30 results about "High gain observer" patented technology

The present invention discloses a hypersonic aerocraft neural network composite learning non-backstepping control method. The technical problem is solved that a current hypersonic aerocraft control method is bad in practicality. The technical scheme comprises: performing transformation of an attitude subsystem strict feedback form, obtaining an output feedback form, employing a high-gain observer to perform estimation of newly defined variables, and providing basis for subsequent design of a controller; allowing the controller to consider the lump nondeterminacy of the system, and only requiring one neural network to perform approximation, wherein the controller is simple in design and is convenient for engineering realization; aiming at control of unknown cases of a gain function, designing the controller based on the parameter linearization expression mode; and introducing system modeling errors, and constructing a neural network composite updating rule and a parameter adaptive composite updating rule to realize fast tracking of a hypersonic aerocraft. The effective estimation of unknown states is realized based on the high-gain observer, the repeat design of the virtual controlled quantity is not needed so as to simple the design of the controller. the realization is easy, and the practicality is good.

The present invention discloses a hypersonic aerocraft neural network composite learning control method based on robustness design. The objective of the invention is to solve the technical problem that a current hypersonic aerocraft control method is bad in practicality. The technical scheme comprises: performing transformation of an attitude subsystem strict feedback form, obtaining an output feedback form, employing a high-gain observer to perform estimation of newly defined variables, and providing basis for subsequent design of a controller; allowing the controller to consider the lump nondeterminacy of the system, and only requiring one neural network to perform approximation, wherein the controller is simple in design and is convenient for engineering realization; and considering control of unknown cases of a gain function, introducing the upper and lower bound information, and designing a robustness item to ensure stability of the system. Because the strict feedback form is transformed to an output feedback form to effectively avoid approximation of virtual control amount required for future through adoption of the neural network; aiming at the system nondeterminacy, the robustness item is designed to ensure the stability of the system; and modeling errors are constructed to design a neural network composite learning updating rule so as to improve the neural network learning speed.

A physically-based, generalizable method to estimate the in-cylinder oxygen fraction from production viable measurements or estimates of exhaust oxygen fraction, fresh air flow, charge flow, fuel flow, turbine flow and EGR flow. The oxygen fraction estimates can be sensitive to errors in the EGR and turbine flow, and in other embodiments, a high-gain observer is implemented to improve the estimate of EGR flow. The observer is applicable to engines utilizing high pressure cooled exhaust gas recirculation, variable geometry turbocharging and flexible intake valve actuation as well as other engines.

The invention discloses an effective wind speed obtaining method of a wind generating set based on a High-Gain observer. The effective wind speed obtaining method includes the following steps that firstly, a sensor is used for collecting a rotor speed omega r of a wind wheel of the wind generating set and data are filter, sorted and preprocessed, high-frequency disturbance from a wind power plant circumstance and a wind generating set tower body is eliminated, mechanical and electrical features of the wind generating set are combined to set up the High-Gain observer and finally, an effective wind speed estimation value is obtained through a Cp secondary surface fitting polynomial function. The effective wind speed estimation value serves as the important reference input of a wind generating set controlling system, is applied to torque control, pitch angle control and yaw control of the wind generating set and also can be applied to analysis of reliability and economic benefits of a wind power station and estimation and analysis on influences on a power system by the wind power station which accesses to the power grid. The effective wind speed obtaining method of the wind generating set based on the High-Gain observer has great scientific significance and application value in planning and design of grid connection of the wind power station, analysis and calculation on safety and stability of the power grid including wind power and configuration and setting of a protection and safety automatic device.

The invention discloses a no-speed feedback dynamic surface control method for a steering engine pan-tilt. The method comprises camera azimuth angle and pitch angle calculation, steering engine model construction and analysis, high-gain observer design, dynamic surface control input voltage design, control parameter design and adjustment, and tracking performance examination according to a simulation experiment. The control method does not need to add an extra angular speed hardware measurement circuit, control input design is simple, control parameters are easy to adjust, and steering engine control precision is high.

The exact method with small time-lag of detecting injection pressure for controlling pressure in an electric-motor driven injection molding machine without using a pressure detector has been asked for because the pressure detector is very expensive, necessitates troublesome works for mounting, an electric protection against noise and the works for zero-point and span adjustings and causes a complicate mechanical structure. The present invention uses a high-gain observer which contains the discrete-time arithmetic expressions derived from a mathematical model of an injection and pressure application mechanism in an electric-motor driven injection molding machine consisting of state equations and outputs an estimate of injection pressure, which is one of the state variables of the above state equations, by using a screw position signal and a servomotor current demand signal or actual motor current signal as inputs. The high-gain observer obtains the exact injection pressure estimate with very small time-lag without using a pressure detector. Thus the estimate of injection pressure fed by the high-gain observer can be adopted as a feedback signal of actual injection pressure for controlling injection pressure.

