45results about How to "Guaranteed global asymptotic stability" patented technology

The invention discloses an MEMS gyroscope robust self-adaptation control method based on neural network upper bound learning. The method includes the following steps that an ideal kinetic model and an MEMS gyroscope kinetic model are established, a sliding mode function is designed, a control law is obtained based on the sliding mode function, and an RBF neural network upper bound estimated value is used as a gain of a robust item on the basis of the control law together with a feedback item and the robust item; a parameter self-adaptation law and a network weight self-adaptation law are designed based on a Lyapunov method. According to the MEMS gyroscope robust self-adaptation control method based on neural network upper bound learning, the feedback item is added in the control law, the two-shaft vibration trajectory tracking speed and the parameter estimation speed of an MEMS gyroscope are greatly increased, and the vibration amplitude is decreased; the robust item based on RBF neural network upper bound learning is added in the control law, the buffeting problem caused by large external disturbance and fluctuation and the problem that the dynamic characteristics are changed worse are solved, the uncertainty of a structural formula and the uncertainty of a non-structured formula are eliminated, and therefore the robustness of the system is further improved.

The invention discloses a micro gyroscope adaptive control method based on model reference, and the method is applied to a controller comprising a gyroscope. The method comprises the following steps: establishing an ideal dynamics model; designing a reference model as two different frequency sine waves, wherein a state space form is X[m]=A[m]X[m]; establishing a micro gyroscope system dynamics model; controlling a micro gyroscope based on a Lyapunov method, wherein a design control law is u=K<T>X+K[f]e, K<T> is an on-line adaptive update controller parameter, and K[f] is a steady matrix, and selecting K[f] and allowing (A[m]+BK[f]) as a stabilization matrix. According to the invention, a model reference adaptive control method is applied to micro gyroscope control in order to compensate manufacture errors and environment interference, and estimate an angle rate correctly. By employing a designed self-adaptive algorithm based on the Lyapunov method, global progressive stability of a whole control system is ensured, and reliability and robustness to parameter change of the system are raised.

The invention discloses a rotating-speed-estimation-based self-adaptive fuzzy and sliding-mode control method of an ESC, and belongs to the field of automobile hydraulic power assisted steering systems. The control method includes the following steps: 1, an outer rotor of the ESC is connected with an engine, an inner rotor of the ESC is connected with a steering pump, and an ESC type electronic control hydraulic steering system is built; 2, a rotating speed signal of the steering pump in the ESC type electric control hydraulic steering system is collected through an ECU; 3, an ESC dynamic equation is built based on the rotating speed in the step 2, wherein a mathematical model of the ESC under the d-q axis is built; and 4, based on the mathematical model in the step 3, a rotating-speed-estimation-based self-adaptive fuzzy and sliding-mode controller is designed. According to the rotating-speed-estimation-based self-adaptive fuzzy and sliding-mode control method, the beneficial effects of self-adaptive fuzzy control and fuzzy sliding-mode control are adopted, satisfied system responses are generated through a dynamic self-adaptive law, interference is obviously eliminated, and buffeting is reduced.

The invention discloses a micro gyroscope robust self-adaptive control method, and the method is applied to a controller comprising a micro gyroscope. The method includes the following steps of establishing an ideal dynamics model, establishing a dimensionless dynamics model of the micro gyroscope, designing a sliding mode function and making the derivative of the sliding mode function to time be zero to acquire a control law, adding a feedback item and a robust item to the control law, wherein the control law with feedback item and the robust item is used as a robust self-adaptive control law, controlling the micro gyroscope based on the Lyapunov function method, and designing an self-adaptive law. According to the micro gyroscope robust self-adaptive control method, the feedback item is added to the control law, so that micro gyroscope two axle vibration trajectory tracking and parameter estimation speed is greatly improved, and vibration amplitude is reduced; the robust item is added to the control law, so that external interference and parameter uncertainty are removed, and robustness and dynamic characteristics of a system are improved; the self-adaptive law is designed based on the Lyapunov function method, so that globally asymptotic stability of the whole system is guaranteed, and reliability of the system and robustness to parameter change are improved.

The invention discloses an inversion adaptive fuzzy sliding mode control method for a micro gyroscope. On the basis of advantages of inversion design and Lyapunov stability theory proof, the control law of the system is obtained step by step through a regression method, the fuzzy control does not need to rely on a mathematical model of a controlled object, the adaptive algorithm and the fuzzy control algorithm are combined, the defect that the fuzzy control cannot conduct parameter adjustment in time is overcome, a controller has the learning capacity, and in the control process, parameters have the self-learning and adjustment capacity. According to the method, the tracking performance of the micro gyroscope system can be effectively improved, the overall stability of the system is ensured, the robustness and the reliability of the system are improved, and the dependency on a controlled system model is avoided.

