110 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.

The invention discloses a self-adaptive finite time fuzzy safety control method for a flexible joint mechanical arm, which comprises the following steps that a flexible joint mechanical arm state space model influenced by spoofing attack is established, and a control signal comprises an unknown time-varying parameter and an unknown bounded attack function; introducing a Nussbaum function meeting a preset limit integral property; designing a control method based on a finite time stability principle, and ensuring that a system state converges to a bounded residual set within finite time; a fuzzy logic system is adopted to approach an unknown nonlinear function in the mechanical arm model; defining an error variable through coordinate transformation, and constructing a parameter which exponentially decays along with time; constructing a Lyapunov function containing the exponential decay parameter, and designing a virtual control function, a controller and a parameter adaptive law based on the function; and the Nussbaum function is constructed to be used for processing the unknown time-varying parameters. According to the invention, the influence of spoofing attacks on state signals can be reduced.

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 hierarchical optimization control method in the vehicle following scene of the fuel cell hybrid electric vehicle comprises the following steps: acquiring vehicle driving data in real time, and inputting the vehicle driving data into a pre-trained working condition recognition model for recognition to obtain a real-time traffic working condition; inputting the real-time traffic condition into a pre-constructed model prediction vehicle-following controller for prediction, and performing optimization guidance on the prediction process of the model prediction vehicle-following controller based on a particle swarm algorithm to obtain an optimal vehicle control parameter; demand power of the fuel cell hybrid electric vehicle is obtained based on the optimal vehicle control parameters, and weighting Soc is calculated according to the demand power; inputting the required power and the weighted Soc as input variables into a fuzzy logic system for fuzzy processing to obtain an adjustment frequency; and inputting the required power and the adjusting frequency into a self-adaptive low-pass filter for frequency division processing. According to the method, the required power of the fuel cell hybrid electric vehicle can be reasonably distributed in the vehicle following scene.

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 provides a preset time adaptive fuzzy attitude control method and system for multiple unmanned aerial vehicles, and relates to the technical field of multi-unmanned aerial vehicle cooperative control, and the method comprises the steps: obtaining a communication topological relation of a to-be-controlled multi-unmanned aerial vehicle system and the structure information of a single unmanned aerial vehicle; establishing an attitude dynamics model of the single unmanned aerial vehicle according to the structure information of the single unmanned aerial vehicle, and converting the model into a state model; based on the state model, designing a preset time series-parallel estimation model for estimating the state of the unmanned aerial vehicle; designing a controller and a composite adaptive law based on the communication topological relation in combination with a preset time series-parallel estimation model and a backstepping method; and carrying out adaptive backstepping control on the attitude of the multi-unmanned aerial vehicle system under a frame of preset time control by using a controller and a composite adaptive law, the approximation precision of the fuzzy logic system is improved by using a serial-parallel estimation model; and by using a preset time technology, the consistent target tracking of the multi-six-rotor unmanned aerial vehicle is realized.

The invention discloses a bipartite consistency control method of an unmanned aerial vehicle cluster under lagging full-state constraint, and relates to the technical field of unmanned aerial vehicle cluster cooperative control, comprising the following steps: establishing an unmanned aerial vehicle attitude dynamic model subjected to lagging full-state constraint and input saturation; constructing a coordinate conversion mechanism, and converting an original constrained system into an equivalent unconstrained system; constructing a cooperation-competition coexistence topology communication network, and defining an error dynamic state in combination with a self-adaptive backstepping control technology; an interval type-2 fuzzy logic system and a self-triggering communication protocol are integrated, and a self-adaptive fuzzy self-triggering fixed-time bipartite consistency controller is designed. According to the method, feasibility conditions do not need to be preset, through innovative coordinate conversion and control strategies, the problem of fixed-time dichotomy consistency control of the unmanned aerial vehicle cluster under lagging full-state constraint and input saturation is effectively solved, meanwhile, the network communication load is remarkably reduced by means of a self-triggering mechanism, and the system reliability is improved. And the control precision and the resource utilization efficiency of the system are improved.

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 invention relates to the technical field of automatic control, and discloses a sliding mode control buffeting suppression method based on an interval type-2 fuzzy rule. Dynamic balance of buffeting suppression and robustness is realized through deep cooperation of an interval type-2 fuzzy logic system and sliding mode control: an interval membership function is utilized to accurately capture an uncertainty boundary of the system, a double-layer fuzzy reasoning mechanism is combined to cooperatively optimize switching gain and boundary layer thickness, and high-precision tracking is maintained under strong disturbance; the control signal high-frequency oscillation energy is monitored in real time to drive parameter reconstruction, and buffeting is eliminated from the source; the modular design can be directly embedded into an industrial control system, abrasion of an executing mechanism is remarkably reduced, the service life of equipment is prolonged, and the inherent contradiction between dynamic response and stability of a traditional method is solved.

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.

This invention discloses a fixed-time control method for a rotary-wing unmanned aerial vehicle (UAV) in unknown environments. The steps are as follows: 1. Constructing a nonlinear system model of the UAV's position loop and attitude loop, simultaneously considering system uncertainty, output constraints, and unknown external disturbances; 2. Designing a safe desired tracking signal, obtaining the remaining desired signals through inverse kinematics, calculating the errors of the position loop and attitude loop, and designing a barrier Lyapunov function based on the characteristics of the nonlinear system model of the UAV's position and attitude loops, and selecting appropriate upper and lower bounds; 3. Designing a tracking controller based on the barrier Lyapunov function, combined with a fuzzy logic system, a fixed-time disturbance observer, a filter, and an adaptive backstepping method. This invention has the advantage of enabling a rotary-wing UAV to track a preset desired tracking signal within a fixed time while ensuring system safety and stability, under the influence of output constraints, system uncertainty, and unknown external disturbances.

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.