to obtain a state estimation value vector of the current control period; the controller based on the fuzzy logic system carries out backstepping recursion through the state estimation value vector of the current control period and the fuzzy weight vector estimation value of the previous control period to obtain an elastic control input signal; and obtaining a fuzzy weight vector estimator of the current control period through an adaptive law based on the state estimation value vector of the current control period. By adopting the method, state jump and performance damage of attacks can be resisted.

The invention belongs to the technical field of robot control, and discloses a fuzzy adaptive control method and system for uncertain robot system constraints. A dead zone model is integrated into an uncertain robot kinetic equation, a robot system module is established, feedback joint displacement state information is output in real time through the robot system module, and unmeasurable speed and acceleration state information is processed through a filter module, a virtual controller module and a self-adaptive law module. And a virtual control signal and a fuzzy adaptive law parameter are obtained, and dynamic change information is obtained through a fuzzy logic system in a fuzzy adaptive controller module. According to the method, parameter uncertainty caused by unknown robot system parameters or uncertainty and dead zone characteristics caused by non-controlled object factors such as external disturbance are solved, and accurate tracking control of the system state under boundary constraint is realized.

The invention provides a fault-tolerant fuzzy formation control method for preset time triggered by dynamic events of multiple unmanned ships, and belongs to the technical field of heterogeneous unmanned system formation control. Comprising the following steps of 1, establishing a heterogeneous multi-USV kinematics and dynamics model considering input hysteresis quantization, intermittent communication topology, execution mechanism intermittent faults and external disturbance; 2, constructing an execution mechanism intermittent fault model and an input hysteresis quantizer; 3, designing a fuzzy logic system based on a minimum learning parameter strategy; 4, proposing a predetermined time robust adaptive dynamic event triggering strategy; and 5, constructing a fault-tolerant formation control law based on predetermined time sliding mode control, and processing an unknown control gain by adopting a Nussbaum function. According to the method, the anti-interference capability and the engineering applicability of the USV system in a complex marine environment are remarkably improved while the consumption of communication and computing resources is reduced.

The invention provides a bionic robotic fish terrain following control method, and the method comprises the steps: calculating a height error based on the current bottom separation height between the bionic robotic fish and the water bottom and an expected height, and calculating the terrain roughness based on the historical bottom separation height between the bionic robotic fish and the water bottom and the current forward speed of the bionic robotic fish; calculating a weight adjustment factor of a cost function of a model prediction control algorithm through a fuzzy logic system based on the height error and the terrain roughness; and solving the cost function according to the weight adjustment factor to obtain predicted output of a driver control instruction of the bionic robotic fish so as to realize terrain following control. According to the real-time height error and the terrain roughness, the weight of a model predictive control (MPC) cost function is dynamically adjusted, and the track precision and the control smoothness can be improved at the same time.

The invention provides a preset time adaptive fuzzy cooperative formation control method for a multi-water-surface-robot system, and the method comprises the steps: building a motion model containing uncertainty and actuator faults based on a speed state vector, and approaching the uncertainty through a fuzzy logic system; constructing a fuzzy logic expansion state observer containing a time-varying gain function to estimate an unmeasurable speed and compensate interference and uncertainty; based on observer output, distributed errors are defined, a barrier Lyapunov function constraint boundary is introduced, and uncertainty and faults are compensated in combination with fuzzy and multiplicative fault adaptive laws. According to the method, the multiple water surface robots can form and keep the expected formation within the preset time, the tracking error is converged to the small neighborhood of the original point, the method does not depend on initial conditions, and the control precision and robustness are improved.

The invention provides a self-adaptive performance safety control method for a flexible joint mechanical arm, and relates to the technical field of mechanical arm control. According to the method, state transformation is carried out on the kinematics model of the flexible joint mechanical arm, and then the preset time performance function is constructed, so that the flexible joint mechanical arm connecting rod tracking error can be converged to a specified range within the preset time under any initial condition. In order to ensure that the flexible joint mechanical arm always operates in a safe range, function transformation is constructed for a constraint function boundary and the state of the flexible joint mechanical arm, and a fuzzy logic system is used for approximating a coupling part of the state, so that the flexible joint mechanical arm operates safely. And a high-precision and high-efficiency tracking control target is realized.

The application relates to the technical field of multi-spacecraft cooperative control, and discloses a multi-spacecraft grouping consistency restraint control method and device under optimized FLS, which comprises the following steps: acquiring the network topology of a heterogeneous multi-spacecraft system and constructing a multi-subgroup directed communication topology network, constructing a spacecraft attitude control system model based on modified Rodrigues parameters; approximating unknown nonlinear terms in the model through a fuzzy logic system, constructing a fuzzy logic system based on a gradient descent algorithm and Armijo line search criterion; constructing a restraint control term and using a backstepping method to construct a virtual control law, combining the output of the fuzzy logic system based on the gradient descent algorithm and the Armijo line search criterion to construct an actual control input law, so that grouping consistency restraint control of the heterogeneous multi-spacecraft system is realized. The application can improve real-time approximation precision and adaptive step length adjustment capability while guaranteeing grouping consistency, and can enhance topology applicability and task cooperation flexibility.