The invention discloses a cantilever beam robustness self-adaptation control method. An ideal cantilever beam dynamic model is designed and contains frequency signals rich enough to serve as a reference trajectory of a system, it is guaranteed that the actual cantilever beam trajectory can track the reference trajectory for the whole self-adaptation control system, an ideal dynamic property is realized, and compensation of manufacturing errors and environment disturbance is achieved. The parameters of a cantilever beam are taken as unknown system parameters, a parameter error vector theta is formed, a slip form function is designed, the derivative of the slip form function is made to be zero, an equivalent controller is obtained, a feedback term and a robustness term serve as an input signal on this basis, the self-adaptation rule of the parameter theta of the controller is designed based on the Lyapunov method, the stability of the system is guaranteed, tracking error is converged to zero, and all the parameters are converged to true values.

The invention discloses a method for controlling an inversion adaptive fuzzy dynamic sliding mode of a micro gyroscope. On the basis of dynamic sliding mode control, a dynamic control rule and a fuzzy adaptive rule are gradually obtained through an inversion method. In the design of the dynamic sliding mode control, discontinuous items are shifted to a controlled first-order or higher-order derivative, and thus the dynamic sliding mode control rule essentially continuous in terms of time is obtained, thereby effectively reducing system buffeting. In the design of the fuzzy adaptive rule, a practical model of a micro gyroscope system is approached by estimating interference items of a micro gyroscope sensor. With the adoption of the invention, the system buffeting can be effectively reduced, manufacturing errors and environmental interference are offset, and system sensitivity and robustness are improved.

The invention discloses a micro-gyroscope self-adaptive dynamic sliding mode control system based on an inversion design and a method. The method includes the following steps: (1) establishing a micro-gyroscope math model; (2) based on the inversion design, obtaining a self-adaptive dynamic sliding mode controller; (3) carrying out a simulating experiment so as to obtain a target system. System robustness and sensitivity to parameter change are improved and system buffet is effectively reduced; in a self-adaptive design, through parameter estimation of a micro-gyroscope sensor, a parameter of a controlled object is obtained so that global asymptotic stability of the whole closed-loop system is ensured; and after a stable mode is reached, a dynamic characteristic of the micro gyroscope is an ideal mode so that manufacturing errors and environment interferences are compensated for.

The invention discloses a control method for a neural network global sliding mode of a micro gyroscope based on linearization feedback. The control method comprises the following steps of S101, establishing an ideal dynamical equation of the micro gyroscope; S102, establishing a dimensionless dynamical equation of the micro gyroscope according to Newton's law in a rotation system; S103, establishing a neural network global sliding mode control system based on the linearization feedback according to the ideal dynamical equation and the dimensionless dynamical equation, designing a control law, and taking the control law as the control input of the micro gyroscope; S104, designing a self-adaptive law based on the lyapunov function theory, thereby enabling the established control system to perform online update. The control method disclosed by the invention has the advantages that the universality is realized, and transient characteristic, robustness and stability of the system are improved.

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 provides a method for inhibiting the velocity fluctuation of an electric vehicle driving system. The method comprises the following steps: obtaining a rotor position by adopting an encoder, and transmitting the rotor position to a velocity control ring; and adopting a parallel mode combining a prediction PI joint controller, a repetitive controller and a nonlinear adaptive feedback observer by the velocity control ring, adopting the parallel mode of the prediction PI joint controller and the repetitive controller during electric start, substituting two continuous sampling values into a prediction domain of the prediction PI joint controller by the encoder, switching to the parallel mode of the repetitive controller and the nonlinear adaptive feedback observer when an electric vehicle enters a stable operation mode, and then transmitting an output signal of the velocity control ring. According to the method provided by the invention, the prediction PI joint control, the nonlinear adaptive feedback observation control, the repetitive control and the vector control are combined together to improve the stability, the accuracy and the dynamic response ability of the electric vehicle driving system and inhibit the velocity fluctuation in an electric vehicle running process.

The invention discloses a PI passive control method and system of a modular multilevel converter. The PI passive control method comprises the steps of: establishing a bridge arm average model based on a controlled source, and listing a differential equation represented by sigma-delta in a corresponding abc coordinate system; deducing a state space model of the MMC bridge arm average model according to the established differential equation; establishing a port controlled dissipation Hamilton model of an MMC system by using the state space model of the MMC bridge arm average model; utilizing the port controlled dissipation Hamilton model of the MMC system and utilizing a passive control theory to design a PI passive feedback controller of a closed-loop system, and realizing control over the modular multilevel converter through using the PI passive feedback controller. According to the PI passive control method, effective tracking of the expected trajectory of the MMC system can be realized, and the global asymptotic stability of the system can be ensured according to the Lyapunov stability theory, so that the robustness of the system is improved.