The invention relates to an error observer-based feedforward control method of a fast mirror. For the disadvantages in feedforward control of the fast mirror at present, a high-gain observer is achieved by fusing a visual axis error amount provided by a CCD and an output quantity of a position ring controller, so that observation and estimation of a target position are achieved, feedforward control on the target position is achieved and a CCD tracking error is reduced. The method is CCD sensor-based data fusion feedforward, so that the method is effective to target tracking and also effective to CCD-based fast mirror disturbance rejection, and the application range is relatively wide. The method is output depending on a position controller, but not output of an outer ring, so that the method is not limited by the sampling rate of an outer ring sensor, and the control flow of a system can be simplified only by the error amount returned by the CCD. Meanwhile, the control structure is relatively simple in actual engineering realization, the calculation amount is smaller, the control complexity is lower and the error observer-based feedforward control method is more intuitive and easy to understand.

A hypersonic aerocraft nonlinear control method based on a high-gain observer comprises the steps that a dynamical model of a hypersonic aerocraft is determined; the speed, the altitude and an attack angle tracking error of the hypersonic aerocraft are defined; a set sigma is defined as a bicompact set representing that system states of the hypersonic aerocraft are within a set range, and when all system states and inputs of the hypersonic aerocraft is kept in the set sigma, an elastic modal vector has an Euclidean norm upper bound; a speed channel control system of the hypersonic aerocraft is designed, namely the high-gain observer and a speed channel controller are designed, the high-gain observer carries out on-line estimation on unknown functions and disturbance in a speed error system; an altitude channel and attack angle channel control system of the hypersonic aerocraft is designed. According to the method, good robustness for modeling robustness, parameter perturbation and external unknown disturbance of the hypersonic aerocraft is achieved, the calculation amount is small, adjustable control gains are rich, and most flight situations can be met.

The exact method with small time-lag of detecting screw back pressure for controlling the screw back pressure in the plasticizing process of an electric-motor driven injection molding machine without using a pressure detector has been asked for because the pressure detector is very expensive, necessitates troublesome works for mounting, an electric protection against noise and the works for zero-point and span adjustings and causes a complicate mechanical structure.The present invention uses a high-gain observer which contains the discrete-time arithmetic expressions derived from a mathematical model of a plasticizing mechanism in an electric-motor driven injection molding machine consisting of state equations and outputs an estimate of screw back pressure, which is one of the state variables of the above state equations, by using a screw position signal, a servomotor current demand signal or actual motor current signal and a screw revolution speed signal as inputs. The high-gain observer obtains the exact screw back pressure estimate with very small time-lag without using a pressure detector. Thus the estimate of screw back pressure fed by the high-gain observer can be adopted as a feedback signal of actual screw back pressure for controlling the screw back pressure in the plasticizing process.

The invention relates to a design method for a linear active-disturbance-rejection controller based on a prediction tracking differentiator, and the method comprises the following steps: a, designingthe prediction tracking differentiator, and performing the filtering of an obtained state signal through the prediction tracking differentiator to obtain a filter value; b, obtaining an extension state of the system through an extension state observer based on the filter value; c, correcting a PD control law through the extension state to obtain the linear active-disturbance-rejection controller.The method provided by the invention can inhibit the impact on the closed-loop characteristics of a system from the noise pollution, can effectively inhibit the phase delay phenomenon caused by the filtering, solves a problem of noise pollution amplification caused by a high-gain observer, and improves the robustness of a controller. The method improves the robustness of the active-disturbance-rejection controller to the high-frequency noise, and has the engineering practicality.

The invention discloses an adaptive chaos control method of a fractional order brushless direct current motor system. The adaptive chaos control method comprises the following steps of (1) establishing a fractional order mathematical model of the brushless direct current motor system firstly, and replacing a real state variable of a to-be-observed system with a state variable estimation value to perform design of a fractional order high-gain observer; (2) defining an error vector, approaching to the characteristic of a nonlinear function by a Chebyshev nerve network at any low error, and designing expansion value of a suppression differential item of a fractional order Levant differential tracker; and (3) solving virtual control input and actual control input by a fractional order liapunovfunction and performing stability analysis, and establishing an adaptive chaos controller in a backstepping framework through a continuous frequency distribution type model method. By virtue of the adaptive chaos control method, adaptive chaos control of the fractional order brushless direct current motor system is realized while system transient-state and steady-state performance is ensured, sothat influence of chaos vibration and the like to the control performance of the fractional order brushless direct current motor system can be suppressed.