The invention discloses a Delta robot trajectory tracking method based on fuzzy-RBF (Radial Basis Function) control. The Delta robot trajectory tracking method comprises the following steps: firstly, establishing a conventional dynamic model of a Delta robot; a virtual control signal is introduced, a sliding mode surface is constructed, and a control law of a dynamic surface sliding mode controller is designed to ensure the stability and convergence of the system. And constructing a specific fuzzy logic system for adjusting the control gain. A radial basis function (RBF) structure is embedded into a fuzzy membership function, and the center and width of the membership function are iteratively updated by adopting an online adaptive optimization algorithm based on gradient descent, so that dynamic self-learning and parameter adaptation of a fuzzy system are realized. Finally, the output of the designed fuzzy-RBF self-adaptive dynamic surface sliding mode controller acts on a robot joint driving system, and high-precision trajectory tracking control of the Delta robot end effector is successfully achieved. A simulation result verifies the effectiveness and robustness of the provided control method in the aspects of processing parameter uncertainty and external disturbance.

The invention provides a self-adaptive fuzzy course tracking control method for an unmanned surface vehicle with input quantization, state quantization and ship steering engine dynamic characteristics, the ship steering engine dynamic characteristics are introduced, a three-order response type mathematical model is established, and a self-adaptive fuzzy controller which simultaneously considers input quantization and state quantization is designed. Wherein the fuzzy logic system is used for approaching an unknown function of a system model, and the event trigger control can reduce communication and calculation energy consumption while ensuring system performance. After the state and the input signal are quantized, the communication and execution frequency of the system is effectively reduced, and meanwhile, good tracking precision is kept in a limited bandwidth and digital environment. The stability of the system is strictly proved by constructing a Lyapunov function and combining a dynamic surface control method. Simulation results show that the method can improve the course control performance of the unmanned surface vehicle, and the effectiveness and feasibility of the proposed control strategy are verified.

This invention discloses a parallel preset performance trajectory tracking control method for unmanned surface vessels (USVs) oriented towards high-precision operations. This method establishes a real USV system model considering environmental interference, then combines a fuzzy logic system, filters, data stacks, and an extended state observer to construct an artificial USV system model, achieving dynamic interaction between the virtual and real spaces. Subsequently, a preset performance function is designed and an error transformation function is constructed to solve the initial value constraint problem of the preset performance control. Finally, a kinematic guidance law and a parallel preset performance trajectory tracking controller are designed to generate control signals and achieve parallel control of the real and artificial USVs. This invention reduces the cost and risk of real-vehicle testing, improves control efficiency, optimizes the transient and steady-state performance of system trajectory tracking, and broadens the engineering application scope of preset performance control.

The application discloses a fault-tolerant control method for a permanent magnet synchronous motor with intermittent actuator faults, which adopts a fuzzy logic system to process unknown nonlinear dynamics. Through a fuzzy state observer, states that cannot be directly measured can be estimated. In addition, under the framework of dynamic surface technology and backstepping recursive technology, a fuzzy fault-tolerant control method for a permanent magnet synchronous motor system with intermittent actuator faults is proposed. By using Lyapunov stability theory, it is proved that all signals in the closed-loop system remain bounded. Finally, the effectiveness and feasibility of the method are verified through simulation results.

The invention discloses a dynamic event triggering preset time fault-tolerant control method considering a non-affine fault. The method comprises the following steps: carrying out online approximation on an unknown nonlinear term in a dynamic decoupling model by adopting a fuzzy logic system to obtain an optimized system model; constructing a segmented preset time performance function, and introducing an instruction filter based on a dynamic surface control technology to process a virtual control signal of the constructed adaptive virtual control law so as to obtain a filtering error; constructing a dynamic signal compensation mechanism based on the filtering error in combination with a sectional preset time performance function, and obtaining a compensated final tracking error; and constructing a dynamic event triggering mechanism to obtain a final fault-tolerant controller, and realizing dynamic event triggering preset time fault-tolerant control considering the non-affine fault according to the final fault-tolerant controller. According to the method, the technical problems of uncontrollability of convergence time, influence of actuator faults and nonlinearity on control performance and limitation of communication and computing resources in the existing method are solved.

