140 results about "Fuzzy logic system" patented technology

The invention relates to the technical field of communication engineering, and discloses a power line carrier and wireless dual-mode fusion optimized communication method, which comprises the following steps of: 1, acquiring signal quality, noise and delay data of a power line and a wireless link in real time, and constructing a prediction model by utilizing historical data; step 2, dynamically adjusting a judgment threshold value of link switching in real time based on the prediction model, and ensuring that switching is triggered only when the signal change exceeds a preset range; step 3, based on the dynamic threshold, combining multiple indexes of signal-to-noise ratio, delay, packet loss rate and network load; according to the method, the necessity of link switching can be more intelligently judged by dynamically adjusting the judgment threshold of link switching and adopting a fuzzy logic decision mechanism, frequent switching during tiny fluctuation of signals is avoided by dynamic threshold adjustment, and a fuzzy logic system integrates multiple factors, so that the decision is finer, and the reliability of link switching is improved. And unnecessary delay and resource waste are reduced, so that the stability of a communication system is improved.

The invention belongs to the technical field of position tracking control of rigid multi-joint mechanical arms, and discloses a fuzzy self-adaptive visual servo control method and system for a multi-joint mechanical arm system for placing bobbins in a textile workshop, and the method comprises the steps: constructing an image error model according to the positions of feature points on an image plane; the fuzzy logic system is used for processing uncertain items in the system, the instruction filtering and fuzzy self-adaption technology is used for achieving tracking control over the expected trajectory of the system, the problem of calculation complexity is solved, and adverse effects caused by filtering errors are eliminated. In the process of changing the bobbins, the bobbins are placed on a creel through a multi-degree-of-freedom robotic arm, a clamping jaw at the tail end of the robotic arm is required to accurately grab the bobbins for feeding and discharging, and the requirement for accurate and rapid control of the robotic arm is met, so that research on visual servo control of the robotic arm is of great significance. The method has a good control effect, the problem of calculation complexity in the design process of the controller is solved, the trajectory tracking error is smaller, and the system robustness is higher.

The invention relates to the technical field of robot control, and discloses a mechanical arm specified performance fault-tolerant control method based on a fuzzy logic system, and the method comprises the steps: building a kinematics and dynamics model and a fault model of an agricultural robot; establishing a relative error dynamic equation between the actual position and the expected position; designing a specified performance function, and constraining the tracking error within an expected range by using the specified performance function; on the basis, designing a virtual control law by using a backstepping method under the condition of meeting error constraints; designing an adaptive control law by adopting a fuzzy logic system based on an uncertain item, unknown environment interference and a fault model in the kinetic model; a specified performance controller is designed based on a virtual control law, fuzzy logic adaptive control and full-state feedback agricultural robot, and specified performance fault-tolerant control of the agricultural robot is realized. The stability proves that the tracking error of the agricultural robot converges to any small neighborhood near zero within fixed time.

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 invention discloses a vehicle queue preset time output feedback safety control method and system, and belongs to the field of intelligent network connection multi-vehicle queue control, the control method comprises the following steps: designing a non-linear vehicle queue model, designing a fuzzy observer, combining with a fuzzy logic system to approximate an unknown non-linear function in the vehicle queue model, and calculating the preset time output feedback safety of the vehicle queue. Outputting an estimated displacement, an estimated speed, and an estimated acceleration of the following vehicle; mapping the error transformation of the finite time domain to an infinite time domain to obtain infinite time domain error transformation; designing a Lyapunov function, and performing derivation on the Lyapunov function to finally obtain a following vehicle brake input signal; designing a false data injection FDI attack model for injecting an attack signal into the following vehicle brake input signal to obtain an input with an attack; the invention provides a cooperative control architecture based on a time domain mapping mechanism, and tracking errors under any initial condition can be asymptotically converged to zero within preset time.

The invention belongs to the technical field of airplane nonlinear system control, and discloses a deicing vehicle hierarchical control method and system under an airplane idling condition. According to the method, a deicing vehicle overall dynamic model under incomplete constraint is constructed based on a Lagrange equation, a vehicle-arm combined trajectory tracking error state model is constructed, a cost function of a model prediction controller is designed according to a control target of deicing vehicle combined trajectory tracking, a quadratic programming problem is constructed on an upper layer to solve an optimal virtual control quantity, and the optimal virtual control quantity is calculated. And the lower layer analyzes the virtual control quantity into control input which can actually act on an execution mechanism by designing a self-adaptive fuzzy logic strategy so as to realize trajectory tracking control. According to the invention, a hierarchical control strategy is provided by combining model predictive control and an adaptive fuzzy logic system (FLS), the stability and high efficiency of system operation are ensured, and the deicing vehicle is ensured to always maintain a stable operation state in a complex and changeable operation environment.

