2448 results about "Fuzzy control system" patented technology

The invention discloses a trajectory tracking control method used for an automatic driving robot of a vehicle, which relates to electronic control technologies of vehicles. The desired trajectory of a vehicle is described in a data point mode to obtain the current position information of the vehicle; humanoid driving is carried out according to the current driving direction at the current vehicle position to preview a distance ahead; the position obtained by previewing is compared with the desired trajectory to determine the lateral position deviation, the angle deviation and the vehicle speed deviation of the coordinate position obtained by previewing relative to the desired trajectory; then, the vehicle direction is jointly controlled by a trajectory tracking and speed controller according to position deviation and direction deviation; and according to vehicle speed deviation, the control quantities of an accelerator pedal and a brake pedal are determined with a fuzzy control method. The trajectory tracking control method is suitable for controlling the trajectory tracking of any trajectory, various vehicle types and various working conditions, and an automatic driving robot performs the control. The trajectory tracking control method has high precision and good repeatability and has an important meaning for guaranteeing the quality of vehicle reliability experiments and improving experiment safety.

Methods and apparatus for automatically generating a list of recommended links for a user are disclosed. For a particular user, one or more criteria for use in generating a list of recommended links are identified or selected by the user. A list of one or more recommended links is then generated and provided to the user.

The invention discloses an air conditioner control method, which comprises the following steps that the indoor temperature is detected; a difference value between the indoor temperature and the temperature set by a user is calculated according to the indoor temperature and the temperature set by the user; the initial operation frequency increment of an air conditioner compressor is calculated according to the difference value and a fuzzy control rule, and the initial operation frequency is calculated according to the initial operation frequency increment for controlling the compressor to operate at the initial operation frequency; the operation time of the compressor is detected; when the operation time is greater than the preset time, and if the absolute value of the difference value is smaller than or equal to a preset threshold value, the operation frequency modification value is calculated according to the difference value and a PID (proportion integration differentiation) control model, and in addition, the operation frequency modification value is overlapped onto the initial operation frequency for controlling the compressor to operate at the modified operation frequency. Therefore the operation frequency of the compressor can realize fast response to the load change, the fast refrigeration and heating can be realized, meanwhile, the temperature can be precisely controlled, and the energy sources can be effectively saved. The invention also discloses an air conditioner control device.

The invention discloses a wireless charging method with a counterpoint automatic prompting function for an implanted medical instrument, belonging to the field of implanted medical instruments. The wireless charging method is characterized in that after a corresponding device is established, a voltage on a rectifier output capacitance in an in vivo power part is collected by using a first microprocessor in an in vitro charger to serve as feedback quantity, the voltage deviation value between two feedbacks and the rate of change of voltage deviation are calculated, a fuzzy control rule is utilized to determine the variation of in vitro transmitting power, and the variation of the in vitro transmitting power is overlapped on a set transmitting power value for compensating the variation of the voltage on the rectifier output capacitance owing to shifting, charging stage variation and the like. By using the method, the counterpoint situation of in vivo / vitro coils can be automatically prompted in the process of charging, the charging efficiency is improved, the stability is improved and the radiation is reduced. Under the conditions of different counterpoint positions or different charging stages, the in vitro transmitting power is automatically regulated, and the radiation of the in vivo implanted medical instrument is effectively controlled in a reasonable range.

An artificial intelligence system is provided which makes use of a dual subroutine to adapt weights. Elastic Fuzzy Logic ("ELF") System is provided in which classical neural network learning techniques are combined with fuzzy logic techniques in order to accomplish artificial intelligence tasks such as pattern recognition, expert cloning and trajectory control. The system may be implemented in a computer provided with multiplier means and storage means for storing a vector of weights to be used as multiplier factors in an apparatus for fuzzy control.

The invention relates to a machinery automatic steering control method which comprises the following steps that: positional deviation and course deviation are determined; according to the variance in the practical front wheel rotating angle of an agricultural machine, online setting of PID parameter is completed by means of parameter self-setting PID control algorithm; moreover, the expected front wheel rotating angle of the next moment is calculated, thereby realizing automatic steering control of the agricultural machine. Based on conventional PID navigation control method, the invention makes full use of the fuzzy control method; according to the variance in the practical front wheel rotating angle of an agricultural machine, the invention meets the different requirements of the machine on PID control parameter under different errors and error change rate, thereby realizing online setting of PID parameter. The invention not only has the advantages of fuzzy control such as flexibility and adaptability, but also has the characteristics of higher precision of PID control; therefore, the invention can increase the stability and precision of agricultural machine automatic steering control and the robustness of a control system.

