34results about How to "Guaranteed boundedness" patented technology

The invention relates to an adaptive RBF (radial basis function) neural network control technique for three-phase parallel active filters, belonging to an active power filter control technique. The invention provides an adaptive RBF neural network control method for three-phase parallel active power filters, which is used for controlling a compensation current output by a three-phase parallel active power filter through a controller, thereby eliminating harmonic waves and improving the power supply quality of a power grid. According to an adaptive control rule provided by the invention, the boundedness of weights is ensured, and the stability of the controller is proved by using a Lyapunov stability theory; and simulation results show that the control method effectively reduces the distortion factor of harmonic waves and is good in dynamic response, and when parameters change, the controller has good robustness and adaptability.

The invention discloses an active power filter self-adaptive fuzzy control system and an active power filter self-adaptive fuzzy control method. The active power filter self-adaptive fuzzy control system comprises an active power filter and a self-adaptive controller which is used for controlling the active power filter to work. The self-adaptive controller is designed according to a mathematical model of the active power filter, and by adoption of a self-adaptive fuzzy algorithm for designing controller parameters on the basis of a fuzzy theory and a Lyapunov method, the self-adaptive controller is updated automatically on line and can update the parameters in real time to ensure that tracking errors are converged to zero and the robustness of the system to parameter changes is improved; and a monitoring controller is added to ensure the boundedness and the global stability of the parameters.

The invention provides a space manipulator trajectory tracking control method based on a cross-scale model. In the case of analyzing the parameter and nonparameter cross-scale characteristics existed during the modeling of a free-floating space manipulator system, the manipulator joint space is subjected to real-time online tracking control. The control method introduces the radial basis neural network to approximate the variation item of the cross-scale characteristic in the dynamic model of the space manipulator and effectively inhibits the influence of the variation item on the system by means of the learning ability of the neural network, and designs the adaptive law to adjust the weight of the neural network in real time and performs the simulation verification by taking the plane two-connecting-rod space manipulator as an example, thereby realizing the fast and accurate tracking of the desired trajectory in the joint space of the space manipulator.

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 mutually-coupled fractional-order chaos electromechanical transducer acceleration adaptive fuzzy control method. The method comprises the following steps: a) creating a smallnetwork formed by three same electromechanical transducers, wherein each electromechanical transducer has a nearest neighbor coupling structure, and building an electromechanical coupled transducer model with a nearest neighbor based on the small network; and b) designing a controller composed of a feedforward fuzzy controller and an adaptive optimal feedback controller, wherein the feedforward fuzzy controller is integrated by a regression non-single-valued type-2 sequence fuzzy neural network, a velocity function and a tracking differentiator in an inversion control framework, and the adaptive optimal feedback controller is formed by the regression non-single-valued type-2 sequence fuzzy neural network, policy iteration and an execution-evaluation reinforcement learning algorithm, and can solve a Hamilton-Jacobi-Bellman equation. The method not only guarantees boundedness of all signals, realizes chaos suppression and synchronous and accelerated convergence, but also minimizes a cost function.

The invention discloses a cantilever beam vibration control method on the basis of self-adaption neural network control and is designed on the basis of filtered tracking error. A controller comprises proportional differential items and RBF neural network items. By the cantilever beam vibration control method, with unknown functions of a cantilever beam system of an RBF neural network approach, an updating algorithm of the weight of the RBF neural network is designed on the basis of Lyapunov stability theory, and overall stability of the system is guaranteed. Robust items are added into the updating algorithm, boundedness of control input is guaranteed, and the final tracking error is kept within any small range by the aid of proportional differential control items. Under the conditions of no structural or nonstructural parameters of a cantilever and with external interference, the control method is capable of accurately tracking and controlling the cantilever system, and robustness and reliability of the system are improved.

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 four-channel teleoperation bilateral control method based on a disturbance observer. By establishing a nonlinear system dynamics model of a bilateral teleoperation system, aglobally stable nonlinear sliding mode controller design method based on the perturbation observer is proposed to solve the nonlinearity, uncertainty, external interference and other major issues of the teleoperation system. For the nonlinear problem of the bilateral teleoperation system, a four-channel structure suitable for the nonlinear bilateral teleoperation system is designed, through transmission of the main end position, the operating torque of an operator, the slave position and the environmental working torque signal among the communication channels, the better system transparency isobtained; and for the uncertainty and external disturbance of the bilateral teleoperation system, ideal trajectory generators at the main end and the slave end and the nonlinear sliding mode controller based on the disturbance observer are designed, and the global stability of the system is guaranteed based on the Lyapunov theory.