The invention discloses a GPS (Global Positioning System) and inertial navigation fused high-precision navigation method, aiming at the problems that a GPS signal is easily shielded and interfered and an inertial navigation error is accumulated along with time, high-precision navigation is realized through data preprocessing, a self-adaptive fusion algorithm, GPS signal interruption processing and fusion result optimization. In the data preprocessing, multi-path effect detection and correction are performed on GPS data, and zero offset error compensation is performed on inertial navigation data; the adaptive fusion algorithm constructs a model based on a fuzzy logic system, and updates a fusion weight in real time; when a GPS signal is interrupted, map matching is adopted to assist navigation, and the prediction model is utilized to compensate an inertial navigation error; and a fusion result is subjected to wavelet filtering and smoothing processing, and the accuracy is ensured by using consistency check. The method can effectively overcome the limitation of independent use of GPS and inertial navigation, realizes high-precision, continuous and reliable navigation in a complex environment, and improves the adaptability and stability of a navigation system.

The application discloses a design method of a fractional order nonlinear system fuzzy fault-tolerant controller for a flexible mechanical arm, and comprises the following steps: constructing a fractional order nonlinear system with external disturbance and actuator fault; estimating a nonlinear part in the fractional order nonlinear system based on a fuzzy logic system; defining a coordinate transformation related to a tracking error; and designing a finite time adaptive fuzzy fault-tolerant controller by combining a fractional order dynamic surface control method with a backstepping method. According to the application, the output of the fractional order nonlinear system can track a reference signal in a finite time, and all signals in the closed loop system are also bounded in the finite time. Finally, the controller is applied to a flexible mechanical arm system.

The invention relates to a fixed-time sliding mode anti-lock braking control method and system based on fuzzy logic and a neural network. The method comprises the steps that a dynamic model of a vehicle anti-lock braking system is established; designing a fixed time sliding mode controller, and constructing a sliding mode surface containing a terminal attractor and an approaching law; a T-S fuzzy logic system is adopted to approach a non-linear tire-road surface friction function in an ABS system on line; an approaching law of the sliding mode controller is approached online by adopting an artificial neural network; carrying out online optimization on additional control item gains introduced by neural network approximation by adopting a particle swarm optimization algorithm; and the fixed time stability of the whole closed-loop system is monitored based on the Lyapunov stability theory. According to the invention, the braking efficiency, the stability and the robustness of the ABS system are effectively improved, and the braking distance and the braking time are reduced.

The invention relates to the technical field of multi-unmanned aerial vehicle formation fault tolerance, in particular to a multi-unmanned aerial vehicle formation adaptive fuzzy sliding mode fault tolerance control method, which comprises the steps of S1, establishing a multi-unmanned aerial vehicle formation system dynamic model; s2, defining a formation global position tracking error and a speed tracking error based on a navigation-following architecture and a communication topological structure; s3, designing a disturbance interval observer, and estimating upper and lower bounds of unknown external disturbance in real time by establishing a disturbance exogenous model and an auxiliary variable adaptive law; s4, constructing an integral sliding mode surface based on a preset performance function, performing adaptive approximation on the lumped uncertain item by adopting a fuzzy logic system, and designing an adaptive fuzzy sliding mode fault-tolerant controller; and S5, proving that the formation tracking error is gradually converged to zero in finite time through Lyapunov stability analysis. When the unmanned aerial vehicle breaks down, reliable transmission of task information and formation keeping can be guaranteed, good robustness and environmental adaptability are achieved, and the stable control performance of the unmanned aerial vehicle in a complex task scene is effectively improved.

The application discloses a multi-unmanned system fuzzy pre-defined time optimization formation control method and belongs to the field of automation and control. In view of the multi-unmanned system with unknown dynamics and input saturation constraints, the method approaches the unknown dynamics of the system online through a fuzzy logic system, designs a cost function with a pre-defined time convergence characteristic, and constructs a single evaluation network to adaptively solve a Hamilton-Jacobi-Bellman equation to obtain an approximate optimal control law. The application does not need prior knowledge of the system dynamics, can realize accurate formation control within a pre-defined time set by a user, effectively handles control input saturation, optimizes system control energy consumption, and is suitable for cluster collaborative control tasks of unmanned aerial vehicles, unmanned vehicles and the like.

This invention proposes a method and system for reducing the order of FPSO models by integrating time-recurrent neural networks and fuzzy logic, belonging to the field of marine engineering structural analysis and intelligent modeling technology. Addressing the problem of large degrees of freedom and low computational efficiency in FPSO finite element models, this invention extracts key degrees of freedom from the high-dimensional finite element model to construct low-dimensional feature vectors. It then uses a fuzzy logic system to perform regularized modeling of the dominant mechanical properties of the structure, achieving initial order reduction. Furthermore, a time-recurrent neural network is introduced to compensate for high-order residual effects that are difficult to accurately characterize using the fuzzy logic model, ultimately resulting in a low-dimensional, high-fidelity reduced-order model. The proposed method significantly reduces the degree of freedom and parameter dimension of the finite element model while maintaining the main mechanical properties of the structure, effectively reducing computational complexity and improving the model's applicability and practical engineering value in applications such as rapid finite element analysis, online structural response prediction, and engineering decision support.