The invention discloses a variable-frequency air-conditioner control method. The method includes: acquiring room temperature and humidity; according to room temperature and humidity, calculating the difference between room temperature and set temperature and the measured values of temperature difference change rate and air-conditioning output capacity change rate as the input parameters of fuzzy control; respectively quantifying the temperature difference and the measured values of temperature difference change rate and air-conditioning output capacity change rate so as to acquire the converting values of the temperature difference, the temperature difference change rate and the air-conditioning output capacity change rate in the corresponding universe of discourse; according to the converting values of the temperature difference, the temperature difference change rate and the air-conditioning output capacity change rate, calculating and outputting compressor frequency by preset fuzzy control regulations. The invention with the characteristics of accurate control, quick response and small overshooting can satisfy the requirements of high control accuracy, excellent transition process and high comfort, which is conducive to reducing the tear and wear of the compressor and saving the energy. Correspondingly, the invention also discloses a variable-frequency air-conditioner control device thereof.

The invention discloses a central air conditioner intelligent control system based on a wireless sensor network and a method, and belongs to the technical field of central air conditioner control. The central air conditioner intelligent control system comprises an intelligent information management center, wireless communication devices, a central air conditioner information monitoring and control module, a room information monitoring and control module and the like. The invention adopts the method that the central air conditioner intelligent control system is used to monitor information, build a data base, calculate current load of the system, and realize advance, accurate and optimized control of a central air conditioner system by combining a short-term loading forecasting method based on similar dates and fuzzy control technology, as well as taking the temperature variation of supply / return chilled / cooling water of a central air conditioner as the manipulated variable. The central air conditioner intelligent control system has the characteristics of great communication capacity, guaranteed communication quality, powerful network functions, wide monitoring range and extensive control quantity of the central air conditioner system, optimized control, high control accuracy, good energy saving effect and the like, and can be widely used in energy-saving optimizing control of various buildings, and is particularly suitable for energy-saving optimizing control of central air conditioners of intelligent buildings.

The image sensing device or image processing apparatus has a synthesizing capability enabling the device or apparatus to perform blur control using a specified amount of blur and to prepare a blur-controlled image, as well as a blur-controlled image processing capability enabling the device or apparatus to carry out processes other than blur control on the prepared blur-controlled image and to change the degree of processing in accordance with the amount of blur.

The invention discloses a multi-behavior combining auto-navigation method for a mobile robot in an unknown environment. The method is characterized in that the method comprises the following steps that (1) current azimuth angle is obtained in real time according to the relative positions of an object and the mobile robot, and a plurality of distance parameters are obtained in real time according to the status of obstacles around the mobile robot; (2) a multi-output support vector machine fuzzy controller outputs a corner value Theta i and a velocity value vi according to the obtained azimuth angle and distance parameters, wherein i is equal to 1, 2 or 3; (3) a multi-output support vector machine environmental-identification controller inputs signals and outputs weight parameters wi of three subbehaviors according to input signals of the azimuth angle and the distance parameters, wherein i is equal to 1, 2 or 3; and (4) current corner value Theta i and velocity value vi of the mobile robot used for navigation are calculated according to the formula. The multi-behavior combining auto-navigation method adopts intelligent control strategy, and has the advantages of strong self adaptation, high navigation reliability and excellent effect.

The invention discloses a central air-conditioning self-optimization intelligent fuzzy control device and a control method thereof. Technological parameters and indoor and outdoor environmental parameters of all control points of a central air-conditioning system are collected, running efficiency of a circulating system and control ends are tracked in real time according to actual load capacity of the system and end cold and hot source requirements, operation cycle of a main machine is optimized and adjusted, operation energy efficiency of each link of the system is comprehensively controlled, so that the system can always run under working conditions of high energy efficiency ratio. Meanwhile, process data, energy consumption data and equipment running data of the central air-conditioning system are summarized to form an intelligent control base, technical process such as analysis and operation is performed through the a central air-conditioning self-optimization intelligent fuzzy controller, new control strategies are generated, on-line upgrading control algorithm and energy-saving potential mining are achieved, energy consumption of the system is optimized, the operation is simplified, so that the operating efficiency of each device of the system is adjusted, and the aims of high efficiency, energy saving and intelligent control are achieved.

