10 results about "Fuzzy observer" patented technology

The invention belongs to the technical field of robot control, and discloses a mobile robot trajectory tracking control method and system based on a fuzzy observer, and the method comprises the steps: building a dynamic model and a kinematic model of a mobile robot, and obtaining a trajectory control model of the mobile robot based on the dynamic model and the kinematic model; designing a fuzzy observer, and estimating lumped disturbance and an unknown speed state; a tracking error is defined, a tunnel type preset performance function is constructed in combination with the error conversion function, and it is ensured that the trajectory tracking error of the mobile robot is within a preset interval; designing an event triggering condition, and constructing an event triggering strategy of a controller and actuator channel; and based on the tunnel-type preset performance function, the fuzzy observer and the event triggering strategy, constructing a mobile robot control law, and completing trajectory tracking control of the mobile robot. According to the method, the mobile robot can recover an unknown speed state and lumped disturbance, and the tracking error transient performance is ensured while unnecessary communication is reduced.

The application discloses a second-order terminal sliding mode control method based on time delay estimation fuzzy observer backstepping method, and aims at unknown disturbance and trajectory tracking problems of a mechanical arm system in many practical problems, estimates kinematics and dynamics parameters of the system through time delay estimation, estimates total disturbance existing in the system through a novel fuzzy observer, combines the second-order sliding mode control to increase instantaneous response and reduce steady-state error, suppresses chattering phenomenon while guaranteeing finite time convergence of tracking error, and finally proves stability of the system by adopting a Lyapunov method, and according to comparison of different methods, fast and accurate tracking of a desired trajectory in the method is shown.

The application discloses a kind of commercial vehicle double-motor electric drive axle finite time collaborative control methods, comprising the following steps: establishing commercial vehicle double-motor electric drive axle system model, model includes: driving motor electromagnetism model and dynamics model, driving wheel model, double-motor electric drive axle coupling model.Based on the model established, the load torque of two driving motors is taken as input, interval two type fuzzy observer is designed, and the disturbance caused by coupling nonlinearity and gear shifting of driving motor is observed in real time;Based on the established commercial vehicle double-motor electric drive axle system model and interval two type fuzzy observer, design finite time adaptive fuzzy collaborative controller, ensure that double-motor electric drive axle system finite time collaborative control is realized.The method realizes the better collaborative control of double-motor electric drive axle system speed torque, while ensuring that the state of double-motor electric drive axle system can be stabilized in finite time.

The application provides a robust multi-fault synchronous reconstruction method for an uncertain quad-rotor unmanned aerial vehicle under external disturbance, and relates to the technical field of quad-rotor unmanned aerial vehicles. First, a dynamic model of the quad-rotor unmanned aerial vehicle is established, and is converted into a T-S fuzzy state space equation to construct an augmented system containing system states, process faults and sensor faults; for the obtained augmented system, a controllable order fuzzy observer is designed, and an error system is derived; the parameter matrix of the controllable order fuzzy observer is solved, and the stability of the error system is analyzed; based on the controllable order fuzzy observer, synchronous reconstruction of multi-faults of the quad-rotor unmanned aerial vehicle is realized. The nonlinear model of the quad-rotor unmanned aerial vehicle is approximated through T-S fuzzy technology, a controllable order fuzzy observer is designed, synchronous accurate reconstruction of multiple faults, effective suppression of external interference and complete decoupling of unknown inputs are realized, and the order of the observer can be flexibly adjusted according to actual requirements.

The invention belongs to the technical field of nonlinear system control, and discloses a method and a system for introducing composite learning into nonlinear system output feedback adaptive control. According to the method, the existing K-filter in the standard output feedback backstepping control is directly multiplexed to construct the extended prediction error, and an additional observer or a state estimation model (such as a serial and parallel estimation model, a fuzzy observer and the like) parallel to the K-filter does not need to be established. According to the function multiplexing design, the calculation overhead of an extra dynamic system is eliminated, the storage requirement and the real-time calculation burden of the controller are reduced, and the hardware implementation cost and the system debugging complexity are remarkably reduced. Meanwhile, through composite driving of a tracking error and an extended prediction error, accumulated information is continuously introduced by using historical memory of a regression quantity, so that the self-adaption and self-learning capabilities of the control system are remarkably enhanced, and the parameter estimation convergence and the system response performance are improved.

