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 resource allocation method for time-delay optimization, which relates to the field of wireless communication technologies. The resource allocation method comprises the steps of: firstly, determining users to be scheduled on a current time slot by means of a wireless resource manager, calculating a priority factor of each service of the users to be scheduled according to QSI of all the users to be scheduled on the current time slot and a previous time slot, and generating a user service priority order table which is used for instructing decisions of the wireless resource manager in controlling transmission power required for transmitting data of the service with the highest priority of the users to be scheduled and allocating wireless resource blocks, and then transmitting resource allocating results to the corresponding users to be scheduled; and finally, updating the QSI of a buffer queue corresponding to each service of each user. The resource allocation method for time-delay optimization ensures the boundedness of all the buffer queues in a network, so as to reduce the average transmission time delay of the services, and makes network energy efficiency performance approaching the optimal value on the premise of realizing network stability.

The invention discloses a self-adaptive multilateral control method based on fuzzy logic for a nonlinear remote operating system. According to the method, non-power parameters of nonlinear environmental dynamics are estimated based on a fuzzy logic function, and the non-power parameters are transmitted back to a main terminal through a communication channel with time delay for reconstitution of main-terminal environmental forces. For various uncertain problems existing on a master robot and slave robots, the method is based on a fuzzy logic system, and the parameters of the nonlinear functioncontaining unknown system model information is online updated by designing the self-adaptive rate; for the position tracking property of the system, according to the nonlinear self-adaptive multilateral control method based on the fuzzy logic system, when communication delay exists in the system, a track signal of the master robot is accurately tracked through the slave robots; and for the operation force distribution problem during collaborative operation of multiple robots, a collaborative control algorithm of multiple robots is designed, and then operation force distribution of multiple slave robots is achieved.

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 discloses a robot control method with optimal energy. The method comprises the following steps that the state variable of a robot and the control torque of the robot are initialized; the current robot state is read through a robot sensor; a Jacobian matrix and a reference acceleration at the current moment are obtained through calculation; the inequality constraints of the current robot state are uniformly described in an acceleration layer, and an upper bound and a lower bound are determined; convex optimization processing is conducted on a to-be-optimized function, and a final constraint optimization model is obtained; a dynamic neural network is adopted to update the current robot state and the control torque, and the updated robot state and the updated control torque are obtained; based on the updated robot state and the control torque, whether the working time of the robot is longer than preset time or not is judged; and if not, the control torque is executed, and returning to the robot sensor is conducted so that the current robot state is read. In the implementation of the robot control method with optimal energy, the method is optimal in energy and high in efficiency.

The invention discloses a neural network sliding mode control method based on an error conversion function. The neural network sliding mode control method comprises the following steps of: acquiring various parameter matrixes of a micro gyroscope and a designed sliding mode surface; adopting a hyperbolic tangent function as input of an RBF neural network to select a center and a base width on thebasis of a tracking error and the sliding mode surface, then estimating an interference upper bound, and designing a micro-gyroscope control law according to the sliding mode surface and an estimatedinterference upper bound parameter matrix; and finally realizing accurate estimation of spring parameters. The neural network sliding mode control method can guarantee that the input of the RBF neuralnetwork is within a determined range, then the proper center and base width of the network are selected, estimation of an interference upper bound parameter matrix is completed, self-adaptive adjustment of the weight is completed by designing a neural network weight self-adaptive rule, the stability of the system is guaranteed, and the measurement precision of the gyroscope is improved.

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.