The invention discloses a stepping motor control system and a control method thereof. The stepping motor control system comprises a controller, a stepping motor controller, a stepping motor, a transmission mechanism, a load and a photoelectric encoder, wherein the controller sends a plurality of pulses with a certain frequency and a certain quantity to the driver which receives the pulse information and a direction signal and acts on the stepping motor, the stepping motor drives the load through a driving mechanism, and the photoelectric encoder feeds back and transfers position information to the controller. The invention realizes high-accuracy positioning through a fuzzy control method, achievesprovides higher operation effect by adopting the combination of dual compensation feedback and open-loop control, has the desynchronizing detection function for compensatingrehending and feeding back failure information and also hasprovides higher failure suitability.

The invention provides a multi-robot path planning and coordination collision prevention method based on fuzzy logic. The method comprises the steps of confirming an input variable and an output variable of a fuzzy controller by setting up the fuzzy controller with robots as a system, using related languages to describe the input variable and the output variable of the fuzzy controller, confirming a qualitative reasoning principle according to the fuzzy control theory, setting up fuzzy control rules according to input signals and output signals of the fuzzy controller, selecting a subordinating degree function of each input language variable and a subordinating degree function of each output language variable, finally, conducting defuzzification, conducting voting on the basis of the maximum principle of the subordinating degree functions, conducting corresponding motions on the robots, and completing the task for planning a path of a single robot. On the basis of the method, the other robots are regarded as dynamic front barriers, and the aim of coordination collision prevention of the multiple robots can be achieved.

The invention provides an unknown environment route planning method of an underwater cleaning robot, which is a self-adaptive learning fuzzy control method for planning a track in an unknown environment. The method is characterized in that: by simulating the route planning thought, obstacle avoiding through and intelligent thoughts for making a decision for determining the priority of quick planning and obstacle avoiding, three different sub fuzzy controllers are put forward correspondingly for solving three kinds of problems. Meanwhile, according to the estimation of the movement results of a controlled robot, the rule base of the fuzzy controller is modified correspondingly to make the fuzzy controller can handle more complex external disturbance and effectively solve the problem of U-shaped obstacles and symmetric obstacles, so that the robot can complete a planned task and arrive at a destination in a more complex environment. The method eliminates the uncertainty influences of an underground complex environmental factor on the route planning of the robot, improves the movement safety of the robot and enables the robot to well adapt to the complex underground industrial environment.

The invention discloses an intelligent automobile path tracking hybrid control method, and belongs to the technical field of intelligent automobile lateral control. The intelligent automobile path tracking hybrid control method comprises the following steps that step 1, an automobile lateral control preview kinematics model at a low speed is established; step 2, an automobile lateral control dynamic model at a high speed is established; and step 3, a path tracking hybrid controller is designed, the path tracking hybrid controller comprises a lateral controller, a supervisor and a switching stability fuzzy controller, wherein the lateral controller is designed based on PID control and model predictive control, the supervisor determines a tracking mode based on longitudinal vehicle speed, and the switching stability fuzzy controller is designed based on a fuzzy control theory. The intelligent automobile path tracking hybrid control method effectively solves the problem of lateral controlperformance requirements under high-speed and low-speed working conditions of an intelligent automobile, and the easy realization, accuracy and stability of path tracking of the intelligent automobile are improved.

A control system using a genetic analyzer based on discrete constraints is described. In one embodiment, a genetic algorithm with step-coded chromosomes is used to develop a teaching signal that provides good control qualities for a controller with discrete constraints, such as, for example, a step-constrained controller. In one embodiment, the control system uses a fitness (performance) function that is based on the physical laws of minimum entropy. In one embodiment, the genetic analyzer is used in an off-line mode to develop a teaching signal for a fuzzy logic classifier system that develops a knowledge base. The teaching signal can be approximated online by a fuzzy controller that operates using knowledge from the knowledge base. The control system can be used to control complex plants described by nonlinear, unstable, dissipative models. In one embodiment, the step-constrained control system is configured to control stepping motors.