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 backstepping self-adaptive fault-tolerant control method for a fixed-wing unmanned aerial vehicle under an actuator fault. The backstepping self-adaptive fault-tolerant control method is used for solving the problem that the execution efficiency of an existing fixed-wing unmanned aerial vehicle is reduced due to the actuator fault. The method comprises the following steps: firstly, converting an attitude dynamic model of the fixed-wing unmanned aerial vehicle into an affine form, and establishing a fixed-wing unmanned aerial vehicle actuator fault model by considering the condition that the actuator fault efficiency is reduced; secondly, estimating an efficiency factor in the fault model by designing an adaptive law, and introducing a projection operator to ensure the boundaries and authenticity of the efficiency factor; and then, obtaining a fault-tolerant controller based on a back-stepping method deduction design. The method is used for fault-tolerant control of execution efficiency reduction caused by fixed-wing unmanned aerial vehicle actuator faults.

The invention discloses an acceleration self-adaptive stabilization method of a fractional order mechanical centrifugal speed regulator system. The accelerated self-adaptive stabilization method of the system comprises the following steps that a, the convergence speed of the system state is accelerated in a preset time by introducing a speed function; b, learning or approximating unknown nonlinearterms are carried out in a system mathematical model by using Chebyshev neural network, the expansion state tracking differentiator is designed to estimate the derivative of the virtual control input, and then the accelerated self-adaptive stability controller is constructed under the backstepping control framework. According to the acceleration self-adaptive stabilization method, the stability problem of the fractional order mechanical centrifugal speed regulator system under the condition of unknown parameters and disturbance, the acceleration stability problem at a given convergence rate and the problem of repeated derivation of a traditional inversion method are overcome, so that the problem of chaotic oscillation of the fractional order mechanical centrifugal speed regulator system is effectively solved.

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 an acceleration self-adaptive stabilization method for a fractional-order mechanical centrifugal governor system. The accelerated adaptive stabilization method of the system includes the following steps: a. introducing a speed function to accelerate the convergence rate of the system state within a preset time; The state-tracking differentiator estimates the derivative of the virtual control input, and then builds an acceleration adaptive stability controller under the backstepping control framework. The invention overcomes the stability problem of the fractional-order mechanical centrifugal governor system under the condition of unknown parameters and disturbance, the acceleration stability problem at a given convergence rate, and the repeated derivation problem of the traditional inversion method, and then effectively solves the problem Chaotic oscillation problem of fractional order mechanical centrifugal governor system.

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 relates to a fingertip three-dimensional contact force sensing device and method capable of keeping touch sense, the device is of a symmetrical structure and comprises a base, an adaptive pad is installed on the upper portion of an inner cavity of the base, side plates are connected to the two sides of the base, and radial film sensors are installed on the inner sides of the side plates; sensor grooves are symmetrically formed in the lower portion of the base, axial film sensors are installed in the sensor grooves, and the axial film sensors are correspondingly matched with contact heads symmetrically installed on the front portion of an inner cavity of the base. The method comprises the following steps: applying pressure to the three-axis pressure sensing device along different directions after the device is worn, and collecting four paths of film sensor signals and three paths of three-axis pressure sensor signals; then, sending into a multiple regression learning system, and establishing a fingertip contact force estimation model through an index GPR (General Purpose Regression); finally, the human finger wearing device applies pressure to the object, signals of the four thin film sensors are sent into a fingertip contact force estimation model, and estimated three-dimensional contact force is obtained. The invention has the characteristics of light weight, human body fitting, short calibration time and the like.

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 discloses a multilateral adaptive sliding-mode control method for a nonlinear teleoperation system. Based on a radial basis function neural network, the method estimates the non-power environment parameters of slave environmental dynamics to transmit to a main end to perform main end environmental force reconstruction through a communication channel; for the nonlinear and various uncertainty problems of master-slave robots, the method respectively designs trajectory builders on the main and slave ends and a radial basis function neural network based nonlinear adaptive sliding-mode controller and also designs the online training adaptive rate of a nonlinear function including system modeling information, so that the stability and accurate position tracking performance of a system can be guaranteed; and for signal communication problems between multiple robots, the controlling force distribution of multiple slave robots can be realized by designing a collaborative force allocation algorithm, and therefore, the collaborative operation performance of the multiple robots on work tasks can be enhanced.

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.