The application belongs to the technical field of nonlinear system control, and discloses a method and system for introducing composite learning into nonlinear system output feedback adaptive control. The application directly reuses the existing K-filter in standard output feedback backstepping control to construct an extended prediction error, and does not need to establish an additional observer or a state estimation model (such as a series-parallel estimation model, a fuzzy observer, etc.) in parallel with the K-filter. This functional reuse design eliminates the calculation overhead of the additional dynamic system, reduces the storage requirement and real-time calculation burden of the controller, and significantly reduces the hardware implementation cost and system debugging complexity. Meanwhile, through the composite driving of the tracking error and the extended prediction error, the cumulative information is continuously introduced by using the historical memory of the regression quantity, so that the adaptive and self-learning ability of the control system is significantly enhanced, and the parameter estimation convergence and the system response performance are improved.

The invention belongs to the technical field of textile mechanical arm trajectory tracking control, and discloses a textile mechanical arm visual servo trajectory tracking control method and system based on a fuzzy observer. In order to solve the problems that visual velocity information is difficult to obtain and a system model is uncertain, a fuzzy observer is designed to obtain a visual velocity estimated value, and a fuzzy logic system is utilized to approach unknown nonlinear dynamics. Besides, an instruction filtering technology is applied, the problem of calculation complexity of the visual servo system of the mechanical arm is solved, an error compensation mechanism is introduced to eliminate adverse effects caused by filtering errors, and the control effect of the visual servo system is improved; and meanwhile, the negative influence of the input dead zone on the system performance is also compensated. The method can effectively solve the problems that the visual speed information is difficult to obtain and the calculation is complex when the controller is designed, has a good control effect, and is suitable for a scene where the textile industry production robot has high control precision requirements.

The application discloses a kind of underactuated ASV dynamic event trigger adaptive fuzzy pre-set performance control methods under network attack, including the underactuated ASV model under network attack including model uncertainty term is constructed;According to the underactuated ASV model, an adaptive fuzzy observer is constructed to obtain a state prediction model, and the state prediction error of the underactuated ASV is obtained according to the state prediction model;Performance control function for maintaining follower and leader formation control is constructed;According to the performance control function, a ship fuzzy pre-set performance virtual control law is constructed;According to the state prediction error of the underactuated ASV and the ship fuzzy pre-set performance virtual control law, an adaptive law is constructed;Switching dynamic event trigger mechanism is constructed to obtain the actual input of the controller;Based on switching dynamic event trigger mechanism and adaptive law, and according to the pre-designed N-type function, a switching dynamic event triggered formation controller is constructed.The problems of maliciously changing system signals by information deception attacks, limited system communication resources, the need to achieve specified performance indicators, external marine environment disturbances, autonomous underwater vehicle underactuation, and uncertain nonlinear models in current ASV trajectory tracking tasks, which lead to reduced ASV path tracking robustness and even instability of the entire system, are solved.

This invention discloses a vehicle queuing preset time output feedback safety control method and system, belonging to the field of intelligent connected multi-vehicle queuing control. The control method includes the following steps: designing a nonlinear vehicle queuing model, designing a fuzzy observer, and combining a fuzzy logic system to approximate the unknown nonlinear function in the vehicle queuing model to output the estimated displacement, estimated velocity, and estimated acceleration of the following vehicle; mapping the error transformation in the finite time domain to the infinite time domain to obtain the infinite time domain error transformation; designing a Lyapunov function, and by differentiating the Lyapunov function, finally obtaining the braking input signal of the following vehicle; designing a spurious data injection (FDI) attack model to inject an attack signal into the braking input signal of the following vehicle to obtain an attacked input; this invention proposes a cooperative control architecture based on a time domain mapping mechanism, which can asymptotically converge the tracking error under any initial conditions to zero within a preset time.

The application provides a multi-unmanned aerial vehicle (UAV) distributed adaptive formation and dynamic obstacle avoidance method against compound attacks, and relates to the technical field of multi-agent system cooperative control. The application constructs a communication topology and a UAV dynamics model considering a directed denial of service attack and a deception attack; an adaptive topology recovery mechanism is designed to smoothly recover the attacked link; a leader state is estimated based on neighbor node information, a distributed fuzzy state observer is designed to estimate unknown system dynamics, and an attack compensator is designed to offset the influence of the deception attack on the output signal; a distributed formation controller based on an event triggering mechanism is designed, and a dynamic obstacle avoidance mechanism is integrated. Through a unified nonlinear error function, dynamic obstacle speed estimation, attack compensation mechanism and fuzzy observer design, the application can realize safe, stable and cooperative operation of the multi-agent system in a complex environment with deception attacks and unknown dynamic obstacles, and realize high-precision and high-robustness formation control.