The invention provides a microgrid economic operation optimization method based on a fuzzy particle swarm algorithm and an energy saving system. The method comprises the following steps of: (1) determining an economic dispatch strategy of a micro source in the microgrid; (2) building a microgrid grid-connected economic operation mathematical model combined with economic benefit and environment benefit; (3) putting forward a fuzzy control strategy for charging / discharging control of the energy saving system; and (4) optimizing based on the particle swarm algorithm, and determining the output of all micro sources and the generation cost per day. Compared with a method in which an optimization dispatch scheme is not adopted by the energy saving system, the method has the advantages that the generation cost per day of microgrid running and pollution emission are reduced; and compared with a method in which the fuzzy control charging / discharging strategy is not adopted by the energy saving system, the method has the advantages that optimal solution quality is improved, operational convergence rate is increased, and the reliability of microgrid running is further enhanced.

The invention belongs to the field of application of photovoltaic power generating system control technology, and relates to a maximum power tracking control method for a monopole three-phase photovoltaic grid-connected system. The method comprises the following steps of: firstly, by testing voltage and current outputted by a photovoltaic array, working out a differential coefficient k of the output power to the voltage of the photovoltaic array and a change rate delta k of the k; secondly, according to the k and an output Step1 of a fuzzy controller (an input is k and delta k, and the output is Step1), jointly determining the disturbance step length and direction of a disturbance voltage delta U which is superposed on a reference voltage U ref of a maximum power point of the photovoltaic array, and making the U ref quickly iterative and then converged at the optimal reference voltage corresponding to the maximum power point of the photovoltaic array under the current environment; and finally, realizing the quick and accurate tracking of the output voltage of the photovoltaic array on the U ref by a double closed loop control system consisting of a voltage PI controller and a synchronous vector current PI controller with feedforward compensation.

A control system for optimizing a shock absorber having a non-linear kinetic characteristic is described. The control system uses a fitness (performance) function that is based on the physical laws of minimum entropy and biologically inspired constraints relating to mechanical constraints and / or rider comfort, driveability, etc. In one embodiment, a genetic analyzer is used in an off-line mode to develop a teaching signal. An information filter is used to filter the teaching signal to produce a compressed teaching signal. The compressed teaching signal can be approximated online by a fuzzy controller that operates using knowledge from a knowledge base. In one embodiment, the control system includes a learning system, such as a neural network that is trained by the compressed training signal. The learning system is used to create a knowledge base for use by an online fuzzy controller. The online fuzzy controller is used to program a linear controller.

The invention discloses a neural network and fuzzy control fused electrical fire intelligent alarm method. The method comprises the following steps of: 1, acquiring a leakage current signal, current and voltage signals, an arc light signal, a temperature signal and a field electromagnetic environment parameter signal by using a sensor on site, and pre-processing signals acquired by the sensor by using a velocity detection algorithm; 2, transmitting processed data to a three-layer feedforward error counterpropagation neural network and processing, wherein the neural network is subjected to supervised learning and establishes a weight matrix in advance; and 3, transmitting electrical circuit undamage probability, electrical circuit damage probability, and electrical circuit fire probability output by the neural network to a fuzzy inference module and performing fuzzy inference to acquire a forecast result of electrical fire. In the method, the probability of the electrical fire is accurately forecast by using the advantages of advanced theories, such as neural network, fuzzy control and the like, and without depending on deep knowledge of an object, the electrical fire forecasting accuracy is obviously improved and the damage of the electrical fire can be effectively prevented and reduced.

