30 results about "High gain observer" patented technology

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 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.

This invention discloses an adaptive chaos control method of the fractional-order brushless DC motor system which covers the following steps:(1) firstly build the fractional-order mathematical model of the brushless DC motor system, use the estimated values of state variables to substitute real state variables of the observed system wherein the fractional-order high-gain observer is designed, (2)define the error vector and employ the Chebyshev neural network to approximate nonlinear function with a small error, then design the fractional-order Levant tracking-differentiator to solve the repeated derivative associated with backstepping, (3)use the fractional-order Lyapunov function to obtain virtual and real control inputs, and deal with the stability analysis, meanwhile establish an adaptive chaos controller by the aid of the continuous frequency distributed model in the framework of backstepping. This invention not only guarantees the transient and steady state performance of the fractional-order brushless DC motor system, but also suppresses the chaotic oscillation.

{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 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.

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.