34 results about "Fuzzy predictive control" patented technology

The invention discloses a fuzzy predictive control system for a combustion process of a lime kiln as well as a control method of the system and belongs to the field of control on the combustion process. The fuzzy predictive control system comprises a reference information input module, a fuzzy controller, a fuel valve, a lime kiln control device, a prediction module, a feedback module and a correction module. A combustion temperature control system of the lime kiln has the characteristics of nonlinearity, time variability, model structure uncertainty and the like, a predictive control and fuzzy decision combined control method is adopted, an advanced prediction function of predictive control and the optimal control effect of fuzzy control in a large deviation range are integrated, and the temperature control problem of a large-time-delay nonlinear system is solved; the system can be put into actual production operation, the dynamic quality in the lime calcining temperature adjustment process is effectively improved, and the good actual application effect is realized.

A maximum power point fuzzy prediction control method for an uncertain photovoltaic system comprises the following steps: firstly, introducing MPV and DMP fuzzy control configurations with PV voltageand power slope as control outputs; secondly, using a T-S fuzzy model representation method to represent the two fuzzy MPPT control configurations as output adjustment problems in a unified form; then, for the deviation of a DC / DC converter, proposing a predictive PDC control law based on an integral fuzzy observer to directly drive the system to reach the maximum power point. The proposed fuzzy prediction controller can ensure that the photovoltaic system reaches the maximum power point within a limited time. The maximum power point fuzzy prediction control method for an uncertain photovoltaic system can solve the controller and the observer gain online by the newly developed LMI type control algorithm with no need to perform coordinate transformation and calculation on the maximum powerpoint of the converter, and compared to the conventional MPPT method, allows for strict stability analysis and online control design of the system in the event of rapid atmospheric changes.

The invention discloses an intelligent temperature control device for an infrared carbon-sulfur analyzer electric arc combustion furnace body. The intelligent temperature control device is composed of a DSP, a GPRS module, a Zigbee transmitting and receiving module, a temperature sensor, a semiconductor refrigeration and heating piece and a GPRS network. In the working process, the temperature sensor is installed on the electric arc combustion furnace body and transmits temperature signals to the DSP, the semiconductor refrigeration and heating piece is controlled by a fuzzy algorithm to make the temperature stabilized at 60 DEG C, and temperature information is transmitted to a smart phone through the GPRS module and the Zigbee transmitting and receiving module so that the infrared carbon-sulfur analyzer can be controlled to be started. Intelligent control is achieved through a fuzzy prediction control algorithm, and therefore the control process of the temperature of the electric arc combustion furnace body can be stabilized and reasonable; the Zigbee protocol wireless control is applied, connection lines are omitted, and multiple electric arc combustion furnaces can be detected through a wireless sensor network; the smart phone participates in furnace body temperature control so that the temperature of the furnace body can be more conveniently controlled.

A robust fuzzy prediction control method for a nonlinear injection molding asynchronous switching process belongs to the field of industrial process control, and comprises the following steps: 1, establishing a state space model of a nonlinear injection molding asynchronous switching system; 2, establishing a state space model of the nonlinear injection molding asynchronous switching system into aTS fuzzy state space model through a fuzzy criterion; 3, converting the built TS fuzzy state space model of the nonlinear injection molding asynchronous switching system into an extended TS fuzzy state space model; 4, designing a controller of the extended TS fuzzy state space model based on the nonlinear injection molding asynchronous switching system; 5, calculating the gain of the controller;6, calculating the average residence time of each stage. According to the invention, the influence of model mismatch caused by model linearization on the control effect of the injection molding systemcan be effectively avoided.

The invention discloses a tire protection device based on intelligent prediction and a control method thereof. The tire protection device comprises a tire device, a detection system, an acquisition module, a control system and a brake system; the tire device comprises a tire and a hub base. The detection system is mounted on the tire device for detecting the distance between the hub base and an inner wall of a tire, detecting a tire pressure signal in the tire and detecting a temperature signal of the tire; the acquisition module transmits the collected distance signal, tire pressure signal and temperature signal to the control system; the control system is used for controlling the brake system to further control the operation of a vehicle, and is used for reminding the tire to be repaired. The control method adopts T-S fuzzy prediction control, according to an input signal, the tire driving safety factor is obtained through a control algorithm, and the warning, maintenance and emergency stop control functions of the tire are realized according to different driving safety factors, so that the vehicle is safer to drive.