The invention discloses a fuzzy control system and a method of a throttle mechanism. According to the invention, a refrigeration cycle system comprises a compressor, a condenser and an evaporator. The control method comprises the following steps: monitoring the refrigerant vaporization temperature and water supply fluid temperature of the evaporator, and defining the small temperature difference of the evaporator as the temperature difference of subtracting the refrigerant vaporization temperature from the water supply fluid temperature; monitoring the temperature and the saturation temperature of the refrigerant discharged by the condenser, and defining the condenser depression of the refrigerant discharged by the condenser as the difference of subtracting the refrigerant temperature from the refrigerant saturation temperature; and calculating the opening of the throttling orifice of an electronic expansion valve by utilizing the small temperature difference of the evaporator and the condenser depression of the refrigerant discharged by the condenser. The method and the device of the invention can solve the problem of failure to realize stable and precise regulation on the opening of the electronic expansion valve due to the unstable liquid level signals of the evaporator in the prior art.

The invention relates to a yaw control method for a large-sized wind powered generator unit. A current yaw angle reference value alpha reference is obtained according to a current wind direction, a current yaw angle practical value alpha practice is obtained by a encoder or a tachometer, a yaw angular error delta alpha is obtained by subtracting the yaw angle practical value alpha practice by the current yaw angle reference value alpha reference, and a corresponding yaw mechanism control rule is calculated by integrating the yaw angular error delta alpha and the variation tendency thereof by a fuzzy controller. Furthermore, an obtained electrical signal of the yaw mechanism control rule is sent to a yaw actuating mechanism to control a propeller pitch angle, and the yaw angle of the wind powered generator unit traces the yaw angle reference value alpha reference to enable the yaw angle practical value alpha practice and the yaw angle reference value alpha reference of the wind powered generator unit to be kept consistent. The invention obviously enhances the total precision, is favorable to increasing the year total generated energy of the wind powered generator unit, has high adaptability for complex and extreme cases, greatly enhances the operating effectiveness, furthest prolongs the mechanical life of the yaw actuating mechanism, and also has low remodel difficulty and low cost.

The invention provides a control method for an electronic expansion valve. When the outdoor environment temperature(Tho-A)is larger than or equal to 43 DEG C, opening of the electronic expansion valve is determined according to frequency change of a compressor. When the outdoor environment temperature(Tho-A)is smaller than 43 DEG C, the temperature is divided into different sections, and linear calculation curves of different target exhaust superheat degrees are set for each section; after the target exhaust superheat degrees and actual exhaust superheat degrees are obtained, fuzzy control is conducted, and then the opening transformation quantity of the electronic expansion valve is obtained. According to the section control method, fuzzy control is used, and different temperature ranges correspond to the calculation curves of the different exhaust superheat degrees, so that precise and optimal control is achieved. In addition, in combination with feedforward control technology, the reply efficiency of the electronic expansion valve is improved; accordingly, all air conditioner systems running in the different temperature ranges can run smoothly, and vibration is completely avoided. The control method has the advantages of being high in energy efficiency ratio, good in low-temperature heating effect, and the like.

The invention relates to a navigation control system based on vision and ultrasonic waves, which comprises a caterpillar robot body, an ultrasonic ranging subsystem, a vision subsystem and a motion control subsystem, wherein the ultrasonic ranging subsystem is arranged in front of the robot, the vision subsystem is arranged over the robot, and the motion control subsystem is positioned above the robot. The ultrasonic ranging subsystem comprises ultrasonic transmitters, ultrasonic receivers and a signal processing circuit, and a plurality of transmitters and a plurality of receivers are distributed in an equally spaced mode. The vision subsystem comprises a CCD (Charge Coupled Device) camera, an analog-to-digital conversion circuit and a DSP (Digital Signal Processor) image processor, and the motion control subsystem comprises a DSP motion controller and corresponding peripheral circuits. Information processed by the vision subsystem and the ultrasonic ranging subsystem is transmitted to a fuzzy controller inlaid in the motion control subsystem, and the fuzzy controller outputs control information to control the motion of the robot. The system has the advantages that the advantages of visual navigation and ultrasonic navigation are combined, so the navigation accuracy is increased; as the DSP high-speed processor is adopted, the instantaneity and the expandability are increased; and the anti-jamming capability is enhanced through applying a fuzzy control method.