{Problem} The exact method with small time-lag of detecting screw back pressure for controlling the screw back pressure in the plasticizing process of an electric-motor driven injection molding machine without using a pressure detector has been asked for because the pressure detector is very expensive, necessitates troublesome works for mounting, an electric protection against noise and the works for zero-point and span adjustings and causes a complicate mechanical structure.{Solution} The present invention uses a high-gain observer which contains the discrete-time arithmetic expressions derived from a mathematical model of a plasticizing mechanism in an electric-motor driven injection molding machine consisting of a state equation and an output equation and outputs an estimate of screw back pressure, which is one of the state variables of the above state equation, by using a screw backward velocity signal, a motor current demand signal applied to a servomotor for injection or actual motor current signal and a screw revolution speed signal as inputs. The high-gain observer obtains the exact screw back pressure estimate with very small time-lag without using a pressure detector. Thus the estimate of screw back pressure fed by the high-gain observer can be adopted as a feedback signal of actual screw back pressure for controlling the screw back pressure in the plasticizing process.

The present invention uses a high-gain observer which contains the discrete-time arithmetic expressions derived from a mathematical model of an injection and pressure application mechanism in an electric-motor driven injection molding machine consisting of state equations and outputs an estimate of injection pressure, which is one of the state variables of the above state equations, by using a screw position signal and a servomotor current demand signal or actual motor current signal as inputs. The high-gain observer obtains the exact injection pressure estimate with very small time-lag without using a pressure detector. Thus the estimate of injection pressure fed by the high-gain observer can be adopted as a feedback signal of actual injection pressure for controlling injection pressure.

The invention discloses a high supersonic speed aircraft neural network composite learning control method based on a high gain observer and aims to solve a technical problem of poor practicality of ahigh supersonic speed aircraft control method in the prior art. The method is advantaged in that a strict feedback mode of an attitude subsystem is transformed to acquire an output feedback mode, a high gain observer is utilized to estimate unknown variables, and base is provided for subsequent controller design; system lumping uncertainty is considered, only one neural network is needed for approximation, the controller is simple to design, and engineering realization is facilitated; a system modeling error is introduced, composite new neural network learning rules are constructed, stable high supersonic speed aircraft neural control under uncertain situations is realized, and good practicality is realized.

The invention provides an extended high-gain observer based disturbance estimation method for a gyrowheel system, belongs to the field of inertial navigation and aims to solve the problem about dynamic disturbance estimation of a gyrowheel rotor in a large heeling angle working state. The method comprises the following steps: step 1, a gyrowheel system state equation containing unknown disturbance is established according to a kinetic equation of the gyrowheel system; step 2, an extended high-gain observer is designed according to the gyrowheel system state equation containing unknown disturbance; step 3, observation error convergence is verified and an observer design parameter epsilon is adjusted; step 4, disturbance estimation of the gyrowheel system is performed. The extended high-gain observer based disturbance estimation method is applicable to disturbance estimation of the gyrowheel system.

The invention discloses a nonlinear sensor fault diagnosis method based on self-adaptive learning and a neural network. The method comprises the steps that S1, a single-output consistent observable standard model is established for a system; S2, learning training and standard observation in a normal mode are performed, wherein S21, the diagnosed system is subject to learning training and state observation during normal running; S22, a high-gain observer provided with the neural network is adopted to perform learning training and state observation; and S23, the high-gain observer provided withthe neural network is adopted to perform second learning training and state observation; S3, learning training and state estimation in a fault mode are performed; S4, a mode bank is established; S5, adynamic estimator is established; S6, a state vector in the dynamic estimator is compared with a state vector of the detected system, and a residual error is generated; and S7, the residual error isassessed, so that fault isolation is performed. The method is used for fault diagnosis of the complicated nonlinear system, an unknown system mode can be learnt and quickly simulated, and therefore afault is discovered and approached.

