34results about How to "Guaranteed boundedness" patented technology

The invention relates to a small unmanned helicopter nonlinear control method. A small unmanned helicopter self-adaptive control method based on an immersion and invariant set method is provided, and realizes the effect that a small unmanned helicopter can maintain a control effect with stable posture under the situation of parameter uncertainty. Thus, the technical scheme adopted by the invention provides a posture nonlinear self-adaptive control method for the small unmanned helicopter, which applies the immersion and invariant set method to the control of a small unmanned helicopter posture system while the small unmanned helicopter is under the situation of parameter uncertainty. The posture nonlinear self-adaptive control method is mainly applied to small unmanned helicopter nonlinear control.

The invention discloses a nonlinear system self-adaptive neural fault-tolerant control method. A multi-input multi-output nonlinear system is established, and an intermittent fault model is adopted toobtain an actuator state; a multi-input single-output extreme learning machine neural network is adopted to approach an uncertainty function in the multi-input multi-output system; then an inequalityis introduced to avoid the singularity problem of the controller; and finally the bounded performance of the estimated parameters is guaranteed by adopting a projection operator. According to the method, the uncertain function of the system is approached by adopting the extreme learning machine, so that the dependence on system prior knowledge is reduced; a continuous inequation is introduced, sothat the singularity of the controller is avoided, and meanwhile, the bounded performance of the estimated parameters is guaranteed by adopting the projection operator; the boundary of jump parameters caused by the fault of the intermittent actuator is determined clearly, so that the influence of an unknown actuator intermittent fault and an unknown correlation item on the system can be effectively compensated.

The invention relates to a fractional order self-sustaining electromechanical seismograph system acceleration stability control method with constraints, and belongs to the field of seismic exploration. The fractional order self-sustaining electromechanical seismograph system acceleration stability control method comprises the following steps of: S1, carrying out system modeling, which is implemented by establishing a mathematical model of a fractional order self-sustaining electromechanical seismograph system according to a Newton's second law and a Kirchhoff's law, and defining constraint conditions; S2, and designing an acceleration stability controller, wherein the design is implemented by constructing an acceleration feedforward controller and an optimal feedback controller, the acceleration feedforward controller is integrated by a molding behavior function based on a fractional order inversion method, a fuzzy wavelet neural network and a tracking differentiator, and the optimal feedback controller is formed by fusing a fuzzy wavelet neural network and an adaptive dynamic programming strategy. According to the fractional order self-sustaining electromechanical seismograph system acceleration stability control method, the boundness of all signals of a closed-loop system is ensured, the safe operation of the system under the constraint condition is ensured, and meanwhile, chaotic oscillation can be suppressed and a minimum cost function can be realized.

The invention discloses a control method of a neural network full adjustment based on a nominal controller. The control method mainly comprises the two steps that a trace tracking controller and a neural network full adjustment compensating controller are designed based on a nominal value model, and the control output ends of the two controllers are combined to be used as the control input end of a micro-gyroscope. According to the control method of the neural network full adjustment based on the nominal controller, the advantage of a model control method is used, meanwhile, the powerful approximation capability of a neural network is used, model errors and an outside disturbance effect are estimated and compensated on line in real time, the tracking performance and the robustness of a system can be greatly improved, the adaptive algorithms of a neural network weight and the center and the sound stage width of a gaussian function are designed based on a Lyapunov stability theory, and the global stability of a closed-loop system and the boundedness of control input can be guaranteed.

The invention discloses an adaptive backstepping optimal control method for a fractional-order chaotic electromechanical transducer system. The method comprises the steps of (1) system modeling, (2) dynamics analysis, (3) design of an adaptive feedforward controller, (4) design of an optimal feedback controller, and (5) stability analysis. According to the invention, the boundedness of all signals of the fractional-order chaotic electromechanical transducer system is ensured, and oscillations originating from chaos and dead zones are suppressed; a cost function is minimal; and the simulation experiment results show the effectiveness of the proposed method.

The invention provides a garbage classification simulation method and system based on OOPN refined operation, and the method comprises the steps of building a rough OOPN system model based on the function and module division of a garbage classification system; determining a class net model of the rough OOPN system model based on a first constraint condition; performing fine processing on the rough OOPN system model by using the class net model to obtain an accurate OOPN system model; and based on the obtained accurate OOPN system model, realizing simulation of the garbage classification process. According to the scheme, the activity and bounded theorem about the OOPN system is given, and the necessary and sufficient conditions that the system still keeps activity and bounded after class net refinement operation are put forward; and the activity and the boundedness of the OOPN model after the refined operation are effectively ensured. The intelligent garbage classification picking and placing system is subjected to simulation analysis by using an OOPN class network refined operation method, so that the working process of the garbage classification system can be accurately reflected, a worker can conveniently research and manage the system, and the garbage classification accuracy is improved.