Aiming at the robust fuzzy predictive control method of nonlinear injection molding asynchronous switching process, which belongs to the field of industrial process control, the method includes the following steps: Step 1: Establish a state space model of nonlinear injection molding asynchronous switching system; Step 2: Through fuzzy Criterion, the state space model of the nonlinear injection molding asynchronous switching system is established as a T‑S fuzzy state space model; Step 3: Transform the T‑S fuzzy state space model of the nonlinear injection molding asynchronous switching system into an extended T‑S S fuzzy state-space model; Step 4: Design a controller based on the extended T‑S fuzzy state-space model of the nonlinear injection molding asynchronous switching system; Step 5: Calculate the controller gain Step 6: Calculate the average dwell time of each stage ; The present invention can effectively avoid the influence of the model mismatch problem caused by model linearization on the control effect of the injection molding system.

The invention discloses a heat load control method of a micro gas turbine combined heat and power system based on robust fuzzy predictive control, comprising the following steps: collecting input fuel quantity and hot water temperature data of the micro gas turbine combined heat and power system, and establishing a heat load control process The T‑S fuzzy affine model of the heat load control process is established; the global amplification state T‑S fuzzy model is established; the offline fuzzy observer is established based on the global amplification state T‑S fuzzy model; the global amplification state T‑S fuzzy model is established Model and Offline Fuzzy Observer to Build a Robust Fuzzy Predictive Controller. The method of the present invention utilizes the T-S fuzzy model to weaken the influence of the nonlinearity of the system on the controller, adopts the observer to overcome the problem of unmeasurable state, uses the state amplification to overcome the steady-state error, and strengthens the degree of freedom of the controller. A high-quality stable model predictive control method for practical complex industrial processes.

The invention discloses a commutation failure prevention method based on direct current fuzzy predictive control. In view of a direct current predictive control method in which limitation exists in a parameter k random value in a direct current predication setting valve calculation formula and direct current at a rectifier side is reduced for a long time, limitation of slow current recovery of the direct current system is caused, a fuzzy controller is adopted to control the direct current predication setting valve, a direct current fuzzy predictive control method is brought forward, a control time duration T selected via a simulation result is set, and the direct current at the rectifier side is only reduced with a time duration T after a CFPREV module detects failure happens to an alternating current system. Through modeling and simulation, compared with the prior direct current predictive control method, effectiveness of restraining the commutation failure ability by the HVDC (high-voltage direct current) system is verified to be enhanced.

The invention discloses a fuzzy prediction control analysis system of an infrared carbon and sulfur analyzer. The fuzzy prediction control analysis system is used for overcoming effects of different environment temperatures, partial pressures, sample weights and analysis air flows on analysis results and the problem of errors of measurement result caused by ageing of a detection unit after long-time use. The fuzzy prediction control analysis system comprises an automatic sampling module, a high-frequency combustion furnace, an oxygen supply module, a gas conveying module, a flow control module, an infrared detection tank, a data acquisition module, an analyzing module, a processor, a display module and a storage module. A formula of ZCi=Ci+Ci*ln[(Qi-Qb)*k1+(Ti-Tb)*k2+(Pi-Pb)*k3+Gi*k4]u1 isutilized to obtain an actual value ZCi of the concentration of CO2 gas; by means of the obtained actual value of the gas concentration, the effects of the environment temperatures, the gas partial pressures, the sample weights and the air flows on the analysis results are reduced, and accuracy and precision of the analysis results are improved; a correction factor u1 is obtained through a fuzzy prediction formula, so that errors of measurement result caused by ageing of the detection unit after long-time use are reduced.

The invention provides a cold-rolled strip steel plate shape prediction control method, comprising the following steps: for cold-rolled strip steel of the same specification, establishing a corresponding plate shape prediction control fuzzy reasoning model; setting fuzzy membership functions of various parameters in the plate shape prediction control fuzzy reasoning model by being combined with the characteristics of influence of rolling force change on the strip steel plate shape; establishing plate shape fuzzy prediction control models by utilizing a Takagi-Sugeno fuzzy model modeling rule; selecting a corresponding plate shape fuzzy prediction control model to carry out online adjustment on a working roller bending device. A dynamic relationship is established among rolling force variation, forward pull variation of a roll mill, backward pull variation of the roll mill, and online adjustment variable of the working roller bending device by using a fuzzy modeling method, the adverse effect of transmission time lag existing between the roll mill body and a plate shape instrument on the plate shape control at the outlet of the cold-rolled strip steel, and two familiar plate shape defects of intermediate waves and edge waves existing in the cold-rolled strip steel products are effectively overcome.