The invention discloses an adaptive fuzzy sliding mode controller for a micro gyroscope, which is characterized by comprising an adaptive fuzzy control system based on a reference module, and a switching control system. In the adaptive fuzzy control system, a sliding mode surface is used as the input of the fuzzy controller, and the weight is automatically adjusted by a dynamic adaptive law so as to realize fuzzy approximation to an equivalent control law. The adaptive fuzzy sliding mode controller for the micro gyroscope of the invention has the following benefits that: when the system is in a steady state, the dynamic performance of the micro gyroscope is an ideal mode, and the manufacturing error and the environment interference are compensated; an adaptive algorithm designed based on a Lyapunov method can ensure the global asymptotic stability of the whole closed-loop system; and with adaptive adjustment on the upper bound of the approximation error, buffeting is reduced obviously.

An automatic regulation system for the combustion of circulating fluidized-bed boiler (CFBB) features that it has several regulation loops for the discharged cinder quantity, primary blowing rate, supplied coal quantity, secondary blowing rate and draught air volume, which are based on the coordination between fluidizing, combustion and heat exchange, the expert system, fuzzy control and composite cascaded PI regulation are combined, and the protecting tactics such as override protection, output restriction and lock distribution choosing and tracking are used.

The invention discloses an automatic intelligent double cross limiting range combustion control method for a heating furnace, and belongs to the technical field of heating furnace combustion control. By combining temperature self-learning, fuzzy control and PI parameter self-learning, the intelligent double cross limiting range control in the combustion process of the heating furnace is realized,the problem of control accuracy of the combustion of the heating furnace is effectively solved, and a hot rolling production requirement is well met. The method has the advantages that: gas and air flow rates are reasonably, accurately and quickly regulated and controlled, the regulating function of a regulating valve is effectively realized, and the temperature of a hearth of the heating furnaceis reasonably and accurately controlled.

The invention discloses a moment distribution method for a self-adaptive cruise control system of a four-wheeled independent drive electric automobile. An ideal acceleration command is calculated through an upper layer control method and input in a lower layer control method, the lower layer control method is used for calculating the ideal drive moment according to the ideal acceleration command calculated through the upper layer control method and distributing the moment to four wheels, and the problem that a traditional self-adaptive cruise control system cannot be directly applied to the four-wheeled independent drive electric automobile is solved. Smoothly-restrained model prediction control is adopted in the upper layer control method, the practicality of the self-adaptive cruise control system is improved, and the requirements for safety, comfort and economical efficiency of a driver are met. Fuzzy control is adopted in the lower layer control method, so that the ideal longitudinal moment is obtained, the moment is distributed to the four wheels according to the vertical load proportions, and the robustness and practicality of the self-adaptive cruise control system of the four-wheeled independent drive electric automobile are improved while the dynamic quality of the four-wheeled independent drive electric automobile is guaranteed.

Disclosed is a Docker self-adaptive scheduling system with a task sensing function. A Docker self-adaptive scheduling method comprises the steps that a data collector collects the load of each container and the resource usage rate of a CPU and a memory system; based on a queuing theory and with the resource usage rate as a benchmark, a performance modeler constructs an application performance model and characterizes an association relationship of the load and response time; a response time predictor estimates parameters of the performance model during running through a Kalman filter, wherein estimation is conducted under a convergence condition of meeting a predicted and measured response error expectation; a feed-forward controller analyzes a residual mean and variance through a fuzzy controller to obtain a feed-forward adjustment value of a control parameter of the Kalman filter; a container scheduler judges whether or not a predicted value of the response time violates application service quality and conducts scheduling according to a scheduling algorithm; the container scheduler conducts container expansion or shrinkage or migration.

The invention discloses an asymmetric four-motor UAV and a control method thereof. According to the asymmetric four-motor UAV, the connection line between two large motor propellers is perpendicular to that between two small motor propellers, and the connection line between the two small motor propellers is shorter than that of the two large motor propellers; the problem of control system divergence is solved through a Smith prediction algorithm; fuzzy control rules are designed and fuzzy inferences are utilized to achieve the optimized adjustment of PID (proportion-integral-derivative) parameters under different flight working conditions. The asymmetric four-motor UAV has the advantages of large take-off weight and good control robustness. The control method of the asymmetric four-motor UAV solves the problems of inaccurate control model and large system interference and reduces the control coupling degree of the four-rotor UAV, thereby reducing the control difficulty and achieving the smooth control of the four-rotor UAV.