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 discloses a random predefined time tracking control method for a heterogeneous cluster unmanned system, which belongs to the technical field of unmanned system tracking control, is used for unmanned system tracking control, and comprises the following steps: constructing a nonlinear leader-follower random multi-follower system, carrying out controller design of actual preset time random consistency, and carrying out time tracking control. Carrying out stability analysis and simulation verification by combining a Lyapunov stability theory; and setting the unmanned system in the heterogeneous cluster unmanned system as a follower, taking the control input of the unmanned system as the control input of the follower, and taking the control output of the controller with the random consistency of the actual preset time as the control information of the random predefined time tracking control. By introducing the adaptive law and the fuzzy logic system, unknown dynamics and interference are effectively estimated and compensated, and the robustness of the system to random noise and nonlinearity is enhanced.

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 sea surface ship formation preset time output feedback control method with multiple quantization mechanisms, and the method comprises the following steps: building a sea surface ship formation system model, and carrying out the approximate processing of an unknown nonlinear function in the sea surface ship formation system model through a fuzzy logic system; a fuzzy state observer is constructed to process an unmeasurable state, and in addition, a state quantization mechanism and an input quantization mechanism are introduced; a dynamic surface technology is adopted and a second-order filter is introduced to process a discontinuous problem of a virtual control signal so as to construct a Lyapunov function; in combination with a backstepping method technology, an adaptive law and a preset time quantization controller are designed, and the output state of the sea surface ship formation system is converged to an expected precision range within a preset time. On the basis of guaranteeing the stability of the system, the control precision is improved, the communication overhead is effectively reduced through a state quantization and input quantization mechanism, and it is ensured that the sea surface ship formation system can still operate stably and reliably in a complex environment.

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 relates to the technical field of intelligent pet behavior correction based on image recognition, and particularly discloses a pet dog behavior correction method based on training image feedback recognition, which comprises the following steps: capturing activity image data of a pet dog by using a camera, and extracting behavior characteristics such as social interaction distance and energy release level; analyzing the time sequence data of the social interaction distance by using a multi-scale convolutional neural network, and calculating an abnormal feature value of the social interaction distance; performing dimension reduction processing and clustering analysis on the energy release level through a manifold learning algorithm, calculating energy release abnormal feature values, constructing a comprehensive feature vector by the two abnormal feature values, inputting the comprehensive feature vector into a random forest model to predict an abnormal behavior score of the pet dog, and when the score exceeds a preset threshold value, judging that the pet dog shows an abnormal behavior. Based on the behavior correction value calculated by the fuzzy logic system, the intensity of the correction measure is dynamically adjusted.

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 present application discloses a preset-time formation control method for a multi-agent system based on fuzzy reinforcement learning. The method includes: establishing a communication network among multi-agents based on graph theory; constructing a fuzzy logic system; constructing a multi-agent system; taking the multi-agents to complete the target formation control within a preset time as the goal, constructing an auxiliary function and an error transformation function; defining the optimal formation control theory, determining the optimal performance index function, and introducing the HJB equation to solve for the optimal control input; applying the ACI structure, designing an identifier, a critic, an actor, and corresponding update rules to complete the update of the parameter matrix for optimal formation control. The present application can achieve the robust control of the multi-agent system in a complex environment, thereby ensuring that the multi-agent system reaches the target formation within the preset time and having strong anti-interference ability.

The present invention relates to a trajectory control method based on time-varying fuzzy sliding mode. The method includes: establishing a system dynamics model, designing a high-order observer to estimate the position, velocity, acceleration and lumped disturbance in real time, and avoiding the conservatism of fixed gains through adaptive gain adjustment; constructing a fuzzy sliding mode control law, combining the equivalent control term and the switching control term, and dynamically adjusting the error weight by using a fuzzy logic system; designing a dynamic limiter and a delay compensation mechanism to smoothly limit the control command to the physical limit of the actuator, and at the same time correcting the control command based on the observer feedback to offset the delay effect. The present invention effectively improves the trajectory tracking accuracy and system robustness through time-varying fuzzy sliding mode control, adaptive observer gain and anti-saturation compensation.

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.

The invention discloses a quantitative control method and device for preset time output feedback of a double inverted pendulum system, and relates to the field of automation, and the method adopts a fuzzy logic system and a fuzzy state observer which are respectively used for processing unknown nonlinear dynamic characteristics of the system and estimating an unmeasurable state. In the design process of the controller, a second-order command filter and a backstepping technology are combined, a state quantization mechanism is introduced, and a novel self-adaptive quantization control method is provided. According to the method, the problem of discontinuity of virtual control signals can be effectively solved, and meanwhile control singularity is avoided. By introducing the preset time stability criterion, it is proved that the closed-loop system can achieve stable convergence within the preset time. A simulation result verifies the effectiveness of the control method, which indicates that the method can ensure that the output state of the double inverted pendulum system is converged to an expected precision range within a preset time, and the response speed and the control precision of the system are improved.