The invention discloses a fuzzy prediction control system and method for a power supply for electrostatic dust removal. The analog signal acquisition unit converts the collected analog signal into an electrical signal, performs filtering and other adjustments, and then outputs it to a T-S fuzzy controller. The T-S fuzzy The controller first identifies the antecedent and conclusion part of the T-S fuzzy model according to the conditioned signal, obtains the T-S fuzzy model, then converts the T-S model into a step response model, and outputs the step response model to the prediction The controller 23 is used as a predictive model; the predictive controller uses the step response model as a predictive model to perform rolling optimization and feedback correction, and output the control signal to the digital logic unit; the control signal is used as a drive signal for controlling the drive unit after passing through the digital logic unit. Fuzzy predictive control of the power supply; solves the problem that the electrostatic precipitator system is difficult to establish a mathematical model, and reduces the dependence of the predictive controller on the precise mathematical model.

The invention relates to the technical field of electric vehicle starting and stopping, in particular to an electric vehicle braking follow-type fuzzy prediction starting and stopping control circuit. The electric vehicle braking follow-type fuzzy prediction starting and stopping control circuit comprises a direct current contactor QF, a precharge branch YC, alternating current contactors QF3, a recognition unit B, a fuzzy prediction control unit VC, a drive and executing device YE, electric reactors L and a capacitor V. The recognition unit B and the fuzzy prediction control unit VC are connected with an electric tractor power supply through the direct current contactor QF1 and the precharge branch YC, the rear end of the recognition unit B and the rear end of the fuzzy prediction control unit VC are connected with the drive and executing device YE, the rear end of the drive and executing device YE is connected with the electric reactors L and capacitors C, the rear ends of the electric reactors L and the rear ends of the capacitors C are connected with the alternating current contactors QF3, and the alternating current contactors QF3 are connected to an electric grid. According to the electric vehicle braking follow-type fuzzy prediction starting and stopping control circuit, the main circuit topology is simple, control is flexible and convenient, and the quality of output waveforms is good.

The invention discloses an intelligent temperature control device for an infrared carbon-sulfur analyzer electric arc combustion furnace body. The intelligent temperature control device is composed of a DSP, a GPRS module, a Zigbee transmitting and receiving module, a temperature sensor, a semiconductor refrigeration and heating piece and a GPRS network. In the working process, the temperature sensor is installed on the electric arc combustion furnace body and transmits temperature signals to the DSP, the semiconductor refrigeration and heating piece is controlled by a fuzzy algorithm to make the temperature stabilized at 60 DEG C, and temperature information is transmitted to a smart phone through the GPRS module and the Zigbee transmitting and receiving module so that the infrared carbon-sulfur analyzer can be controlled to be started. Intelligent control is achieved through a fuzzy prediction control algorithm, and therefore the control process of the temperature of the electric arc combustion furnace body can be stabilized and reasonable; the Zigbee protocol wireless control is applied, connection lines are omitted, and multiple electric arc combustion furnaces can be detected through a wireless sensor network; the smart phone participates in furnace body temperature control so that the temperature of the furnace body can be more conveniently controlled.

The present invention provides a fuzzy predictive control method based on the enhanced robustness characteristic of a disturbance observer. The steps are as follows: 1. Establish a discrete fuzzy disturbance observer model, an auxiliary controller and a robust predictive controller; 2. Solve the feedback gain of the auxiliary controller and the gain of the disturbance observer, obtain the minimum robust invariant set, and calculate the tight constraint set of the control input and state variables of the robust predictive controller; 3. Initialize the system state variables and assign them to the state variables of the nominal model; 4. For the The current nominal model state variables, solve the optimization problem that minimizes the upper bound γ of the predictive control performance, and obtain the current disturbance estimation value; 5. Calculate the control input of the system and act on the controlled object; 6. Apply the control of the nominal system The input acts on the nominal model and calculates the output of the current state, and the output of the current state quantity of the sampling system; 7. Substitute with and set k=k+1, and then jump to step 4. The present invention improves the stability of the predictive control system.

The invention discloses a network teleoperation robot system and a time delay overcoming method. The system is formed by linking two manipulators with the same structure and symmetrical functions through a network communication link. A main manipulator is operated by an operator so that the main manipulator is acted; the main manipulator transmits an action signal to a secondary manipulator through force and angle displacement sensors, and a communication control link, so that the secondary manipulator can move in the same direction along the main manipulator. When the secondary manipulator is affected by the environment, the secondary manipulator can return the signal to the main manipulator along the original path, thus, the main manipulator can feel the same action of the environment and feed back to the operator. By adopting a modeling method based on an event and a fuzzy predictive control algorithm, the system has good robustness on the model mismatch; therefore, the system can overcome the influence caused by the network time delay.