The exact method with small time-lag of detecting screw back pressure for controlling the screw back pressure in the plasticizing process of an electric-motor driven injection molding machine without using a pressure detector has been asked for because the pressure detector is very expensive, necessitates troublesome works for mounting, an electric protection against noise and the works for zero-point and span adjustings and causes a complicate mechanical structure.The present invention uses a high-gain observer which contains the discrete-time arithmetic expressions derived from a mathematical model of a plasticizing mechanism in an electric-motor driven injection molding machine consisting of state equations and outputs an estimate of screw back pressure, which is one of the state variables of the above state equations, by using a screw position signal, a servomotor current demand signal or actual motor current signal and a screw revolution speed signal as inputs. The high-gain observer obtains the exact screw back pressure estimate with very small time-lag without using a pressure detector. Thus the estimate of screw back pressure fed by the high-gain observer can be adopted as a feedback signal of actual screw back pressure for controlling the screw back pressure in the plasticizing process.

The present invention discloses a hypersonic aerocraft neural network composite learning non-backstepping control method. The technical problem is solved that a current hypersonic aerocraft control method is bad in practicality. The technical scheme comprises: performing transformation of an attitude subsystem strict feedback form, obtaining an output feedback form, employing a high-gain observer to perform estimation of newly defined variables, and providing basis for subsequent design of a controller; allowing the controller to consider the lump nondeterminacy of the system, and only requiring one neural network to perform approximation, wherein the controller is simple in design and is convenient for engineering realization; aiming at control of unknown cases of a gain function, designing the controller based on the parameter linearization expression mode; and introducing system modeling errors, and constructing a neural network composite updating rule and a parameter adaptive composite updating rule to realize fast tracking of a hypersonic aerocraft. The effective estimation of unknown states is realized based on the high-gain observer, the repeat design of the virtual controlled quantity is not needed so as to simple the design of the controller. the realization is easy, and the practicality is good.

The invention relates to a ship rudder rolling-reducing control method. The method is characterized by including performing mathematical description of ship rudder rolling reducing motions; inputting rolling angle signals output by a ship rolling model to a high gain observer; inputting rolling angular velocity signals output by the high gain observer to an analytic model predictive controller in a feedback manner; allowing the analytic model predictive controller to output command rudder angle signals, and realizing ship rudder rolling reduction.

A method for synchronously estimating ammonia injection rate and state of a diesel engine Urea-SCR system is disclosed. The present disclosure first proposes a high-gain observer design method for general nonlinear systems, which can simultaneously obtain system state and unknown parameter estimation. The outputs are derived, assuming only that these input dynamics are bounded, and no assumptions are made about how these unknown inputs vary. According to the above method, a high-gain observer is designed for the Urea-SCR system, which can obtain the ammonia injection rate, NOx concentration, NH 3 Simultaneous estimation of states such as concentration, ammonia coverage, etc. The ETC test cycle simulation and error analysis results show that the high-gain observer designed in the present invention has high estimation accuracy, and these estimation information can be used for fault diagnosis of Urea‑SCR system, and can also be used for full-state feedback control strategy or adaptive control strategy , can save the cost of installing the sensor.

{Problem} The exact method with small time-lag of detecting injection pressure for controlling pressure in an electric-motor driven injection molding machine without using a pressure detector has been asked for because the pressure detector is very expensive, necessitates troublesome works for mounting, an electric protection against noise and the works for zero-point and span adjustings and causes a complicate mechanical structure.{Solution} The present invention uses a high-gain observer which contains the discrete-time arithmetic expressions derived from a mathematical model of an injection and pressure application mechanism in an electric-motor driven injection molding machine consisting of a state equation and an output equation and outputs an estimate of injection pressure, which is one of the state variables of the above state equation, by using an injection velocity signal and a servomotor current demand signal or actual motor current signal as inputs. The high-gain observer obtains the exact injection pressure estimate with very small time-lag without using a pressure detector. Thus the estimate of injection pressure fed by the high-gain observer can be adopted as a feedback signal of actual injection pressure for controlling injection pressure.

The invention provides an extended high-gain observer based disturbance estimation method for a gyrowheel system, belongs to the field of inertial navigation and aims to solve the problem about dynamic disturbance estimation of a gyrowheel rotor in a large heeling angle working state. The method comprises the following steps: step 1, a gyrowheel system state equation containing unknown disturbance is established according to a kinetic equation of the gyrowheel system; step 2, an extended high-gain observer is designed according to the gyrowheel system state equation containing unknown disturbance; step 3, observation error convergence is verified and an observer design parameter epsilon is adjusted; step 4, disturbance estimation of the gyrowheel system is performed. The extended high-gain observer based disturbance estimation method is applicable to disturbance estimation of the gyrowheel system.

