6 results about "Fuzzy observer" patented technology

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 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 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.