The invention relates to the technical field of remote education, and discloses a remote region remote education heterogeneous network vertical switching method, system and device and a medium. The method comprises the following steps: acquiring a current teaching service type and a network type of a user terminal; according to a membership function of the fuzzy logic system, through the current teaching service type and a plurality of different network performance indexes of the current teaching service type under each network type, determining the membership of each network performance index of the current teaching service type under each network type; performing weighted summation on the membership degree of each network performance index under each network type to obtain a service quality score of the current teaching service type under each network type; based on a Q learning algorithm, according to the service quality score of the current teaching service type under each network type, a switching strategy of the current network type is determined, and a reward function of the switching strategy is constructed according to the change of the service quality score after the action is executed and the cost of executing the action.

The present application belongs to the technical field of robot control, and discloses a fuzzy adaptive control method and system for constraints of an uncertain robot system. A dead zone model is integrated into a robot dynamics equation, a robot system module is established, real-time output of joint displacement state information can be fed back through the robot system module, unmeasurable speed and acceleration state information is processed through a filter module, a virtual controller module and an adaptive law module, and a virtual control signal and a fuzzy adaptive law parameter are obtained, and dynamic change information is obtained through a fuzzy logic system in a fuzzy adaptive controller module. The present application solves the parameter uncertainty caused by unknown parameters of a robot system or the uncertainty caused by external disturbances and dead zone characteristics of non-controlled objects, and realizes accurate tracking control of system state under boundary constraints.

The application discloses a rigid aircraft composite adaptive predetermined time attitude fault-tolerant control method, constructs a rigid aircraft kinematics and dynamics model based on modified Rodrigues parameters, and considers the actuator fault problem in the model; constructs a virtual controller in the form of hyperbolic tangent, avoids the controller singularity problem caused by derivation of the virtual controller; adopts a fuzzy logic system to approximate nonlinear uncertain items of the system, and designs an adaptive law to estimate the upper limit of composite disturbance; on this basis, a composite adaptive predetermined time attitude fault-tolerant controller is designed. The application can realize that the rigid aircraft attitude converges to the neighborhood of the original point within a predetermined time under the conditions of uncertain rotational inertia, external disturbance and actuator fault, wherein the upper limit of the minimum convergence time can be predetermined by adjusting a simple parameter.

This application provides a visual inspection method and system for surface defects of integrated busbars in power batteries. The method includes: acquiring multimodal images and a view transformation matrix of the integrated busbar; wherein the multimodal images include structured light depth images, visible light images, and infrared thermal images from multiple viewpoints; generating geometric point cloud maps, visible light point cloud maps, and infrared point cloud maps using a cross-modal fusion neural network based on the structured light depth images, visible light images, infrared thermal images, and view transformation matrix; identifying defect types based on the geometric point cloud maps, visible light point cloud maps, and infrared point cloud maps, obtaining corresponding diagnostic results and confidence levels for each; and determining the final detection result based on the diagnostic results and confidence levels through comprehensive reasoning using a fuzzy logic system. This approach can improve the accuracy of defect detection in integrated busbars of power batteries.

This invention provides a finite-time consistency control method for multi-manipulator systems with output constraints. Addressing the cooperative control problem of multi-manipulator systems with model uncertainties, external disturbances, and output constraints, this invention proposes a finite-time convergent adaptive control strategy based on a backstepping design framework, combined with an event-triggered mechanism and a fuzzy logic system. First, a dynamic model of the follower manipulator is established, and the synchronization state error is defined. Second, a cascaded Lyapunov function is designed to handle the output constraints, and the fuzzy logic system is used to approximate the model uncertainties. Third, an event-triggered mechanism is constructed to save communication resources, and control and adaptive laws are designed based on finite-time convergence theory. Simulation results show that the proposed method enables multiple manipulators to achieve high-precision trajectory tracking consistency within a finite time, with the tracking error strictly controlled within a minimal range. The control voltage always meets the output constraints, improving system safety and engineering applicability.

The application provides a cooperative control method and system for a nonlinear multi-agent system, considers a communication link fault, establishes a nonlinear dynamics model of each single-link mechanical arm, and uses a fuzzy logic system to fuzz unknown uncertain nonlinear dynamics in the single-link mechanical arm; a mixed event trigger mechanism is introduced to estimate leader state information and effectively reduce a communication frequency; based on the estimated state information, a virtual controller and an actual controller are constructed by using a backstepping method to control the position of the follower mechanical arm to keep synchronization with the expected trajectory of the leader mechanical arm under the influence of the communication link fault. The method can ensure that the states of the multi-single-link mechanical arm system keep consistency and realize stable tracking control under the conditions of the communication link fault and the nonlinear uncertainty without global information.