The present invention discloses a hypersonic aerocraft neural network composite learning control method based on robustness design. The objective of the invention is to solve the technical problem that a current hypersonic aerocraft control method is bad in practicality. The technical scheme comprises: performing transformation of an attitude subsystem strict feedback form, obtaining an output feedback form, employing a high-gain observer to perform estimation of newly defined variables, and providing basis for subsequent design of a controller; allowing the controller to consider the lump nondeterminacy of the system, and only requiring one neural network to perform approximation, wherein the controller is simple in design and is convenient for engineering realization; and considering control of unknown cases of a gain function, introducing the upper and lower bound information, and designing a robustness item to ensure stability of the system. Because the strict feedback form is transformed to an output feedback form to effectively avoid approximation of virtual control amount required for future through adoption of the neural network; aiming at the system nondeterminacy, the robustness item is designed to ensure the stability of the system; and modeling errors are constructed to design a neural network composite learning updating rule so as to improve the neural network learning speed.

The invention discloses an effective wind speed obtaining method of a wind generating set based on a High-Gain observer. The effective wind speed obtaining method includes the following steps that firstly, a sensor is used for collecting a rotor speed of a wind wheel of the wind generating set and data are filter, sorted and preprocessed, high-frequency disturbance from a wind power plant circumstance and a wind generating set tower body is eliminated, mechanical and electrical features of the wind generating set are combined to set up the High-Gain observer and finally, an effective wind speed estimation value is obtained through a secondary surface fitting polynomial function. The effective wind speed estimation value serves as the important reference input of a wind generating set controlling system, is applied to torque control, pitch angle control and yaw control of the wind generating set and also can be applied to analysis of reliability and economic benefits of a wind power station and estimation and analysis on influences on a power system by the wind power station which accesses to the power grid. The effective wind speed obtaining method of the wind generating set based on the High-Gain observer has great scientific significance and application value in planning and design of grid connection of the wind power station, analysis and calculation on safety and stability of the power grid including wind power and configuration and setting of a protection and safety automatic device.

The invention discloses a high supersonic speed aircraft neural network composite learning control method based on a high gain observer and aims to solve a technical problem of poor practicality of ahigh supersonic speed aircraft control method in the prior art. The method is advantaged in that a strict feedback mode of an attitude subsystem is transformed to acquire an output feedback mode, a high gain observer is utilized to estimate unknown variables, and base is provided for subsequent controller design; system lumping uncertainty is considered, only one neural network is needed for approximation, the controller is simple to design, and engineering realization is facilitated; a system modeling error is introduced, composite new neural network learning rules are constructed, stable high supersonic speed aircraft neural control under uncertain situations is realized, and good practicality is realized.

Memristor based chaotic oscillators exhibit complex dynamics. They are often chosen for secure communication owing to their interesting feature. In chaos-based secure communication applications using the master-slave configuration, synchronization is a central issue. Most of the synchronization methods proposed in the literature are asymptotic. In practice, it is desirable that synchronization be established in a predefined time. This invention provides new developments in the design of high-gain observers with an unknown input dedicated for predefined-time synchronization of memristor based chaotic systems. The proposed predefined-time extended high gain observer is constructed on the basis of a time-dependent coordinates transformation based on modulating functions that annihilate the effect of initial conditions on the synchronization time. Both noise-free channel and noisy channel are considered. Simulations performed on a numerical example illustrated the efficiency of proposed approaches.

A hypersonic aerocraft nonlinear control method based on a high-gain observer comprises the steps that a dynamical model of a hypersonic aerocraft is determined; the speed, the altitude and an attack angle tracking error of the hypersonic aerocraft are defined; a set sigma is defined as a bicompact set representing that system states of the hypersonic aerocraft are within a set range, and when all system states and inputs of the hypersonic aerocraft is kept in the set sigma, an elastic modal vector has an Euclidean norm upper bound; a speed channel control system of the hypersonic aerocraft is designed, namely the high-gain observer and a speed channel controller are designed, the high-gain observer carries out on-line estimation on unknown functions and disturbance in a speed error system; an altitude channel and attack angle channel control system of the hypersonic aerocraft is designed. According to the method, good robustness for modeling robustness, parameter perturbation and external unknown disturbance of the hypersonic aerocraft is achieved, the calculation amount is small, adjustable control gains are rich, and most flight situations can be met.

The invention discloses a no-speed feedback dynamic surface control method for a steering engine pan-tilt. The method comprises camera azimuth angle and pitch angle calculation, steering engine model construction and analysis, high-gain observer design, dynamic surface control input voltage design, control parameter design and adjustment, and tracking performance examination according to a simulation experiment. The control method does not need to add an extra angular speed hardware measurement circuit, control input design is simple, control parameters are easy to adjust, and steering engine control precision is high.